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https://devpost.com/software/fraud-detection-bot

Architectural Diagram Theme- Insurance Industry Problem Statement: Workers' compensation claim fraud contributes significantly to the $40 billion insurance frauds per annum. A significant impact of COVID-19 has been the ‘Work from home’ culture. This presents new challenges to insurance companies as validation of claims from employees working at home is quite a challenging task. Inspiration: The inspiration behind this solution was to make our contribution to the community during and post-COVID. Automation Anywhere's IPA technology has enabled us to employ the most sophisticated digital technologies to solve problems. Additionally, our team has representation from Europe, the Middle East, and Asia and this allowed us to bring our different perspectives and skills on the table. Solution: Our solution, the Fraud Detection Bot is an intelligent process automation solution that leverages machine learning blocks such as natural language processing and computer vision to detect fraudulent claims. The solution is advanced in capabilities to extract information from unstructured documents and communicate with third-party sources for verification purposes. Based on the extracted information, the bot calculates a Fraud Score and generates the next action steps for claims adjusters. Claim adjusters can use the 'human-in-the-loop' feature of the solution to interact with the bot by viewing high-level insights and claims-specific information. The bot has been designed with digital building blocks and uses APIs and python scripting. The Automation Anywhere IPA technology orchestrates the whole process allowing for end-to-end automation. The modularity in the design of the solution allows for quick and easy upgrades to the solution based on the insurance company preferences. Challenges: The biggest challenge in implementation was an initial lack of understanding about the insurance domain, however, after reaching out to some connections in the insurance industry, we were on track with a better idea about the problem. Impact: The impact of Fraud Detection Bot is not just limited to the current situation. Post COVID-19, we see that a lot of employees will continue to work from home and the Fraud Detection Bot will play an instrumental role in detecting fraudulent claims. The Fraud Detection Bot empowers insurance claims adjuster teams to identify fraudulent claims in an intelligent manner. Leverages AI tech stack Comprehends unstructured information Generates Fraud score and next action steps Facilitates bot-human interaction Results in faster and error-free processing Built With amazon-web-services azure ipa kpi-dashboard natural-language-processing python textract vision Try it out github.com

https://devpost.com/software/student-evaluation

Flow Chart 1 Flow Chart 2 Flow Chart 3 Average marks of Class in Different Subjects Inspiration :: During Covid-19 crisis, maximum numbers of persons are doing Work from home, also children are doing Schooling from home. Maximum schools send students classes videos, assignments through whats up/ youtube or any other digital platform. But they are not able to manage each student profile. Maximum schools are updating Student profile, curricular, Leave Request on paper and some are updating in excel sheet manually. Hence we can provide support such schools to track student Academics. What it does :: Software can read the Student data through excel file or database and then calculate attendance percentage for each student. If attendance is less than 80% for a month drop a mail and send SMS to Parents for less attendance. Software can read the marks for each student and send the subject wise result to parents over email and SMS. Software can check if fees is pending for any student and send reminder mail and SMS to parents to submit fees and maintain Reminder count. How I built it :: Used AA2019 & Python Challenges I ran into :: I was not sure how to send SMS online and how different graphs can be plotted using Python libraries. Accomplishments that I'm proud of :: We can help number of government or other private schools which are not able to afford School Administrative Software and can help them to maintain academic tracker for student. What I learned :: I learned about AA2019 and number of new things in Python. What's next :: Whatever we do for one school, can be used for other schools also. We can also plan some Inter school tournaments and use this data to select best student among schools. Built With excel folium mail matplotlib pandas python Try it out aa-botathon-us.my.automationanywhere.digital

https://devpost.com/software/productivitech

Statistics Statistics How ProductiviTech Works Our Features About Us User Profile Productivity Report Attendee Breakdown Recomendations Inspiration Due to the Covid-19 pandemic, each of our team members had to work or study remotely. Some of us were in the United States while others were in Nepal but we all experienced the same transition to online medium. Faced with this new work environment, we realized that online meetings are quite difficult to oversee, especially when there is a large number of attendees. Meeting hosts cannot easily keep track of attendance throughout the entire meeting and it is a real challenge to keep track of which meeting attendees are paying attention. Some of the scenarios that we personally experienced were the following: Professors could not easily keep track of the students who were paying attention to lectures and could not easily take attendance if the class was large. Managers who held online meetings could not easily keep track of which employees were actively paying attention to specific segments of a presentation. Based on our observations, we decided to create a tool that would allow the user to quickly analyze a recorded meeting and evaluate its productivity level based on the attentiveness of the attendees. Our goal is to help meeting hosts capture the attention of their audience by learning from their past experience reflected in a "productivity dashboard". What it does Takes a meeting video recording as an input Processes the video frame by frame and detects the faces and the eyes of the attendees Saves the number of attendees. For each individual attendee, frames are classified as "attentive" or "not attentive" based on the following parameters: - Attendee face present/not present in the frame - Attendee eyes present/not present in the frame - Distance of detected face from the camera (chose a threshold) After all the video is processed, the software calculates what percentage of the time that each individual attendee was attentive Finally, the metrics are displayed in a dashboard with 3 tabs: - Overall meeting productivity - Individual attendee attentiveness - Evaluation of the host based on attendee attentiveness together with personalized recommendations on how to improve meetings How we built it As our goal was to help meeting hosts capture the attention of their audience, we started by detecting the face and eyes of the person in the frame. We implemented this feature by using OpenCv library and HaarCascades algorithm. Once the faces and eyes were detected, we started to track the depth (the distance between the webcam and the attendee). Using the face, eye, and depth we calculated whether the student is attentive or not. Using all the obtained data, we were able to calculate the attention and the engagement of the individual student. We were able to extract the name of the student from the frame by using Tesseract (optical character recognition engine). At last, all the results were provided to the host, using Plotly graphics. For the front end, we used HTML, CSS (Bootstrap) and JavaScript. We also created graphics using tools like Adobe Photoshop and Illustrator. We first did research on what problems we are currently facing. We then created graphics of the problems faced, how we can solve it through our project and the main features of our project. In the development phase, we tried to keep it simple yet informative. We created an interface where you can view the important information at a glance. The user experience was also taken under consideration and an easy to use application was built. Challenges we ran into There are many popular video meeting platforms and each provider's application has a slightly different format (Google hangout meetings display the attendee names while Zoom meetings do not etc.). It was very difficult to implement a processing technique that would adapt to any video recording of a meeting, whether it is Zoom, Skype, Webex, Google Hangouts etc. We are still working on making our software more robust. Splitting the frames of each individual attendee Extracting the name from the video recording (text recognition in an image) There is a very large number of corner cases in a video meeting: - Camera off - Multiple people in the same user frame - Bad lighting - Confusing background - Poor connection leading to blurry images - Too many attendees so their video stream is displayed on different screens Accomplishments that we're proud of We identified a challenge created by the new circumstances that the pandemic forced us into and we implemented a working solution We built a visually pleasing and easy to interact front-end We solved a few difficult challenges in the back-end (for example, identifying names in the frame) We collaborated harmoniously and made use of each of our top skills. Our team had people in the United States and in Nepal and despite the 9 hours and 45 minute timezone difference, we had very productive meetings We strongly believe that our presentation video is top notch We are very proud of the brand name we chose. In the future, we would like to build a suite of productivity tools under this brand name What we learned Most of us did not have any previous experience with automation bots. It was great to learn how to use the Automation Anywhere functionality and it was very rewarding to see our process streamlined What's next for ProductiviTech Track the engagement of each attendee by identifying the instances when they speak in the meeting. Add enhancements to the gaze tracker. We currently detect faces and eyes but we would like to analyze the direction of the gaze and correlate with what is happening on their screen. Build more products that will be part of the "ProductiviTech productivity enhancing tools" Built With adobe-illustrator bootstrap css3 filmora9 flask heroku html5 javascript opencv pandas photoshop plotly pytesseract python sqlite Try it out github.com

https://devpost.com/software/trending-bot

Provide NEWS from different Sites. Our bot provides Trending NEWS of a country with its NEWS Title and its link from different and popular NEWS sites and provides a maximum of 20 results. For this bot, we have used API's from www.newsapi.org Most of the cases, people waste their time by looking up for NEWS and this bot is their solution. Built With api rpa ui

https://devpost.com/software/health-scout

Team lead views Employees health information through Bot Insights Automation Workflow Bot downloads the Form responses Bot logs into Google Form Employee submits Google Form Non respondents list generation Employee receives message from health scout bot WhatsApp message to employees Bot input file preperation Inspiration Employee Wellness, Ease of Use and Improving effectiveness What it does Collection of health information from team members, reminding team members and send a consolidated information to team lead • The bot reaches to all the team members through WhatsApp to update their Health status, at the beginning of the shift • Track their responses in a Google form • Send 3 follow on remainders only to defaulters over the course of four hours to have each team member fill the form • An email is sent out to the Team Lead/Manager on health status of entire team with Bot Insight link to visualize the information How we built it After designing the solution along with technical specifications, we have followed 'divide and conquer' approach and succeeded. We have modular designed solution; we have developed each module independently and then integrated it to an end to end solution. Challenges we ran into Initially we were in a search to find easiest mode of communication to get better coverage for employees, in our experience emails were not an effective medium. We shortlisted on Web-WhatsApp and Google Forms as effective medium for updating, collation and reporting There were few limitations in Google Forms which we had to work around. Accomplishments that we're proud of Major accomplishment we all can say that as a team "This Botathan event helped us to be a pioneer on A2019 journey within a short span of time" What we learned It’s not just building the Bot is our learning, we learnt about Usability, Ease of use, integrating multiple solutions to build a effective solution. Health Scout Bot development and A2019 Botathon experience by itself is a great learning. Exploring towards new features of A2019, Bot Insights, WhatsApp API through Twilio, Google App Scripts for Forms are our learning. What's next for Health Scout As an organization we plan to re-use & leverage the bot for the many activities like, sharing important organization level notification with employees sending organization level survey responses/training feedback automating reminders to be sent for any process collecting information through customized google forms, tracking and reporting the response Built With botinsights googleforms macro webwhatsapp Try it out drive.google.com

https://devpost.com/software/providing-legit-job-postings-0i6soj

Team Aerica Singla, Arushi Madan and Arun Venu Inspiration COVID-19 pandemic is affecting economies in every continent. Unemployment rates are spiking every single day with the United States reporting around 26 million people applying for unemployment benefits, which is the highest recorded in its long history, millions have been furloughed in the United Kingdom, and thousands have been laid off around the world. These desperate times provides a perfect opportunity for online scammers to take advantage of the desperation and vulnerability of thousands and millions of people looking out for jobs. We see a steep rise in these fake job postings during COVID-19. In the grand scheme of things, what may start off as a harmless fake job advert, has the potential of ending in human trafficking. We are trying to tackle this issue at the grassroot level. What it does We have designed a machine learning model that helps distinguish fake job adverts from genuine ones. We have trained six models and have drawn a comparison among them. To portray how our ML model can be integrated into any job portal, we have designed a mobile application that shows the integration and can be viewed from the eyes of a job seeker. Our mobile application has four features in particular: 1) Portfolio page: This page is the first page of the app post-login, which allows a job seeker to enter their employment history, much like any other job portal/app. 2) Forum: A discussion forum allowing job seekers from all around the world to share and gain advice 3) Job Finding: The main page of the app which allows job seekers to view postings that have been run through our Machine learning algorithm and have been marked as real adverts. 4) Chat feature: This feature allows job seekers to communicate with employers directly and discuss job postings and applications. How I built it We explored the data and provided insights into which industries are more affected and what are the critical red flags which can give away these fake postings. Then we applied machine learning models to predict how we can detect these counterfeit postings. In further detail: Data collection: We used an open-source dataset that contained 17,880 job post details with 900 fraudulent ones. Data visualisation: We visualised the data to understand if there were any key differences between real and fake job postings, such as if the number of words in fraud job postings was any lesser than real ones. Data split: We then split the data into training and test sets. Model Training: We trained various models such as Logistic regression, KNN, Random Forest etc. to see which model worked best for our data. Model Evaluation: Using various classification parameters, we evaluated how well our models performed. For example, our Random Forest model had a roc_auc score of 0.76. We also evaluated how each model did in comparison to the others. Further on, we designed an app titled JOB, using Adobe XD. We also integrated a Twilio framework so as to add a system to notify job posters whose job postings may have been flagged. This is because, since our ML algorithm is not a 100% accurate, we would have cases of 'False Positives' wherein a true job posting would be marked as fake. In these times we would want to allow the job posters to challenge the decision by providing evidence. Immediate impact Especially during but also after COVID-19, our application would aim to relieve vulnerable job seekers from the fear of fake job adverts. By doing so, we would be re-focusing the time spent by job seekers onto job postings that are real, and hence, increase their chances of getting a job. An immediate consequence of this would be decreasing traffic onto fake job adverts which would hopefully, discourage scammers from posting fake job adverts too. Police departments don’t have the resources to investigate these incidents, and it has to be a multi-million-dollar swindle before federal authorities get involved, so the scammers just keep getting away with it. Hence our solution saves millions of dollars and hours of investigation, whilst protecting the workers from getting scammed into fake jobs and misused information. What's next for Providing legit job postings We wish to completely automate the notification system built using 'Twilio'. Submission info We have submitted this project to RookieHacks and Oxford Digithon after confirming that cross-hackathon submissions are allowed for the hackathons held over the same weekend. DOMAIN.COM - Oxford Digithon MLH Challenge kidsusepickuplinesiusepullrequests.space Built With javascript python tensorflow twilio Try it out kidsusepickuplinesiusepullrequests.space github.com

https://devpost.com/software/lab-assay-bot

Lab Assay Bot is a suite of bots that automates the data collection process of laboratory equipment and integrates with a Laboratory Information Management System (LIMS). Lab Assay Bot automatically captures raw experimental data [measurements, equipment settings, calibration readings] from a variety of lab devices [microscopes, oscilloscopes, scanning electron microscopes, spectrometers]; assembles relevant data sets and visualizations that are stored securely; and generates descriptive reports containing Jupyter notebooks and ML model predictions. Each bot is tailored to a specific lab device and corresponding LIMS. Try it out bitbucket.org

https://devpost.com/software/safemenu

Logo with QR code to our chat bot. Inspiration Problems that many of enterprenues facing, including my mother What it does Creates safe menu for visitors How I built it With help of my team Challenges I ran into Stong client focus. reaching the clients Accomplishments that I'm proud of That we are able to grow fast What I learned Team means a lot What's next for SafeMenu Growth. product development new functions. new solutions. Built With facebook java-(spring) javascript messenger mysql Try it out m.me

https://devpost.com/software/machbarschaft-25fcwg

Vision Machbarschaft connects millions without internet access with their neighbours who are willing to provide help for basic supplies in order to keep vulnerable groups safe. In this crisis, Machbarschaft allows to bridge human to human contact through a hybrid solution linking both analogue and smart digital technology. Our vision is to build a stronger community and a more equitable society where no one is left behind! Inspiration Every 9th household in the EU does not have any internet access. This makes it difficult for especially vulnerable people to request help. We are experiencing a huge movement of solidarity, especially in communities and neighborhoods. However, these volunteers are often are organized on digital platforms. Tens of millions are not able to access these platforms in the EU, leaving them in a drastic care gap. Our mission is to close this gap through Machbarschaft. This solution enables vulnerable people to use their well-known devices to connect with their neighborhood. Especially in times of crises, strengthening neighborhood ties helps to build a more equitable and inclusive future. This way society can ensure everybody safe access to support. Machbarschaft - how does it work? We offer a smart bridge combining three key elements: the analog telephone, a phone bot powered by AI and an app. Our app and hotline represent the link between the digital-native generation and people in need of help and are not familiar with the use of the internet. With Machbarschaft, people in need can make a simple phone call and provide information about the category of their request and a few additional logistical information. The service hotline is run by an AI powered bot. The information will then be processed and finally displayed in our app which helpers download in advance. Registered and verified helpers receive push-notifications and they can look up requests near their current location on a map in the app. As soon as they select a request, they can contact the care recipient by phone to arrange further details. After, the helper sets off, and fulfils the request. In case of the much needed grocery shopping request, the helper brings the agreed items to the care recipient and receives the money for the groceries in exchange. In order to ensure appropriate protection against infection, the money is packed in an envelope and handed over at an appropriate distance and without any body contact. This way, we hope to make the ongoing crisis a little less threatening, especially for vulnerable groups and those in need of help. The solutions impact to this crisis Machbarschaft addresses the group that has been left behind so far: The vulnerable people without a wide social network and without internet access. Further, we offer our system to e.g. already existing societies that currently help in the segment of neighborhood aid, but perform their processes manually. We hope to enhance efficiency of their process through digitalizing the process. Since every 9th household in the EU is possibly compromised in this crisis and beyond the crisis, there is a huge demand to cover. In addition, the gap between poor and rich is continuously growing leading to deteriorating situations. Therefore, scalability is key to Machbarschaft. How did Machbarschaft improve during the Botathon? Although our product is already in an advanced state, it is under constant development. Our main goal for the Botathon was to finish our Apps in order to start a first testing phase utilizing the whole "technology pipeline". We also tried to focus on our Bot Technology Part. With our Telephone Bot, it is possible to convert the resources of a central call center to a decentralized helper network with a direct connection to regional helpers. This provides protection against contagion, especially during corona, and also makes it easier to organize the process afterwards. Furthermore, we have tried to find approaches how we can overcome language barriers, which arise in particular from dialects of elderly people, by means of skillful helpers using artificial intelligence. A further important point for us is that humans remain humans and a psychosocial interaction is given despite the automation with the bot. Through the bot, a large network of helpers can be created, which gives people a sense of belonging and enables direct social contacts. We have also set our focus on digitalizing analog tasks. What is needed and what are the next steps? The immediate next step is to run further simulations and tests. In order to optimize our concepts and technology, we have already established various partnerships with civil societies as well as local authorities in Germany to run early testings with authentic target groups. This is why we already planned to our first pilot project in the city of Passau, Germany, we are already partnering with. After this proof of concept testing, we need more expertise in the areas of business strategy and product ownership, as the next steps will be constant optimization of our product while scaling up nationally. To prepare for scaling up, we already worked out a combined B2C and B2B concept. We will have to intensely expand our already large network in preparation for expansion. Further, our business plan has to be implemented in order to finance this non-profit project. Therefore, further partnerships and cooperations have to be established. At the same time, we plan to continue our already exisiting cooperations with similar non-profit projects with a similar solution. The reason is, that as a non-profit it is not easy to find ressources. Therefore, we aim to combine existing ressources and powers to really be able to implement our solution quickly and help people as soon as possible. Thus, we aim to quickly expand our easy scalable solution nationally, EU-wide and globally in order to support millions as soon as possible. The value of Machbarschaft after the crisis Machbarschaft has high sustainability goals. We aim to connect people beyond the current crisis. Why? Because, there are millions who struggle with everyday errands, and they still will also after the COVID-19. Machbarschaft hopes to contribute a small piece to solve this problem in the long-term perspective. We offers a smart platform to meet the age-old basic need of human to human connection - to be there for those who need it most. This is why we are not limited to the time of the current crisis. We aim to accompany those connections built through as well as fostering new connections to be built. As soon as there is no immediate infection risk, we are already planning on expanding our currently offered services far beyond of pharmacy and grocery shopping. Our vision is to build a stronger community and a more equitable society where no one is left behind! Try it out! Try out our hotline bot (24/7)! Our phone numbers for various countries: +358 75 3250960 (Finland) +44 131 510 5863 (United Kingdom) +46 10 551 53 69 (Sweden) +49 40 299960980 (Germany) +420 910 902 652 (Czech Republic) +1 779 201 6897 (USA) Our apps and hotline are still under active development – please excuse any errors or bugs at this point and feel free to report it on our GitHub repository. You can download a first version of our Android app here . Also, take a look on the videos of our current Apps on iOS here and on Android here ! How we built it Hotline In order to be able to process calls from as many people as possible at the same time without having to resort to a conventional call center (also regarding the COVID-19 infection risk of call center employees), we developed an automatic service with Twilio. In this service, we modelled an easily understandable call flow in which all necessary data for a request for help is collected. In order to scare older people by using robotic voices, we recorded the questions ourselves (German only). We are also currently trying new solutions with voximplant to directly coordinate the caller with a helper with our bot system and in case there is no helper currently available the bot will create the help seek in the app directly. Backend The collected transcribed data from the phone call is then sent to a Firebase function (Javascript/NodeJS) via POST request which tries to generate longitude and latitude from the spoken address via Google Maps API. This data is then stored in a Firestore database along with the name and request, making it visible and sending push notification to nearby volunteers based on the geolocation. Another Firebase function triggers a callback to the help seeker via Twilio after 24 hours if no volunteer could be found or if an order has been marked as completed. App To ensure the best possible user experience, we program the app natively for iOS (Swift & UIkit for the frontend) and Android (Java). These apps communicate with the central database of Firebase, Cloud Firestore (NoSQL). For this we used the available SDK for Android and iOS from Google. Function and design of both apps are based on the created wireframe. After the users have verified themselves via an identification procedure. This is, for now, an optional step for us and may depend on the country and their legal requirements for the registration of phone numbers to be able to assist local authorities in case of fraud. After that, the user have to create their account via SMS confirmation. The user is informed about the process in the app and the procedure for dealing with requests for help. After the successful login, the user will see help requests in his or her area. These are visualized on a map (using the Google Maps API) and in a list. In advance, the user only sees the most necessary information in order to offer him a small decision support. As soon as he decides to accept a request for help, he has to confirm this and will receive further information. The user can contact the person seeking help again and exchange information about the details. Now the helper can go and take care of the request. As soon as he has completed the assignment, he can confirm this in our app. If an order has not been accepted after 24 hours, the person will be informed about the situation by a callback and it will be up to him/her to decide whether to continue the order or to withdraw it. Website Our simple website serves as a central information platform for interested volunteers. The purely static website machbarschaft.jetzt is based on a bootstrap theme, which was implemented using HTML5 and CSS3 (SCSS) technologies. A CMS has not been implemented yet. Since the website can be called up in any situation, we attached great importance to a responsive layout. The website serves not only to recruit new volunteers for our native apps but also to promote our entire concept to future supporters. Since our target group of people seeking help is mainly informed via analogue channels, supporters can download an attractive Machbarschaft flyer which can be hung up in the neighbourhood or distributed in mailboxes. With this, both target groups can be addressed in parallel – volunteers and people seeking help. Current state Our hotline and backend are fully functional and secure. Data from orders completed with our hotline are saved in our Firestore database and are available in our apps. The apps are working as well, they are fully connected to our database and can communicate to it. We are doing bug testings and are preparing our next phase to test the app with more users in a closed beta. The hotline is being tested by our team and we are preparing to test it with old people as soon as possible. In the future, we are going to improve the underlying technology to be even more AI-driven and making the hotline more flexible and adaptable to our users. Who are we? We are a diverse team of more than 30 people from all over Germany with a unique and unstoppable team spirit! We were brought together by the mission to provide safe and fast access to neighbourhood assistance for elderly people without internet access during the Corona pandemic. We are convinced: Together everything is possible! University students and high school students Software Developers Designers (Marketing, Product and UX) Doctors Experts in Legal, Process management, Project management, Human Ressources, Design Thinking, Business Development, Data Specialists, Psychology, Marketing, Press, Public Relations Business plan Vision The supply of all people in need is secured. Mission We connect needy people of the risk group without internet competence with digital- affine helpers from the neighbourhood in order to guarantee the supply of the needy. Strategic goals are long-term goals that make it tangible whether we are fulfilling our mission. Offer help to those who are vulnerable towards Covid-19 disease and who at the same time have no access to the internet and online solutions. Connect neighbors and bring them together across generations. Qualify the future workforce of Europe with regards to AI and digital technology. Offer a sustainable solution, that will be used also after the Covid-19 pandemic. Build the solution in a way it can be transferred to other use cases by NGOs. Goal 1: Offer Help Number of successful matches (in a period) Proportion of successful matches = Number of successful matches / Number of requests for help Number of verified helpers on the apps Goal 2: Connect Neighbours over Generations Proximity of residence Goal 3: Qualification Number of technical team members working on our solution (per year) Goal 4: Sustainability of the Solution Number of use cases for which Machbarschaft offers its services Goal 5: Transferability of the Solution Publication as open source for other non-profit organizations Number of institutions / associations that use parts of our technical solution KPIs (different target numbers of KPIs depending on local, national, EU-wide or global setting) No. of help requests, no. of downloads (android vs. iOS), no. of verified app users, no. of successful matches, proportion of successful matches, no. of recurrent requests from the same care seeker, no. recurrent request completion by the same app user and helper Go to Market Strategy Machbarschaft uses a combination of B2B and B2C approach. B2B is particularly important to reach the final target groups with the aid of already existing and care providing community services, civil society organizations, NGO´s etc. B2C care recipient main characteristic: does not use internet different segments (elderly, poor, internet illiterate, language barrier…) Channels: social welfare offices, community services, doctors offices, hospitals, pharmacies, nursing care, outpatient physiotherapists,. churches, communities, bakeries, supermarkets, TV, radio, local newspaper, magazines, kids, grandkids, family, friends... volunteers main characteristic: wants to support vulnerable group and frequently uses internet different segments (between-14-55 yo., pupils to professionals, family or stand-alone…) Channels: mainly internet based, social media, TV, radio, newspapers, family, friends... B2B and Strategic Partners non-profit: NGOs, citizens society groups/ initiatives that already established contact with care recipients and/ or helpers industrial partners: telephone services, digital companies, marketing companies, companies specialized in elderly services public authorities: local, regional, national, EU, global Customer Journey & Personas There are three main personas and customer journeys to be considered: care recipient, helper and civil societies that use us as support for digitalization of their service. On Boarding Concept Tools: care recipients: explanation on flyers, TV etc. and re-explanation on our hotline helpers: explanation on Social Media/ flyers and introduction within the app Retention measured through no. of recurrent requests from the same care seeker, no. recurrent request completion by the same app user and helper Successes Probably the greatest achievement was to digitally bring together about 30 strangers from all over Europe with different experiences and levels of knowledge, to implement a great idea together and to develop a runnable app that carries the core goal of the idea within itself. Despite the circumstances that a personal meeting was not possible at any time, the communication ran smoothly. The team members were reliable, punctual and it was no problem that the communication was purely digital. Thanks to some tools provided by the organization, data exchange, various uploads and even communication within the team was no problem. Thanks to high quality input throughout the EUvsVirus Hackathon we were able to very quckly improve and optimize our solution, we were able to achieve a goal-oriented implementation. Thanks to this, we have done a considerable amount of work and can deliver a presentable result. What we've learned We have learned that extraordinary situations require extraordinary actions and that with the right will and motivation you can achieve incredible things! We have also learned to get out of our comfort zone. It was necessary to get into direct and fast close contact with complete strangers. We then received excellent advices and support. Even if we came to our limits in terms of our abilities and energy from time to time, we were able to motivate each other because, for all of us at Machbarschaft, our work is for "the greater good" of helping people. More detailed info can be found in our pdf: Machbarschaft Details Flyer Facebook , Instagram , Twitter , YouTube , GitHub and LinkedIn How did we work Ticketing tool: Trello Communication: Slack Repository: GitHub Website: HTML, CSS, Bootstrap machbarschaft.jetzt/en Built With css3 firebase firestore html5 java javascript kotlin swift twilio Try it out github.com machbarschaft.jetzt

https://devpost.com/software/covid-19-update-bot

Email Sample considering the current situation and wave of rumors on COVID-19, I have created a free bot to trigger an email with the current count on COVID-19 cases across different states in India Current status will be fetched from Govt of India Ministry of Health website and displayed in a table format for clear understanding. Also bot will send you an pdf attachment containing Protective measures suggested by the Government Built With robotic-process-automation Try it out botstore.automationanywhere.com

https://devpost.com/software/cryptovote

Log in Page OCR Verification DEMO FOR OCR READER (Since it got corrupted in our video): https://imgur.com/xcbU6Gi Inspiration The COVID-19 Pandemic has led to many people wondering how they are going to vote for their selected leaders in the upcoming elections, especially in the United States where the presidential election will take place this November. People are hesitant to go to voting booths to vote due to the risk of catching the virus. The only options left are online voting, and mail-in voting. However, our traditional system for online voting is very unsafe and there have been many cases in various countries of hackers messing with the votes. As well, many have expressed fears of mail-in voting due to vote spoofing, and a rigging of the election. Motivated by this demanding issue that people keep forgetting about, we have decided to create CryptoVOTE. What it does CryptoVOTE is a decentralized web application that securely counts people's votes using a blockchain. The web application also has a verification Optical Character Recognition (OCR) system which reads the user's Voter ID or driver’s licence and verifies it with a simulated government database using MongoDB Atlas to make sure that the individual voting is eligible and real. The utilization of an OCR alongside the blockchain system makes it virtually impossible to hack, through brute force or any other malicious strategy - creating a safe way for people to vote online. How we built it We built CryptoVOTE using a wide variety of tools and frameworks. These include ReactJS for the front end, a Blockstack Gaia storage/MongoDB Atlas combo to act as a decentralized backend and store user votes in a blockchain format, Blockstack for the decentralization log in/log out process, and TesseractJS for the OCR verification as well as NodeJS and Express for the non-voting backend. Challenges we ran into Most of our challenges lied in deploying the actual application and backend. Due to unknown issues, we spent hours trying to deploy our front-end to firebase, and backend to Google App Engine. However, we later realized it was a combination of Blockstack and MongoDB issues. Due to CORS, we were unable to send POST requests to our backend, until we found out how to allow it in our application. MongoDB also didn’t allow us to deploy to Heroku, as we were unable to whitelist the dynamic IP address for MongoDB Atlas. Lastly, Blockstack caused some hiccups on the way, as it would not allow us to deploy our frontend to Firebase static hosting. However, in the end we were able to figure out the issues, despite it costing us hours. Accomplishments that we're proud of We are proud to have learned to create a truly decentralized and safe way of voting in just 24 hours. It was all of our first time creating a blockchain app and we are very excited to have succeeded in making it. We hope that our creation will be able to impact many and provide a safe and secure way to vote. What we learned It was our first time building a blockchain app and we learned a lot. We learned how the Blockchain system works, its technicalities, why it is so secure, its potentials for the future and how to build one. We also learned a lot about how backend databases work. After studying the Gaia Storage database and MongoDB Atlas one, we were eventually even able to combine the two to make a decentralized database and that was very exciting. What's next for CryptoVOTE CryptoVOTE has the potential to be an extremely useful and versatile app. It solves an urgent and complex COVID-19 issue that determines the fate of entire countries. The election process is extremely important and can not be messed around with at any cost and our app is integral in solving the unsecure online voting problem. We hope to talk to various organizations (whether it be as small as schools having a valedictorian election or as big as the American Presidential election) and offer them our solution to this grave problem. Built With blockstack css3 express.js gaia-storage ganache html5 javascript metamask mongodbatlas tensorflowjs tesseract Try it out github.com mihirkachroo.github.io cryptovote.tech

https://devpost.com/software/botparty

Inspiration Learning RPA What it does Performs automated functions How I built it Using AutomationAnywhere Software Challenges I ran into Time Accomplishments that I'm proud of Learned how to use RPA software What I learned Using RPA is a lot of fun What's next for botParty More botParties Try it out github.com

https://devpost.com/software/efficient-economic-lockdown-equlibrium

Inspiration I was inspired by my high-school project about history of pandemics, study of aerodynamics, kinetics in physics, random walks, psychology of well-being in isolation, and following news of very different approaches of different countries, in a try to find an 'equilibrium approach'. It assumes that 2m away, always, no matter the circumstances, would be too rough of an approximation to use in every possibility in the world. What it does Simulates society on micro and macro scale to measure/approximate exposure, and what minimizes loss to society (economic loss, mental health loss, loss of life and different losses) How I built it Simple implementation. Challenges I ran into Lack of teammates, and a lot of real-life challenges. Accomplishments that I'm proud of Simplifying SIR model so much, succesfully. What I learned Syntax What's next for Efficient economic lockdown equlibrium Impact of those decisions on well-bing of people according to character, and on economy (closed places)- macroeconomic view. Built With java r Try it out github.com

https://devpost.com/software/employee-welfare-management-bot

Since we are now working from home due to this pandemic situation, we plan to develop a BOT which can get the pulse of all employees and understand their challenges and address their concerns in a best way. That's how we got inspired and developed our Employee Welfare Management BOT. Actually, we build it using a new environment - A2019, there was a lot of challenges in learning the new functionalities and finally we did it.We learned lot of new technical aspects in A2019 by doing this project. We also plan to suggest this 'Employee Welfare Management BOT' to our leadership team to implement in our whole organization.

