hackathon_id int64 1.57k 23.4k | project_link stringlengths 30 96 | full_desc stringlengths 1 547k ⌀ | title stringlengths 1 60 ⌀ | brief_desc stringlengths 1 200 ⌀ | team_members stringlengths 2 870 | prize stringlengths 2 792 | tags stringlengths 2 4.47k | __index_level_0__ int64 0 695 |
|---|---|---|---|---|---|---|---|---|
10,529 | https://devpost.com/software/bigfridge-big-data-of-food-bkomcv | BigFridge App SS 1
BigFridge App SS 2
BigFridge App SS 3
BigFridge App SS 4
Inspiration
What is the problem about Food during this Covid-19 Crisis?
There may be shortages in food supply and ambiguity in food demand due to lockdowns.
If the isolation period prolongs over months there can be shortages of some food. Some vegetables, fruits and grains cannot be planted for the next year because of social distancing is necessary for farmers. Some ingredients cannot be transported from local producers to other countries because the borders are closed.
Food stocks should be planned carefully in this disaster. Otherwise food will be a serious problem along with virus disease health issues.
In the coming months, local authorities, food producers, big companies and governments will require data. In normal times they use their own data and projection tools in order to plan the food demand properly and distribute evenly. But this is a crisis that the world had not been experienced before. Everything can fall into chaos. Supply chain might be disrupted. Demand routines might change abruptly. Chaos in food supply might cause serious destruction. Therefore accurate predictions are essential keys to handle this disaster.
What it does
If we count what we have in every individual fridge, then we will be able to know the big data of all available food stock.
BigFridge is an app where individuals can contribute to the macro planning of food supply by providing information of what they have in their own household stocks and what they will need in the coming months.
When all food stock is gathered in the BigFridge’s pool, Big Data is provided to food producers, big grocery chains and local governments.
Business Model Canvas
KEY PARTNERS
Key Partners in Food Production and Supply: (and/or)
i)Big Grocery Chains
ii)Online Grocery Companies
iii)Food Producers
Motivated by optimization in operations and stock control. Improvement in profit margins due to reduction in waste with prepaid orders.
Key Partners in Planning of Food Supply: (and/or)
i)Local Government Authorotiries
ii) Agriculture Departments and Bureaus
iii) Ministry of Food & Agriculture
Motivated by better coordination due to reduction of risk in food supply chain.
Key Partner in Data Analytics:
i) A reliable and trustworthy data analytics company in order to share data.
Motivated by the acquisition of particular data resources gathered by the app from the activities and personal information provided by BigFridge users.
KEY ACTIVITIES
i) Building and promoting an online medium (eg. ios/android mobile apps) for the users in order them to provide the data of their household food stocks.
ii) Making agreements with Big Grocery Chains and Online Grocery Markets about the gifts and discounts that they are going to offer to BigFridge Users.
iii) Organizing of the gathered raw data according to the partner Data Analytics Company’s requirements.
KEY RESOURCES
i) Creative designs and ideas are important in order to build this brand new mobile app. Our young and creative team members are the key resource.
ii) Strategic partneships and coloborations among food business circles is at the heart of this model. Our mentors and advisers are industry experts having +60 years experience totally.
VALUE PROPOSITIONS
VP for Users:
i) Being a part of the solution for the possible problems in Food Supply Chain.
ii) Receiving discounts, giftcards and paychecks for grocery shopping.
iii) Access to healthy meal recipes that can be prepared with the existing goods in the fridge.
VP for Food Poducers and Grocery Markets:
i) Prepaid orders provide win-win for consumers as well as food producers and big grocery stores. Producers will be able to plan their production better.
ii) If supply chain is planned according to accurate demand projections there will be less food to be wasted.
Characteristics:
Newness, Convenience, Accessibility, Design, Customization, Cost Reduction, Risk Reduction
CUSTOMER RELATIONSHIPS
BigFridge is positioned as a novel domestic mobile brand which provides some benefits and have fun when using it.
Therefore it is important to keep the online product of BigFridge always updated, attractive and functional by continuous flow of fresh content on easy cooking, healthy recipes, kitchen tips, food hygiene, etc.
