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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

Compost Share

An interactive website for users to share, post, and look for composting stations.

['Jena Alsup', 'Meia Alsup', 'meena21r']

['Grand Prize']

['css', 'express.js', 'google-maps', 'heroku', 'javascript', 'mongodb', 'netlify', 'node.js', 'react']

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

Cleaner Disposal

Best practices for medical dispose waste

['Jessyca Moraes', 'Cesar Bacovis']

["People's Choice"]

['adobe-illustrator', 'autodesk', 'html', 'photoshop', 'php']

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

Reuse-a-Box

Produce glass containers for take out and delivery to minimize single use plastics, especially during increased take-out under shelter-in-place.

['David Monical', 'Lina Gannon']

['Honorable Mentions']

[]

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

The MassGass

MassGass may save us all, providing a way to harness energy from food waste.

['Myla Kahn', 'Sage Quinn']

['Honorable Mentions']

['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

iCook

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.

['Trang Trần', 'Dayeong-git']

[]

['dart', 'firebase', 'flutter', 'intellij-idea', 'kotlin', 'swift', 'visual-studio']

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

GardenBox

Making the art of gardening an easy, efficient, and reliable source of food.

['Abhinav Kokala', 'Abhinav Emani', 'Ranak Bansal', 'Harsheet Kummaraguntla']

[]

['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

SAFETY OF THE ENVIRONMENT

Safety of the environment.

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[]

[]

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

Destroy FakeNews effectively

FakeNews arise from the different views on life, our perception and our questions. These can only be solved through communication and integration of different opinions.

['Gudrun Dara Müller']

[]

['wordpress']

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

Foovie

Dont waste the taste!

['Shraddha Neerputh', 'Akeel Rajak']

[]

['css', 'html', 'javascript', 'localhost', 'php']

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

GLARE

App that helps feed the needy and reduce food wastage

[]

[]

['android-studio', 'c#', 'firebase', 'google-maps', 'java', 'photoshop']

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

Divoc

DIVOC - An Antidote For - COVID

['Sanket Kankarej']

[]

['mongodb', 'node.js', 'vue', 'webrtc']

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

Fighting COVID Today

Supporting communities to Fight against Covid-19. Working together to find real solutions to support Life. Think Globally, Start Locally, Expand Regionally! Common platform for resources and requests.

['eapo sztrof']

[]

['css', 'html5', 'javascript']

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

CoNetRes - The Community Network Resource Mapping toolkit

The CoNetRes it’s a toolkit 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.

['eapo sztrof', 'Artem Pavlov', 'Szabi Green', 'wigy', 'ArinaVenusRising']

[]

['codepad', 'css3', 'holochain', 'html5', 'javascript', 'json', 'mongodb', 'node.js', 'python', 'raspberry-pi', 'srvctl', 'ssoiler', 'vue.js', 'xmpp']

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

Reducing food waste by predicting end-of-year crop yield

Machine learning to reduce wasted crop

['Arushi Madan', 'Arun Venugopal', 'Aerica Singla']

[]

['python']

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

ACESO - the first feasible SARSCov2 Test & Trace Network

ACESO digitizes and automates rollouts of extensive testing and contact tracing in compliance with personality rights through a self governed blockchain network.

['Tin Stribor Sohn']

[]

['fabric', 'hyperledger', 'ibm-cloud', 'ibm-watson', 'kubernetes', 'node.js', 'react', 'react-native']

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

Virtual Health Checkup|Modelling of CoronaVirus|Technoband

Future prediction with Virtual checkup online and Smart electronic band

['Shreyansh Pagaria', 'Maor Mashiaxch']

[]

['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

Data Visualization and Crowd Analysis using ML Techniques

Real time crowd analysis in crowded places by using AI in CCTV footages

['Rithik Jain', 'Vimal Kumar', 'Godi SaiTeja', 'Vijay Krishnaa']

[]

['android', 'css', 'firebase', 'html', 'java', 'javascript', 'python', 'regression', 'sdc-net', 'spyder3', 'website', 'xml']

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

ظرفشویی صنعتی

ظرفشویی صنعتی

['toos tajhiz']

[]

[]

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

Keep6

A mobile app, Arduino hardware, and website based platform to encourage and monitor safe social-distancing

['Elias Wambugu', 'Arya Tschand', 'Albert Zou', 'Elias Wambugu', 'Sai Vedagiri']

['2nd Place', 'First Overall', '2nd Place']

['amazon-web-services', 'arduino', 'c++', 'css', 'dart', 'firebase', 'flutter', 'html', 'javascript', 'node.js', 'react']

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

QuickBites

QuickBites is proud to help local restaurants in Toronto to ensure that they are profitable despite of lack of customers due to COVID-19, by letting restaurants sell stocked food to the public.

['Kevin Xu', 'Jiale Tom Tian', 'Dennis Bae']

[]

['bootstrap', 'css', 'firebase', 'google-maps', 'google-places', 'html', 'javascript', 'react']

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

Planetry Voice Social

An app that provides Immediate solutions and future solutions to all Planetry pandemic crisis

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['ajax', 'bootstraps', 'css', 'jqery', 'mysql', 'php']

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

COVIDSeek

Serving healthcare systems and people through accurate data tracking, visualizations, and forecasting of the coronavirus

['Shreyas Chennamaraja', 'Aryan Agarwal']

[]

['bootstrap', 'css3', 'fbprophet', 'flask', 'google-maps', 'html5', 'javascript', 'matplotlib', 'numpy', 'pandas', 'python', 'sqlalchemy']

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

CovAid

CovAid is a web application that facilitates deliveries to those in need during these pressing times. The website connects at-risk users with volunteers willing to donate necessities.