https://devpost.com/software/asd-qunovb

Inspiration - Effective testing for COVID-19 is key to control the pandemic— knowing who is infected, who has been exposed, and who is immune. Though there have been major efforts around the world to increase the production capacity of Covid-19 testing kits, the demand is likely to exceed the actual capacity. Thus, there is a need to identify the potential patients of COVID-19 to ensure optimum testing and prevent any unnecessary disease spread while testing. This is what inspired us to create this solution What it does - This solution will ask all relevant questions from the users and provide assessment results & recommendations based on the user’s symptoms. It also has the capability to book an appointment for the patient with the nearby doctors based on their availability How I built it - This has been achieved by centralizing health care physicians’ data along with their availability schedule and combining the potential of RPA & Conversational AI. Chatbot assesses the user's symptoms and RPA helps in booking appointment with the doctor Challenges I ran into - Building the connection between chatbot and RPA was a tricky task but FireBase database helped us resolve this. We were able to connect to Firebase database both through chatbot and RPA to make this handshake possible Accomplishments that I'm proud of - Our solution is capable of increasing awareness towards Covid-19 self assessment and prevention. It also helps in booking appointment with doctors in case of medical assistance required. Also, the overall reach of this solution is around 2 billion people worldwide which will significantly improve the current Covid-19 testing across the world What I learned - We have learnt a lot about how technology can help during these kind of situations. A simple solution can be also very helpful to the normal people and can create a big impact. Customizing the chatbot to interact with RPA was something very interesting that we learnt during this project. What's next for COPE - Covid-19 Patient Evaluation - We can add features like video conferencing with doctors & placing IVR call to the patient's emergency contact in case of medical emergency. Also, this solution can be evolved to a Universal Patient Self-Assessment suite for other ailments as well Built With amazon-ec2 api chatbot database excel firebase rpa Try it out github.com

https://devpost.com/software/anti-covid19-active-mask

prototype The current pandemic has pushed me to seek solutions for a reusable, effective and stable face mask over time. In fact, common filter masks tend to gradually lose the ability to filter viruses and bacteria. On the contrary, this system is absolutely stable because it does not filter but inactivates the virus / bacterium, keeping it caught while it is destroyed by UVC rays and electrostatic charge. Built With hardware Try it out www.hackster.io

https://devpost.com/software/simple-payment-app

Inspiration: Creating a simple payment app on the Marqueta platform What it does: Simple payment interface How I built it: Using Marqueta API and Javascript Challenges I ran into: Using Marqueta API and the encryption required for the product Accomplishments that I'm proud of: Creating the final product What I learned" Everything related to the Marqueta platform What's next for Simple Payment App: Improve it further taking today's payment app challenges Built With api java

https://devpost.com/software/oksur

cover Web site Logo Inspiration To prevent the COVID-19 infections spreading, lots of diagnosis test-kits have been developed all around the world. Diagnosis is important for the isolation of the infected people, preventing them from spreading the disease and is also important for the observation and study of the infection. Most of the test kits based on the samples taken from blood, mucosa layer inside the nose and throat. These samples are used in RT-qPCR, antigen and antibody tests. RT-qPCR technique is the one with highest confidence, however it is costly, not portable and takes too much time. The problems with RT-qPCR raise the need for cheaper, efficient and portable new methods. According to WHO, * dry cough is an important symptom for 67,7% of the patients. * WHO A dry cough is when the cough does not include mucus secretion. Coughing of people with COVID-19 can have a distinct sound profile that can be differentiated from other coughing sounds. The different sound structure can be analyzed by algorithms we have developed, and these algorithms can be used as a novel diagnosis tool for COVID-19 that is cheaper, efficient and portable. The Aim This research aims to build up machine learning algorithms that can inform the diagnosis of COVID-19 based on the people's coughing and breathing sounds. After developing an algorithm by using large scale, crowdsourced data collection from healthy and non-healthy participants, we can create a novel diagnostic tool for COVID-19 that is cheaper, efficient and portable and we can use it as an early diagnostic tool. If successful, all the countries and people in the world will be the beneficiaries. Moreover, the work in this project can also be scaled for the other respiratory diseases rather than COVID-19. ML Model This is the research repository of Covid-19 Cough Sound Classification. You can read the details of research from the web site we prepared. https://caglayan.github.io/covid19/en/research Team Our team is made up of two graduate students from Boğaziçi University, Istanbul. Çağlayan Şerbetçi is a graduate student in System and Control Engineering and M. Görkem Durmaz has a bachelor’s degree in Molecular Biology & Genetics and she is now a graduate student in Computer Engineering department. Project Web Site In this project, COUGH AI, we have developed a website. This web site record coughs with various diagnostic information. It works on all mobile devices. Please record your cough sounds and help our research! Web site: https://caglayan.github.io/covid19/en/ more details. Built With javascript machine-learning matlab node.js react Try it out caglayan.github.io github.com

https://devpost.com/software/evisitor-card

Inspiration The motivation originated from the present circumstance where everyone is looking for minimal touch to the common things. Most organizations see visitors every day to their office regardless of whether it is an interview candidate, vendor, or salesman. And all the visitors will be accessing the same gadget either tablet, visitor register or any medium to collect visitor entry and common ID cards which may not be reasonable in this circumstance. What it does eVisitor Card digitizes the visitor check-in to the workplace premises. This solution sends the approval code (QR Code) to the visitor. This QR Code will be validated upon visit without contacting any gadget. If the QRcode gets authorized, the bot will snap the picture of the visitor and will send the entry card over email. How I built it Below are the features and brief idea of how it was built: 1. Generate the QR Code - Read the details from Google sheet and based on the email address generate the QR Code using Google Chart API. 2. Send the QR Code to the visitor - Using the built-in email functionality of A2019 sends the email to the visitor with the QR Code generated at the above step. 3. Scan the QR Code - Call python script to show the window to scan QR Code and once it finds the QR Code the code will return the content. 4. Validate the QR Code based on the details provided by visitors - Based on the content returned from the above step, validate the content in visitor details Google sheet to check if any entry exists. 5. Show the camera window and take the photograph of the visitor - If visitor details exist in Google sheet, Call Python script to show the camera window and Security can take the photo by clicking on Enter button. 6. Send digital entry card to the visitor over email - Call DLL file and pass parameters i.e. username, password, visitor email address, and so forth which will send entry card to the visitor. 7. Delete the visitor details from google sheet - Delete the visitor details from Google sheet once the entry card has been issued. 8. Add the visitor details in another google sheet to keep the details for future - Store the visitor details from the above step in variable and pass these values in Google sheet Challenges I ran into Problem: The challenge was to send the entry card over Gmail with the inline image (only for an image stored in the local machine) using the built-in email functionality where the image was getting displayed as blank however the same functionality was working fine while sending the email in outlook. Solution: Used DLL file to send the email with an inline image which is sending the email with an entry card to the visitor. Problem: Providing the dynamic cell in set cell option of Google sheet was throwing error and was not allowing to give the variable like A$countRow.Number:toString$ even though the count variable was typecasted to string. Solution: Assigned the cell address in a variable and used that variable in the Google sheet set cell option. Accomplishments that I'm proud of I am pleased with the idea of helping society in not spreading the deadly COVID-19 virus. What I learned I learned many new features of A2019 i.e. Forms, Google Sheet, etc while developing this solution. Apart from that I also learned how to create DLL files and use the same in A2019. What's next for eVisitor Card Scanning Government ID Auto Face-detection Built With a2019 google-chart google-spreadsheets python Try it out github.com

https://devpost.com/software/bot-o-ship

NOTE: In the end I was unable to move the bot to a public domain for some reason even after following the instructions here ( https://aai-covid19-botathon.devpost.com/updates/12930-enterprise-a2019-guidelines ). I have included set of images that show every line in the bot and their setting that can be found in the repository linked to this project. This submission is for the Travel, Supply Chain & Logistics category. Inspiration I experienced a lot of problems with delivery during Covid-19. Many inexperienced delivery people are hired to offset the huge increase in orders. They often lose packages, deliver them incorrectly, or skip your house. The bootstrap shipping method also means I rarely get notified when my package arrives. I wanted a way to help fix this. What it does It allows delivery orders to be tracked by just sending an email. The delivery driver takes a picture of where they left the order and sends it by email to the company they deliver for (This means that delivery drivers don't need to install an additional app). From there, an automation anywhere bot gets triggered and downloads the image and the metadata from the email. It is able to extract the gps location from the image metadata, and the order number using OCR. If the product is shipped in the correct location, it sends an email to the customer saying their order is there for pickup. If the location is incorrect, it sends an email to the delivery person that they made a mistake. All of this is updated in a customer order spreadsheet. How I built it Automation Anywhere with python scripts to handle the metadata scrape and address to gps coordinate conversion. Challenges I ran into Learning the automation anywhere tools and debugging some errors Accomplishments that I'm proud of Getting the entire project completed and working at 100% What's next for Bot-O-Ship Creating a map to follow the delivery route of the driver to determine if they are doing their job efficiently. This can be done using the gps metadata from the images they take for every order. Built With automationanywhere gps python Try it out github.com

https://devpost.com/software/safeapp-1qhtub

Inspiration Currently there are 12300 persons in institutionalized quarantine and over 10200 people are hospitalized in Romania. Imagine 336 hours in the life of a COVID-19 suspect, quarantined alone, isolated away from his family, without human interaction and who is constantly measuring every change in his body condition, fearing C-positive. Imagine >336 hours in the life of a sick person who is lucky enough to not be in the Anesthesia Intensive Care Unit but who is isolated from doctors, family and who is afraid for his own life and needs to communicate more with doctors, a psychologist, state institutions or other people in order to distract himself from his own fears and thoughts. And now imagine how easy it is to have a mental crisis while in quarantine or in the hospital and at the same time to not have anyone around with sufficient time to see you, hear you, calm you or help you. SAFEapp is created from these real needs of patients/suspects. What it does SAFEapp connects patients/suspects with those who are separated from them by the disease: doctors, psychologists, juridical counselors, state institutions (City Hall, Police, Emergency Situations Department), other hospitalized or quarantined people, all in a MultiChat, full of warmth and color through entertainment options (games, videos, movies, e-books) to increase their well-being, immunity and faster healing. And because our goal is health, we also have a package of options for healthy people: free medical, psychological and legal consultations, e-market offers, COVID hospitals map, positive information, news about NGOs and a space where to attach his contribution to the COVID reality. The key element of our solution is the construction of the patient's mental map, so that a virtual psychologist (AI) can estimate in advance the appearance of crisis states of the patient or of the COVID suspect. How I built it Initially it all started with a quick, simple and targeted solution for patients, suspects and their families ( www.caz-covid.ro ). Challenges I ran into In a short period of time the website proved to be too unattractive and too targeted on a single link in a vast system, which determined us to use other channels to reach the targeted group: Facebook, Instagram, press, epidemic communities, etc. Accomplishments that I'm proud of The time was too brief and people’s panic was too big to concentrate on other things than the 4000 online consultations and 100 psychological and juridical counselling sessions, lists with material support, applications for financing from a variety of sources. What I learned We understood and we haven’t forgotten that we could do it differently, better, more complex, fresher and more positive. That’s why we increased the team with people of various ages (teenagers, bachelors, young people) with different educational backgrounds: IT, arts, psychology, mathematics and computer science. We also introduced the function of virtual psychologist for crisis estimation. What's next for SAFEapp So, SAFEapp is the improved version, adapted to both the needs of state institutions to communicate and answer promptly and positively to patients/suspects and also to the needs of patients/suspects to feel safe for themselves and their families. SAFEapp has the governmental version that we will deliver to hospitals, quarantine centers, City Halls, Police and the Emergency Situations Department, as well as the community version for healthy citizens. We know that this kind of application, inspired by the COVID reality, can be adapted to any other disease/infectious disease or in case of a future different regional epidemic, so we aim to easily adapt it to wider causes and to the requirements of several European states and beyond. The benefits of implementing the application on a large scale are: •Public administrations interact more efficiently and promptly with people most affected by the epidemic (patients, suspects and their families). •The existence of such a vast simultaneous interaction can interconnect not only patients in a transnational network, but also doctors. •Anticipating the occurrence of psychological crisis situations of patients and suspects. •Creating a positive environment and framework for patients/suspects. •Providing a wider range of interactions than family members and virtual network (in which they do not want to expose themselves). Development: 12 months Built With ai android css digitalocean htm java javascript mysql php rocket.chat wordpress Try it out safeapp.caz-covid.ro

https://devpost.com/software/covid-19-virtual-travel-assistance

Advice Email Request and Response Automation Flowchart (High Level) Inspiration During the initial dates of the lockdown in India , a close relative of neighbors died in a district far away from our city (Mumbai) , and they had no clue what to do? How they can travel during lockdown? If they apply for travel pass, how many people can travel in a car then? This incident inspired me to create this solution when I came to know about CoVID-19 Botathon in the last week of May’20. What it does Travelling during this pandemic is not recommended ; however, being social animals, needs arises causing us to travel from a place to another. CoVID-19 Virtual Travel Advisor assist us find how safe our travel or commute is! The Govt. of India has classified all the districts in the country into RED\ORANGE\GREEN Zones . Government has also defined rules for each of the zones. There are special travel related advisories to be followed while traveling through these zones. CoVID-19 Virtual Travel Advisor BOT leverages MapMyIndia Geo APIs to find the optimal route from Origin to Destination addresses fed to the BOT via Email. It then identifies all the districts the route passes through. The districts are then classified into RED\ORANGE\GREEN Zones using information provided by the Govt. of India on its CoVID19 dedicated website www.mygov.in/covid-19 . The BOT makes the suggestions based upon the findings and provides a report listing all the districts and their CoVID19 zone classification. This can help us plan the travel as per government's advisory for travel in RED\ORANGE\GREEN Zones. This will avoid hurdles we have not planned for. How I built it I have used Emails received in MS Outlook desktop application as the start point. A rule defined in MS Outlook moves the request email from Inbox to a dedicated folder when an email with a defined subject arrives. The BOT iterates through each email when manually invoked. This BOT is built in Automation Anywhere’s A2019 (Community Edition). BOT leverages several Geo APIs from MapMyIndia in its course of action. It starts the operation with Geocoding API converting the Origin and Destination addresses into geographic coordinates (latitude/longitude) to be placed in Routing API which calculates optimal (used as default calculation type) driving routes between specified locations including via points\intersections. It then uses distinct intersections to be able to find the district in which the intersection are. Reverse Geocoding is a process to give the closest matching address to a geographical coordinate (latitude/longitude). BOT uses MapmyIndia’s Reverse Geocoding API to identify the districts on the intersections in the route. To identify CoVID19 Zone Classification of the districts, I have used the information available on CoVID19 dedicated website www.mygov.in/covid-19 of Govt. of India . Challenges I ran into First, I had very less time to work upon on the solution. Secondly, it was my first time to build a complete BOT using A2019 and very first time to work with Geo APIs . Building a working solution in a short period was itself a challenge. Understanding working of Geo APIs and decoding Routing API's result was the main challenge I faced. Accomplishments that I'm proud of The working solution itself is a great accomplishment for me. I am proud of that I have built an end to end solution using Geo APIs. What I learned Building BOTs using A2019 and usage of Geo APIs What's next for CoVID-19 Virtual Travel Assistance Initially, I had decided to build a mobile app to feed the inputs to the BOT. However, due to very less amount of time to implement the solution along with office work, I have used MS Outlook to feed input requests to BOT. We can have a mobile app integrated with AWS S3 to feed inputs to BOT and receive output. A very sophisticated solution can be developed to provide travel advisories. Also, it is currently implemented for India only. The idea was to show how BOTs can help humans in safeguarding their travels. The solution has potential to serve peoples from other countries\regions. Built With a2019 email geocoordinates mapmyindiaapi maps mygov.in outlook

https://devpost.com/software/covid-19-workplace-health-safety-plan

Bot Making Call if Person not wearing mask or Maintaing Social Distance A2019 working as Bridge For integrating Computer Vision and Chatbot and Phone Call and creating E2E Solution Chatbot For Helping Employess for increasing there productivity and safety Mandatory Wearing Mask and keeping Social Distance # Stay Safe Inspiration As We All know Everyone in the world is talking about new Normal During or Post Covid-19 Situation and Government allowed to open Factories, Offices, Malls, Supermarkets with limited no of people. As Social Distancing is must for Employees as well as Employer Safety and we know it's not possible for the Employer to detect all employees all time whether they are wearing mask or maintain social distance or If Max number crossed. And there is no way employees can check before going in Public area whether it's free or not. we all should realize by now why china, Germany , Italy & Iran had flattered their curve , it just because they started following the social distancing & Wearing Face mask .Its important to us to follow the same , so this is the Bot does this job on behalf of man power. What it does 1.This Automation Anywhere Bot will keeps on monitoring live CCTV Footage inside organization. And if it will find any employee not wearing Mask it will perform Facial Recognition and fetch Employee details from the Employee Database and Call Employee at his Phone and SMS as well for alerting for wearing mask. Bot will also call admin at his phone if people are not maintaing Social Distancing and give him area details so that he can take action. Bot will also call admin at his phone if Max Number reached inside area and give him area details so that he can take action. This Solution Provides Chatbot for employees using which Employee can check before accessing free areas like Cafeteria, Smoking Zone, Etc. How I built it I built this bot using Technologies:- Automation Anywhere Computer Vision OpenCv Python 3 Twilio (For Making Phone Call) IBM Watson (Chatbot, Discovery, NLU) Challenges I ran into Integration of multiple technologies. Accomplishments that I'm proud of I created a solution which will help not only Factories or Corporate Office for caring about there Employees Safety even it will be use in Supermarkets, Malls, Schools, Government Offices. What I learned re-invent the new wheel…utilize the existing Integration of multiple Technologies for creating single solution for battling against Covid 19. What's next for Covid 19 Workplace Health & Safety Plan Creating Mobile Application and Web portal for creating Touch less Environment. Built With automationanywhere2019 computervision html5 ibm-watson keras opencv python sklearn tensorflow twilio Try it out github.com github.com github.com

https://devpost.com/software/deployment-automation

Process Flow Diagram of Deployment Automation Inspiration I got an inspiration, when I started deployment manually and when there is the unavailability of the workforce. It becomes a very tedious task and time-consuming. What it does The "Deployment Automation" has the capability to timely deploy continuous software deliveries at the onsite environment and offshore environments. It picks the ". properties file from an absolute path and perform tasks accordingly. After deploying the packages to the offshore environment, it creates the docker image of the whole application from the dockerfile and then pushes the image to the docker hub, from where the client site, pull the image and run it as a container. How I built it I create a shell script, which read ". properties" file. This file includes artifactory link of packaged deliveries, environments name(where to deploy) and it's IP with defined variables respectively. I create jobs in Jenkins with "init" job as a starting job and schedule it every 2 min. This init job read variables from ". properties" file. And, deploy packaged deliveries in respective environments. Challenges I ran into The challenge is how to append incremental files in the complete files in different environments. Since each incremental files have a different format of representation. So, I need to find out the pattern that definitely looks out for incremental file and append accordingly. Accomplishments that I'm proud of I made the whole manual deployment to automation deployment. I feel happy now since I do not need to remain online for deployment. The job picks the PDN(Processed Definition Number) file which includes artifactory path server name, and user and does the processing as required. And, using the docker image, the whole application will get deployed at onsite without doing manual deployment at onsite. This also enables offshore environment in sync with the onsite environment. What I learned I learned shell scripting, Jenkins, Git, SVN, Docker. And, more important I learned how to make a designed process and workflow for automation. What's next for Deployment Automation That depends on business needs and new updates. Built With docker git jenkins shell-scripting svn

https://devpost.com/software/aayushman-ai-powered-smart-multi-purpose-covid-19-bot

Home Page Screening / Diagnosis Prediction Psychiatric Therapy Employee Checkin Diagnostic Report Geo fencing Common Questions Facebook Messenger bot Whatsapp bot Inspiration My brother is a Frontline Health Worker who treats Covid-19 patients in Bangalore, India. Everyday my family and me constantly pray nothing happens to him because he is constantly under risk of getting infected with Covid-19. In order to solve this problem that effects millions of brave hearted people who sacrifice their lives to stop this pandemic all over the world, AAYUSHMAN was built. "AAYUSHMAN" in Sanskrit means Safe and Healthy. What it does Features / Functionalities : Comprehensive Covid-19 Screening / Diagnosis with Prediction & Diagnostic Reports. Geo Fencing Based Nearest Covid-19 Home Quarantined Person's Information Notification. Answers to Hundreds of Covid-19 related Queries & FAQ's. Virtual Psychiatric Therapy to Combat Loneliness / Depression. Employee Checkin for Companies to Track and Monitor. Voice Controlled Dashboard for Covid-19 Healthcare Professionals for Easy Data Monitoring. AAYUSHMAN also Deployed in WhatsApp & Facebook Messenger Platforms. How we built it Using Dialogflow Framework to create Google Cloud deployed Bot that is also be deployed over multiple platforms such as Whatsapp and Facebook Messenger for Feasibility and Vibrancy. We used Node.js for the frontend and backend. We used NLP for voice/speech recognition. Challenges we ran into In training our bot to process speech accurately. Deployment in other platforms. Accomplishments that we're proud of We have successfully deployed our bot in Google, Whatsapp and Facebook Messenger Platform after elaborate beta testing and is ready for use. What we learned Apart from experience in learning new technologies and frameworks. We learned to leverage technology for the good that can impact people well. We learned to Innovate for the society and we are now obsessed with it. We look to develop products in the future that can help and save humanity. What's next for AAYUSHMAN - AI Powered Smart Multi - Purpose Covid-19 Bot Especially in India where the Medical System still lacks advanced technology as a result people still practice traditional screening and testing. Our bot could certainly eliminate this making it faster and safer. Hence we would like to see AAYUSHMAN being utilised in Public and Private Hospitals. This would certainly eliminate the risk factor for Health workers to an extent. Built With dialogflow google-cloud natural-language-processing node.js Try it out github.com

https://devpost.com/software/call-center_outboundcalls_automation_planchange_a2019

Inspiration To help Society and Business in COVID situation by providing an immediate solution that serves a business need and so business can serve Customer/society better Real need for Solution to solve Contact Center Customer challenges. Talking to big heads driving BPOs like Accenture, TCS, CST, firmed our idea. Build Production Ready solution that supports Business to continue following the same process from the office laptop/desktop which is now at home. Data Privacy and Security. Keep the Agent productivity & monitoring intact Automated solution with Cloud Telephony platform integration What it does Agent to use Interactive Forms in A2019 and deploy a bot via WLM technology on Remote Runner Machine. The Bots are integrated with the IVR and ACD systems. Telephony and CTI integrations with leading contact center vendors available. Single pane view of all customer data on one screen, using interactive forms. Agent triggers bot to provide more information or update fields across multiple applications Next-step-guidance is presented to the agent, to eliminate errors and fraud, and improve upon training. The bot auto-completes the after-call work by setting reminders SMS, sending emails, and updating multiple systems. How I built it We used latest web based AA platform A2019. Integrated with Cloud Telephony platform Exotel (trial version) with A2019 – using Google Suite Script and AA APIs Covered end to end flow , like Self Service Module, In Call Guidance, Post Call Automation and Performance and QC dashboard using Bot Insight We built a Custom Command pkg for WLM Work item creation. We used few APIs to automate Work Queue creations & device association as soon as Agent executes the bot. Solution helps executing bot on remote runner machine and provide interaction between bot & human to support any process need Human-bot collaboration. ## Challenges I ran into Choosing right Cloud Telephony platform that allows to automate end to end flow. (we used trial of Exotel) Design integration of Exotel with A2019. Aspects to be covered to serve Pre Call Transfer, Post Call transfer and Post call Completion activities. No inbuilt mechanism to execute a bot on Remote machine. (we achieved using WLM technology). No inherent ability to automate WLM, so to reduce Operation Overhead creating WLM queues/workitems for thousands of Agents. (we automated it 92% to 95%, remaining 5% to 8% is one time Job) Accomplishments that I'm proud of We could build Solution that is: Most suitable for Contact Centers need and support business Continuity. Helps companies supporting/helping their Customers & society, in adverse time like COVID - 19. Industry/domain agnostic. Be it Healthcare, Finance, Banking, Logistic, Supply chain etc, it works. Saleable & scalable and support all aspects e.g. Security/Scalability/Performance/HA-DR etc Fully Automated, reduce Manual/operation overhead. Production Ready. What I learned Business: Support Business Continuity is the most important factor even in COVID like situation. Forecasting problems and start building solutions to support business and the whole society. Pick a right problem as there is always a way to solve the problems. Technical: IVR and ACD systems. Better way of utilizing powerful web based Automation Anywhere platform A2019. How helpful Custom Command Package & AI Senses features have been. How individual components can be integrated to derive a solution to support Business Continuity. What's next for Call Center Solution We would like to try fit in the solution for other Call Center use cases Try to integrate with few more IVR platforms. Built With a2019 exotelapi googleappscript Try it out github.com

https://devpost.com/software/precon-prevent-contagious-ar-ar-training-simulation

Inspiration The inspiration comes when I see a lot of peoples still acting improperly to current situation (COVID-19). Many of them still forgot about this viruses and act like this is nothing, they didn't wash their hand, touching carelessly, etc What it does With this AR application, we can simulate the event in daily life, what should we do and shouldn't. So people can learn and remember what exactly action should be taken when it comes to prevent contagious of COVID-19. And more importantly, Using AR for training simulation is the one of many ways to do a safe simulation, without being infected from the virus. It can helps other people for keep their self healthy from viruses based on their reaction. How I built it I'm using one of the common game engine, Unity, and AR Foundation for the AR part. I'm combining the 3D assets and make the simple training simulation. I'm also trying echoAR for 3D cloud services but not fully implemented. Challenges I ran into I need to find more information about Augmented Reality also do trial and error, sometimes makes me dizzy. Also, this is the first time using echoAR services. Got some problem about the limitation for using it's API, since currently I'm using free version. Accomplishments that I'm proud of This project actually. Somehow proud that I can be part of "helping others" in this pandemic with my own way. Especially helping medical team to prevent other people being infected. Helping people with Extended Reality technologies are something that really change the views of AR/VR/MR application. What I learned From this project, I'm more learn new things about AR, how to solve problem, how to adapt to the new things, also more exploring about COVID19 also for my self I'm learning more about what should we do and should't. More exploring some advice for being healthy during this pandemic. What's next for test project Maybe if I can really continue with this project, I hope can upgrade more simulation regarding COVID19, and maybe will try to do Virtual Reality Training Simulation, so it can be more immersive to do a training. Built With android arcore arfoundation c# echoar unity-technologies Try it out precon.rgplays.com

https://devpost.com/software/call-center_automation_covid-19_creditcard_replacement_a2019