Giftcards, paychecks, discounts and delightful surprises will be waiting for loyal users and BigFridge will soon become a love mark.
CHANNELS
The channel of the main business activity for user interface are Mobile Apps (ios/android). Promoting the mobile apps to reach as many young ypeople as possible will be held by digital and conventional marketting tools. Getting the support of local authorities and governments for promotion campaigns as well as cooperating with local communities and food banks are important in order to spread the usege of the apps. BigFridge is planned to be a part of the daily kitchen and grocery shopping routines of individual users.
CUSTOMER SEGMENTS
Young generation are the target customers while every person with a mobile phone who is willing to download an app can use BigFridge. It is so easy to use but there is a limitation in terms of place: It can only be used at home, in the kitchen. The user should look in the fridge and write down what is there, check the lists on the app, take photos of the food. When the weekly task is complete, the user receives a QR code and views the gifts. Although these activities are all simple and fun, people over 50 of age are generally not prone to use such apps.
There are more than 1.2 billion households worldwide. The number of total population under the age 25 is more than 3 billion. We target 1% of these households to use this app, which is 12 million users. During this quarantine times, we expect the young members of the families will adopt this new app and contribute big food data while winning.
COST STRUCTURE
BigFridge is value driven rather than costs. The requiriments for fixed costs are almost negligible and all the workers are paid in certain percentages of the income. Therefore they have to create revenue in order to be paid well.
Variable costs are due to digital marketting and promoting activities. All the operational activities are held within a cost sensitive perspective. No offices, no luxirius utilities. Costs like hosting and cloud storage are afforded by foundations supporting startups.
REVENUE STREAMS
BigtFridge app is free to use. No payment or fee is expected from the user. Similarly nothing is charged to partner Grocery Chains while they are expected to offer discounts, giftcards and paychecks frequently to loyal users while sponsorship supports are all welcome from partner companies.
When data of food stocks of the users are gathered, big data is provided to food producers, big grocery chains and local governments within the partnership of a data analytics company.
The customers of the partner data analytics company who are willing to pay are going to be the main source of income. Tehrefore the price or value of the collected data as a resource is going to be dynamic and depends on the negotiations according to current market circumstances.
Team
ARTUN AKDOGAN (21)
Sophomore in Electrical and Electronics Eng. Bogazici University /
QuID for Developers Certificate – ETH Zürich /
Applied AI Certificate – inzva (Sanctuary of the Turkish Hacker Community) /
Cyber Security Certificate – BUSIBER /
Excelled in Python and Linux /
github.com/artun-akdogan
ILAYDA ISERI (17)
Koc High School Junior /
Google Science Fair 2019 Regional Finalist with ‘The Woof Project’ /
MIT Research Science Institute Summer Program 2020, Candidate in the national short list /
Awards and honors in national math contests /
Studying to be a mathematician, composer, cartoonist and filmmaker.
BARKIN E. OKSUZ (16)
First year in Robert College, RC24 /
IDTech Camps in Game Design and Development, Stanford Campus, 2018 /
He builds web sites and apps, wants to be a gamer
Built With
javascript
Try it out
oksbar24.wixsite.com
www.appsheet.com | BigFridge - Big Data of Food | BigFridge is an app where individuals can contribute to the macro planning of food supply by providing data of what they have in their own household stocks and what they will need in the next months. | ['İlayda İşeri', 'Artun Akdogan', 'Barkın E. Öksüz'] | ['Environment - 3rd Place'] | ['javascript'] | 5 |
10,529 | https://devpost.com/software/justlearn-platform | Landing page for users
Register Page
Courses available from volunteer tutors
Displays courses available related to interest of job
Featured tutors to attract more students
Displays job listings available
Discovery
According to a study, there is a surge in global online education in response to COVID-19.
Millions of retrenched or unemployed individuals are looking for new ways to join the challenging market,
or those currently with jobs are unsure with what the future holds for them.
With dozens of educational platforms to learn from, there is one thing most of them are missing, securing a job for their students. With a plethora of courses available, everyone is spoilt for choice. But this could also be a bad thing.
Inspiration
Millions of individuals are currently suffering from losing their jobs or uncertainty with their current skillset.
We were inspired to help them out by providing free resources for the future.