['Atin Pothiraj', 'Aarav Khanna', 'Shrey Gupta', 'Anirudh Bansal']

['2nd Place']

['bootstrap', 'css3', 'flask', 'google', 'html', 'javascript', 'jquery', 'keras', 'machine-learning', 'numpy', 'pandas', 'python', 'sqlalchemy']

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

Advanced A.I Health Bot

An A.I bot with: Telephone calling,NLP,24*7 health coverage,total automatic data management,wipes rumors,Easy navigation,HD pictures,Customer service help etc

['Udipta Koushik Das']

['Accessibility: Second Prize', 'Healthcare: Second Prize']

['dialogflow', 'facebook', 'google-cloud', 'javascript', 'json']

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

plantAId

plantAId is a machine learning app which allows farmers to scan their crops for diseases, and preventing devastation of crops.

['Krish Malik', 'Advait Jagannathan', 'Anish Kataria']

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['computer-vision', 'coreml', 'keras', 'machine-learning', 'natural-language-processing', 'python', 'swift', 'uikit']

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

Masked AI: AI Solution for Face Mask Identification

Masked AI is a cloud-based AI solution for real-time surveillance that keeps an eye on the human who violates the rule by not using face masks in public places.

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['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

M-VIC19: McMarvin Vision Imaging for COVID19

M-VIC19 is an AI Diagnosis platform is to help hospitals screen suspects and automatically locate the infected areas inside the lungs caused by the Novel Coronavirus using chest radiographs.

[]

['1st Place Overall Winners', 'Third Place - Donation to cause or non-profit organization involved in fighting the COVID crisis']

['amazon-web-services', 'flask', 'google-cloud', 'javascript', 'keras', 'nvidia', 'opencv', 'python', 'sqlite', 'tensorflow']

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.

Food & Secure Jobs

We Grow Green is working to increase the food supply with novel technology while improving the soil and our environment.

['guido mattei', 'Keith Johnson', 'Glenn Barrett', 'Corinne Stewart', 'Renee Chewning']

[]

[]

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

COVID-19 Detection System

It is a web based application.You need to Upload Your X-RAY And you will Get Results : COVID + or COVID - if you get detected with COVID+ then you must contact to specialist !! STAY HOME ,STAY SAFE !!

['Anukriti joshi', 'Arayaman Dubey', 'Kirti Paithankar', 'ruchir toshniwal']

[]

['css3', 'flask', 'html5', 'javascript', 'machine-learning', 'opencv', 'os', 'php', 'python']

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

Hospit-AI

We wanted to create a machine learning project that tells hospitals when they will reach their maximum capacity, so they can plan ahead.

['Reshma Kosaraju', 'Alice Tao']

[]

['automl', 'google-cloud']

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

Generating Electricity By Walking

Generate a lot of electricity just by walking!

['Tarun Ravi']

[]

['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

CorBall

Creating a Coral Research ball that provides information of the underwater world in real time along with its live footage with machine learning for quicker research and interpretation.

['Anupam Tiwari', 'Neel Desai', 'Anushka Purohit']

[]

['ai', 'machine-learning', 'opencv', 'raspberry-pi', 'tensorflow']

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

Monetic

The COVID-19 pandemic has left businesses and millions of families in shambles. Monetic gives impacted individuals and businesses a platform to create short videos where they can crowdsource money.

['Ken Hinh', 'Kevin Ko', 'Michael Sun', 'Sophy Sun']

[]

['firebase', 'gofundme', 'heroku', 'javascript', 'react', 'tiktok']

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

UNIGO

Blockchain-based Environmental Community platform.

['Devarsh Panchal', 'Harsh Mauny', 'Meet Bhavsar', 'dwij patel']

[]

['ajax', 'blockchain', 'css3', 'html5', 'javascript', 'jquery', 'python']

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

Ad-Dabbas

We are not regular dustbins, we are Sanitizing Ad Bins

['Divyank Jain']

[]

['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

Autonomous Drone Delivery System

An autonomous drone delivery system to provide efficient and virus-free deliveries.

['Allen Ye', 'Gavin Wong', 'Michael Peng']

['Best COVID-19 Hack', '2nd Place Hack']

['drone', 'firebase', 'keras', 'opencv', 'python', 'swift', 'tensorflow', 'xcode']

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

smart farming

To make farming smart

['Harsh Naik']

[]

['motor', 'nutrients', 'oxygenpump', 'pipes', 'seeds']

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

Auto watering system

when you are away these will give yours plants a way.

['Harsh Naik']

[]

['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

waste food to fodder

food is never been called waste

['Harsh Naik']

[]

['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

veggies cleaner

virus and disease free veges in 2mins

['Harsh Naik']

[]

['belt', 'box', 'fan', 'fogger', 'pipes', 'pump', 'thermalimagecmera', 'u.vlight']