Inspiration : 1 To help Society and Business in COVID situation by providing immediate solution that serve business need and so business can serve Customer/society better 2 Real need for Solution to solve Contact Center Customer challenges. Talking to big heads driving BPOs like 3 Accenture, TCS, CST, firmed our idea. 4 Build Production Ready solution that supports 5 Business to continue following the same process from the office laptop/desktop which is now at home. 6 Data Privacy and Security. 7 Keep the Agent productivity & monitoring in tact 8 Automated solution with Cloud Telephony platform integration What it does : 1 Agent to use Interactive Forms in A2019 and deploy a bot via WLM technology on Remote Runner Machine. 2 The Bots are integrated with the IVR and ACD systems. 3 Telephony and CTI integrations with leading contact center vendors available. 4 Single pane view of all customer data on one screen, using interactive forms. 5 Agent triggers bot to provide more information, or update fields across multiple applications 6 Next-step-guidance is presented to the agent, to eliminate errors and fraud, and improve upon training. 7 The bot auto completes the after-call work by setting reminders SMS, sending emails, and updating multiple systems. 8 Enforce data privacy and Standard Operating procedures (SOP) i.e. Agent will receive only Non-PII data from Bot for Validation and further usage. 9 Provides Real time analysis of transactions/tickets processed by Agent in one single Dashboard – using Bot Insight How I built it: 1 We used latest web based AA platform A2019. 2 Integrated with Cloud Telephony platform Exotel (trial version) with A2019 – using Google Suite Script and AA APIs 3 Covered end to end flow , like Self Service Module, In Call Guidance, Post Call Automation and Performance and QC dashboard using Bot Insight 4 We used Interactive Form feature to gives single pane view for Agent. 5 We built a Custom Command pkg for WLM Work item creation. 6 We used few APIs to automate Work Queue creations & device association as soon as Agent executes the bot. 7 Solution helps executing bot on remote runner machine and provide interaction between bot & human to support any process need Human-bot collaboration. Challenges I ran into : 1 Choosing right Cloud Telephony platform that allows to automate end to end flow. (we used trial of Exotel) 2 Design integration of Exotel with A2019. 3 Aspects to be covered to serve Pre Call Transfer, Post Call transfer and Post call Completion activities. 4 No inbuilt mechanism to execute a bot on Remote machine. (we achieved using WLM technology). 5 No default capability to let Human and Remote bot collaborate (exchanging information) with each other. (we used Interactive Form and WLM) 6 No inherent ability to automate WLM, so to reduce Operation Overhead creating WLM queues/workitems for thousands of Agents. (we automated it 92% to 95%, remaining 5% to 8% is one time Job) Accomplishments that I'm proud of : We could build Solution that is: 1 Most suitable for Contact Centers need and support business Continuity. 2 Helps companies supporting/helping their Customers & society, in adverse time like COVID - 19. 3 Industry/domain agnostic. Be it Healthcare, Finance, Banking, Logistic, Supply chain etc, it works. 4 Salable & scalable and support all aspects e.g. Security/Scalability/Performance/HA-DR etc 5 Fully Automated, reduce Manual/operation overhead. 6 Production Ready. What I learned: Business: 1 Support Business Continuity is the most important factor even in COVID like situation. 2 Forecasting problems and start building solutions to support business and the whole society. 3 Pick a right problem as there is always a way to solve the problems. Technical: 1 IVR and ACD systems. 2 Better way of utilizing powerful web based Automation Anywhere platform A2019. 3 How helpful Custom Command Package & AI Senses features have been. 4 How individual component can be integrated to derive a solution to support Business Continuity. What's next for Call Center Solution - 1 We would like to try fit in the solution for other Call Center use cases 2 Try to integrate with few more IVR platforms. 3 Try Outbound Call type use case. Built With a2019 acd aht attended creditcard interactiveform ivr unattedned wlm Try it out github.com

https://devpost.com/software/call-center_automation_covid-19_hold-order_a2019

Inspiration : 1 To help Society and Business in COVID situation by providing immediate solution that serve business need and so business can serve Customer/society better 2 Real need for Solution to solve Contact Center Customer challenges. Talking to big heads driving BPOs like 3 Accenture, TCS, CST, firmed our idea. 4 Build Production Ready solution that supports 5 Business to continue following the same process from the office laptop/desktop which is now at home. 6 Data Privacy and Security. 7 Keep the Agent productivity & monitoring in tact 8 Automated solution with Cloud Telephony platform integration What it does : 1 Agent to use Interactive Forms in A2019 and deploy a bot via WLM technology on Remote Runner Machine. 2 The Bots are integrated with the IVR and ACD systems. 3 Telephony and CTI integrations with leading contact center vendors available. 4 Single pane view of all customer data on one screen, using interactive forms. 5 Agent triggers bot to provide more information, or update fields across multiple applications 6 Next-step-guidance is presented to the agent, to eliminate errors and fraud, and improve upon training. 7 The bot auto completes the after-call work by setting reminders SMS, sending emails, and updating multiple systems. 8 Enforce data privacy and Standard Operating procedures (SOP) i.e. Agent will receive only Non-PII data from Bot for Validation and further usage. 9 Provides Real time analysis of transactions/tickets processed by Agent in one single Dashboard – using Bot Insight How I built it: 1 We used latest web based AA platform A2019. 2 Integrated with Cloud Telephony platform Exotel (trial version) with A2019 – using Google Suite Script and AA APIs 3 Covered end to end flow , like Self Service Module, In Call Guidance, Post Call Automation and Performance and QC dashboard using Bot Insight 4 We used Interactive Form feature to gives single pane view for Agent. 5 We built a Custom Command pkg for WLM Work item creation. 6 We used few APIs to automate Work Queue creations & device association as soon as Agent executes the bot. 7 Solution helps executing bot on remote runner machine and provide interaction between bot & human to support any process need Human-bot collaboration. Challenges I ran into : 1 Choosing right Cloud Telephony platform that allows to automate end to end flow. (we used trial of Exotel) 2 Design integration of Exotel with A2019. 3 Aspects to be covered to serve Pre Call Transfer, Post Call transfer and Post call Completion activities. 4 No inbuilt mechanism to execute a bot on Remote machine. (we achieved using WLM technology). 5 No default capability to let Human and Remote bot collaborate (exchanging information) with each other. (we used Interactive Form and WLM) 6 No inherent ability to automate WLM, so to reduce Operation Overhead creating WLM queues/workitems for thousands of Agents. (we automated it 92% to 95%, remaining 5% to 8% is one time Job) Accomplishments that I'm proud of : We could build Solution that is: 1 Most suitable for Contact Centers need and support business Continuity. 2 Helps companies supporting/helping their Customers & society, in adverse time like COVID - 19. 3 Industry/domain agnostic. Be it Healthcare, Finance, Banking, Logistic, Supply chain etc, it works. 4 Salable & scalable and support all aspects e.g. Security/Scalability/Performance/HA-DR etc 5 Fully Automated, reduce Manual/operation overhead. 6 Production Ready. What I learned: Business: 1 Support Business Continuity is the most important factor even in COVID like situation. 2 Forecasting problems and start building solutions to support business and the whole society. 3 Pick a right problem as there is always a way to solve the problems. Technical: 1 IVR and ACD systems. 2 Better way of utilizing powerful web based Automation Anywhere platform A2019. 3 How helpful Custom Command Package & AI Senses features have been. 4 How individual component can be integrated to derive a solution to support Business Continuity. What's next for Call Center Solution - 1 We would like to try fit in the solution for other Call Center use cases 2 Try to integrate with few more IVR platforms. 3 Try Outbound Call type use case. Built With a2019 acd aht attended interactiveform ivr sap sfdc telephony unattended Try it out github.com

https://devpost.com/software/dis-cretion-cnhlfw

Log in to the unparalleled experience of decision making on employee request The Request Dashboard Review request details, alongside specific policy information, anonymised historic requests and areas of considerations highlighted for you See the anonymised historic requests Look out for the latest changes of guidance Highlight the changes for you to review Inspiration Many organisations couldn’t process a large number of employee queries timely during the COVID-19. There are evolving policies and guidance from all directions that HR practitioners need to maintain. Employees and Managers exhaust their time and efforts on searching for the right information. Many decisions on employee requests are not consistent across the business. Discretionary decisions are likely to be made using previous experience. Junior HR practitioners can be inexperience in making discretionary decisions, hence the discretion offering may not be at the optimal level. What it does Dis-cretion is a tool to empower staff to make informed decisions on employee requests efficiently and consistently. It highlights specific internal policies, external resources (e.g. government guidance), anonymised historical requests, and areas of consideration. How it works An employee submits a request, which is sent to the line manager to review. The manager sees the dashboard of requests and can review the details of them. Dis-cretion presents specific policy information that is relevant to the request. It also shares anonymised summary of some relevant historical requests. Dis-cretion encourages structured, constructive discussions between manager and employee by suggesting areas of consideration and topics to both of them. The manager can now make an informed decision. The automation anywhere Email bot is triggered to send a customised update on behalf of the manager to the individual. Decision and rationale are fed back to the repository. Challenges we encountered Limited backend capacity to run the machine learning model - need manual refreshing Next steps Look into alternative ways to capture employee requests, e.g. use chatbot or other major HR systems Contribute to smarter Business Continuity Planning Built With adobe-xd google-cloud heroku javascript machine-learning python react typeform Try it out dis-cretion.herokuapp.com hellodiscretion.typeform.com github.com github.com

https://devpost.com/software/bpo-work-from-home-solution-a2019

Inspiration : To help Society and Business in the COVID situation by providing an immediate solution which leverages Automation Anywhere’s wen native A2019 platform which serves an enterprise’s need and so they can serve their Customer/society better Real need for Solution to solve challenges which a BPO’s customer face. Talking with senior leadership at BPOs like Accenture, TCS, CST, firmed our idea. Build Production Ready solution that supports Business Continuity Plan - Business to continue following the same process (workflow, rdp, citrix login, etc.) from the office laptop/desktop which is now at home. Data Privacy and Security. Keep the Agent productivity & monitoring intact What it does : Provides a single pane view to Agent, for all customer data on one screen, using Interactive Forms. Agent to use Interactive Forms and deploy a bot via WLM technology on Remote Runner Machine, so Agent does not access the target application at all (if the use case demands). Alternatively, the agent can trigger the bot on a target application on a local machine as well. Remote bot takes Agent’s input and perform actions on Target application (e.g. SAP as XenApp or XenDesktop) reside on Citrix machine. AI Sense 2.0 (A2019) helps performing actions accurately even with screen Resolution changes. Provides Real-time analysis of transactions/tickets processed by Agent in one single Dashboard – using Bot Insight Remote bot validates PII data on the remote machine itself. Only Non-PII data are sent back on the Agents Form view for further updates/validation. Solution Reduces WLM Operational Overhead by 92% to 95%, creation of Work Queues/Device assignment/Work item creation etc as we have Automated the whole concept. As an instance : Agent = 500 Steps/Process = 5 Execution/Day = 100 Total Manual efforts is [(500) Work Queue creation + (500 Agent x 100 Runs x 5 steps) Work Items creation] All of above is Automated. How I built it: We used latest web-based AA platform A2019. We used Interactive Form feature to gives a single pan view for Agent. We built a Custom Command pkg for WLM Work item creation. We used few APIs to automate Work Queue creations & device association as soon as Agent executes the bot. Solution helps executing bot on remote runner machine and provide interaction between bot & human to support any process need Human-bot collaboration. BPO customers do not allow Citrix Agent to be install, so we used AI Sense 2.0 (AI in true sense) to perform Image base Automation accurately, even works on various machine resolutions. Bot Insight for Realtime Dashboarding. Challenges I ran into : No inbuilt mechanism to execute a bot on Remote machine. (we achieved using WLM technology). No default capability to let Human and Remote bot collaborate (exchanging information) with each other. (we used Interactive Form and WLM) No inherent ability to automate WLM, so to reduce Operation Overhead creating WLM queues/workitems for thousands of Agents. (we automated it 92% to 95%, remaining 5% to 8% is one time Job) Right approach to design Work Queue, Per Users or Per Steps. (we finally created User-based Work Queue for smooth and consistent results) Right approach to design Remote Bot Creation/Execution flow. Because One WLM Queue can associate only One Bot, but Agent has to execute multiple Bots (workitems) to complete the whole process. (we created a Master Bot and conditioned it based on parameters passed from Agent (via workitems) and executes right Child Bot and also pass Child Bot output to Agent) Citrix Agent is not allowed as a BPO practice, challenge was to automate target application (SAP) using IR/OCR and make it work even when screen resolution changes. (We used AI Sense 2.0) Accomplishments that I'm proud of : We could build Solution that is: Most suitable for BPOs & Contact Centers, and support business Continuity. Helps companies supporting/helping their Customers & society, in adverse time like COVID - 19. Industry/domain agnostic. Be it Healthcare, Finance, Banking, Logistic, Supply chain etc, it works. Saleable & scalable and support all aspects e.g. Security/Scalability/Performance/HA-DR etc Help driving Sales and build Customer trust until an inbuilt capability comes in to A2019. Fully Automated, reduce Manual/operation overhead. Production Ready. What I learned: Business: Support Business Continuity is the most important factor even in COVID like situation. Forecasting problems and start building solutions to support business and the whole society. Pick a right problem as there is always a way to solve the problems. Agents get bogged down by juggling multiple screens and repetitive tasks. AHT and NPS suffer as a direct result. RPA can free the agent up to do more judgment based/non-repetitive work. Resolve calls faster and improve customer experience. Remote agents need virtual setup and training. RPA gets them started faster. Reduce setup and training time. Expensive to integrate systems for an omnichannel customer engagement experience. RPA reduces the time, effort and dollar investment to get you the same experience, faster. Technical: Better way of utilizing powerful web based Automation Anywhere platform A2019. How helpful Custom Command Package & AI Senses features have been. How individual component can be integrated to derive a solution to support Business Continuity. What's next for BPO Work from Home Solution - Considering wide usage of Excel, we would like to extend the solution using Excel Productivity plugin, by creating Forms in Excel, using Macros etc. Enhance easiness by creating more Custom command pkgs, so one Command can server multiple actions. To extend the solution for Contact Centers along with IVR/CTI integrations to support Self Service, Post Call transfer to Agent and Post call Complete, activities. Built With aisense automationanywherea2019 botinsight citrix interactiveforms sap wlmapi Try it out github.com

https://devpost.com/software/smart-care-2-0

Inspiration As of May 31, 2020, 103,700 Americans have died from Covid-19. 62,000 doctors, nurses and health care providers have been infected and about 300 have died. Around the world, covid-19 has presented a number of challenges from absence of a vaccine to shortages of personal protective equipment (PPE). Healthcare providers are risking their lives to test and save others, but there is a safer method to test patients while protecting healthcare professionals. What it does Smart Care leverages EOSIO blockchain technology and smart contracts with facial recognition to automate the process of testing for Covid-19. To start, patients enter basic health screening questions and a computer screen with a webcam watches as they swab themselves. The patient must turn sideways to show a side profile. A good specimen is calculated after the swab has reached 50% of the diameter of the patient's head. To show that a good specimen has been collected the border around the screen changes from red to green. That information is then stored on the blockchain and sent to a doctor or other healthcare professional to notify them that a good specimen is collected at which time healthcare professionals can report the findings of the Covid-19 test. How I built it A lot of EOSIO smart contracts, tensorflow, opencv, blockchain. Challenges I ran into This is actually my first time using blockchain. I went from knowing close to nothing about blockchain to making my own blockchain with EOSIO. Understanding the concepts of blockchain proved to be the hardest part of the project. A Accomplishments that I'm proud of Coming up with and developing an application that could save thousands of lives. Whats next for Smart Care I would like for this to also be an app so that patients can pick up swabs from a safe location and swab themselves from their home or car without ever having to enter a medical facility, infecting themselves or others. Built With eosio javascript tensorflow Try it out github.com

https://devpost.com/software/chatbot-micro-coaching-for-employees-via-teams-slack

Remote work is a big change for most companies from the usual face to face business. The emotional toll, challenging environment, adoption of digital tools, prioritization, new way to lead people and self-management all make remote work a nightmare. Remote work transition (and especially 100% work from home) can be tough for employees. It disrupts collaboration, makes people lonely, requires new processes and remote leadership style. We hope to support thousands of people whose performance suffers. We empower key people to manage themselves better, gain clarity, collaborate effectively, and develop as remote leaders. Traditional coaching is a powerful tool that supports change. Panda’s micro-coaching solution brings it a step further by (a) making coaching scalable and affordable, and (b) allowing everyone to be aware of what’s going on in the company through knowledge sharing and data collection. All this made possible by utilizing both human expert-led coaching via text/Teams/Slack and AI chatbot tech. We hope to support thousands of people whose performance suffers. We believe we can support them emotionally, help them become more productive, communicate better and generally solve personal and professional challenges. 50K EUR revenue last year. working with Universal Pictures (in LA), Bayer, Posti, Cramo, SAP and a few smaller firms like Futurice, Gofore, Vastuu Group. Won in Latvian HackForce COVID hackathon, recognized by Microsoft, Swedbank, Startup Wise Guys. Expressed desire to pilot, looking for a project: Ramboll, Comcast, KONE, Finnair, UN, Stora Enso, S-Group, KPMG. Built chatbot product MVP. Built a podcast with following in Finland and sponsors. Built a network of freelance coaches we use for our projects. Won 2 hackathons: DigiEduHack in the category "The future of work" and Sanako EdTech hackathon. The product is 1 year old, the company is 4 years old - pivoted. The main challenge we have ran into was clarifying our value proposition. The problem is very diverse and people have different takes on it. We ended up with the conclusion that the management seems to care the most about the performance of the employees and the data about how their people are doing. On the tech side, chatbot integration with Teams has been challenging (especially authorization) but we solved it with Botkit. Tech-wise, the solution is ready. What we need is to develop the content for the free chatbot trial package and nail the go-to-market strategy. That's why we worked hard on signing up organizations to test our solution: we will interview their people and then let them use the solution before releasing it out to the world. We been signing up test users and organizations who want to spread the word. It's been a huge unexpected success with people signing up from Google, United Nations, Cisco, Innova (Swedish innovation agency), City of Detroit, Solvay, Bluehealth Innovation Center (part of Microsoft), ITMO University, Tampere University, Sofigate, Rigilog, NurseBuddy, Redbridge, Dutch chamber of commerce and 30 more organizations! Built With amazon-web-services express.js jquery node.js react Try it out www.fibofy.com

https://devpost.com/software/social-distancing-screen

Social Distancing Screen Inspiration I was inspired by World Hackathon Day for COVID19 Emergency Response. What it does It's a hologram camera 3d screen application. It can act like a toll-booth, and make suggestions like, "tap your phone" or "tap your card". How I built it I got a projection screen from Amazon and downloaded software from AIY Projects Cube with Google. https://aiyprojects.withgoogle.com/vision/ Challenges I ran into Like anything, I have to try and try again until I make a sale. Accomplishments that I'm proud of Well, it enables customers to interact with a safe Artificial Intelligence that could never be infected or dangerous. What I learned I learned that we can indeed, imagine and deploy solutions at the speed of thought. It's no longer a barrier, it's a portal or window... from the friendshipcube edge, to the 22core optical neural network, and into the hybrid cloud. What's next for Social Distancing Screen Sales and installations in a reliable partnership. Built With 3d camera distancing hologram screen social Try it out aiyprojects.withgoogle.com www.developers.google.com www.github.com

https://devpost.com/software/smart-tourist

Inspiration COVID-19 pandemic has brought the world into a halt. People were forced to stay at home. But, there would be a time when people will go out after the pandemic is over. But, travel in post-pandemic era would be harder. It requires longer hour, many health-related checking and a lot of documents.Therefore, a simpler way for checking in the post-pandemic travel would be crucial. What it does SMART helps travelers to go abroad safer in a post-pandemic era by utilizing blockchain and machine learning. Documents that would be needed for travel could be placed in blockchain and machine learning could be utilized to understand itinerary data. How I built it SMART is built using blockchain for cross distributed document-checking. Challenges I ran into Implement blockchain for cross distributed document-checking Integrate machine learning into the project Accomplishments that I'm proud of What I learned We learn how to use blockchain, deploy machine learning models, and integrate it into our application. What's next for SMART Smart contract development; testing; fine-tuning; improve application Built With amazon-web-services blockchain Try it out smart.acacio.my.id

https://devpost.com/software/mvhealthbot

Inspiration Health care systems have to be improved and gain some cleverness in the area of big data management , analyses and predictions using well-trained AI models. What it does MvHealthBot is providing predictive health services by using LinearRegression AI models and NLP for identifying Covid19, diagnose diseases based on symptoms and alert the patient in case of disorders. MvHealthBot also simulates the process of fetching data from a public health API using a patient's medical ID and provides a diagnosis via the well trained AI model that has been built for the purposes of the app. How I built it I used python and google big query for the AI models creation and training. The models were published as APIs using google AI platform and cloud functions. Dialogflow has been used for the NLP part, in order to identify free text sentences context and intents. Google cloud functions have been used as the api layer. Node.js has been used for the backend api services. Fire base database and big query for data storage. Google cdn for content management. Challenges I ran into Model training and accuracy was a big challenge Accomplishments that I'm proud of MvHealthBot could be used in the health sector and help people get faster diagnosis and results What I learned To never give up What's next for MvHealthBot MvHealthBot is a prototype now. I would like to find funds and traction to extend it in order to be used in the health sector. Built With ai angular.js dialogflow firebase gcp google-bigquery ionic node.js python Try it out mvhealthbot-883e9.web.app

https://devpost.com/software/covi_fight

Inspiration The virus has affected humanity in various ways, be it our economy, our freedom of movement, and the loss of loved ones. Then how do we live on, comfortably, and safely with this virus around? Even after the lockdown is over, there is a massive possibility that traces of the virus will remain, and it can spread again. We wanted to bring people back their mobility and keep them safe at the same time. We wanted people to know about their status while they leave their houses. What it does CoviFight alerts me about the risks of catching the virus if I have come in contact with an infected person within the past three weeks. It also informs the healthcare system accurately about the spread of infection. CoviFight also generates a map with hotspots for what places have virus traces , so that people can prevent travelling at these places and authorities can sterilize or lockdown these places efficiently rather than having a complete lockdown of a country. How we built it We develop a three-tier app: • A user's app • A provider's app • An official's portal. While utilizing Bluetooth and GPS of your phone, CoviFight makes sure that the confidentiality of every individual is secured and can not be compromised. Data is encrypted using a secret key, and no one can view it without your permission. It only traces the past data of positively tested patients. This way, CoviFight also meets the GDPR compliance. By using Geo-fencing and Machine Learning , we predict your chances of catching the infection so that you can take preventive measures. A provider's app for aggregation points like shops, restaurants, and public transport synchronizes with the nearby user app. This interface is the key to the detection of infection points, be it a stationary workplace or a moving vehicle . If McDonald's installed CoviFight and had an infected customer in the past 15 days, all the customers after the positive tested patient would get alerted, and hence the restaurant can be sterilized. Only the medical system may update a person's status over the official's portal, and the authenticity of the app is maintained hence preventing false positives or self-reporting, which might lead to falsification of records. So, with the help of our app, people can move around while being alerted about their status, stay away from the virus, and be free from the worry of their privacy maintenance at the same time. Take a look at our demo by clicking here Challenges we ran into To maintain the authenticity of the predictions and analysis, we were initially in a fix as to how to update a person's status as positive or negative. Then we decided to come up with a three-tier system, and we developed a Doc App or official's portal, which is only accessed by the medical system so that the authenticity is maintained. No one else can manipulate the data. Accomplishments that we're proud of • We have made sure that the privacy of every individual is maintained and can not be compromised. The encryption algorithms meet the standards of the leading social networking apps existing in the market. • CoviFight not only alerts people about their own risks but provides heatmaps of the traces of the virus too. CoviFight also shows what specific restaurant or public transport( like a bus or a train) may be infected precisely. • We do not need to compare data between people, thus making computation very cheap and exponentially faster and efficient. Our Journey So Far • Winners( Runner up) in the #EUvsVirus, a Pan-European Hackathon Organised by the European Innovation Council to counter COVID-19 pandemic with more than 9k participants and 2000 teams. We stood second in the Real time Communication and Prevention challenge. • Top 6 finalists of The Global Hack, by Garage48, April 2020 The Global Hack is a hackathon designed to share and rapidly develop ideas for urgently needed solutions in the face of the COVID-19 crisis, and to build resilience post-pandemic, with over 12k participants from 100+ countries. The team developed a mobile application solution for the containment and tracking of this virus. We were in the top 6 teams in the Crisis Response Track. • We were also in the Top 23 Student Innovators in COVID-19 SAMADHAN MHRD( Ministry of Human Resource Development, India) Mega Online Challenge What we learned It has been an enjoyable experience to work with people who have not even met each other before and still successfully develop this amazing app. We learned a lot through the hackathon, from interacting with the mentors and getting their guidance, to develop the app. What's next for CoviFight We plan to get this deployed at its earliest so that people may get their safe mobility back. We plan to deploy this on the Play Store and make a version for iOS as soon as we can. We are also in touch with the Indian Government and we might be able to save lives in India also. The necessities to continue the project: • Approval from government authorities to implement and track data. • Participation from Hospitals/government bodies to update the status of a patient so that system can generate realtime alerts and mark hotspots. • Cloud resources to scale up the project. Currently limited by the free tier of cloud infrastructure. The value of our solution after the crisis: • This application can be used for any contagious disease management. • It can be used in disaster management to understand the right victims and relief reaches all rightful beneficiaries( such as in the case of floods and storms). • It can be used by Providers such as McDonald's and Public transport systems to implement targeted location-based marketing complying with data collection practices. What we have done till now. • Implemented masked identities for users to comply with GDPR and privacy requirements. • Identification of hotspots in realtime based on the patient status update. • Fixed bugs in the flow and to make it work E2E. • Produced a product demo. New Technology introduces • Blockchain to manage user identification data(making it immutable), adding the security of ECC digital signature • Moving from NoSQL mogoDB to a hybrid of BlockChain and GraphDB for better analysis and prediction while keeping the user's id secrete on the Graph • Use of Kubernetes for creating multiple threads for gaining concurrency •Adding caching mechanism to mobile devices to handle any kind of network failure. Built With firebase google-directions har java machine-learning maps python Try it out github.com sidhantha.medium.com

https://devpost.com/software/messenger_assistant

First Aid Kit Inspiration Chronic diseases are defined broadly as conditions that last 1 year or more and require ongoing medical attention or limit activities of daily living or both. Chronic diseases such as heart disease, cancer, and diabetes are the leading causes of death and disability in the United States. They are also leading drivers of the nation’s $3.5 trillion in annual health care costs. https://www.cdc.gov/chronicdisease/about/index.htm What it does dealing with an attack for chronic disease How I built it https://developers.facebook.com/ https://dialogflow.com/ Challenges I ran into learn how to manage more than theology in one project Facebook & google cloud Accomplishments that I'm proud of that is my first chatbot What I learned “ANYONE WHO STOPS LEARNING IS OLD, WHETHER AT TWENTY OR EIGHTY.” —HENRY FORD What's next for First Aid Kit add more diseases to deal with Built With api developers.facebook dialogflow google webhooks Try it out www.facebook.com

https://devpost.com/software/internet-speed-analyzer

Inspiration Once we started working from home, our daily video stand-up ran into internet speed issues. Teammates would complain about the internet speed and we had to turn off the camera or drop and join back. Most of the team faced issues while having screen share meetings because of a drop in audio and video quality. Eventually, many of us realised that we need to switch to a different provider, upgrade our plan or change our meeting to a different time of the day. The solution was to have the speed data statistics over time and other analysis done regarding internet speed. Our project theme is Employee Care & Productivity . What it does Recommends if current internet speed plan works for the customer. Suggest upgrade plan details from the same provider. Provides top 5 alternate internet providers and plans based on the customer’s zip code. Checks if any unknown devices are stealing the wifi. Reports the number of devices connected to wifi every hour. Proposes the best and worst time for download and upload in 3 distinct shifts. Notes the highest and lowest download and upload speed over a fixed period. Summarizes the information in tables, graphs and emails to the customer in a readable format. How we built it Bot logs into customer’s internet provider account to capture speed plan and customer’s zip code details. For the botathon we have designed a UI for internet service provider website using google sites Truefast Configurable bot will perform the speed test by running every 30 minutes over a period of 1 to 3 days and capture the download and upload speed. The captured date will be put in excel sheet for consolidation and calculation. Based on the hourly average speed collected, bot will calculate the best download and upload speed in 3 distinct shifts and generate a graph (please check the email in the video). Speed is deemed acceptable if the average speed is above 80% (configurable) of plan speed. Bot emails the user if the internet speed is acceptable. Bot will search for top 5 internet providers and plans based on the customer zip code. This is done by logging into Broadband Search website and searching the best internet providers for the extracted zipcode. Scheduled bot will log into software “Who is on my wifi” and identify any unknown/suspicious devices is stealing the wifi and number of devices connected. Reducing the number of devices connected to wifi will help in improving the speed. Bot will generate a graph on number data connected for the desired time interval. Email the consolidated data to the customer after the scheduled processes are completed. Challenges we ran into Design solution on how to develop a configurable bot which can capture internet speed over 1 to 3 days. We spent time in figuring out how to test the prototype without waiting for scheduled bot. Explain the complete process and feature in less than 3 minutes in a video. It was a tough to decide on what features should we include and what feature should we drop from the timed video. Applying excel formulas for calculation. Accomplishments that we're proud of Come up with an idea for a common problem and then add various features to it. Started as a novice RPA developer for A2019 version but this botathon has encouraged us to explore more in Automation Anywhere. Providing different options to the customer to make him aware of all possible issues and fixes. Configurable design. What we learned Exploring the advance options of A2019 and Microsoft excel. Botathon is a fun way to learn and give us liberty to implement ideas and explore the tool. Decision making and problem solving. What's next for Internet Speed Analyzer In case the speed issues are found, request a router reset time to the customer. Bot to reset the router from command prompt. Check the internet latency. Provide online tips on how to improve the internet speed. Built With aa excel google-sites html rpa Try it out drive.google.com sites.google.com