What it does
JustLearn is a platform for passionate volunteers who have experience in a certain skillset to upload their classes to our platform.
It is also a platform for those who have lost their jobs to learn and equip themselves with new skills relevant to jobs that they are interested in.
The platform will be looking for jobs and would curate which classes to take or certificates to pass to possibly land a job offer.
Being an online platform, we are accessible everywhere. We will also be looking for courses specific to the demands of each region.
How we built it
With limited experience in website deployment, we chose to build a mock site instead, using Boxmode.com
The mock site is currently static with limited functions but captures the vision we have for the future end product.
Challenges we ran into
Coming from South East Asia, it was a challenge looking for mentors in the same time zone as well as participating in ZHack events. However, we made sure that we attended a clinic and adjusted it. Initially, we used bubble.io to build the website. However, we realised that there were limited features and we quickly switched to Boxmode to develop the website.
Being our first Hackathon, we struggled to look for resources that would help us out. We had issues picking out the best ways of developing our idea into a prototype. We had limited skills but participating in this hackathon has given us a lot of new skills to learn and equip ourselves for future projects.
Accomplishments that we're proud of
We managed to develop a concept site in a short amount of time. We were not confident at the start, but we knew that we had to get it done, no matter what.
What we learned
Initially, we thought of using complicated solutions. Using machine learning, blockchain, AR and so much more.
But we realised that we had to create a simple solution.
That led us to just creating a basic, functional website. It might not stack up against machine learning, blockchain, and other technologies, but it didn't have to.
We found a problem, we found a group of individuals suffering from this problem, and we knew that we had to create a simple, functional solution.
What's next for JustLearn Platform
Early Stage Initiatives
a) Developing it into a fully functional site.
i) With the use of ReactJS and Firebase Database
ii) Creating a UI friendly site
b) Scrape through job-posting sites such as LinkedIn/Glassdoor and to post them on our site as well.
i) This is an early-stage strategy
ii) Once the platform has reached a certain appeal from companies, we will allow them to directly post on our site as well.
c) Convene with a group of experts to discuss the relevant skills and certifications required for the job postings.
Later Stage Initiatives
a) Eventually, we are also looking at having a program where experts can list paid classes. Volunteers can sign up for the class and complete the course and would only have to pay after they have secured a job from a company.
b) Have a regional focus to curate the demands for each region.
Built With
boxmode
css3
html5
Try it out
justlearn.site.bm
docs.google.com | JustLearn Platform | Providing free resources for an invaluable future | ['James Conde', 'Abu Bakar'] | ['Pandemic - 1st Place'] | ['boxmode', 'css3', 'html5'] | 6 |
10,529 | https://devpost.com/software/test-7wpoyz | Our Logo
Landing Page
Our Post Board on our Functioning Website Back-End
Profile Screen for a User on KAYR
Sample Screenshots for our Future App Plans
Inspiration
KAYR drew inspiration from the caremongering movement that grew around the world in light of COVID-19. Here, we saw the creation of hundreds of social media groups where people rallied together, often in the thousands, to offer or request help in their local communities. And when people needed advice, grocery delivery or extra supplies, these groups played an important role in helping those in need. Here's an article from the BBC about the movement, which indeed started in our very own Canada:
https://www.bbc.com/news/world-us-canada-51915723
.
Further, our group has been inspired by the actions & sacrifice of so many essential workers on the frontlines. COVID has revealed many gaps in our systems, but also the good in people who to want to serve.
There are many who need help with everyday things in these COVID times, whether for childcare, grocery delivery, advice or more. The Caremongering movement shows just how many people are willing to give that help.
Going beyond social media & Facebook, how do we better connect help with those who need it?
What it does
KAYR provides a platform to connect those who want to help with those who need it. It provides a user interface that is much cleaner than Facebook threads or social media , allowing for users to easily post when they want to offer help or need a hand, filter across these posts & communicate with those that may be a match.
And there's another unique element to this platform, inspired by Blockchain technologies: a Karma system.
Here, we create a community where everyone who joins is rewarded with a certain amount of Karma (1,000 points). Karma can then be shared with other users who help you, thus allowing one to accumulate Karma points... Think of it like XP (Experience Points) in modern games. And with many points, you can move to different Karma Levels. And with new levels, one can unlock new rewards, such as store discounts or coupons (as we envision in the future).