https://devpost.com/software/chattering-space

WE ARE MAKING YOU AWARE OF COVID19 THIS COVID19 HAS BEEN SPREAD IN THE WHOLE WORLD Inspiration WE CAN DO THE PEOPLE AWARE OF COVID19 BY CREATING APP What it does IT AWARES THE PEOPLE HOW TO PROTECT YOURSELF FROM COVID19 How I built it I BUILT IT USING BLOCK PROGRAMMING Challenges I ran into NOTHING Accomplishments that I'm proud of IT IS VERY CLEAR What I learned NOTHING IS IMPOSSIBLE What's next for Chattering space Built With javascript Try it out studio.code.org

https://devpost.com/software/germ-blocker

Thumbnail Pulley System The circuit The box Inspiration My family gets takeout a lot. At our popular local pizza parlor, during the COVID-19 lockdown , employees typically stand outside rain or shine delivering takeout food to your car. The problem is that this isn't truly contact-free pick-up of food. We are still interacting with people who could be infected without knowing and surfaces that they have touched potentially increasing the spread of the virus . What it does Instead of handing the food/drinks for restaurant takeout, the people in the restaurant place the food/drinks in a box, then visit a link with a password (like restaurant.com/pass1) , making the box closed and secure with ultraviolet light killing all germs on the takeout containers. Then they send another password (like pass2) to a customer when they order takeout. When the customer comes, they go to the box and visit another link (like restaurant.com/pass2) , making the box open again. Then, they could pick up the food and go on their away. This process reduces the risk of transmission in takeout significantly and reduces the amount of people who need to be actively managing the pickup process, allowing restaurants to better allocate employees. How I built it I used an Arduino MKR1010 , a step motor, a box, leds, breadboard, and a few other miscellaneous parts to build it. I programmed it using C++. The Arduino creates a web server, and when the user makes a get request to the server by visiting a link, the box opens or closes using the stop motor and a pulley system with threads. Challenges I ran into I was limited to the supplies that I had in my house so I created a pulley system powered a step motor to open and close the door. The threads often got tangled up when the motor was pulling them because I was only using 1 motor and it was opening and closing a big door. For a better prototype, I would create automated hinges to open and close the door. Accomplishments that I'm proud of I was able to open and close a physical box using the internet . What I learned I now know how to use a step motor with Arduino and create a web server with my Arduino. What's next for Quarantine Pickup I'd like to increase the quality of my prototype using better materials and redesign the open/close mechanism. I also can make a more secure, and user friendly website for customers to interact with, and restaurant employees to administer. I believe that as well as reducing contact during pickup this also has the potential to increase efficiency for a restaurant after lock-down since less people will need to manage the pick-up process. Built With arduino c++ iot Try it out github.com

https://devpost.com/software/healing-frequencies-mix

Electromagnetic frequency healing for coronavirus(covid-19) Inspiration Audio frequencies mixing What it does Helps healing of Covid-19. How I built it Mixing audio frequencies using Mozilla and audacity to record. Challenges I ran into Choosing the correct frequencies. Accomplishments that I'm proud of The mix is high quality and all the frequencies can be heard. . What I learned How to record in high quality sound without distortion of frequencies. What's next for Healing frequencies mix I hope many people will find it useful to fight the infection. Built With audacity mozilla Try it out audiomack.com

https://devpost.com/software/stay-home-stay-safe-self-protection

Regularly and thoroughly clean your hands with an alcohol-based hand rub or wash them with soap and water. Why? Washing your hands with soap and water or using alcohol-based hand rub kills viruses that may be on your hands. Maintain at least 1 metre (3 feet) distance between yourself and others. Why? When someone coughs, sneezes, or speaks they spray small liquid droplets from their nose or mouth which may contain virus. If you are too close, you can breathe in the droplets, including the COVID-19 virus if the person has the disease. Avoid going to crowded places. Why? Where people come together in crowds, you are more likely to come into close contact with someone that has COIVD-19 and it is more difficult to maintain physical distance of 1 metre (3 feet). Avoid touching eyes, nose and mouth. Why? Hands touch many surfaces and can pick up viruses. Once contaminated, hands can transfer the virus to your eyes, nose or mouth. From there, the virus can enter your body and infect you. Built With advise care

https://devpost.com/software/myfacemaskapp

Prototype model 1 "Dany" pink opaque Prototype model 1 "Dany" white opaque AiRFace.it (App) A new customizable, economic and ecological mask tailored to you Millions of disposable masks are thrown out every day, which in addition to having a significant environmental impact on nature also have a high monetary expenditure. To meet all these needs, the AiRFace.it App team has developed a project that allows the customization of the mask or the creation of transparent masks that can adapt to the features and therefore customizable by scanning the face in 3D. This is AiRFace.it App , a mobile application that can be downloaded free on IOS and Android devices. The scanning process is quick and easy: it happens through the use of the mobile phone camera. In addition, the material used in the production of the masks, biodegradable and hypoallergenic allows a repeated use, being able to sterilize the washing at high temperatures . The mask is therefore ergonomic and environmentally friendly, because you just need to change the filters. The 3D model can also be printed from the comfort of the house without leaving. The advantage is that, being custom-built, the signs released by normal masks after hours of use will be reduced. Considering the difficult and delicate situation we are experiencing, AiRFace.it App would ensure protection and prevention with zero impact on the environment and would meet individual needs. It would be an optimal solution, without going out to buy it if you already have a 3D printer or otherwise we print and deliver directly to your home! The App is free as well as the availability of the various basic 3D models. Ergonomic My face mask with its 3D scanning process, allows you to create ergonomic masks suitable for every feature of your face. This custom adaptation will allow you to reduce the marks released by normal masks after hours of use. 3d Printer The realization of the 3D masks allows anyone to create them independently. In fact, a 3D printer and a mobile phone are enough to quickly create personalized templates based on the desired quantity. Ecological The material of these masks is absolutely ecological, a very important feature if you consider high consumption daily, especially in some sectors. In fact, these masks can be reused several times later washing at high temperatures which allows sterilization. Trasparent One of the main features of AiRFace.it is transparency. The idea of ​​making these masks with a transparent material was born mainly from the awareness of the importance of lip reading for deaf people. Privacy For us your privacy comes first, that's why AiRFace.it complies with all Gdpr ( https://gdpr.eu ) regulations and no personal data will be transmitted. The problem solved by the project With AiRFace.it App we want to find the solution to the problem of the availability of personal protection devices for everyone, in fact thanks to this app anyone can print his mask, following our guidelines for the use of safe and biodegradable materials, with their own 3D printer,only if you are certificated member of our network to ensure the quality and safety of the mask produced according to all applicable regulations, so that you have a mask for you and your family that can last for all this complicated period that we are living. The solution you bring to the table AiRFace.it is installable for free on all iOS and Android devices that have compatibility requirements and in a simple automated way and can create your own 3D mask and send it directly to the 3d printer certificated The impact of the solution on the crisis The idea behind this project is to create a safe, cost-effective product that respects nature for us and future generations The needs to continue the project This project needs funds in order to create the best mask with the best materials and for this a significant investment in research and development in addition to wanting to make and print masks to those who do not have the opportunity to have their own 3D printer The value of your post-crisis solutions We believe a lot in this project because it is a valuable help to anyone who does not have the opportunity to always have a disposable mask and this mask can be reused even after this crisis (hopefully it ends as soon as possible) in any sector that needs personal protection devices The AiR net blockchain We are creating a network of certified makers to be able to print masks for doctors and nurses for free to thank them for their valuable help. We are working with other startups to create a network of certified makers using the Ethereum blockchain to create smart contracts in order to be able to transparently verify the entire network. We are creating a decentralized system that is based on blockchain eos to ensure the total transparency of certification of the materials used to print the masks. Currently our team also collaborates in the creation of protective equipment for doctors and nurses to thank them for their difficult work. Built With blockchain c# html5 java javascript kotlin objective-c python swift Try it out airface.it bitbucket.org

https://devpost.com/software/patient-health-history-record-system-f8iyxt

Screens Maps Control of epidemics - Patient Control - Control of Vaccination Campaigns - Expert needs control by location - More reliable and secure data according to the new personal data protection law - More objective targeting of public health policy - Faster reaction to public health emergencies - Doctors with access to all health history and with more accurate patient information including emergency care - Patients with an updated and accessible health history when they need medical care - Public authorities using mathematical models to make projections with greater reliability - Patients using mathematical models to make projections and personalized predictions - Access to vulnerable population to technologies given only to people with private health insurance - Access to information of people using private health insurance by the government - People who have a private health plan and switch to the public health system without losing data - More accessible and reliable data than in DATA SUS - Data held by patients - Data released only for those patients who wish to access - Health History x Digital Health Record - Simplification of the database used today using data lake methodology Built With datalake flutter ionic react Try it out github.com

https://devpost.com/software/masked-ai-masks-detection-and-recognition

Platform Snapshot Input Video Model Processing Model Processing Output Video Saved Output Video Snapshot Output Video Snapshot Output Video Snapshot Output Video Snapshot Output Video Snapshot Output Video Snapshot Inspiration The total number of Coronavirus cases is 5,104,902 worldwide (Source: World o Meters). The cases are increasing day by day and the curve is not ready to flatten, that’s really sad!! Right now the virus is in the community-transmission stage and taking preventive measures is the only option to flatten the curve. Face Masks Are Crucial Now in the Battle Against COVID-19 to stop community-based transmission. But we are humans and lazy by nature. We are not used to wear masks when we go out in public places. One of the biggest challenges is “People not wearing masks at public places and violating the order issued by the government or local administration.” That is the main reason, we built this solution to monitor people in public places by Drones, CCTVs, IP cameras, etc, and detect people with or without face masks. Police and officials are working day and night but manual surveillance is not enough to identify people who are violating rules & regulations. Our objective was to create a solution that provides less human-based surveillance to detect people who are not using masks in public places. An automated AI system can reduce the manual investigations. What it does Masked AI is a real-time video analytics solution for human surveillance and face mask identification. Our main feature is to identify people with masks that are advised by the government. Our solution is easy to deploy in Drones and CCTVs to “see that really matters” in this pandemic situation of the Novel Coronavirus. It has the following features: 1. Human Detection 2. Face Masks Identification (N95, Surgical, and Cloth-based Masks) 3. Identify human with or without mask in real-time 4. Count people each second of the frame 5. Generate alarm to the local authority if not using a mask (Soon in video demo) It runs entirely on the cloud and does detection in real-time with analysis using graphs. How we built it Our solution is built using the following major technologies: 1. Deep Learning and Computer Vision 2. Cloud Services (Azure in this case) 3. Microservices (Flask in this case) 4. JavaScript for the frontend features 5. Embedded technologies I will be breaking the complete solution into the following steps: 1. Data Preparation: We collected more than 1000 good quality images of multiple classes of face masks (N95, Surgical, Clothe-based masks). We then performed data-preprocessing and labeled all the images using labeling tools and generated PASCAL VOC and JSON after the labeling. 2. Model Preparation: We used one of the famous deep learning-based object detection algorithm “YOLO V-3” for our task. Using darknet and Yolo v-3, we trained the model from scratch on 16GB RAM and Tesla K80 powered GPU machine. It took 10 hours to train the model. We saved the model for deploying our solution to the various platforms. 3. Deployment: After training the model, we built the frontend which is totally client-based using JavaScript and microservice “Flask”. Rather than saving the input videos to our server, we are sending our AI to the client’s place and using Microsoft Azure for the deployment. We are having on-premise and cloud solutions prepared. At the moment, we are on a trail so we can’t provide the link URL. After building the AI part and frontend, We integrated our solution to the IP and CCTV cameras available in our house and checked the performance of our solution. Our solution works in real-time on video footage with very good accuracy and performance. Challenges we ran into There are always a few challenges when you innovate something new. The biggest challenge is “The Novel Coronavirus” itself. For that reason, we can’t go outside the home for the hardware and embedded parts. We are working virtually to build innovative solutions but as of now, we are having very limited resources. We can’t go outside to buy hardware components or IP & CCTV cameras. One more challenge we faced was that we were not able to validate our solution with drones in the early days due to the lockdown but after taking permission from the officials that problem was not a deal anymore. Accomplishments that we're proud of Good work brings the appreciation and recognition. We have submitted our research paper in several conferences and international journals (Waiting for the publication). After developing the basic proof-of-concept, We went on to the local government officials and submitted our proposal for a trial to check our solution for better surveillance because the lockdown is near to be lifted. Our team is also participating in several hackathons and tech event virtually to showcase our work. What we learned Learning is a continuous process. We mainly work with the AI domain and not with the Drones. The most important thing about this project was “Learning new things”. We learned how to integrate “Masked AI” into Drones and deploy our solution to the cloud. We added embedded skills in our profile and now exploring more features on that part. The other learning part was to take our proof of concept to the local administration for trails. All these “Government Procedures” like writing Research Proposal, Meeting with the Officials, etc was for the first time and we learned several protocols to work with the government. What's next for Masked AI: Masks Detection and Recognition We are looking forward to collaborating with local administrative and the government to integrate our solution for drone-based surveillance (that’s currently in trend to monitor internal areas of the cities). Parallel, The improvement of model is the main priority and we are adding “Action Recognition” and “Object Detection” features in our existing solution for even robust and better solution so decision-makers can make ethical decisions as because surveillance using Deep Learning algorithms are always risky (bias and error in judgments). Built With azure darknet flask google-cloud javascript nvidia opencv python tensorflow twilio yolo

https://devpost.com/software/covidcentral-u21txv

Landing Page Landing Page Landing Page Landing Page - Contact Us Section Signup Page Login Page Content Summarizer Comparison of 4 Types of Content Summarizer Text Insights Preprocessing Inspiration This year has been really cruel to humanity. Australia is being ravaged by the worst wildfires seen in decades, Kobe Bryant’s passing , and now this pandemic due to the Novel Coronavirus originated from the Hubei province (Wuhan) of China. Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. More than 3 million people are affected by this deadly virus across the globe (Source: World O Meters). There have been around 249,014 deaths already and it’s counting. 100+ countries are affected by this virus so far. This is the biggest health crisis in the last many years. Artificial Intelligence has proved its usefulness in this time of crisis. The technology is one of the greatest soldiers the world could ever get in the fight against coronavirus. AI along with its subsets (Machine Learning) is leveraging significant innovation across several sectors and others as well to win against the pandemic. After Anacode releases “The Covid-10 Public Media Dataset” , we took this as an opportunity to use Natural Language Processing on those data composed of Articles. According to Anacode “It is a resource of over 40,000 online articles with full texts which were scraped from online media in the timespan since January 2020, focussed mainly on the non-medical aspects of COVID-19. The data will be updated weekly”. Anacode further says “We are sharing this dataset to help the data community explore the non-medical impacts of Covid-19, especially in terms of the social, political, economic, and technological dimensions. We also hope that this dataset will encourage more work on information-related issues such as disinformation, rumors, and fake news that shape the global response to the situation.” Our team leveraged the power of NLP and Deep Learning and built “CovidCentral” , a PaaS (Platform as a Service) . We believe our solution can help media people, researchers, content creators, and everyone else who is reading and writing articles or any kind of content related to the COVID-19. What it does Our tagline says “Stay central with NLP powered text analytics for COVID-19”. CovidCentral is one of its kind NLP driven platform for fast and accurate insights. It generates a summary and provides analytics of large amounts of social and editorial content related to COVID-19. STAY CENTRAL INSHORTS. It does three things: 1. CovidCentral platform can help to understand large contexts related to COVID-19 in a matter of minutes. Through the platform, Get actionable insights from hundreds of thousands of lines of texts in minutes. It generates an automated summary of large contents and provides word-by-word analytics of the texts from total word count to the meaning of each word. The user can either enter an URL to summarize and getting insights or enter the complete content directly into the platform. 2. The large content of text data is hard to analyze. It is very difficult to analyze the large content of texts. CovidCentral can help people to get insights within minutes. Manual analysis of texts leads to a number of hours. Media people, researchers, or anyone who is having the internet can access our platform and get the insights related to the COVID-19. 3. Humans are lazy in nature and people want to save time. This platform can generate content’s summary within minutes via a single URL. CovidCentral uses NLP and Deep Learning technologies to provide an automated summary of texts. Very helpful for getting short facts related to the COVID-19. Why Use CovidCentral? 1. Fast 2. Ease of Use (User-friendly) 3. High Accuracy 4. Secure (No content or data will be saved in the server rather we are sending NLP to you at the frontend.) How we built it We built CovidCentral using AI technologies, Cloud technologies, and web technologies. This platform uses NLP as a major technique and leverages several other tools and techniques. The major technologies are: a. Core concept: NLP (Spacy, Sumy, Gensim, NLTK) b. Programming Languages: Python and JavaScript c. Web Technologies: HTML, CSS, Bootstrap, jQuery ( JS) d. Database and related tools: SQLITE3 and Firebase (Google's mobile platform) e. Cloud: AWS Below are the steps that will give you a high-level overview of the solution: 1. Data Collection and Preparation: CovidCentral is built on mainly using “Covid-19 Public Media Dataset” by Anacode. A dataset for exploring the non-medical impacts of Covid-19. It is a resource of over 40,000 online articles with full texts related to COVID-19. The heart of this dataset are online articles in text form. The data is continuously scraped from a range of more than 20 high-impact blogs and news websites. There are 5 topic areas - general, business, finance, tech, and science. Once we got the data, the next step is obviously “Text Preprocessing”. There are 3 main components of text preprocessing: (a) Tokenization (b) Normalization (c) Noise Removal. Tokenization is a step that splits longer strings of text into smaller pieces, or tokens. Larger chunks of text can be tokenized into sentences, sentences can be tokenized into words, etc. Further processing is generally performed after a piece of text has been appropriately tokenized. After tokenization, we performed “Normalization” because, before further processing, the text needs to be normalized. Normalization generally refers to a series of related tasks meant to put all text on a level playing field: converting all text to the same case (upper or lower), removing punctuation, converting numbers to their word equivalents, and so on. Normalization puts all words on equal footing and allows processing to proceed uniformly. In the last step of our Text preprocessing, we performed “Noise Removal” . Noise removal is about removing characters digits and pieces of text that can interfere with your text analysis. Noise removal is one of the most essential text preprocessing steps. 2. Model Development: We have used several NLP libraries and frameworks like Spacy, Sumy, Gensim, and NLTK. Apart from having a custom model, we are also using pre-trained models for the tasks. The basic workflow of creating our COVID related NLP based summarizer or analytics engine is like this: Text Preprocessing (remove stopwords, punctuation). Frequency table of words/Word Frequency Distribution – how many times each word appears in the document Score each sentence depending on the words it contains and the frequency table. Build a summary or text analytics engine by joining every sentence above a certain score limit. 3. Interface: CovidCentral is a responsive platform that supports both i.e. Mobile and web. The frontend is built using web technologies like HTML, CSS, Bootstrap, JavaScript (TypeScript, and jQuery in this case). We have used a few libraries for validation and authentication. On the backend part, it uses python microservice “Flask” for integrating the NLP models, SQLITE3 for handling the database, and Firebase for authentication and keeping records from the User forms. 4. Deployment: After successfully integrating backend and frontend into a platform, we deployed CovidCentral on the cloud. It runs 24*7 on the cloud. We deployed our solution on Amazon Web Services (AWS) and use an EC-2 instance as a system configuration. Challenges we ran into Right now, the biggest challenge is “The Novel Coronavirus”. We are taking this as a challenge and not as an opportunity. Our team is working on several verticles whether it is medical imaging, surveillance, bioinformatics and CovidCentral to fight with this virus. There were a few major challenges: Time constraint was a big challenge because we had very little time to develop this but we still pulled CovidCentral in this short span of time. The data which has more than 40K articles are pretty much messy, so we got difficulties dealing with messy data in the beginning but after learning how to handle that kind of data, we eliminated that challenge to some extent. We also got challenges while deploying our solution to the cloud but managed somehow to do that and still testing our platform and making it robust. Accomplishments that we're proud of Propelled by the modern technological innovations, data is to this century what oil was to the previous one. Today, our world is parachuted by the gathering and dissemination of huge amounts of data. In fact, the International Data Corporation (IDC) projects that the total amount of digital data circulating annually around the world would sprout from 4.4 zettabytes in 2013 to hit 180 zettabytes in 2025. That’s a lot of data! With such a big amount of data circulating in the digital space, there is a need to develop machine learning algorithms that can automatically shorten longer texts and deliver accurate summaries that can fluently pass the intended messages. Furthermore, applying text summarization reduces reading time, accelerates the process of researching for information, and increases the amount of information that can fit in an area. We are proud of the development of CovidCentral and to make it Open Source so anyone can use it for free on any kind of device to get important facts related only to COVID-19. What we learned Learning is a continuous process of life, the pinnacle of the attitude and vision of the universe. I tell my young and dynamic team (Sneha and Supriya) to keep on learning every day. In this lockdown situation, we are not able to meet each other but we learned how to work virtually in this kind of situation. Online meeting tools like Zoom in our case, GitHub, Slack, etc helped all of us in our team to collaborate and share our codes with each other. We also strengthen our skills in NLP (BERT, Spacy, NLTK, etc) and how to integrate our models to the front-end for end-users. We spent a lot of time on the interface so people can use it and don’t get bored. From design to deployment, there were many things that helped us improve our skills technically. We learn many things around us day by day. Since we are born, we learn many things, and going forward, we will add more relevant features by learning new concepts in our platform. What's next for CovidCentral We are adding features like “Fake News Detector” to spam fake news related to the COVID-19 very soon on our platform. CovidCentral’s aim is to help content creators, media people, researchers, etc to only read that matters the most in a quick time. APIs to be released soon so anyone who wants to add these features in their existing workflow or website can do it so they won’t need to use our platform rather they can just use our APIs. We are also in discussion with some text analytics companies to collaborate and bring an even more feasible, robust, and accessible solution. In the near future, we will make CovidCentral an NLP powered text analytics platform in general for all kinds of text analytics for anyone, free to use from anywhere on any kind of devices (Mobile, Web, Tablet, etc). Built With amazon-web-services bootstrap css firebase flask html javascript natural-language-processing nltk python sqlite Try it out covidcentral.herokuapp.com

https://devpost.com/software/covnatic-covid-19-ai-diagnosis-platform

Landing Page Login Page Segmentation of Infected Areas in a CT Scan Check Suspects using Unique Identification Number (New Suspect) Check Suspects using Unique Identification Number (Old Suspect) Suspect Data Entry COVID-19 Suspect Detector Upload Chest X-ray Result: COVID-19 Negative Upload CT Scan Result: Suspected COVID-19 Realtime Dashboard Realtime Dashboard Realtime Dashboard View all the Suspects (Keep and track the progress of suspects) Suspect Details View Automated Segmentation of the infected areas inside CT Scans caused by Novel Coronavirus Process flow of locating the affected areas U-net (VGG weights) architecture for locating the affected areas Segmentation Results Detected COVID-19 Positive Detected Normal Detected COVID-19 Positive Detected COVID-19 Positive GIF Located infected areas inside lungs caused by the Novel Coronavirus Endorsement from Govt. Of Telengana, Hyderabad, India Endorsement from Govt. Of Telengana, Hyderabad, India Generate Report: COVID-19 Possibility Generate Report: Normal Case Generated PDF Report Inspiration The total number of Coronavirus cases is 2,661,506 worldwide (Source: World o Meters). The cases are increasing day by day and the curve is not ready to flatten, that’s really sad!! Right now the virus is in the community-transmission stage and rapid testing is the only option to battle with the virus. McMarvin took this opportunity as a challenge and built AI Solution to provide a tool to our doctors. McMarvin is a DeepTech startup in medical artificial intelligence using AI technologies to develop tools for better patient care, quality control, health management, and scientific research. There is a current epidemic in the world due to the Novel Coronavirus and here there are limited testing kits for RT-PCR and Lab testing . There have been reports that kits are showing variations in their results and false positives are heavily increasing. Early detection using Chest CT can be an alternative to detect the COVID-19 suspects. For this reason, our team worked day and night to develop an application which can help radiologist and doctors by automatically detect and locate the infected areas inside the lungs using medical scan i.e. chest CT scans. The inspirations are as below: 1. Limited kit-based testings due to limited resources 2. RT-PCR is not as much as accurate in many countries (recently in India) 3. RT-PCR test can’t exactly locate the infections inside the lungs AI-based medical imaging screening assessment is seen as one of the promising techniques that might lift some of the heavyweights of the doctors’ shoulders. What it does Our COVID-19 AI diagnosis platform is a fully secured cloud based application to detect COVID-19 patients using chest X-ray and CT Scans. Our solution has a centralized Database (like a mini-EHR) for Corona suspects and patients. Each and every record will be saved in the database (hospital wise). Following are the features of our product: Artificial Intelligence to screen suspects using CT Scans and Chest X-Rays. AI-based detection and segmentation & localization of infected areas inside the lungs in chest CT. Smart Analytics Dashboard (Hospital Wise) to view all the updated screening details. Centralized database (only for COVID-19 suspects) to keep the record of suspects and track their progress after every time they get screened. PDF Reports, DICOM Supports , Guidelines, Documentation, Customer Support, etc. Fully secured platform (Both On-Premise and Cloud) with the privacy policy under healthcare data guidelines. Get Report within Seconds Our main objective is to provide a research-oriented tool to alleviate the pressure from doctors and assist them using AI-enabled smart analytics platform so they can “SAVE TIME” and “SAVE LIVES” in the critical stages (Stage-3 or 4). Followings are the benefits: 1. Real-world data on risks and benefits: The use of routinely collected data from suspect/patient allows assessment of the benefits and risks of different medical treatments, as well as the relative effectiveness of medicines in the real world. 2. Studies can be carried out quickly: Studies based on real-world data (RWD) are faster to conduct than randomized controlled trials (RCTs). The Novel Coronavirus infected patients’ data will help in the research and upcoming such outbreak in the future. 3. Speed and Time: One of the major advantages of the AI-system is speed. More conventional methods can take longer to process due to the increase in demand. However, with the AI application, radiologists can identify and prioritize the suspects. How we built it Our solution is built using the following major technologies: 1. Deep Learning and Computer Vision 2. Cloud Services (Azure in this case) 3. Microservices (Flask in this case) 4. DESKTOP GUIs like Tkinter 5. Docker and Kubernetes 6. JavaScript for the frontend features 7. DICOM APIs I will be breaking the complete solution into the following steps: 1. Data Preparation: We collected more than 2000 medical scans i.e. chest CT and X-rays of 500+ COVID-19 suspects around the European countries and from open source radiology data platform. We then performed validation and labeling of CT findings with the help of advisors and domain experts who are doctors with 20+ experience. You can get more information in team section on our site. After carefully data-preprocessing and labeling, we moved to model preparation. 2. Model Development: We built several algorithms for testing our model. We started with CNN for classifier and checked the score in different metrics because creating a COVID-19 classifier is not an easy task because of variations that can cause bias while giving the results. We then used U-net for segmentation and got a very impressive accuracy and got a good IoU metrics score. For the detection of COVID-19 suspects, we have used a CNN architecture and for segmentation we have used U-net architecture. We have achieved 94% accuracy on training dataset and 89.4% on test data. For false positive and other metrics, please go through our files. 3. Deployment: After training the model and validating with our doctors, we prepared our solutions in two different formats i.e. cloud-based solution and on-premise solution. We are using EC-2 instance on AWS for our cloud-based solution. Our platform will only help and not replace the healthcare professionals so they can make quick decisions in critical situations. Challenges we ran into There are always a few challenges when you innovate something new. The biggest challenge is “The Novel Coronavirus” itself. One of the challenge is “Validated data” from different demographics and CT machines. Due to the lockdown in the country, we are not able to meet and discuss it with several other radiologists. We are working virtually to build innovative solutions but as of now, we are having very limited resources. Accomplishments that we're proud of We are in regular touch with the State Government (Telangana, Hyderabad Government). Our team presented the project to the Health Minister Office and helping them in stage-3 and 4. Following accomplishments we are proud of: 1. 1 Patent (IP) filled 2. 2 research paper 3. Partnership with several startups 4. In touch with several doctors who are working with COVID-19 patients. Also discussing with Research Institutes for R&D What we learned Learning is a continuous process. Our team learnt "the art of working in lockdown" . We worked virtually to develop this application to help our government and people. The other learning part was to take our proof of concept to the local administration for trails. All these “Government Procedures” like writing Research Proposal, Meeting with the Officials, etc was for the first time and we learned several protocols to work with the government. What's next for M-VIC19: McMarvin Vision Imaging for COVID19 Our research is still going on and our solution is now endorsed by the Health Ministry of Telangana . We have presented our project to the government of Telangana for a clinical trail . So the next thing is that we are looking for trail with hospitals and research Institutes. On the solution side, we are adding more labeled data under the supervision of Doctors who are working with COVID-19 patients in India. Features like Bio-metric verification, Trigger mechanism to send notification to patients and command room , etc are under consideration. There is always scope of improvement and AI is the technology which learns on top of data. Overall, we are dedicated to take this solution into real world production for our doctors or CT and X-rays manufacturers so they can use it to fight with the deadly virus. Built With amazon-web-services flask google-cloud javascript keras nvidia opencv python sqlite tensorflow Try it out m-vic19.com

https://devpost.com/software/ar-mask-line-for-covid-19

line3 line2 line1 line4 Inspiration When I go out in the morning, there is a line in front of the drugstore that people want masks. I always think it's really dangerous. What it does Using smartphone or Hololens, people can put their avatar in the line instead of themselves. So every time, everyone can keep social distance. How I built it Using unity with ARCore, I developed the application. See the tutorial of Azure Spatial Anchors ( https://docs.microsoft.com/en-us/azure/spatial-anchors/ ). It will help you. Challenges I ran into It was difficult to share the avatar with others. However, I implemented using Azure Spatial Anchors. So easy and cool. Accomplishments that I'm proud of It can reduce COVID-19 !!!!! What I learned The fundamental of AR and the method to share AR objects. Microsoft Learn was really useful. What's next for AR Mask Line for COVID-19 Build Hololens version and improve quality of design. For now, users can put only one character. I improve that users can create their own original character. Built With android azure c# ios microsoft-hololens unity web Try it out github.com

https://devpost.com/software/handsfree-basin

Inspiration This is pandemic condition. Be a part of mission against a coronavirus What it does It will be used in public spaces or slum areas or army/medical camps. Also, useful for isolated people How I built it Using arduino, hardware components and sensors, etc and coding Challenges I ran into Lack of components or accessories Accomplishments that I'm proud of Really helpful What I learned Proud to be an Indian, use my knowledge for Nation What's next for Handsfree basin Helping others.. Built With knob machine-learning mechanical-parts-like-spring pedal Try it out drive.google.com github.com

https://devpost.com/software/auto-health-monitoring-system

Inspiration We all know that doctors, healthcare workers are trying to do their best to fight against COVID-19. Being an engineer we also have some responsibility to our society. So in this horrible situation we have to step forward to do something for the society and help people to fight against COVID-19. Problem statement As per the guidelines of World Health Organization (WHO), “Home quarantine / Quarantine in non-health care settings is intended for anyone who believes they have been exposed to COVID-19 and are required to be home quarantined to prevent community transmission”. Now it is not possible to monitor so many people who have mild symptoms or who believes that they have been exposed to COVID-19 during their ‘Home Quarantine’ period. So, if we have an auto monitoring system by which a patient’s health condition can be monitored or recorded by which doctors can come to a decision that if the patient is required to be hospitalize for further treatment or they are safe in other case. What I have built What I have planned to build is having a portable or wearable device that patient will be wearing throughout the day. The device will be consisting of sensors to sense the body temperature, blood pressure, heartbeat or other certain parameters. The sensors will generate data and then that sensor data will be captured by a node mcu. We will develop an application that will receive the data sent by node mcu and store it into a database. After consecutive 14 days, our application will make 14 days statistics and plot a graph based on daily recorded data and that will be sent to local hospital or to the doctor. The statistical data or the graph will be able to identify that whether the patient health condition is critical or not or he/she is required to be hospitalized or not for further treatment. How I built it First I have planned to make a portable device and connect it to the various sensors to sense the data from human body. Then connect it with a programmable device like node mcu. Now I will build an android app that can receive the data generated by the sensors and sent from the node mcu and store the data into a database. And using Machine Learning models the app will run a productive analysis on the data to predict the patient's health condition. Challenges I ran into The availability of the necessary products in the market is a big challange to me. And also not having a team support is a big problem. Accomplishments that I'm proud of I recently grabbed the 6th place on the innovatieve idea competition Eradicate, organized by JIS college of engineering, Kalyani, in association with IIC. What I learned Programming and automations using devices like node mcu, developing android app and implementing ML models on it etc. What's next for Auto health monitoring system We can further upgrade the functionality of the application where after storing the consecutive 14 day’s data, it can be able to run a predictive analysis on the dataset stored on the database and then it will automatically predict whether the patient health is so critical as to be hospitalized or not. Built With android kotline nodemcu sensors tensorflow

https://devpost.com/software/kishanyojna

kishanyojna kishanyojna What it does How I built it Challenges I ran into Accomplishments that I'm proud of What I learned What's next for kishanyojna Built With former Try it out www.kishanyojna.com

https://devpost.com/software/spark-k6txdh

Inspiration - Needed a place to track and plan our projects. - Frustrated over apps that are specialized to do only one thing resulting in having to keep many tabs/apps open at the same time. - Saw a need for an all-in-one productivity app. - Believed it had the potential to increase the productivity of those affected by COVID-19. What it does - Features: Calendar, Tasks, Team Administration, Project Tracking, Messages, Meetings and Zoom Integration, Discussion Board - A cumulative productivity app that puts all the apps you need into one - Assists teams and organizations by improving productivity and tracking the progress of projects - Highlights team collaboration with SparkRooms to coordinate team members How we built it - Written in HTML/CSS/JS, Python - Written in Visual Studio Code - Utilized Travis for continuous deployment and autonomous configuration - Divide and Conquer - Frontend, Backend - Git, VSCode Live Share, and Discord Challenges we ran into - Responsiveness & Mobile compatibility - Rendering iframes on the dashboard - Saving arbitrary user data within Firestore - Git collisions: Committing changes to the same lines at the same time Accomplishments that we're proud of - Firebase for hosting, database, and authentication - Fully functional login system with Google Oauth 2.0 Authentication - Dashboard to render iframes to show lots of content in a single page - Travis CI/CD - Lots of backend and Javascript to process website What's next for Spark - Expand Spark for function enterprise and educational usage - Increase responsiveness of site to enable mobile usage - Create a way to send personal messages to team members - Create more tools for users eg. a personal File Storage Method Built With bootstrap css3 flask fullcalendar google-cloud html5 javascript jquery node.js python travis-ci Try it out sparkapp.cf github.com docs.google.com

https://devpost.com/software/coronator

Inspiration I am mechanical engineer and teacher for Methods of construction and design on a technical highschool. First time I became aware of the danger of virus / pandemia was during my military service – I was educated as a ABC – soldier (Atomar – Biological – Chemical weapons). I am sad about all the victims, those who are suffering and that we are facing the biggest challange since 2nd World War to protect our society , social life and economics. Since we have the global lockdown – the only solutions to avoid exponatial growths of infections is to seperate people (social distancing with all negative impacts on social and economical life) and create a high standard on hygienic behaviour. What it does Goal is to kill the virus or get resistant. Today , killing the virus is only based on medical solutions (vacintion or medication for people who are suffering) Another method to fight the virus , ist o avoid that he is entering the mouth / nose / eyes channel. So it is crucial to seperate the face from contact with fingers and aerosols which are contaminated with virus particles and avoid that air with contaminated aerosols will be breath in. The actual used masks (self-made or FFP x – mask) are limited in protection . They have leakages or are only usable for a short time. How I built it I created a mask , with the following working principle - it will kill the virus in a 3 – Step Filter System: Step: Filter by an Filament / Electrostatical part (realized in the Prototyp with a Swiffer to clean your home from Dust) Step : Destroy the Virus in heated part (Mini electrical oven)  (Temperature up to 60°C are enough ) this is the « Hell-fire » for the Virus (realized in the prototype with a cigarette lightner inside the car) The remaining air will be guide through a washer and get in contact with water / alcohol aequeus solution (realized in the prototype with a self-build part with 3-D-printer and a glass for Marmelade) It`s like a «mechanical vacination » Each part of the CORONATOR Mask is cleanable, desinfectable and re-usable. It`s personalized. It protects yourself and all other people around you. To make it «Hipp» or « Trendy » it can also added with earphones to connect with a cellphone which includes a Corona Tracking App. If an infectet person is near to you , you get a warning sound. A protection glass or whole transparent hull for your eyes can be integrated After the crisis it can also help people who are suffering on allergical reaction in spring time on pollen. The principle is shown in the sketches / draft. The design has to be optimized Locking forward to work in this "EUvsVIRUS" community to bring our Knowledge together and fight down this Virus with our swarm intelligence. I love to go for social dance and really pissed off to be seperated from my friends and dance partners…… So, let`s go and kill the virus ! I am proud to be a part of this Campaign. Next challange is to find Partners and companies to realize this product. Built With 3-d-printer solidworks

https://devpost.com/software/personal-and-family-coping-with-covid-19-in-the-global-south

If you are interested in how you and your family are dealing with the COVID-19 crisis, please complete the following questionnaire We are a team of Rwandan scientists of the Mental Health and Behaviour Research Group at the University of Rwanda. Momentarily, the COVID-19 crisis is causing relatively few direct health victims, but it is causing already a very real economic disaster in countries that have very few reserves to absorb such shocks. Reading reports from the Global North on how COVID-19 negatively impacts on people's lives, we expect similar negative outcomes to happen in the Global South. As researchers from a Global South University, we felt that we had a responsibility to act and try and help the citizens from the Global South, as we have a unique knowledge about what solutions can be useful in our contexts. We have developed an online interactive questionnaire that asks people in the Global South how they and their families are coping with the COVID-19 crisis and we give personalized feedback and advice on how they can deal with the situation. We ask people a number of questions (using standardized validated scales) on how COVID-19 impacts on their daily life, their overall well-being, their social situation, their relationship (in case they are in a relationship), parenting issues (in case they have children), their alcohol use and their level of worry. Importantly, we compare their scores to other people in their country and give personalized feedback on these issues (disclaimer; the psycho-educational materials are developed and validated by a group of Rwandan scientists, and are now being programmed). We constructed the questionnaire firstly by deciding what indicators we felt were relevant and after looked up standardized validated surveys, with a preference for those previously used in the Global South. After, using authoritative psycho-social advice resources from WHO, MHPPS and others, combined with expertise available in our local research team, we developed the psycho-educational advice materials and developed the system of how we present to offer feedback in a personalized way. The key to the whole project is to be able to provide personalized feedback to the people who complete the questionnaire. We did not want our research to be extractive, but on the contrary offer real and direct help to people who might need it. We believe that the strength of our health messaging is the fact that people themselves solicit for the feedback and advice, as it is they who freely decide to take the online questionnaire. Research has convincingly showed that people are much more receptive to advice when they look for it, compared to when it is pushed on to them. Secondly, getting personalized feedback touches people more deeply, as they cannot so easily ignore the feedback. For example, reading that you use 'much more/more/about the same/less/much less' alcohol compared to other people from your gender group in your country, might be eye-opening. This effect is further strengthened by visually displaying this information as you belonging to a 20% interval group. We again have good reasons to believe that this leads to higher receptiveness for our (very hands-on) psycho-educational messages. Interestingly, we are capable of measuring the impact of our intervention (i.e., the psycho-educational messages), through people who complete the questionnaire multiple times. A major challenge was to find the right platform that would allow us to give the personalized feedback and advice. We struggled to find a good system, until we started collaborating with the University of Antwerp, Belgium who were so generous to share with us the platform they developed for a different COVID-19 related questionnaire. These technical challenges set us back for almost two weeks. We are very ambitious about this project. We started reaching out through our networks to have research teams support the project and use the questionnaire and feedback/advice messages in other Global South countries. Recently, we came in contact with the University of Antwerp who allowed us to use the platform they developed and our now assisting us in reaching out to twenty more Global South Countries. Through our own networks, we intend to reach out to even more countries. In conclusion, we hope that this project will actively help people coping with aggression control, alcohol control and other issues during this COVID-19 crisis. We also want to demonstrate how South Universities contribute to solutions for the Global South. This entire project is running without any funding whatsoever for the time being, but it is very strongly alive through the engagement and efforts of so many of our colleagues in other Global South countries. Built With online questionnaire Try it out www.icpcovid.com

https://devpost.com/software/easyvent-cf01

#ventilator pulmonary easyvent Inspiration. we started 20 days ago with the challenge What it does the ventilator is easy to manage, easy to produce and has a lot of options that others don't How we built it we build it with the components of our products Challenges we ran into OBTAIN THE CERTIFICATION Accomplishments that we're proud of very fast reactivity and be apart of a special team What we learned if you want , you can do it! What's next for easyvent cf01 start the production Built With 3d

https://devpost.com/software/compost-share

Map Search for nearby compost bins or add/update/remove a personal compost bin to the map Click on a compost drop-off location Inspiration Due to coronavirus budget cuts, New York City will be suspending curbside composting beginning on May 4, 2020 and ending in June 2021. Residents will no longer be able to discard food scraps and yard waste as compost and compostable items must be collected as garbage. In order to allow people to continue composting, we designed Compost Share to allow people with backyard compost bins to collect compost from their neighbors. This allows for reusing of compost materials within a community or neighborhood! What it does Users may either (1) add a drop off location for others to bring their compost to or (2) look for nearby compost drop-off areas to bring their personal food scraps and yard waste to. How we built it Our stack comprises of MongoDB as well as a back-end server built with Express, and React for our front end. We also utilized the Google Maps Javascript API. Challenges we ran into One challenge we ran into was deployment . All of our code lives in a single git repository even though we have two servers. We have a front-end and back-end server that must be deployed separately. We were originally going to deploy both with Heroku. Heroku requires a remote back end git repository in order to deploy. This means we would need nested git remotes, which proved difficult since we are unfamiliar with it. Instead, we deployed our front end react server with Netlify and back end with Heroku. We found Netlify difficult to work with. We wanted an environment variable to act as a flag between development and production. But we found it hard to figure out how to access environment variables with Netlify. We ended up utilizing the Node Environment variables as a work-around for our production specific configurations. Accomplishments that we're proud of We are excited to have a deployed website that is not solely on a local host url, but can be accessed by anyone who goes to our public link! On top of successfully deploying, we are all really proud of styling it to function on mobile and web well. What we learned We learned how to use the Google Maps API, React, and set a project up from scratch. None of us have built a website from start to finish before so it was exciting to work on every step of the process collaboratively. We also learned some basic modern javascript and css. We also learned a ton about environment variables, node environment variables, and exporting configurations separately in production in order to keep API Keys private. What's next for Compost Share In the future, Compost Share will be used by people worldwide to allow organic material to be discarded sustainably. This website was designed to allow for continued composting during the coronavirus pandemic in NYC and other cities with cancelled services. However, Compost Share may continue to be used in cities that don't offer composting well after the pandemic is over. In the future, we are hoping to allow users to create an account with the ability to manage their drop-off locations personally. Built With css express.js google-maps heroku javascript mongodb netlify node.js react Try it out compost-share.netlify.app github.com

https://devpost.com/software/cleaner-disposal

window.fbAsyncInit = function() { FB.init({ appId : 115745995110194, xfbml : true, version : 'v3.3' }); // Get Embedded Video Player API Instance FB.Event.subscribe('xfbml.ready', function(msg) { if (msg.type === 'video') { // force a resize of the carousel setTimeout( function() { $('[data-slick]').slick("setPosition") }, 2500 ) } }); }; (function (d, s, id) { var js, fjs = d.getElementsByTagName(s)[0]; if (d.getElementById(id)) return; js = d.createElement(s); js.id = id; js.src = "https://connect.facebook.net/en_US/sdk.js"; fjs.parentNode.insertBefore(js, fjs); }(document, 'script', 'facebook-jssdk')); Cleaner Disposal Brain storm Inspiration Impossibility of recycling medical dispose waste. The moment we're living - proportionate by COVID-19 - when people were so preoccupied in find a solution for straw because of a Youtube video with a turtle hurt by on in its nose. Suddenly, nothing more was important. They were fighting about toilet paper and left the masks they're using behind. It's important to remember before the virus we had problems with drugs trail in the hospital sewage. What it does The main idea is provide correct information to the society and healthcare professionals, once the WHO said lots of them were infected by the COVID-19 because they weren't able to use appropriately the PPE. My idea is providing all the necessary information to avoid new contamination and reduce the unappropriated medicine disposal. Other important fact is what we should do with all this disposal and recent research provides a possible to use part of those waste in a Biodigester. How I built it I programmed the website part was ready party was edited in php html. I used Adobe, Photoshop and Autodesk for design it. Methods are indicated by WHO, the dissertation and a paper o Journal of Cleaner Production of a team member. link Challenges I ran into The same way the society has changed and evaluated so the medicine. But we won't be able yet to recycle medical dispose. The reason is biological hazard. The World Health Organization use some best practices to find a way to dispose efficiently and sustainably. Some of those practices will be discussed with the community. Also, doctors will be able to share information, ideas and best practices with community. Accomplishments that I'm proud of Always there's a way to share some new information. So, in the same way it was hard to make a review of different field, not only different but involving so many different ways of risk in passing the wrong information. I can say it will be amazing see the website but the solutions the whole world will be able to share and minimize the the ground or water contamination. What I learned How wonderful is the Earth with the adaptive environment capable to transform itself. Almost all the infected healthcare professionals were infected because they didn't know how to use or dispose protection equipment. What's next for Cleaner Disposal Improving the website. Making an app. The app is being improve. Provide the engineering and coast to make a biodigester. Built With adobe-illustrator autodesk html photoshop php Try it out jessy1201.wixsite.com

https://devpost.com/software/reuse-a-box

Inspiration - Plastic use from food take out during shelter in place and lack of recycling due to low gas prices. What it does - Begins to eliminate plastic waste from food take-out Challenges we ran into - Accounted for how to carry, how to store, what materials to use. Major challenges are integration into society. How to assure return of product and make this profitable for businesses to participate. Accomplishments that we're proud of - This takes into account many challenges with producing a better take-out container. It is exciting how many obstacles are yet to be worked out in this project. What we learned - There are many stages of product development. What's next for Reuse-a-Box - Speaking with businesses and consumers about needs for practicality and continued use. Then we may revise product design, begin testing and develop mobile app. Try it out docs.google.com

https://devpost.com/software/the-massgass

Exploded View Side View to demonstrate how drawer connects to mixer Demonstrating functionality of food drawer Introduction: Every year in America, over 80 billion pounds of food is discarded. This throw-away food is worth over $100 billion and is over 30% of the US food supply. Not only are we wasting food that could feed millions of hungry people, money that could be spent on bettering the planet, and expendable resources that our lives depend on, this food waste is directly contributing to the climate crisis. The majority of food waste-- from homes, stores, farms, transportation, etc.-- ends up in landfills where it is left to rot for years. As waste accumulates, lower layers are buried and therefore oxygen-deprived. As food continues to decompose, now anaerobically, it produces methane-- and a lot of it. Landfill gas composes over 15% of US methane emissions which greatly affects the climate as methane is anywhere between 25 and 80 times more effective at trapping heat in our atmosphere than carbon dioxide. Because of the environmental implications and the fact that we can use methane for energy, some landfills have incorporated systems to capture, process, and send the gas to the grid for utility use. But this does not provide use for the wasted food itself, methane still escapes, and this is not yet widely adopted. In our project, we aim to tackle both the problem of unused food and unused methane. It is estimated that over 40% of food waste happens at the consumer level. Then, all of this waste has to be dealt with and transported to landfills, further increasing its environmental impact. The solution that we are proposing is a trashcan-like appliance known as the MassGas that allows food to decompose to the point of usable soil and captures released methane that is filtered into the natural gas line of the home and used for household energy needs. Product Dec: The MassGas is designed as a high functioning composting and energy-producing unit. It is composed of a 15 gallon air sealed bin with a drawer for dispensing the food waste, and a door for removing the compost. The drawer opens, allowing the consumer to place their food waste in the compartment. When the drawer closes, the food is forced through a sharp metal grate that divides the food waste into smaller pieces. When the drawer is then reopened, the waste falls into the main bin. Dividing the waste into smaller bits accelerates the decomposition process. A plastic flap is installed behind the grate, providing an airtight seal until pushed by the drawer to allow the food waste in. The drawer also connects to a mixer located at the bottom of the main bin by means of a long rod. When the drawer is opened or closed, the mixer spins, churning the compost with it. Mixing the waste allows for circulation and some aeration to further expedite the composting process. While sitting in the largely anaerobic container, the waste will be decomposed by bacteria into humus, the desired product of compost. This process, under these air-sealed conditions, produces methane, an extremely potent greenhouse gas that largely contributes to climate change. The power of our product lies in harvesting this gas for a usable purpose. To make the MassGas a standard appliance in any modern-day home, this menthane will be directly fed into an already initialized natural gas line that connects to the home. The home’s natural gas line will pump the pressurized fuel into the MassGas where it will, in turn, pressurize the methane produced from anaerobic food waste decomposition, push it through a standard natural gas filter, and into an input pipe to the homes’ natural gas line. This process has a zero energy input, utilizing the already existing gas pressure in the home. The outlet pipe will have a lower pressure than the input’s so the gas will naturally flow down the pressure gradient. The methane will then no longer be a harmful byproduct but a utilized resource in the home. The methane from this decomposition process will also offset the need for conventionally produced natural gas. When the MassGas reaches its capacity of food waste, the natural gas line may be turned off with a valve, and the MassGas’s door can be opened to remove the humus. The humus can then be used for gardening, soil health, or sold to a local municipality. Analysis: Once the MassGas is manufactured it can immediately be bought and used by the consumer. The only installation necessary is to connect the two lines on the back of the appliance to the home’s natural gas line. We aspire for the MassGas to become a standard appliance in every home, drastically cutting down the need for other sourced natural gas. The MassGas is a major stepping stone in absolving the global demand for natural gas production and the use of other fossil fuels. As of right now, a large majority of energy comes from these environmentally unfriendly sources. Not only do they release large sums of greenhouse gases, but their extraction, refinement, and transportation use absurd amounts of energy and other resources. The MassGas will allow for the fuel to be produced onsite, without mining, fracking, or other harmful extraction methods. Despite the fact that utilizing methane is not a completely carbon neutral process, the released gases are far less harmful than the methane itself, natural gas is a fuel much cleaner than coal or oil, the gas is naturally produced so the resources and energy for extraction are greatly diminished, and the gas is produced on the site of use which greatly increases efficiency by eliminating most transport and transmission losses. Once the food waste breaks down into humus, it can be used in agriculture and gardening, further sequestering CO2 and offsetting emissions. Additionally, the MassGas will be a relatively cost-neutral or even eventually a cost positive appliance. The upfront cost is the only direct monetary input to the product. After that, the MassGas only uses resources that are available and would otherwise be wasted. By taking these materials and making useful products out of them-- both humus and methane-- our product offsets its initial cost greatly. The methane that is produced will be energy that is not drawn from the grid, reducing electricity and gas bills, and the compost produced can be used to grow food or sell. Due to both the cost neutrality and the user-friendliness, the MassGas is a product that can be easily implemented in nearly any residence. As the world develops, so will our product. As we phase out fossil fuels, and subsequently natural gas, the purpose of the MassGas will adapt to using methane harvesting for electricity generation. The basic framework of our appliance will remain, but with modifications to fit the modern, electrically driven home. The MassGas can also be enhanced to meet the food waste needs of a larger consumer such as an industrial-sized farm, a food processing center, or a market. This will greatly reduce the strain on landfills and repent uncapitalized methane from food waste decomposition. Not only is the MassGas a very unique product, it is also more practical than most home energy and food waste solutions. Although the end goal is to eliminate virtually all food waste and step completely away from fossil fuels, our society is not capable of doing this overnight. We must crawl before we can walk. So even though cutting out fossil fuels or only producing what exactly will be consumed are ideal, they are not possible at this point in time. The MassGas is an appliance that makes use of the fact that we cannot solve the world’s problems in a night, but we can get closer. In addition to providing a use for food waste and producing energy, our product will foster knowledge and awareness about the topic. It will encourage people to waste less and make use of what they do waste, pay attention to where their energy is coming from and how they are using it, and hopefully further inspire developments to a waste and fossil fuel free planet. If executed on a wide scale, the impact of our product will be drastic. The MassGas will limit the need for extracted natural gas, greatly reduce the amount of food waste sent to landfills, provide compost for agriculture at the home or to be transported to a farm, and increase consciousness of food waste and energy use. Built With solidwork solidworks

https://devpost.com/software/icook

iCook Inspiration We have all been in that situation where we look at the food we have in our home and have no clue what meal we can make. So we came up with iCook, an app that help you solve this problem. During the Covid-19 pandemic, it is nessacery for us to self-isolate to flatten the curve. This mean essential trips to get grocery should also be minimized. As a result, many of us have had to get more creative with what we have at home. What it does iCook lets you add and remove from a persional ingredients list. A user can then use that ingredient list to look for recipes, or simply look for a recipe by name. Recipes can also be saved for easy access in the future. How we built it We use Figma to mock-up an app design, Dart and Flutter to implement the app, and Firebase to implement authentication. Challenges we ran into We both have little to no previous experience with Dart/Flutter and Firebase. We've also ran into a number of error at the beginning of the project. Accomplishments that we're proud of Successfully implement basic features of the app. What we learned How do use Firebase Authentication How to use some widgets in Flutter such as BottomNavigationBar What's next for iCook Users can upload recipes Categorize ingredients Keep track of amount for each ingredient Remove/change ingredients from list Expiration date or when they bought it and the app will reminds user when their ingredients go back Built With dart firebase flutter intellij-idea kotlin swift visual-studio Try it out github.com

https://devpost.com/software/gardenbox

Temperature Readings/Information Installation Instructions Information For One Current Plant Suggestions Based on Compiled Information GardenBox Home Page Plant Library Humidity Readings/Information Inspiration GardenBox is an innovative mobile app changing the way you garden. These difficult times have left many people wondering how they can better their community and make a positive impact. Unbeknownst to many, one of the best ways to make that impact is by going green and gardening. Gardening has many benefits: weight loss, stress reduction, and a healthier environment. However, one of the biggest benefits of gardening is that it is one of the healthiest and safest means of food production. Despite these positive benefits, however, many people struggle to successfully grow a garden. Inspired by this problem, as well as our joint passion for gardening, we created GardenBox. What it does GardenBox’s two primary components – a physical device and a mobile application– allow the user to maximize their crop output, allowing for a steady stream of food to sustain themselves. The user links the device to their phone, chooses the crop they are trying to grow and installs the device by placing it into the soil, next to the crop. After the link has been established, the device reads in the light, humidity, and temperature data of the plant the user is trying to grow. Using an API, the device sends the data to the mobile application, which compares the given data to the ideal conditions in which the plant grows. After comparing the data, the app provides the user with intuitive suggestions in order to promote the growth and food output of these plants. By doing so, we hope to provide users with a safe and reliable avenue for food consumption, as well as limiting food wastage. How we built it The application user interface was made using the iOS app development SDK powered by swift. The physical device was built using a moisture sensor and photo-resistor connected to an arduino. These two are integrated using an arduino plug-in and the Google Sheets Api. The user interface is designed so that any user can easily understand and use the app from the moment they pick it up. Challenges we ran into The biggest challenge we ran into was working on the hardware aspect of our project, while being miles apart from each other. When it comes to coding the project, there were multiple ways to work together, but not nearly as many for working on hardware. This proved to be our primary issue throughout the hackathon. Accomplishments that we're proud of We are really proud of the fact that we were not only able to create such a helpful and reliable app in a short amount of time, but that we were able to make it look really good as well. We all strive to do whatever we do to the best of our ability, and making a clean looking app was something that was important for us. What we learned We learned so much about working together, and many other things as well. One of the biggest things however, was how to operate under pressure. Hackathons are a high pressure coding environment, and learning how to handle that pressure was a great experience for all of us. What's next for GardenBox GardenBox has so much potential, and we plant to tap into that. We want to expand upon the crops that we currently offer, and add things like fruits as well. We believe they sky's the limit for GardenBox. Built With arduino google-sheets google-sheets-api swift

https://devpost.com/software/safety-of-the-environment-of-the-organisms

Greetings from Greece. SAFETY OF THE ENVIRONMENT First of all, scientists should use fusion energy (when it will be ready) in order to do not pollute the environment & they must create artificial forests (with artificial trees) inside & near the cities & villages in order to clean the environment. Later, directors of educational institutions should use school excursions for tree planting in order to become the kids friendly with the nature from small ages & to learn to do not pollute it through specific lessons. Governments should use also drones which use automation & which exist already & they can implant thousands – millions of evolved spores (with evolved their genetic – biological material) everyday inside the soil through launches from the air. Finally, scientists should create biodegradable plastics in order to be dissolved after some days of their liberation without to harm the environment & they should discover microorganisms, molecules, submolecules & etc which will eat the plastics that exist already in order to clean the environment. Sources: https://www.google.com/ https://scholar.google.com/ https://www.technologyreview.com/ http://news.mit.edu/ https://news.stanford.edu/ https://hms.harvard.edu/news https://www.broadinstitute.org/ https://wyss.harvard.edu/ https://www.utoronto.ca/news https://www.anu.edu.au/news https://www.cam.ac.uk/news http://www.ox.ac.uk/news-listing https://news.tsinghua.edu.cn/ http://www.iitd.ac.in/media https://www.ncbi.nlm.nih.gov/ https://www.nature.com/ https://www.cell.com/ https://www.sciencemag.org/ https://www.nejm.org/ https://www.thelancet.com/ https://jamanetwork.com/ https://www.embopress.org/ https://phys.org/ https://medicalxpress.com/ https://rupress.org/ https://www.genengnews.com/ https://www.embl.org/ https://www.researchgate.net/ https://www.ted.com/ https://www.youtube.com/ https://www.ieee.org/ https://techxplore.com/ https://sciencex.com/news/ https://www.wikipedia.org

https://devpost.com/software/destroy-fakenews-effectively

Ergebnis unserer Forschungen im Studiennetzwerk für integrative Medizin Wie entstehen FakeNews? FakeNews, Lügen u.ä. entstehen automatisch in unserem Gehirn, wenn wir unser inneres Gleichgewicht verlieren. Wie das im Gehirn passiert und was dann getan werden kann, findest sich hier in diesem Beitrag Die größten FakeNews des Gesundheitssystems (1) Der Glaube an die Existenz von Krankheiten ist die größte FakeNews auf die wir seit Generationen reingefallen sind. Weder im eigenen Körper noch in den Körpern der Patienten ließen sich je Krankheiten wahrnehmen. Es waren immer nur Symptome zu erkennen, die sich in der Zeit verändern. Diese Symptome korrelieren mit den Änderungen im Verlauf des Lebens des Menschen. (2) Die Verschiebung der Schuld an Symptomen auf Mikroben, Viren, Erbanlagen und andere Menschen hat uns in der Vergangenheit unserer Macht beraubt, unser Leben so zu verändern, dass wir ein Leben lang gesund bleiben können. Was haben wir in vorherigen Hackathons bereits getan? Aufbau des Centers of competence. Dort integrieren wir verschiedene Perspektiven zu einer integrativen Medizin. Du findest es hier Aufbau der Plattform FragDichGesund.de, auf der Menschen Fragen stellen können, die in einer integrativen Medizin beantwortet werden Du findest sie hier Zugang zur Ursachenforschung für jedermann Du findest ihn hier Gesundheitsversicherung in Selbstverantwortung. Dort lernen wir es, in unserer Mitte zu bleiben und mit anderen Menschen zu kommunizieren. Du findest sie hier Was haben wir in diesem Hackathon getan? kurze klare Zusammenfassung der FakeNews-Quellen der offiziellen Seite und Hintergründe der Corona-Krise im obigen Video FakeNews-Quellen der alternativen Seite eingebunden und klare Zusammenfassung des Themas auf der Plattform FragDichGesund.de Hier findest du das Ergebnis Erstellung der Facebook-Seite fürs Marketing Hier ist das Ergebnis What's next for Destroy FakeNews effectively Beschreibung des Aufbaus des Grundeinkommens für Investitionen in Gesundheit und Nachhaltigkeit Zusammenarbeit zwischen Therapeuten politische Arbeit neu gestalten Zusammenarbeit mit Unternehmern Was ist sonst noch möglich? Welche Ideen hast du? Built With wordpress Try it out findewissen.de

https://devpost.com/software/foovie

Inspiration We have acknowledged the profound impact food wastage has on the environment and communities and thus have come up with an environmentally friendly solution to the problem What it does The website allows you to find and post eco-friendly recipes and attaches an eco rating to them How we built it Using a free boostrap template and some php knowledge Challenges we ran into Time constraints Accomplishments that we're proud of It looks great and is partially functional What we learned We learned to work under pressure What's next for Foovie Complete the build and add a donation service Built With css html javascript localhost php Try it out thesustainabilitychallenge.co.za

https://devpost.com/software/glare-xu4ozt

Inspiration According to the Food and Agriculture Organization (FAO) of the United Nations, An estimated 1.3 billion tonnes of food is wasted globally each year, one-third of all food produced for human consumption. Due to pandemic conservation of basic resources like food has become mantatory, rather than throwing away food how can it be utilized? According to the latest estimates, 9.2 percent of the world population (or slightly more than 700 million people) were exposed to severe levels of food insecurity in 2018, implying reductions in the quantity of food consumed to the extent that they have possibly experienced hunger. So much food is wasted in events like marriages and parties which are thrown, hence wasted. What can be done to reduce this? Due to food wastage--- Environmental Issue Morally Unacceptable – Fighting Hunger Waste of Labour, Time, and Natural Resources What it does GLARE is an Android/IOS application that helps people locate all the nearest NGOs and other social service organizations which actively collect food, to feed the people in need. Integrated with Google Maps, then the person can send in a food pick-up request, and the food can be collected by the people from the organization rather than throwing away additional food, it can hence be used to feed the needy. How I built it It is integrated with Google maps which help find all the organizations in the locality and Firebase system manages the authentication, storage of the information so that people can register using email-id and other details Challenges I ran into Integrating Maps with the app, real-time two-end response system. As I had to run the app on the phone to record the video is a bit blurry. Accomplishments that I'm proud of Looking at what is happening in the world today, I realized that sustainability is one of the most important things. Hence I am proud of building something that helps the society and also in many ways helps in healing the planet. What I learned Technically, cloud-based application building. Most importantly I learned about what the outside world today is, realized the importance of apps like GLARE as they help save the planet and more importantly lay the path for humanity to stand strong. What's next for GLARE In-order to be of use, apps like GLARE, awareness among individuals play a huge role. Hence the first step would be making a few changes in the app, publish it, make a web version as well. Then start creating awareness among people about the pros of It, as due to the current Pandemic, with thousands out there without proper food, Glare could make a difference. Built With android-studio c# firebase google-maps java photoshop Try it out github.com

https://devpost.com/software/divoc-e0fywm

Flow chart depicting the working of the whole system. Homepage of the application Teacher Login Student Login Teacher Dashboard Student Dashboard Canvas as a blackboard Asking question in middle of a lecture Tab Change alert to gain students attention to the lecture Inspiration There is an old saying, The Show Must Go On , which kept me thinking and finding out a way to connect teachers and students virtually and allow teachers to take lectures from home and to develop a completely open source and free platform different from the other major paid platforms. What it does This website is completely an open source and free tool to use This website whose link is provided below, allows a teacher to share his / her live screen and audio to all the students connected to meeting by the Meeting ID and Password shared by the teacher. Also this website has a feature of Canvas, which can be used as a blackboard by the teachers. Including that, this website also contains a doubtbox where students can type in their doubts or answer to teachers questions while the lecture is going on. Again this website also has a feature of tab counting, in which, tab change count of every student is shown to the teacher. This will ensure that every student is paying attention to the lecture. Also, teacher can ask questions in between the lecture, similar to how teacher asks questions in a classroom. How I built it 1) The main component in building this is the open source tool called WebRTC i.e. Web Real Time Communication. This technology allows screen, webcam and audio sharing between browsers. 2) Secondly Vuetify a very new and modern framework was used for the front end design. 3) Last but not the least NodeJS was used at the backend to write the API's which connect and interact with the MongoDB database. Challenges I ran into The hardest part of building this website was to find a open source tool to achieve screen and audio sharing. This is because Covid crisis has affected most of the countries economy due to lockdown. Hence, it is of utmost important that schools and colleges do not need to pay for conducting lectures. Accomplishments that I'm proud of I am basically proud of developing the complete project from scratch and the thing that anyone who has the will to connect to students and teach them can use it freely. What I learned I learned a new technology called WebRTC which I believe that is going to help me more than I expect in future. What's next for Divoc Integrating an exam module and allowing teachers to take exams from home. Built With mongodb node.js vue webrtc Try it out divoc.herokuapp.com

https://devpost.com/software/fighting-covid-today

Inspiration Working on a resource mapping toolkit one of our mentors showed us a relevant video of Destin @SmarterEveryDay , then at the end of the video, I ordered the fightingcovid.today domain then published the call to action on it. What it does Supporting communities to have their easy-to-use webpage under a fightingcovid.today subdomain. Providing tools and strategies for collaboration and the emergence of communities. Listing #FightingCOVID solutions, resources, and other databases. How I built it The current page was created with Godaddy's free webpage creator but needed to rebuild in a more adaptive way with simple HTML5 , CSS , JavaScript , .json technology with some kind of NoSQL database. Challenges I ran into I am too slow with coding today and hard to find good programmers who are available for agile development. Accomplishments that I'm proud of It was a great feeling to find the perfect available domain for it and setting up a quick MVP under. Many people were giving positive feedback about the idea. What I learned Today it's hard to find agile developers and more effort is needed to spread the word. What's next for Fighting COVID Today The next step is to replicate the current site on a node.js compatible hosting to be able to start the development and bring more people to the team, make connections with similar projects and find incentives for the community to build spread the world and build a bigger database. Built With css html5 javascript Try it out fightingcovid.today

https://devpost.com/software/i2we20-community-network-resource-mapping-toolkit-dev

GIF resources for collaborative cooperation The incentive growing of network of resources and needs Bottom-up self-sufficient collaborative resource management Inducing collaborative cooperation and undivided common property Incentive human intelligence towards artificial intelligence Sequence diagram of a simple use case Importance of Collaboration Permaculture ethics Resources for hope Multi-dimensional toroid map of nodes (people, community and resources) Organic farmers are the caretakers of our future! Support them with all the resources needed. lang:hu tags: #COVID19 #I2We20 #HacktheCrisis #save_communities #EUvsVirus #COVIDathon #cohesion #community #cooperation #collaboration #efficiency #permaculture #sustainability #eco-friendly #self-sufficient #cryptography #holochain #DID #DAC Inspiration I came up with this idea when I realized: The globally failing economic , political and *social institutions * induce bottom-up collaborations, because fixing our problems from above does not provide lasting solutions to our needs anymore. For more than a month in our region with a group of volunteers, we are trying to help mostly the elderly. It was a long and painful journey to map the available resources and to reach the segment. People still don't know what can be the best help in the current situation and how to do it accurately. I dare not mention how personal data was handled, and what kind of abuses took place in the process. What is unresolved … is the intra-community resource management and fair allocation of resources , because communities lack the technology to collect , process , and share their available supply, support, and aid capacity as well the shortages. The pattern Permaculture (the "science of nature") reveals the patterns for any kind of sustainable system development. It is time to apply nature's ethics ( people care , earth care , and fair-share ) to have effective IT solutions available to every collaborative community on the planet. What it does Community Network Resource Mapping ( CoNetRes ) software it's a toolkit that provides a big picture of the supply , support , and aid capacity and needs of a community, to support effective and efficient collaboration inside and between communities. The toolkit … can be used to build progressive web applications for resource mapping, on most of the digital platforms, and using gateways in special needs (SMS, Nat, IoT). The data is collected using sociocybernetic techniques, stored in MongoDB using decentralized identity and access management with GDPR compliant privacy and data security, aggregated through APIs (in relevant cases) and open protocols like Murmurations ; the data collection and actualization is incentivized using ThankYou points , validated by the community with the help of embedded personal value formula variable (advanced reputation system) processed with data anonymization using decentralized IDs (DIDs). The data owner (User/Community) fully controls the stored data and accessibility. Authentication … can happen through the main contact channels (email, phone, social), OAuth as secure delegated access, cryptographic hash function with mnemonic Simple use case How We built it We started to develop an open-source resource mapping solution done in an independently and collaboratively , with anonymous data collection and secure information management , which are key roles today! We are developing a highly customizable open-source toolkit for this. We are using HackMD to clarify the bigger picture and to collaborate on all related information. Using social interactions and google forms we researched different communities. The technical background for the development is provided by other open-source solutions, like ßoiler JavaScript framework with codepad , the collaborative development environment running on srvctl . The main language is JavaScript with node.js , d3.js and Vue.js , using JSON , HTML5 , CSS , and python for data processing. Challenges I ran into The biggest challenge was to reduce the big vision to a clear and simple workflow to be able to write the first lines of the code. It was challenging and energy-consuming to have a bigger picture of our human resource availability and to communicate our needs in a clear way using incentives. We have no committed programmers available at the moment. Accomplishments that I'm proud of We have wide experience-based knowledge about bottom-up communities and socio-cybernetic tools to help resource-mapping and to facilitate collaboration. We found a collaborative development environment and the core developer is in the team. What We learned The importance of having a clear picture of our available resources and knowing collaborative ways of managing them. What's next for CoNetRes, the Independent Community Network Resource Mapping toolkit. During the #COVIDathon … we were able to work on the project definition , clarify the main vision for different markets; reach an agreement with communities about the common goals; gaining promise of support from the regional council and cooperation with the online network of 50.000 registered users in Hungary; having software and mentorship support from the Internet of People professionals to include the Decentralized Access Control framework to protect personal data; doing promising research on Holochain and having mission statements with developers; realize visual storyboard about a simple use case scenario: CoNetRes/I2We20-story.pdf including the Murmurations protocoll in our project; setting up funding channels and preparations for a crowdsourcing campaign; setting up the domain and launching the Resources.CollaborativeSociety.eu site on GitHub; develop basic user authentication and user management for the web application created during the #theglobalhack create and share the English version of the latest questionary on an easy-to-manage format with google forms Fill the form: https://forms.gle/Ma92PzQWP2asmfNu6 For the MVP The next steps for the MVP of the Community Network Resource Mapping toolkit are Clarify the UI/UX of frontend/backend to have a fully customizable form for data collection, [0--(40)--100] list available COVID-19 and pandemic solutions as resources and possible deficiencies, ability to have anonymous profiles with decentralized identity and access management [0--(20)--100] Good to have multiple views with filters to display the collected data, such as an egocentric zone view, where resources can be listed in related zones, like categories [0] a dependency map, where missing resources can be revealed easily [0] a geolocation view, where resources can be located providing easy access to them [0] To reach these steps good open-source programmers are needed, stable motivation/incentive is needed (funds, events, perks, etc) the continuous presence of mentors are needed mostly on the fields of #Data_science #Psychology&Sociology #UIUX_design The impact … to the crisis Because of the globally failing economic , political and *social institutions * fixing our problems from above does not provide lasting solutions to our needs anymore. Many newly formed collaborations are trying to find bottom-up solutions (growing numbers of hackathons are proving that), but there is a high chance for them to fail because of the lack of resources, like technology, money, professionals, etc. CoNetRes providing a bottom-up independent solution for effective allocations of resources -- like technology, money and professionals -- to support collaborative communities to thrive . This process will relieve organizations across the globe, saving a lot of energy, lowering carbon footprint, and providing the chance to adapt to crisis situations in a resilient way. After the crisis There will be no "after the crisis" for a while, emergency collaborations and resource allocation will be needed. Open-source alternatives are always needed for communities who prefer independence and secure solutions to support cooperation inside and between communities &hellp; because these are the pillars of a healthy society. Monetization The toolkit can't be monetized, crowdsourcing, donations, and even business can be built on it with a pro-rata share to gain money, development, and necessary resources. Contribute I don't want to tell people what to do, but hope more and more people will realize the need of the advanced tools for mapping resources and effective allocation and they will use and develop the Community Network Resource Mapping toolkit by their needs to support their community. Do you see the value … and want to contribute: Join our team on DEVPOST Follow and contribute on GitHub hop in to our Discord server Built With codepad css3 holochain html5 javascript json mongodb node.js python raspberry-pi srvctl ssoiler vue.js xmpp Try it out resources.collaborativesociety.eu hackmd.io github.com forms.gle

https://devpost.com/software/reducing-food-waste-by-predicting-end-of-year-crop-yield

Vision for the app Inspiration When presented with the challenge of reducing food waste, we, as a team were sure to tackle it. The first process in food waste is the production of crop itself. Hence, we wanted to tackle crop production at the grassroots level. Back home in India, we have heard of numerous cases of farmers committing suicide due to crop failure and crop loss. Alongside, the amount of resources (pesticides etc.) that are invested in growing crops that eventually fail, is a huge amount, both in terms of cost and the environmental impact they have. To tackle this issue, we wanted to use machine learning to help the government and farmers predict their yield at the end of each season depending on various factors such as area, season, weather conditions, Methane levels, soil quality etc. What it does Using Machine learning, we have trained the Random Forest Regressor model on the yield that was produced in previous years as a result of input parameters such as area and season. The model has been tested for accuracy and can be used to approximate the yield for future years. The model can be embedded into a web or mobile application but due to shortage of time we have not been able to embed it yet. The concept has been demonstrated through mobile application graphics. We do wish to delve deeper than the top level prediction approach we have used. We do wish to let this be a personalised service for each farmer to let them monitor their farms, themselves. We aim to introduce more parameters into consideration, other than the existing parameters of 'area' and 'season', such as Soil Quality (pH sensor) etc. that would help the farmer detect any issues in their farm early on and accordingly invest resources (eg. pesticides, irrigation methods etc.) to fix avoidable issues. How I built it We built it using python, html, css and graphics. Challenges I ran into We got stuck at merging a ML code written in Python with an iOS Swift app. Accomplishments that I'm proud of A working model! What I learned Optimization What's next for Reducing food waste by predicting end-of-year crop yield Delve deeper and make it a personalised service for each farmer to access and monitor their own farms! Built With python Try it out github.com

https://devpost.com/software/aceso-the-first-feasible-sarscov2-test-trace-network

Track your Virus tests & trace statistics. Have conversations with a personal AI driven health assistant. Scan the QR code in order to activate the digital Health ID. As a government, test lab or other official entity, participate in the network and create automated policies with smart contracts The Problem. It is generally known that extensive and widespread testing as well as contact tracing to identify infection chains is crucial for overcoming the SARSCov2 pandemic and gradually returning to normality. Currently, however, even though the testing itself is not that complicated, the logistics around testing and investigation (infection tracing) of positively tested patients requires Lots of effort and man-hours and goes beyond the borders of available capacities. The related processes are just not automated and digitized. As a consequence, lots of infections are not reflected in statistics making it extremely difficult to cope with the virus as well as spread and isolate it, lockdowns are inevitable in order to stay beyond the intensive care capacity borders. For contact tracing, the EU has decided to follow the track of controversial software architectures and apps like PEPP-PT, or now "the decentralized" approach DP3T, which cause not only privacy issues but also don't deliver any direct value-add to the users. Another issue is, that tracking without integrated, optimized and automated end2end test rollout management still leads to data lacking behind the real time state and an inefficient value chain. To sum up, it still lacks a feasible end2end test management and contact tracing platform, connecting governmental institutions, test labs, healthcare facilities and citizens in order to automate the prioritized rollout of test and trace back infection chains after positive test results, without significantly attacking the personality rights of citizens. How we solve it. We leverage the properties of the blockchain technology, artificial intelligence and state of the art cryptography to provide an end2end SARSCov2 testing and tracing network. But how does this work? The solution consists of three parts: a permissioned blockchain network for governing test rollouts / logistics and access to personal data in case of infections a dashboard for government, testing labs and healthcare facilities a unique Health assistant and "passport"-type health id for citizens in form of a mobile app. Additionally personal data is encrypted with a hash and stored off-chain in a decentralized cloud-database whilst solely a smart contract contains the key in order to decrypt and display the data to responsible entities in case of infections and direct contact to infected persons. ACESO Healthpass Each citizen is provided by the government with a unique digital Health ID which he maintains in an interactive app keeping an anonymized log of relevant events, for instance nearby contact with another person or visiting a public location, for instance a supermarket. Additionally citizens have other value added services like conversations with a chatbot (assistant), or seeing the current load of people on public places. The healthpass collects all the logs anonymously mapped to the non-personal blockchain health pass id and warns the citizen if he behaves too risky , like for instance having lots of contact with other people. Sensors used for Contact Tracing Instead of deploying expensive gateways, we believe there is already a mass of options available. For the purpose of not tracking personal data we do not use GPS sensors, but rather diverse options available on public places. For people to people tracking our app leverages bluetooth technology and available WiFi Networks to register check-ins at public places. Additionally, at public places, so called sound beacons can be used by registering a signal through the public speakers (for example in supermarkets), we also currently train a neural network, using IBMs Watson Studio, in order to identify different public places based on sound recognition. As we want to be an open source solution, we want to offer a plug and play sensor interface for easily incorporating additional sensors. The deployment of new sensors has to be voted by the network in the blockchain. ACESO Test & Trace Network The blockchain network, at which governments, healthcare facilities and testing laboratories can take part, governs automated policies for data access and testing logistics through smart contracts empowered by Machine Learning and optimization algorithms in order to achieve ideal capacity planning and real time data transfer. Even though data is anonymized outside the recognized infection chains, it still can serve as a very valuable data source for epidomologic research. How this will impact the crisis. ACESO Test & Trace network provides an ideal trade-off between value add for citizens, personal data protection, and effective insights and testing / infection chain management for governments. With the help of this technology, governments can isolate the spread of the virus by real-time capacity planning and logistic automation and quickly deploy and measure new policies whilst citizens stay informed and can stay safe with the help of their personal health assistant. Additionally it could be extended to manage Intensive Care Capacities cross-border through the whole European Union. What we have achieved during 2 days. During this weekend we have not only elaborated the idea, but also deployed a full scale blockchain network with already running smart contracts for privacy rules, and an off chain encrypted database as well as created the first fully functional prototype of the ACESO health pass for citizens with an AI-driven chatbot interface and all mentioned sensors for contact tracing. How we want to continue and what could the solution bring after the crisis. We want to get in contact with public entities as well as healthcare facilities to establish an open-source project with a longterm goal beyond the testing & tracing use case during the pandemic. With the help of the digital health pass for each EU-citizen we could automate cross-border patient information transfer and inter-country healthcare research knowledge transfer through smart contracts on a self-governed blockchain network. Built With fabric hyperledger ibm-cloud ibm-watson kubernetes node.js react react-native Try it out github.com

https://devpost.com/software/virtual-health-checkup-modelling-of-coronavirus-technoband

Technoband Software Modelling of Future conditions of CoronaVirus Inspiration Daily surge in cases, health conditions of citizens pushed me to work hard What it does It predicts the curve of future conditions of any country w.r.t. data set available How I built it I built it through software, that have been mentioned. Challenges I ran into Lots of challenges, but overcomes and got the results as expected Accomplishments that I'm proud of That I did something, which satisfies and help at least one citizen, then the chain will follow up. What I learned I learned new softwares, skills What's next for Virtual Health Checkup|Modelling of CoronaVirus|Technoband If got success, wanna make it open source. Built With arduino c++ embedded matlab python webex

https://devpost.com/software/data-visualization-and-crowd-analysis-using-ml-techniques

Splash screen User app Home page User App- Home screen Website Home page Admin app - Authentication Admin app - Set limit In recent years, the human population is growing in extreme rate and hence the growth has indirectly increased the incidence of the crowd. There is a lot of interest in many scientific research in public service, security, safety and computer vision for the analysis of mobility and behavior of the crowd. Due to a crowded crisis, there are large crowds of confusion, consequence in pushing, mass-panic, stampede or crowd crushes and causing control loss. To prevent these fatalities, automatically detection of critical and unusual situations in the dense crowd is necessary. People visiting various malls and students studying in universities face a lot of difficulty because of the rush. So far there has not been any significant improvement to tackle this problem effectively. Our project aims to tackle this issue by providing a system for collecting, processing and visualizing the crowd behaviour. The end result of our system is a web and app user interface where users can browse through a range of information related to the crowd distribution and crowd movement within a campus and a city. This project combines the power of Wifi devices, Big data, Machine learning and Data Visualization techniques to promote smart living and management. The main idea of this project is to analyze the CCTV in real time and tracking the Wifi probe requests of users for automatically sensing the crowd distribution and to provide statistical data to the users. The use of big data is to analyze and predict the level of crowdedness among the various places in the city and inside the campus also. It also captures the crowd movement to locate critical and crowding spots effectively. Furthermore, it monitors the crowd conditions and waiting time at important locations such as bus stops, railway stations, airports, religious places, campus canteen and uses Artificial Intelligence techniques to predict the upcoming crowd. For example, people can check the current crowdedness conditions and waiting time at bus stations and make smarter decisions on their mobility. Through big data analysis, people can not only compare the crowdedness but also can avoid the peak hours by AI prediction. In this Pandemic situation, Social distancing is in much importance, without which there is a high risk of people getting affected by the virus. So we can use the surveillance cameras that are available at many location and compute, analyze the crowd density of a particular location. If the crowd density of a particular location is found to be greater than the allowed crowd density, we can alert the police authorities and it would be a great use for them to track the people who are not following the rules that are put forward by the government ie, we can prevent large crowd gathering which may lead to more people getting affected. Crowd detection and density estimation from crowded images have a wide range of application such as crime detection, congestion, data driven smart campus, public safety, crowd abnormalities, visual surveillance, urban planning, bus stations, restaurants and various other places. Nowadays, crowd analysis is the most active-oriented research and trendy topic in computer vision. As a result, it will definitely help to make emergency controls and appropriate decisions for security and safety. This system can be used for the detection and counting people, crowd level and also alarms in the presence of dense crowd. The objectives of this project are: Develop an automated system for collecting and processing input data. Develop algorithms for observing the crowd size in various places and predicting the crowd. Raise alarms in the case of over crowdedness. Design and build database for data storage. Build an intrusive app and web user interface for visualizing the crowd distribution and crowd movement information. Thus, our project handles the difficult issue of including the quantity of items in pictures, a universal, principal issue in computer vision. While people and computer vision calculations, are profoundly blunder inclined, our algorithms and IOT devices consolidate the best of their abilities to convey high accuracy results at moderately low expenses providing an effective solution for this imminent problem.All these are back-end working of a web interface and a app that allow authorized person to sign in and gather the details about the targeted location from anywhere. Built With android css firebase html java javascript python regression sdc-net spyder3 website xml Try it out he-s3.s3.amazonaws.com github.com drive.google.com

https://devpost.com/software/project-14tacrvo2mp0

Inspiration What it does How I built it Challenges I ran into Accomplishments that I'm proud of What I learned What's next for Try it out bit.ly

https://devpost.com/software/keep6-p846kn

Keep 6 Logo Arduino Mega Wiring RFID Read From 6+ Feet (Blue Light = Safe) RFID Read From <6 Feet (White Light = COVID-19) Flutter App Settings Page With RFID Input Flutter App Add Whitelisted User Page Flutter App Safe Social Distancing Interface Flutter App Social Distancing Violation Interface Website Map With Plotted Densely Packed Locations and Lat/Long Table Website Sign Up Page Firebase Database Entries For Backend Data Storage Inspiration Without a doubt, the most pressing global issue right now is the fight against COVID-19. Currently the battle against this global pandemic is fought on two fronts. While our healthcare heroes seek to end the disease one patient at a time, the rest of us must do our part to keep the global community safe and healthy. All across the world, people are quarantining themselves and practicing social distancing when going outside. According to the Center for Disease Control and Prevention, social distancing is defined by two main parts: not gathering in groups and staying at least 6 feet apart. With people still leaving their houses, whether to run essential errands or just to exercise, it becomes near impossible to keep track of everybody that has come near you. We wanted to create a widespread platform for people to be able to track the coronavirus status of the people that they have come near through factors like recency of interaction, proximity of interaction, and latest COVID-19 testing results. What it does Keep6 is a mobile app, Arduino hardware, and website based platform that encourages and monitors safe social-distancing. Using an RFID sensor, the platform is able to track the distance from the user to the other people around them. An ESP8266 module is then used for communication to a server which will log the distances and communicate them with the mobile app and website. The mobile app is used to determine whether the user has been within 6 feet of anyone and would therefore be at risk, while the website displays a Google Maps API detailing the most concentrated areas of people to avoid. How we built it Hardware We used an Arduino Mega with an MFRC522 module for RFID distance functionality and an ESP8266 module for public server communication. A portable device was made for users to carry with them in their outdoors excursions. These RFID sensors are used to track the proximity of neighboring users and relay that to a server. Compared to Bluetooth and GPS location services, RFID distancing is accurate to the foot and has a very large range. Furthermore, because RFID is secure, does not track users’ data or absolute location, and only returns relative distances with other users, there are no privacy concerns for the user. Server The server is the middleman for the entire project. It handles all requests coming in from the arduino, iphone app, and the website. The first process that the server handles is users logging and signing in. From there the server gets a request from the arduino to update the device location. This information is then sent to a web socket that the iphone also connects to in order to view the location of other arduino devices in the area. The server then calculates the distance between devices in order to determine whether the user is within 6 feet of another user. App We created the app using the Flutter programming language and it allows you to connect to your RFID reader. This app will allow you to see if you are near someone else and notify you of your risk of COVID and the distance between you and the closest person. It also gives the user the option to set whitelists for people that are in their family by adding their email addresses. The app collects information on whether or not the user has been diagnosed with COVID-19 and accordingly updates the risk levels for those around them. All of the app communications are routes through the server to enhance security. Website We created the website using a Google Maps API paired with javascript code marking concentrated areas of people on the map. HTML/CSS was used for the formatting of the website which allows user login to view the personal coronavirus information in the form of maps and tables that is obtained through the server. Challenges we ran into We ran into numerous problems with wiring/programming the Arduino hardware for sensor reads and Wi-Fi communication. We were originally using a NodeMCU, but found out that it cannot handle Wi-Fi communication and RFID read simultaneously because of serial port limitations. After switching to the Arduino Mega and debugging server request issues, we were able to seamlessly implement the hardware. Furthermore, we had some issues with whitelist users on the backend because of the multiple links required for its functionality. However, we were able to develop a reliable algorithm for editing and parsing the storage structure to provide this functionality with no errors or miscommunications. Accomplishments that we're proud of We are especially proud that we were able to accomplish seamless Wi-Fi based server communication between 4 distinct components. Each of our 4 team members took on a component and worked together to mesh each piece together into a single, connected platform. With all of the user information, RFID identifiers, distances, and other data points that are passed between components as API parameters, we are proud that we created a platform that handles it all accurately and efficiently, all while working remotely. What we learned This was our first hackathon in a remote setting, so we had to learn to collaborate with each other through voice calls and coding live shares. While it was difficult at times to work on frontend/backend integration and hardware communication with other components, we were able to complete all of the functionality we originally envisioned. What's next for Keep6 A potential next step for Keep6 would be to find a medically approved algorithm to determine the likelihood of infection for the user based on the data that the Keep6 server already tracks. Another important step to maximizing the effectiveness of this platform would be to expand to as many users as possible so that the server has more information. One way of doing this would be to make the RFID Arduino mechanism smaller and more convenient for users to carry around so that they would be more incentivized to use it. Built With amazon-web-services arduino c++ css dart firebase flutter html javascript node.js react Try it out github.com

https://devpost.com/software/quickeats-x7fdp4

Home Page Information Page Sends the restaurant's data to Firebase Retrieves the restaurant's information from Firebase to showcase the offered products Uses the restaurant's address from Firebase to calculate the latitude and longitude and to show where the stores are located through APIs Firebase Portal Inspiration COVID-19 brought much of global economic activity to a halt, hurting businesses and causing people to lose their jobs. In particular, restaurant owners suffer a great loss due to few customers and wasted food. A new study suggests that one in 10 restaurants around the country have permanently closed due to COVID-19. Restaurants Canada says an estimated 800,000 jobs have been lost across the country in the past month and more than 300,000 of those jobs are in Ontario alone. We feel an urge to help them endure this hardship and thought of a platform for them to resell their stocked food to everyone. Although it is at a lower price, it benefits not only the restaurants in minimizing costs but also the general public in saving money, moreover the environment for not wasting resources. What it does QuickBites allows restaurants to make postings of the food they are selling on the "Partners" page. Then the information will be stored in the database for everyone to see. Consumers can buy specific products as a discounted rate on the "Products" page. In addition, QuickBites have a "Location" page that shows all of the restaurant partners so that consumers can easily pick one that is most convenient to their home. This portion is completed with the support of Google Maps and Place API. How we built it We built this project using JavaScript, HTML/CSS, Bootstrap, React, and Firebase. We designed our website with React framework and managed all details with HTML, CSS and Bootstrap. Furthermore, we implemented our database using JavaScript and Firebase, and we incorporated the Google Maps and Place API to show the restaurants near the users. Challenges we ran into When making this project, one of our struggles was designing a visually appealing and functional website. By using Bootstrap and carefully designing the details, we were able to overcome this problem. The other issue we came across was managing different states in React and integrating everything together with Firebase. The large number of interactions our web app is making causes the issue and we resolved it at the end by continuous debugging and checking over. Accomplishments that we're proud of We successfully completed the project and it worked perfectly in the end. We are proud of ourselves since we do not have a lot of experience with React + Firebase. However, we persist to complete the project. What we learned We reinforced our knowledge on implementing React and Firebase, and we learned to integrate Google Maps and Place API into our website which created a convenient experience for users. What's next for QuickBites In terms of technical aspects, we hope to implement more APIs and explore more with Google Cloud services along with other databases such as MongoDB. QuickBites can not only help restaurant owners but also other local retail stores in the COVID-19 crisis. We hope to implement it in the near future to contribute our part to the economy amidst the pandemic. Built With bootstrap css firebase google-maps google-places html javascript react Try it out github.com

https://devpost.com/software/planetry-voice-social

Inspiration: Solving immediate and future Planetry crisis. Solving Immedate Planetry Crisis and issues: They are alots of things that can create planetry hazards like carbon emisions, wasting of water,consumption of excessive can foods and bottles drinks etc. 1.) Educational Posts, Blogging and Enlightenment: People need to be educated and enlightened on the danger of all this hazard. TO this effects, we created an application to allow community to post, blogs, events, news and share environmental information's via Blogs, photos, Videos, Posts etc while allowing the community to respond via likes, comments, replies etc. 2.) Donations: Donations can be anything eg. money, gift etc. The application allow the community members and philanthropist to donate money that will be use to keep the earth free. The Application uses Paypal Payment Gateways to collect the payment donations. The site Admin has to update his Paypal wallets Email address and other information's from the admin dashboard. 3.) Immediate Connections to Site Admin(Planetry Experts) Social Networks: On the landing page,the applications allows the community members to reach out to Planetry Experts on their various social media Eg. Facebook, Whatsapp, Twitter to learn and gather more information's from there on how to keep the planet safe. All the site admin has to do is to login to the admin dashboard and updates all their Social Links so as to reflects on the landing page. 4.)Contact Us Mail: Community members can use this to send emergency messages to the site admin on environmental emergency situations. All this forms has a built-in internal defense against spam-bots. All the site admin has to do is to login to the admin dashboard and updates all their Contact us email address, Phone numbers etc so as to reflects on the landing page. This will enable site admin to receive messages straight to his email addresses Solving Future Planetry Crisis: I was reading about origin of covid-19 virus and I came across a CNN posts on how Chinese Governments tries to silence doctor Li Wenliang who boldly blows whistle on the novel corona virus so that world will take actions and protective measures. CNN Source Here: https://edition.cnn.com/2020/02/03/asia/coronavirus-doctor-whistle-blower-intl-hnk/index.html Some other doctors in China could have done the same but just that they are afraid of their governments and the same thing could happen to other Whistle Blowers in any other government oppressing countries. Solutions: Posting, Sharing Educative and emergency information's anonymously. We provide a system to allow people with good mind to post their Environmental, planetry observations, hazards in the form of Posts, Blogs, photos, videos etc. anonimously without anyone knowing it was you. The Blogs, post will not be seen from the Whistle Blower profile so you have nothing to be afraid of anyone tracking your back. . Only the site admin can have access. other community members can only comments, likes, replies etc. on the anonymous post without ever knowing who shared it. How to use/test the Applications: (For testing this apps, Mozila Browser is my best) The applications has both Admin and members sections though some other features are yet to be implemented due to lack of time. Accessing the application as Admin: You will require to make registration with 8 digit access code good1982 Without this access code, you cannot register as an admin. The Admin form is protected with built-in spambot shields. Accessing the application as a Member: As a community members, Environmental Bloggers, Experts etc, you will have to register with a valid email address. Upon registration, a verification link will be sent to your email address to complete your registration. Admin can do other minor customization's on the landing page. More features coming soon as can be seen from the apps members and admin dashboards. Devices already Tested: Highly mobile responsive and works on all devices ranging from mobiles smart phones to desktop devices. Issues Currently having on testing The app runs excellent locally on all browsers like Mozila, UC, internet explorer 11 etc and some version of chrome. For instance the Chrome in my laptops allows all the bootstraps files to load and the app is working excellent while chrome version in my desktop does not allow 2 bootstraps files to be loaded remotely saying error integrity check unless i loaded the 2 bootstrap files locally. I know I can solve this little chrome issue later. its just some chrome browser issues Built With ajax bootstraps css jqery mysql php Try it out equationdev.com

https://devpost.com/software/covidseek

Inspiration Since the beginning of this pandemic, many people globally are in a state of confusion and panic. Many healthcare systems need a way to allocate resources properly based on the density of the pandemic. Furthermore, many people do not know when this virus will keep spreading. We built COVIDSeek to answer these problems through providing an accurate visualization and predictions/forecasts of the pandemic. What it does COVIDSeek is a web application that connects people and healtchare systems through accurate information, and predictive analytics. Users enter their location to see a density heatmap of the virus on an international scale, which is also useful for medical practitioners and the healthcare system. They also will see the specific number of cases and deaths in their respective area on a given day. Finally, they are provided with a forecast of what cases might rise/lower to in the next 1-2 months. How we built it On the front end, we used html, css, and javascript through the bootstrap web framework. On the backend, we first use the google-maps api in python (through gmaps) to visualize the heatmap, and we passed this into an html file. Furthermore, we used Flask to serve the json data of the cases and deaths (across the world) to our front end, and SQLAlchemy as a way of storing data schema in our database. We use the FBProphet library to statistically forecast time-series data and future cases through Bayesian analysis, logistical growth, and predictive analytics, by factoring in trend shifts as well. Challenges we ran into We ran into challenges regarding the visualization of the heatmap, as well as the creation of our forecasting algorithm, as we didn't have much experience with these areas. Furthermore, serving some parts of the data to the front-end from Flask had some errors at first. It also took time to assemble data into a consolidated file for analysis, which was a bit hard in terms of finding the right content and sources Accomplishments that we're proud of We are proud of how much progress we've made considering how new we were to libraries such as FBProphet and Flask, and the unique, special, and effective way we learned how to implement it. We learned how to create opportunities to benefit different areas across the world through data analytics, which is something that we're very proud of doing. What we learned In terms of skills, Aryan learnt how to develop his front-end skills with Bootstrap and using different ways of styling. Shreyas also developed his front-end skills while working with Aryan to structure the front-end, as well as finding new skills in learning Flask and the Gmaps API. We learnt that there are numerous ways that an individual can help the world around them through computer science. What's next for COVIDSeek In the future, we want to add a user-interactive search bar that places a marker on their location and zooms into the map, as well as a way for users to report symptoms/cases on the map. We also want to add more features, such as nearby testing sites, hospitals, as well as nearby stores with a certain amount of resources that they might need. Overall, we want to make this web app more scalable worldwide. Built With bootstrap css3 fbprophet flask google-maps html5 javascript matplotlib numpy pandas python sqlalchemy Try it out github.com

https://devpost.com/software/covaid-53hv21

CovAid Register Page CovAid Login Page CovAid Requests Page CovAid Requests Viewer CovAid Request Submission CovAid Home Page Inspiration The world we live in has changed dramatically amidst the COVID-19 outbreak. Although some of us are safe at home with the proper equipment, a large portion of the population does not have access to essentials. In analyzing the issue, we realized the immunocompromised currently had no access to essentials as they could not simply leave their houses to go to a grocery store. We decided to provide a solution to this problem by creating a website in which we could allow users to make virtual requests for items, such as toilet paper or hand sanitizer, and then enable volunteers to accept these requests to donate supplies to them. As there is no preexisting platform that allows for direct pairings between users and volunteer deliverers, we believe this is the perfect solution to help those most impacted by COVID-19. What it does CovAid is a web application that connects volunteers to those in need during the COVID-19 outbreak using AI-driven intelligence. The website connects at-risk users with volunteers willing to donate necessities. Users can make requests for items to the website and volunteers can respond to those requests. These pairings are created efficiently with a machine learning algorithm that takes into account various factors such as the distance between the user and the volunteer. How we built it Through the development of CovAid, we were able to learn how to integrate Flask, JavaScript, and jQuery as our back-end with HTML and Bootstrap together to develop a website from scratch. We used SQL to operate the database of users and the Google API to calculate the miles and estimated time between users. These topics were new to us and we were able to truly learn how to integrate every part together to create a fully-functioning website. In order to perform the matching between users and volunteers, we developed a Machine Learning Neural Network model to sort the requests on a volunteer’s page, as we wanted requests most relevant to the volunteer to show up when a volunteer is searching for a request to accept. We used Keras, NumPy, Pandas, and a Sequential Machine Learning Neural Network model with Dense layers to develop our model before implementing it into our website. Challenges we ran into We faced numerous challenges when it came to properly communicating with Flask view and the various HTML templates. Since CovAid is a dynamic site form data had to be sent back and forth between the files and stored in a database. Using a database was something new to all of us and understanding how to integrate it for our needs was a major roadblock for a while. Another major challenge was implementing our machine learning sorting algorithm with our Flask and HTML to sort the requests for each volunteer, since we had to learn how to get live user data to enter into the model. Accomplishments that we're proud of We are proud of how we could efficiently push out a website while allowing everyone on our team to contribute equally. After beginning with our entire team working together to create the basic layout of our website, we split up into two teams. Shrey and Atin worked on the front-end and back-end of the website while Anirudh and Aarav worked on the machine learning aspect of the project. We also learned various CS skills while also helping our community at the same time. In addition, we are also pleased that we have created another scenario that AI can help ease our lives. We are excited to see how our project will be able to create opportunities for other people to make a positive impact on their surroundings. What we learned In developing CovAid, aside from exploring new software such as Bootstrap and Flask, we fully understood the broader impacts of our project — that any simple act of kindness can be influential, especially to those that are impacted the most from issues like these. What's next for CovAid In order to create a real difference in our community we hope for CovAid to be more widespread and have a larger impact on the world. We also want to implement a system in which users are able to be further interconnected. Our vision is that through our product everyone will have access to essentials and will stay safe as our world continues to change from COVID-19. Built With bootstrap css3 flask google html javascript jquery keras machine-learning numpy pandas python sqlalchemy Try it out github.com

https://devpost.com/software/stacy-bot

Interface in FB messenger This representation of NLP Features which will be added more as time goes PLEASE NOTE THIS IS A TEST BOT, AS PUBLISHING AND VALIDATION TAKES TIME, SO IF U WANT TO USE THIS THEN U NEED TO BE THE TESTER. BUT U CAN USE THE PHONE CALL FACILITY. CALL AT: +1 463-221-4880 (This is a toll-free number based in US, if you are out of US then only minimal international charges will be applicable, I am from India and it takes 0.0065$/min) If you want to use this app in your Facebook Messenger like shown in the video then please comment your Facebook ID in this project's comment section, I will add you as a tester to this app IT IS JUST AN WORKING DEMONSTRATION OF MY IDEA TO TACKLE THE PROBLEM, IT CAN BE MADE AS PER THE DEMAND OF ANY ORGANISATION. AND THE BEST THING IT IS NOT A CONCEPTUAL IDEA IT IS TOTALLY A REALISTIC IDEA THAT CAN BE DEPLOYED AT ANY MOMENT ACCORDING TO THE DEMAND OF THE ORGANIZATION Our Goal General Perspective Due to the situation of COVID-19 the work force of the world is decreasing(since everyone is maintaining self quarantine and social distancing ), which is creating a big havoc in the world, through this project of mine, I mainly target to tackle this problem and help the health organizations with a virtual workforce that runs 24*7 without any break, and handles all kind of mater, starting from guiding the people to fill up the forms to managing the data of the patients automatically and all-together. Business Perspective(if required) Bot service (it is not a company yet, I am just referring to the thing that we want to build or start this company, we are student developers right now) which adds a virtual work force to every client organisation to bloom in the market. In business perspective Our potential business targets are small business,NGO and health organisations and we help them to be free from human service cost and help them to grab more users by providing 24*7 interaction with there users, thus generating more revenue for them. Inspiration I really got inspired for making this advance A.I bot by seeing the current COVID-19 situations, because of these COVID-19 situations people are restricted from gathering hence work force and user interaction of various health organisation are diversely effected. Through this project I aimed to connect the health organizations with the patient anywhere in the world,using any platform(not limited by android, ios or Web). And also manage the data of the patients automatically thus reducing human effort and maintaining social distancing. MADE THIS PROJECT TO BRING A CHANGE . How is our product different than others 1) There are many types of A.I bots,where most of them are Decision tree based models that work with particular buttons only,our products will be totally based on NLP based models,which are more advanced and are in higher demands than others. 2) Other service A.I bot service providers are confined to only 1 or 2 platforms, whereas we at the same time are providing advantage to the client to choose from a large scale of platforms like FB messenger, google assistant,slack,line,website bots and even in calls 3) For the health organisations that are willing to buy our technology (We are also willing to donate this tech for free), from business perspective we will also be cheaper than our other competitors, when others are taking near about $3300/year for the service, we are doing it in $100-$1500 one-time fee range with more versatility. It will totally be free for any user using it, no charges will be applicable for users What it does Our bot provides the power to every health organisation at such situations of COVID-19 by managing the screening,testing and quarantine data and also connecting the persons that are willing to do the test with the help of diversified digital platforms. In cases where internet is not working (where other bots won't function) still our bot works inside the phone number thus providing fruitful results in such situations.It basically covers all important aspects of an advanced A.I bot. It also connects the health organisations with volunteers that are willing to donate their time as helping hands in this hour of need. How I built it I built it using Google cloud A.I solutions, Google cloud Dialogflow framework(which includes automatic firebase integration) where I trained the bot with NLP with huge datasets from WHO and government and then integrated it with the Facebook messenger through Facebook Developer account. It is also supporting Phone call facility Challenges I ran into I had to go through many challenges, starting from being a solo developer, I really had to face a lot of problems as making such a complex app which all the advanced features as mentioned, all these things together cost me a lot of sleepless nights but i hope my hard-work pays off Accomplishments that I'm proud of I am really proud of the app that I made because it itself is a big milestone for a solo developer like me. What I learned I learned a lot of things through out this journey of developing this app, starting from advance use of Google cloud A.I solutions, Dialogflow and integrating it to Facebook messenger, making filters inside the chat-bot to enhance user experience etc.Connecting it with a phone number to receive phone calls etc. What's next for Health Bot If my work gets selected, then for sure I am going to work really hard to make Health Bot even bigger and to add more amazing functionalities to make my users happy. Built With dialogflow facebook google-cloud javascript json Try it out github.com

https://devpost.com/software/plantaid-7opq9x

Inspiration When we came into the hackathon we didn’t have a clear idea about how we could improve, but after researching environmental issues, we found a huge problem that needed fixing. In the farming world, annual losses of 30 to 50 percent of crops are not uncommon. Why is this number so high? The main answer to this shocking statistic is diseases. Plant based viruses can decimate whole fields if given the time to spread enough, so we made PlantAId. Farms around the world provide us with 70% of all food, so clearly this issue is of utmost concern This app has the potential to be revolutionary for farmers who will be able to find viruses in the plants before it spreads, cutting that annual loss of crops due to diseases down by large margins. Whether the user is a farmer, who depends on these crops to make a living, the consumers, some of whom are starving from this lack of food, whether the disease is on the leaves, stems, or with the buds of the plant, PlantAId is paving the path to a healthier future, one plant at a time. What it does PlantAId has three main functions: Our machine learning model is able to predict what kinds of diseases different crops have or if they’re healthy, and users will then be sent to a screen detailing different symptoms and treatments for the specific disease the crop may have. If the fruit is healthy, they will be taken to a screen detailing how to maintain the prosperity of the plant. Our third element comes in via a disaster bot, where farmers are able to type in concerns about crops and receive answers from said disaster bot. We believe including this information on our app will save farmers precious time and ways to solve problems such as if their crops are being flooded or are experiencing a drought. How I built it The main component of our application is the CoreML based machine learning model. CoreML and CreateML are IOS based machine learning libraries which allowed us to build the machine learning functionalities. We looked for datasets online to help train and test our model in Apple’s CreateML interface, which allows you to easily label and train a machine learning model. After training and testing, we were able to use Apple’s Vision Framework to easily process user images, process them with our model and then output a result. We used AVCam, a camera app framework to create the camera interface within our app. Next, we used Keras and Python to train a Natural Language Processing Chatbot. With a few lines of code, our ChatBot was able to analyze and process user questions and intents and provide sufficient responses. We did research on common agricultural disasters and added solutions to help farmers in whatever situation they are in. Finally, we built a database using SweeterSift and SnapKit to build a user-friendly UI which would be easy to navigate. Our database takes in and stores data from user input and can store images and text for easy use and display. After our user inputs their data, our crop tracker simply holds and projects whatever profile our user added for their plant/crop. Challenges I ran into A large obstacle we ran into was trying to make the chat bot system work. We had plans previously to make the chatbot work for multiple scenarios, but eventually settled for the disasterbot function and were able to make the system work for the specific category. We learnt that if we asked our ChatBot to perform too many functions, it would often get confused and not understand what our user was asking. We eventually settled by narrowing to DisasterBot, and this worked far better for user input. Another challenge was implementing the machine learning model, as analyzing plants required a lot of images and tons of data, which was hard to find and then took patience to train. Originally, our model did not work as needed and debugging and pinpointing the issues was a tedious task. Eventually, after re-training our model started working as intended. Accomplishments that I'm proud of We are proud of our chatbot, as we had never dealt with Natural Language Processing in swift, and building a chatbot was a new and unique experience which we enjoyed. Working with python and Keras was also a learning experience that could be useful in the future, and was good exposure to the extents of NLP. We are also proud of our machine learning model, as re-training and gathering data to make an accurate model was both rewarding and troublesome. This was our 3rd or 4th time ever working with CoreML, so we were learning something new while also making something which can have a huge impact on our food production and farming. What I learned We learnt a ton about NLP and how it works. From understanding intents and entities to going about training our model in Python, building a ChatBot was far harder than I anticipated. We also learnt about the limits of NLP; when a bot is flooded with commands and different ideas, it has a hard time processing information and training, which is why we have to give narrow and straight-forward questions to allow our bot to perform adequately. We also learnt a lot about UI frameworks such as SnapKit, which we had little experience working with prior. We had previously relied solely on UIKit, but using different frameworks was educational and will be helpful for any future apps. Building a clean UI seems like a daunting task, but simplicity and certain design concepts (Proper White Spacing, Color Blocking) allowed us to build a beautiful but functional user interface. What's next for plantAId In the future we will look at implementing a better centralized database system so farmers can keep track of thousands of plants with easier scans. On top of this, adding more features to the chatbot will allow farmers an incredible amount of information at the tip of their fingers and would be a good extension to this app. A huge addition to PlantAId would of course be adding more content, such as more crops that can be analyzed along with diseases that the crops could be carrying. With the clean format we have created, adding new content such as new plants and diseases will be a much simpler task. Disclaimer: Our app file size was larger than 35mb (DevPost limit). If you would like to access the App, please use our Github Repo instead. Built With computer-vision coreml keras machine-learning natural-language-processing python swift uikit Try it out github.com

https://devpost.com/software/masked-ai-masks-detection-and-recognition

Platform Snapshot Input Video Model Processing Model Processing Output Video Saved Output Video Snapshot Output Video Snapshot Output Video Snapshot Output Video Snapshot Output Video Snapshot Output Video Snapshot Inspiration The total number of Coronavirus cases is 5,104,902 worldwide (Source: World o Meters). The cases are increasing day by day and the curve is not ready to flatten, that’s really sad!! Right now the virus is in the community-transmission stage and taking preventive measures is the only option to flatten the curve. Face Masks Are Crucial Now in the Battle Against COVID-19 to stop community-based transmission. But we are humans and lazy by nature. We are not used to wear masks when we go out in public places. One of the biggest challenges is “People not wearing masks at public places and violating the order issued by the government or local administration.” That is the main reason, we built this solution to monitor people in public places by Drones, CCTVs, IP cameras, etc, and detect people with or without face masks. Police and officials are working day and night but manual surveillance is not enough to identify people who are violating rules & regulations. Our objective was to create a solution that provides less human-based surveillance to detect people who are not using masks in public places. An automated AI system can reduce the manual investigations. What it does Masked AI is a real-time video analytics solution for human surveillance and face mask identification. Our main feature is to identify people with masks that are advised by the government. Our solution is easy to deploy in Drones and CCTVs to “see that really matters” in this pandemic situation of the Novel Coronavirus. It has the following features: 1. Human Detection 2. Face Masks Identification (N95, Surgical, and Cloth-based Masks) 3. Identify human with or without mask in real-time 4. Count people each second of the frame 5. Generate alarm to the local authority if not using a mask (Soon in video demo) It runs entirely on the cloud and does detection in real-time with analysis using graphs. How we built it Our solution is built using the following major technologies: 1. Deep Learning and Computer Vision 2. Cloud Services (Azure in this case) 3. Microservices (Flask in this case) 4. JavaScript for the frontend features 5. Embedded technologies I will be breaking the complete solution into the following steps: 1. Data Preparation: We collected more than 1000 good quality images of multiple classes of face masks (N95, Surgical, Clothe-based masks). We then performed data-preprocessing and labeled all the images using labeling tools and generated PASCAL VOC and JSON after the labeling. 2. Model Preparation: We used one of the famous deep learning-based object detection algorithm “YOLO V-3” for our task. Using darknet and Yolo v-3, we trained the model from scratch on 16GB RAM and Tesla K80 powered GPU machine. It took 10 hours to train the model. We saved the model for deploying our solution to the various platforms. 3. Deployment: After training the model, we built the frontend which is totally client-based using JavaScript and microservice “Flask”. Rather than saving the input videos to our server, we are sending our AI to the client’s place and using Microsoft Azure for the deployment. We are having on-premise and cloud solutions prepared. At the moment, we are on a trail so we can’t provide the link URL. After building the AI part and frontend, We integrated our solution to the IP and CCTV cameras available in our house and checked the performance of our solution. Our solution works in real-time on video footage with very good accuracy and performance. Challenges we ran into There are always a few challenges when you innovate something new. The biggest challenge is “The Novel Coronavirus” itself. For that reason, we can’t go outside the home for the hardware and embedded parts. We are working virtually to build innovative solutions but as of now, we are having very limited resources. We can’t go outside to buy hardware components or IP & CCTV cameras. One more challenge we faced was that we were not able to validate our solution with drones in the early days due to the lockdown but after taking permission from the officials that problem was not a deal anymore. Accomplishments that we're proud of Good work brings the appreciation and recognition. We have submitted our research paper in several conferences and international journals (Waiting for the publication). After developing the basic proof-of-concept, We went on to the local government officials and submitted our proposal for a trial to check our solution for better surveillance because the lockdown is near to be lifted. Our team is also participating in several hackathons and tech event virtually to showcase our work. What we learned Learning is a continuous process. We mainly work with the AI domain and not with the Drones. The most important thing about this project was “Learning new things”. We learned how to integrate “Masked AI” into Drones and deploy our solution to the cloud. We added embedded skills in our profile and now exploring more features on that part. The other learning part was to take our proof of concept to the local administration for trails. All these “Government Procedures” like writing Research Proposal, Meeting with the Officials, etc was for the first time and we learned several protocols to work with the government. What's next for Masked AI: Masks Detection and Recognition We are looking forward to collaborating with local administrative and the government to integrate our solution for drone-based surveillance (that’s currently in trend to monitor internal areas of the cities). Parallel, The improvement of model is the main priority and we are adding “Action Recognition” and “Object Detection” features in our existing solution for even robust and better solution so decision-makers can make ethical decisions as because surveillance using Deep Learning algorithms are always risky (bias and error in judgments). Built With azure darknet flask google-cloud javascript nvidia opencv python tensorflow twilio yolo

https://devpost.com/software/covnatic-covid-19-ai-diagnosis-platform

Landing Page Login Page Segmentation of Infected Areas in a CT Scan Check Suspects using Unique Identification Number (New Suspect) Check Suspects using Unique Identification Number (Old Suspect) Suspect Data Entry COVID-19 Suspect Detector Upload Chest X-ray Result: COVID-19 Negative Upload CT Scan Result: Suspected COVID-19 Realtime Dashboard Realtime Dashboard Realtime Dashboard View all the Suspects (Keep and track the progress of suspects) Suspect Details View Automated Segmentation of the infected areas inside CT Scans caused by Novel Coronavirus Process flow of locating the affected areas U-net (VGG weights) architecture for locating the affected areas Segmentation Results Detected COVID-19 Positive Detected Normal Detected COVID-19 Positive Detected COVID-19 Positive GIF Located infected areas inside lungs caused by the Novel Coronavirus Endorsement from Govt. Of Telengana, Hyderabad, India Endorsement from Govt. Of Telengana, Hyderabad, India Generate Report: COVID-19 Possibility Generate Report: Normal Case Generated PDF Report Inspiration The total number of Coronavirus cases is 2,661,506 worldwide (Source: World o Meters). The cases are increasing day by day and the curve is not ready to flatten, that’s really sad!! Right now the virus is in the community-transmission stage and rapid testing is the only option to battle with the virus. McMarvin took this opportunity as a challenge and built AI Solution to provide a tool to our doctors. McMarvin is a DeepTech startup in medical artificial intelligence using AI technologies to develop tools for better patient care, quality control, health management, and scientific research. There is a current epidemic in the world due to the Novel Coronavirus and here there are limited testing kits for RT-PCR and Lab testing . There have been reports that kits are showing variations in their results and false positives are heavily increasing. Early detection using Chest CT can be an alternative to detect the COVID-19 suspects. For this reason, our team worked day and night to develop an application which can help radiologist and doctors by automatically detect and locate the infected areas inside the lungs using medical scan i.e. chest CT scans. The inspirations are as below: 1. Limited kit-based testings due to limited resources 2. RT-PCR is not as much as accurate in many countries (recently in India) 3. RT-PCR test can’t exactly locate the infections inside the lungs AI-based medical imaging screening assessment is seen as one of the promising techniques that might lift some of the heavyweights of the doctors’ shoulders. What it does Our COVID-19 AI diagnosis platform is a fully secured cloud based application to detect COVID-19 patients using chest X-ray and CT Scans. Our solution has a centralized Database (like a mini-EHR) for Corona suspects and patients. Each and every record will be saved in the database (hospital wise). Following are the features of our product: Artificial Intelligence to screen suspects using CT Scans and Chest X-Rays. AI-based detection and segmentation & localization of infected areas inside the lungs in chest CT. Smart Analytics Dashboard (Hospital Wise) to view all the updated screening details. Centralized database (only for COVID-19 suspects) to keep the record of suspects and track their progress after every time they get screened. PDF Reports, DICOM Supports , Guidelines, Documentation, Customer Support, etc. Fully secured platform (Both On-Premise and Cloud) with the privacy policy under healthcare data guidelines. Get Report within Seconds Our main objective is to provide a research-oriented tool to alleviate the pressure from doctors and assist them using AI-enabled smart analytics platform so they can “SAVE TIME” and “SAVE LIVES” in the critical stages (Stage-3 or 4). Followings are the benefits: 1. Real-world data on risks and benefits: The use of routinely collected data from suspect/patient allows assessment of the benefits and risks of different medical treatments, as well as the relative effectiveness of medicines in the real world. 2. Studies can be carried out quickly: Studies based on real-world data (RWD) are faster to conduct than randomized controlled trials (RCTs). The Novel Coronavirus infected patients’ data will help in the research and upcoming such outbreak in the future. 3. Speed and Time: One of the major advantages of the AI-system is speed. More conventional methods can take longer to process due to the increase in demand. However, with the AI application, radiologists can identify and prioritize the suspects. How we built it Our solution is built using the following major technologies: 1. Deep Learning and Computer Vision 2. Cloud Services (Azure in this case) 3. Microservices (Flask in this case) 4. DESKTOP GUIs like Tkinter 5. Docker and Kubernetes 6. JavaScript for the frontend features 7. DICOM APIs I will be breaking the complete solution into the following steps: 1. Data Preparation: We collected more than 2000 medical scans i.e. chest CT and X-rays of 500+ COVID-19 suspects around the European countries and from open source radiology data platform. We then performed validation and labeling of CT findings with the help of advisors and domain experts who are doctors with 20+ experience. You can get more information in team section on our site. After carefully data-preprocessing and labeling, we moved to model preparation. 2. Model Development: We built several algorithms for testing our model. We started with CNN for classifier and checked the score in different metrics because creating a COVID-19 classifier is not an easy task because of variations that can cause bias while giving the results. We then used U-net for segmentation and got a very impressive accuracy and got a good IoU metrics score. For the detection of COVID-19 suspects, we have used a CNN architecture and for segmentation we have used U-net architecture. We have achieved 94% accuracy on training dataset and 89.4% on test data. For false positive and other metrics, please go through our files. 3. Deployment: After training the model and validating with our doctors, we prepared our solutions in two different formats i.e. cloud-based solution and on-premise solution. We are using EC-2 instance on AWS for our cloud-based solution. Our platform will only help and not replace the healthcare professionals so they can make quick decisions in critical situations. Challenges we ran into There are always a few challenges when you innovate something new. The biggest challenge is “The Novel Coronavirus” itself. One of the challenge is “Validated data” from different demographics and CT machines. Due to the lockdown in the country, we are not able to meet and discuss it with several other radiologists. We are working virtually to build innovative solutions but as of now, we are having very limited resources. Accomplishments that we're proud of We are in regular touch with the State Government (Telangana, Hyderabad Government). Our team presented the project to the Health Minister Office and helping them in stage-3 and 4. Following accomplishments we are proud of: 1. 1 Patent (IP) filled 2. 2 research paper 3. Partnership with several startups 4. In touch with several doctors who are working with COVID-19 patients. Also discussing with Research Institutes for R&D What we learned Learning is a continuous process. Our team learnt "the art of working in lockdown" . We worked virtually to develop this application to help our government and people. The other learning part was to take our proof of concept to the local administration for trails. All these “Government Procedures” like writing Research Proposal, Meeting with the Officials, etc was for the first time and we learned several protocols to work with the government. What's next for M-VIC19: McMarvin Vision Imaging for COVID19 Our research is still going on and our solution is now endorsed by the Health Ministry of Telangana . We have presented our project to the government of Telangana for a clinical trail . So the next thing is that we are looking for trail with hospitals and research Institutes. On the solution side, we are adding more labeled data under the supervision of Doctors who are working with COVID-19 patients in India. Features like Bio-metric verification, Trigger mechanism to send notification to patients and command room , etc are under consideration. There is always scope of improvement and AI is the technology which learns on top of data. Overall, we are dedicated to take this solution into real world production for our doctors or CT and X-rays manufacturers so they can use it to fight with the deadly virus. Built With amazon-web-services flask google-cloud javascript keras nvidia opencv python sqlite tensorflow Try it out m-vic19.com

https://devpost.com/software/food-secure-jobs

Inspiration Our project was inspired by our love of good food, concern for the environment and wish for all people to have access to abundant, high quality, nutritious food. What it does The scalable We Grow Green Hub provides a rapidly deployed growing system to generate food quickly for communities; an algae growth system to produce a soil amendment for farmers to increase crop yield while improving the soil; and a local food distribution, education, training, and job center. Our integrated platform produces purified water to grow an algae-based soil amendment in photobioreactors. This soil amendment product is harvested and used for growing fruits, vegetables, grains and all crops. The yield of the crops is at least 30% higher with no chemical or synthetic fertilizer inputs. Our platform integrates with aquaponics to grow fruits and vegetables along with fish. The Center of the Hub acts as a food distribution point and offers job training and opportunities for the community to learn about food, cooking, gardening and other skills. How we built it We started with the cleanest, purest water possible from our nanobubble water purification system. Once we had purified water, we designed a growth system that can be installed in any type building or greenhouse. If it is in a warehouse or building, we use LED light for photosynthesis. We have integrated other systems to further increase the algae production along with an organic nutrient. Our small algae plant produces enough product for 4,000 hectares. The algae farm has no limitations in scaling up. The aquaponics systems benefits from our purified water and algae. The aquaponic system can be scaled from a small teaching system to a large-scale commercial system capable of producing thousands of pounds of fish and produce annually. Challenges we ran into The primary challenges are financial resources and identification of communities to install the systems. We are looking for communities with individuals who would not only benefit from the food produced, but that have people who would be great operational partners, Agricultural Entrepreneurs. There will certainly be additional challenges for development of data management; knowledge of legal requirements in different countries; logistics and shipping; market development; and contracts. It is our plan to develop a traceability system for consumers so they can know where and how the produce they buy has been grown and to learn how much of a positive impact eating foods that have been grown with We Grow Green has made on the environment. Accomplishments that we're proud of We are so excited to share the benefits of this product resulting in the tremendous increase in yield of crops and improvement of the soil. We have been able to develop relationships with farmers and nonprofits who can distribute excess food to low resource communities. We are teaching people about eating nutritious food and how it impacts their health. We are proud of the high tech jobs that we are able to create. What we learned We are learning every day. Our greatest learning has been in working within communities to have them identify their needs and being able to adapt our projects accordingly. What's next for Food & Secure Jobs We hope to work with people across the globe to increase the food supply while improving the soil and providing good quality high tech jobs. We know that nutritious food and a healthy environment will help ensure a healthy population. It is our plan to develop a traceability system for consumers so they can know where and how the produce they buy has been grown and to learn how much of a positive impact eating foods that have been grown with We Grow Green has made on the environment.

https://devpost.com/software/covid-19-detection-system-57wzbc

Inspiration As the coronavirus pandemic sweeps the world, more and more governments are imposing lockdowns on their citizens. As a result, me and my team tried there had on artificial intelligence we have tried to build a web-based application which detect covid-19. Problem Faced Main issue in this situation is lack of detecting resources. We have provided a simple dataset of x-rays to train the system. About Project Our website is a platform which can be used by any one of us from the society from a Civilian to a Medical expert. Just one must have to provide their x-ray of the lungs and the result will show the report instantly on Just 1 Click . Which will help medical experts to detect and provide necessary treatment on time. You will have your reports in your hand within a second. Technology Used We have used Python, machine learning, to build the back end and html, CSS, JavaScript for the front end. The challenges we have faced are mostly related to the modules in machine learning. Today team AaKAR is proud that we have overcome every hurdle and completed the project. What We Have Learned ? This project is an extraordinary challenge we dealt with , especially in the time of COVID 19 . We believe that the most meaningful thing we learned is “ TEAMWORK ” . During working on this project we encountered with a lot of technical and non technical issues and sometimes we fell off as well But We Climbed and Shined in UNITY by learning something new every time. As They Say “Coming Together is a Beginning, Staying Together is Progress, and Working Together is Success” Team AaKAR is working on adding Chatbot to our system, we are working on the latest update that can be done in the project. Built With css3 flask html5 javascript machine-learning opencv os php python Try it out covid19detection.pythonanywhere.com

https://devpost.com/software/hospit-ai-608itu

This is our logo! This is part of the data that we used to build this model. Inspiration: My (Reshma's) mother is a doctor, and she told me about the challenges that hospitals are facing. I wanted to do something about the coronavirus. I (Alice) on the other, was searching for ways to help with the coronavirus crisis and luckily came across this hackathon. I knew that Reshma was big into science and AI so I asked her if we could partner up and create something. We ended up creating Hospit-AI! What it does: Our model tells hospitals when they will reach their maximum capacity. How we built it: We used the AutoML Tables API from the Google Cloud Platform in order to build and test our model. Challenges we ran into: We initially were not sure which angle we wanted to pursue. We wanted to address both the economic and medical impacts of COVID-19. After much thought and discussion, we decided on a project that had elements of both. Later on, we were not sure how to go about this project. A friend recommended the Google Cloud Platform (GCP) to build, optimize, and test our model, so we decided on this. However, it was still challenging to learn how to use this as both of us were completely new to it. Accomplishments that we're proud of: Initially, we had no idea how to go about this project. We are proud that we were able to learn how to use the GCP and successfully accomplish our project. What we learned: We learned how to use the GCP and we learned lots about Machine Learning. Most of all, we learned how to work together as a team and had a great time doing so! What's next for Hospit-AI: We hope to further develop Hospit-AI to reflect the changing circumstances by adding more data. We eventually hope to have it implemented. Built With automl google-cloud Try it out console.cloud.google.com

https://devpost.com/software/generating-electricity-by-walking-mci4kr

The primary hardware components used. A bunch of piezoelectric sensors! An inside view of the shoe. 17 piezoelectric sensors can be seen in this side. There is an additional 16 sensors on the other side. The top down view of the shoe (without the styrofoam) Summary The average American walks approximately 3,500 steps per day; each step creates mechanical energy, energy which ends up being wasted and dispersed into the environment. Tapping into this wasted energy opens a door for opportunities to supplement the user’s actions. Varying amounts of piezoelectric sensors were used to generate this energy which gets stored in a LiPo battery through the aid of the BQ25570 chip. My design used 33 piezoelectric sensors, which generated, approximately 0.27 volts or 23.625 mAh just after 60 steps. If a user wore this shoe and walked the average amount of steps per day, they would generate 1,378.125 mAh! In addition, I developed an add-on to this project that adds an Arduino Nano with an Accelerometer and Gyroscope sensor. The data from these sensors are run through a neural network that predicts the behavior the user is doing. For example, if the user is jumping it will predict they are jumping. How I built it The hardware component of this project has one layer of styrofoam on the top and bottom. This protects the piezoelectric sensors and increases comfort for the user. Then there are two layers of cardboard, each side of the cardboard has 8-9 piezoelectric sensors, connected in series. The two cardboard pieces are connected in parallel. There is then a thin piece of paper between the two cardboard pieces, to make sure no wires short out when they touch each other. The software uses Keras with TensorFlow. I created a Google Cloud Virtual Machine Instance, which runs a python script that reads in data regarding user's motion and then with Keras and TensorFlow creates a model of the data that can be used for prediction. Challenges I ran into Developing the hardware of the shoes took the bulk of my time. I have never used Piezoelectric sensors before, so I had to learn how to use them. In addition, it took me a while to optimize the energy outputted from the shoe. The green BQ25570 chip helped me do that though. Accomplishments that I'm proud of This is the world's most efficient shoe that generates electricity! Other solutions mostly use different means to generate electricity. My solution used Piezoelectric sensors, and then the BQ25570 chip to control the flow of electricity from the two capacitors on the chip to the battery. This minimizes the electricity wasted. What I learned I learned a lot! In general, I am better at software related projects, this project, being a hardware-first project, increased my skills in dealing with hardware. I got better at soldering, understanding the mathematical calculations of voltage and current, Piezoelectric sensors, Arduinos and various hardware compounds. On the software side, this was my first time using Google Cloud. I am now comfortable in creating complex Virtual Machines in the cloud that can run various advanced scripts. What's next for Generating Electricity By Walking I want to add a wifi/Bluetooth chip into the Arduino Nano, this will enable the data from the accelerometer and gyroscope to transfer to a web server in the cloud without the need of a wire. With this advancement, I could develop a mobile/web app that tracks various foot-related fitness activities, including jumping, running and walking. Built With google-cloud keras piezoelectric tensor-flow

https://devpost.com/software/corball-n8ozvd

Inspiration We were inspired to create an idea like this when all of us came together to watch a documentary. Having visited pier 39 recently in San Francisco, we were having a little obsession with sea lions, so we decided to watch documentaries on marine life. We realized how important information about underwater conditions is, and how much effort is put in on a weekly basis to simply gather this information. People manually have to go under and observe information specific to locations, which can easily be replaced by technology. What it does CorBall is a coral research ball, which goes underwater and is held together with the support of a buoy. Since it is housed with sensors and a camera, it is able to generate a live stream of footage of coral underwater, and our unique machine learning algorithm, coupled with Raspberry Pi's detection capabilities, can identify the exact hex codes of the color of the coral. Over time, if there is a change in the color of the coral, scientists will be notified of this, and they can finally quantify it against time, and draw inferences. How we built it We built this by creating a web server using a Raspberry Pi and DHT22 sensor to graph the humidity and temperature in oceans over time. The data can be accessed over a web browser. We have also used image recognition methodology using Tensorflow Lite with help from the OpenCV library, coupled with our own machine learning algorithm, that allows scientists to see the change in color codes over time. Challenges we ran into It was incredibly tough for us to be able to come up with a successful model of this, given that we did not have much background. For this reason, we each split up to handle one aspect, with one of us doing the machine learning research, two of us experimenting with the hardware available, and one of us researching the feasibility of this technology and creating the demo. Accomplishments that we're proud of In the end, we are so excited to share that we were able to create fully functioning technology! We were able to create a demo prototype of this with materials available to us, and have done all the research to ensure this is easy to fit into the real world. What we learned We learned the importance of "if there is a will, there is a way" - we all had a passion to help scientists and researchers around the world with the information they need, so we put in the blood (figuratively), sweat (literally) and tears (literally) to make this happen. We are so excited to see what this does, and hope that it will help us combat climate change! What's next for CorBall We hope to pitch this to investors to get funding and mentorship so that we can actually see it in action in the real world, helping researchers around the world. Built With ai machine-learning opencv raspberry-pi tensorflow Try it out github.com

https://devpost.com/software/monetic

Problems: (1) Lack of access/virality on crowdsourcing platforms (2) Falling disposable incomes (3) No direct payments to individuals Monetic gives financially struggling individuals a platform to create short videos where they can crowdsource money. Business Model Pitch COVID-19 has left businesses, millions of families, and environmental nonprofits/charities in shambles. Monetic gives financially struggling individuals a platform to create short videos where they can crowdsource money. Based on the recipients’ responses, we ensure those most impacted by COVID-19 get recognition. You can kind of think of it like it's TikTok or Youtube + GoFundMe. Inspiration The economic repercussions of the COVID-19 pandemic are far-reaching--ravaging families, businesses, and communities across the country. As 100% of small businesses and nonprofits nationwide have been affected by the pandemic, millions of individuals and families are left in financial ruin. Most Americans do not have the choice to sit at home and satisfy their basic needs. The current remedies provided by the government, such as expanded sick leave, lower interest rates, and loans that help companies will do nothing for the millions of Americans who are living paycheck to paycheck. We believe that people most impacted by the COVID-19 pandemic deserve the dignity to choose for themselves how to improve their loves, and cash enables that choice. Initially, we were thinking about building a new mobile payment system, similar to Cash or Venmo, to help the government issue stimulus checks more efficiently; however, government bureaucracy presented a challenge for us. Moreover, traditional fundraising may have to go through nonprofits or require big name endorsements to be successful, making it inaccessible to the average recipient. Instead, we make it so that anyone who logs on the app can make a direct case as to why they need the money. How do we ensure those who are the most impacted by COVID-19 get the cash they need? According to Matt Klein, director of strategy at Sparks & Honey, fundraisers on traditional platforms, such as GoFundMe, rely on trends, virality, and social marketing gurus to ensure that their campaign receives traction. We solved this with a matching algorithm that ensures that those most impacted by the COVID-19, based on metrics, such as location. Finally, we wanted an innovative and engaging way to connect recipients to donors. If you see the person or child in need, this creates an empathetic and emotional attachment. According to NP Source, 57% of people who watch videos go on to make a donation. And with the rise of short video platforms like TikTok and Snapchat, we decided to implement a short video feature. What it does COVID-19 has left businesses and millions of families in shambles. Monetic gives financially struggling individuals a platform to create short videos where they can crowdsource money directly. Based on the recipients’ responses and a matching algorithm, we ensure those most impacted by COVID-19 get recognition. Potential recipients can upload a 10-second video, address, and income changes due to COVID-19 to crowdsource money. Potential donors can view the videos by clicking the ‘Next’ button. Videos are ranked by the most neediest individuals based on a matching algorithm that takes into account location. To donate money, donors can click ‘Donate’ where they can see the progress of a fundraiser or donate immediately. How I built it Monetic is built in React and a Firebase realtime database. By using react, we were able to split up the pages into components and work in parallel and develop fast and safely. Since React has been a very popular framework, it will be easy to maintain and understand for others to continue development. Another reason we chose React is the portability to mobile with React Native which will minimize the code that needs to be rewritten and can be easily deployed to different platforms. We are using the TikTok API to easily embed videos within our app and using GofundMe to accept donations. Challenges I ran into One of the challenges we ran into was embedding the TikTok videos on our app because the API response doesn’t work nicely with React. Therefore, we had to find workarounds to get the videos to show up. Another challenge we encountered was implementing the Stripe API. Although it has a very detailed documentation, we were not able to set it up to accept payments. Finally, we didn’t have enough time to come up with a good algorithm to decide which recipient to prioritize. However, we thought about adding analytics into these videos using Microsoft Azure API to collect data about speech and sentiment in these videos to get a better understanding of the recipient's needs. Accomplishments that I'm proud of We are very proud of building a working prototype in such a short time and no prior experience in React or any of the APIs we used. We were able to divide tasks and work closely with others remotely and made good progress. We loved learning from the mentors and using various APIs. Thank you Hack:Now for a seamless and great experience even though it was on Zoom. What I learned We learned more React and JavaScript as well as how to use the TikTok API. Also gained more experience working in a team setting and got a lot of advice from mentors on what to focus on for a MVP. We learned the process of building and deploying a web application, prototyping with Figma, and collaborating on a project entirely online. We learned how to develop an idea by thinking about how to solve a problem. Then, I learned how to develop a business model. Furthermore, We learned how to persevere in the light of difficulty. What's next for Monetic We want to overtake GoFundMe as the #1 crowdsourcing platform. In the future, we want to decouple from TikTok and GoFundMe to create our own app with recording and donating capabilities. Furthermore, we hope to revolutionize donations-based crowdsourcing with our idea by continuing to implement features. Such features, include: (1) Adding a "philanthropist" section where potential "philanthropists" can pose challenges to donate money to others. (2) Making our matching algorithm more specific to loss of income, type of job, and number of family members. (3) Creating a leaderboard to highlight donors Built With firebase gofundme heroku javascript react tiktok Try it out monetic.herokuapp.com monetic.herokuapp.com github.com

https://devpost.com/software/unigo-pg2a13

GIF Home Screen - The user can navigate to various features from this page like join the community, check the community page GIF User logged in Screen - The user can register to the community by providing the following details GIF Add post Screen - The community member can post images and description which will be displayed on community page. GIF Community post listing Screen - The community member can post images and description which will be displayed on community page. GIF Donation Screen - The users can donate amount as per their will to the community. Big Concept Heard of Google Local Guides? How they contribute to google maps about various places, and millions of people rely on their contributions to decide where to go and what to do. Similarly, we wish to create a platform for developing a community of local helpers who are concerned about the environment and take initiative to improve changing climatic conditions. Features The local helpers can : Post photos and description of the various initiatives they have taken (like planting trees, posting photos of polluted ponds or factories causing air pollution) For helpful posts they will earn reward points Organize meetups with other local helpers and discuss climate change issues in their area. Why we’re using Block-Chain? Transparent Everyone will be having the same authority / access to the app. Decentralized No organization or person has central control over the platform. Immutable Due to immutable nature of the platform, frauds can be prevented. Built With ajax blockchain css3 html5 javascript jquery python Try it out github.com

https://devpost.com/software/ad-dabbas-caj5n9

Back side of the model, with segmented dustbins Front side of the model with curtains on side to avoid dust and air Closer view to the model Inspiration The current situation due to COVID-19 is really threatening and what is more threatening is post-Lockdown fear of sanitization and cleanliness around oneself all the time!! This problem became my inspiration to build something that can keep you clean and safe even while you travel using public transports. What it does? I have built a physical unit which has the following components: Segmented dustbin for dry and wet waste Gentle shower spray of sanitizer (herbal neem verified spray) Hand tap for hand sanitization Coin collector with IoT element used in user usage data analysis Advertisement on top to generate revenue to run the system How I built it I was by myself this time in EarthX and I have built a 3D model using SketchUp tool and also created an Arduino system code and a circuit diagram as well by which when the unit is placed out in public we can calculate the demographics and which unit is having more traffic and people are using hand sanitizer or body spray more and we can change our model and make timely refills accordingly. Challenges I ran into The major challenge I had to face was with not having the aurdino and it’s component at home, else I could have also built the prototype in real. Sleepless nights were also a challenge but coffee was there for the rescue :p Accomplishments that I'm proud of I am proud that I pushed myself to my limits, being unable to get people on board for my project still not losing hope, and created the entire project and circuit by myself. I would like to take this product to the next level and help people fight against COVID-19 and the fear that it has built-in everyone’s head..! What I learned I learned using Sketchup, how to work constantly, discovered how I willing I am to work for the people and build on my ideas and also how Netflix collaborating watching works :p What's next for Ad-Dabbas I started my idea as a startup with only the advertisement angle to it… in the course of 3-5 days when I talked with all the mentors, I was able to build this for the need of solving COVID19 problems. Up next I want to take this to the municipality of my city and take this unit into the market so people can benefit from it. Built With arduino iot

https://devpost.com/software/deep-learning-drone-delivery-system

Results of our CNN-LSTM Accuracy after training our model on 25 epochs MSE of our CNN-LSTM How we preprocessed data for our model Data preprocessing Picture of Drone Inspiration: The COVID-19 pandemic has caused mass panic and is leaving everyone paranoid. In a time like this, simply leaving the house leads to a high risk of contracting a fatal disease. Survival at home is also not easy: buying groceries is frightening and online ordered necessities take ages to arrive. The current delivery system still requires a ton of human contact and is not 100% virus free. All of these issues are causing a ton of paranoia regarding how people are going to keep their necessity supply stable. We wanted to find a solution that garners both efficiency and safety. Because of this, drones came into the picture(especially since one of our group members already had a drone with a camera). Drone delivery is not only efficient and safe, but also eco friendly and can reduce traffic congestion. Although there are already existing drone delivery companies, current drone navigation systems are neither robust or adaptable due to their heavy dependence on external sensors such as depth or infrared. Because of this, we wanted to create a completely autonomous and robust drone delivery system with image navigation that can easily be used in the market without supervision. In a dire time like now, a project like this could be monumentally applied to bring social wellbeing on a grand scale. What it does: Our project contains two parts. The first part is a deep learning algorithm that allows the drone to navigate images taken with a camera which is a novel and robust navigation technique that has never been implemented before. The second portion is actually implementing this algorithm into a delivery system with firebase and a ios ecommerce application. Using deep learning and computer vision, we were able to train a drone to navigate by itself in crowded city streets. Our model has extremely high accuracy and can safely detect and allow the drone to navigate around any obstacles in the drone’s surroundings. We were also able to create an app that compliments the drone. The drone is integrated into this app through firebase and is the medium in which deliveries are made. The app essentially serves as an ecommerce platform that allows companies to post their different products for sale; meanwhile, customers are able to purchase these products and the experience is similar to that of shopping in actual stores. In addition, the users of the app can track the drone’s gps location of their deliveries. How I built it: To implement autonomous flight and allow drones to deliver packages to people swiftly, we took a machine learning approach and created a set of novel math formulas and deep learning models that focused on imitating two key aspects of driving: speed and steering. For our steering model, we first used gaussian blurring, filtering, and kernel-based edge detection techniques to preprocess the images we obtain from the drone's built-in camera. We then coded a CNN-LSTM model to predict both the steering angle of the drone. The model uses a convolutional neural network as a dimensionality reduction algorithm to output a feature vector representative of the camera image, which is then fed into a long short-term memory model. The LSTM model learns time-sensitive data (i.e. video feed) to account for spatial and temporal changes, such as that of cars and walking pedestrians. Due to the nature of predicted angles (i.e. wraparound), our LSTM outputs sine and cosine values, which we use to derive our angle to steer. As for the speed model, since we cannot perform depth perception to find the exact distances obstacles are from our drone with only one camera, we used an object detection algorithm to draw bounding boxes around all possible obstacles in an image. Then, using our novel math formulas, we define a two-dimensional probability map to map each pixel from a bounding box to a probability of collision and use Fubini's theorem to integrate and sum over the boxes. The final output is the probability of collision, which we can robustly predict in a completely unsupervised fashion. We built the app using an Xcode engine with the language swift. Much of our app is built off of creating a Table View, and customized cell with proper constraints to display an appropriate ordering of listings. A large part of our app was created with the Firebase Database and Storage, which acts as a remote server where we stored our data. The Firebase authentication also allowed us to enable customers and companies to create their own personal accounts. For order tracking in the app, we were able to transfer the drone’s location to the firebase and ultimately display it's coordinates on the app using a python script. Challenges: The major challenge we faced is runtime. After compiling and running all our models and scripts, we had a runtime of roughly 120 seconds. Obviously, a runtime this long would not allow our program to be applicable in real life. Before we used the MobileNet CNN in our speed model, we started off with another object detection CNN called YOLOv3. We sourced most of the runtime to YOLOv3’s image labeling method, which sacrificed runtime in order to increase the accuracy of predicting and labeling exactly what an object was. However, this level of accuracy was not needed for our project, for example crashing into a tree or a car results in the same thing: failure. YOLOv3 also required a non-maximal suppression algorithm which ran in O(n^3). After switching to MobileNet and performing many math optimizations in our speed and steering models, we were able to get the runtime down to 0.29 seconds as a lower bound and 1.03 as an upper bound. The average runtime was 0.66 seconds and the standard deviation was 0.18 based on 150 trials. This meant that we increased our efficiency by more than 160 times. Accomplishments: We are proud of creating a working, intelligent system to solve a huge problem the world is facing. Although the system definitely has its limitations, it has proven to be adaptable and relatively robust, which is a huge accomplishment given the limitations of our dataset and computational capabilities. We are also proud of our probability of collision model because we were able to create a relatively robust, adaptable model with no training data. We were also proud how we were able to create an app that compliments the drone. We were able to create a user-friendly app that is practical, efficient and visually pleasing for both customers and companies. We were also extremely proud of the overall integration of our drone with firebase. It is amazing how we were able to completely connect our drone with a full functioning app and have a project that could as of now, instantly be implemented in the marketplace. What I learned: Doing this project was one of the most fun and knowledgeable experiences that we have ever done. Before starting, we did not have much experience with connecting hardware to software. We never imagined it to be that hard just to upload our program onto a drone, but despite all the failed attempts and challenges we faced, we were able to successfully do it. We learned and grasped the basics of integrating software with hardware, and also the difficulty behind it. In addition, through this project, we also gained a lot more experience working with CNN’s. We learnt how different preprocessing, normalization, and post processing methods affect the robustness and complexity of our model. We also learnt to care about time complexity, as it made a huge difference in our project. Whats Next: A self-flying drone is applicable in nearly an unlimited amount of applications. We propose to use our drones, in addition to autonomous delivery systems, for conservation, data gathering, natural disaster relief, and emergency medical assistance. For conservation, our drone could be implemented to gather data on animals by tracking them in their habitat without human interference. As for natural disaster relief, drones could scout and take risks that volunteers are unable to, due to debris and unstable infrastructure. We hope that our drone navigation program will be useful for many future applications. We believe that there are still a few things that we can do to further improve upon our project. To further decrease runtime, we believe using GPU acceleration or a better computer will allow the program to run even faster. This then would allow the drone to fly faster, increasing its usefulness. In addition, training the model on a larger and more varied dataset would improve the drone’s flying and adaptability, making it applicable in more situations. With our current program, if you want the drone to work in another environment all you need to do is just find a dataset for that environment. As for the app, other than polishing it and making a script that tells the drone to fly back, we think our delivery system is ready to go and can be given to companies for their usage with customers. Companies would have to purchase their own drones and upload our algorithm but other than that, the process is extremely easy and practical. Built With drone firebase keras opencv python swift tensorflow xcode Try it out github.com

https://devpost.com/software/smart-farming

our exhibition model with 60 planters model with 160 planters Inspiration The smile and satisfaction on the face of farmer that they deserve. What it does we help farmers to build an smart farm and using different techniques to make better production then ever and controlling all environment economically viable and best market analysis to get higher price in export also. How I built it we have an team which has agricultural education background as well as field experience we know problems of farmers and there hard work. so we are slowly covering every farmers farm for smart grow. Challenges I ran into The trust of farmer and heart to win is very challenging. to build economic viable farm as farmer cant afford. to have best market analysis. Accomplishments that I'm proud of we got 2nd prize in startup tank 2019, 3rd prize in global challenge, and local appreciation What I learned how to deal with farmer and how to deal with people to take products which are directly available from farm. field and hardwork of farmer What's next for smart farming we are going to reach in each and every corner of india of farmers farm so there can be revolution in india agriculture Built With motor nutrients oxygenpump pipes seeds Try it out naikharsh052001.wixsite.com

https://devpost.com/software/auto-watering-system

model using model Inspiration when i was leaving my home for 20 days i was thinking about my plants that without water they will die and if i put smart system it needs electricity and its costly. so i came up with this. What it does it water the 7 pants like an drip and can last up to 30days without electricity How I built it i just take an tank and iv sets to complete it and it make my day Challenges I ran into to avoid the air blockage into pipes Accomplishments that I'm proud of it can help medal class to help in getting cheaper system with zero maintenance What I learned how to make solution of ideas What's next for Auto watering system i want to develop it more and more and make it complex so no one need to take care of plants just to enjoy its freshness and products Built With i.v.sets tank

https://devpost.com/software/waste-food-to-fodder

flow diagram waste food crushing removing moisture drying drying fodder cake Inspiration when i have seen the food has been wasted in our mess i was thinking something to make creative which can help for waste management. so i just convert the waste by processing it in fodder to help farmer to get fodder in half price then market. What it does It convert waste food to fodder of animals and every unit is been tasted so essential nutrient can be added naturally while crushing. it will help farmers to get the fodder in half price of market. How I built it I have build the machinery that will process this in very small space Challenges I ran into to avoid mixing of unwanted nutrient to avoid food poisoning to make sure all essential nutrient are present. Accomplishments that I'm proud of i have reached upto last level of hackathon but due to some condition i cant build machine hence cant get selected What I learned All things which is essential for animal to get proper productivity and nutrient What's next for waste food to fodder i want to setup the company or plant which will help in best waste management and can reduce global warming, best nutritious fodder, economically viable Built With crushingunit dryingunit packingunit pelletmakingunit

https://devpost.com/software/veggies-cleaner

Inspiration what if we get news that our local vegetable vendor is corona effected. so i got new machine which will instantly in 2 mins clean the grocery as well as vegetables. What it does the particular vegetable or grocery will be kept inside the box first 30 sec it will be under uv light then in 1.30sec they will be fogged by organic sanitizer and then fan will dry it and then by hot water and then again fan will dry it after this process it will be passed by thermal camera if there are more red spot detected on any object will be rejected. hece will get virus free How I built it i have just assembled uv light, fan, pump, pipe, belt, motor, thermal camera, fogger etc. Challenges I ran into to insure customer that particular will be virus free Accomplishments that I'm proud of its just new start What I learned how to be safe from pandemic What's next for veggies cleaner we will be setting a smart door in which person will be sensitize and will be allow if and only if there is not symptoms of virus or disease so our societies and homes will be disease free Built With belt box fan fogger pipes pump thermalimagecmera u.vlight