And why is such a system important? Because of the dozens of people we interviewed, 77% of those who needed help from strangers said that it was important for them to recognize or reciprocate an act of kindness, even if not in traditional means. Karma offers a way that people to show appreciation & create a more caring, unified community.
How I built it
I did not build it. The team did.
Each of us had particular roles in the project, with my role being more on the project management & ideation side. For me therefore, it was really important to do market research, with my reaching out to dozens of people across 30+ different caremongering movements in the country. From here, our two tech specialists worked together on the front-end and back-end sides, building out the posting environment on the one end, and screenshots plus a landing page via Wix on the other. For the back-end, a variety of programming languages were used, and in operation, the systems created mimic some of the mechanisms used in the blockchain, especially in the delivery of Karma points.
Finally, we had a video specialist on the team whose job it was to articular what we do, and showcase the work our team did so far (112,000 lines of code!!), plus all the potential we see for this concept in the future.
Challenges I ran into
The greatest challenge for me is that I am not a technology specialist (more on the business side). And hence, the early part of this journey was centered on recruiting teammates to carry this project forward. This proved to be an exhilarating journey, although we did have some issues: One teammate we initially recruited had to drop out due to his school getting in the way. Others also found it difficult to make time in the middle of other commitments.
And yet, we had a team that often worked till 2-3 AM to get things done, sacrificing many a night of sleep. Although there are always challenges for getting together as a team, I'm proud of what we pulled off.
Accomplishments that I'm proud of
This team. Truly, we found a team that complemented each other exceptionally: Each being a specialist in key project tasks. And really, everything else I am proud of is because of this team: The five-minute video we've developed is the brainchild of animator Mai Tran, the 112,000 lines of code mentioned to create a functioning (if still-developing) website is thanks to the work of Tara Mathers. The front-end designs you see is those of Deanna Castano. And beyond that, we're thankful for so much advice given throughout our journey, and particularly from Nikheel Premsagar of the Government of BC who provided us great insights into maintaining the safety of our platform.
When you have a team like this, you can accomplish anything.
What I learned
Humility. This journey was touching for me, as I saw people willing to invest hundreds of hours of their time to make this vision happen. I am eternally grateful to my team. Truthfully, I was not always the easiest person to work with, often demanding, often confusing. But they stuck with it, and we're excited for what we created.
And another thing... We're proud at a personal level for honing new skills in this hackathon that we had not tried much before. Our back-ender Tara built user accounts for the first time. Our front-ender Deanna learned how to use Wix and expressed learning lots about teamwork as well. Our video creator Mai was excited to teach herself a few new animation hacks. And I learned most about communication as a leader & how important it is to listen.
Very early in this hackathon, our team established the importance of giving it our all, learning & having fun in the process! And, while the journey is just beginning, we do believe that we did just that :).
What's next for KAYR
Canadian Thanksgiving! That's the date we've set us to complete our Version 1 web platform & get things launched. Already, we've been doing some betas in a test environment, and do have a few test-users signed up.
And after that, we want to explore a mobile app, likely for December 2019 if resources allow. This is especially due to ~60% of people in our surveys advising they might prefer a mobile interface, although most liked the web as well.
And finally, moving into next year, we want to actively explore two markets: (1) First, we would like to tap into existing caremongering communities in Canada that are organized via Facebook. This has already been started, and we are connected with ~30 such groups in Canada so far. And (2) We want to explore the $2.4B (in Canada) student services market, as we believe there is potential for us to create private, internal KAYR communities for university clients (i.e. a B2B model), perhaps even in conjunction with other social platforms like Facebook or HiQ.
And this is just the beginning... While our initial target is local-focused (in Canada), we see potential for the KAYR concept to gain traction around the world. It will be excited to see how the project develops!
Built With
adobe
amazon-web-services
css
html
javascript
jquery
laravel
lightsail
mysql
php
react
wix
Try it out
kayr.org
app.kayr.org
github.com | KAYR | Bringing people together to help those in need... With an underlying system of karma-based rewards, implemented via technologies like the blockchain, to appreciate those who give back the most | ['Mai Tran', 'Tara Mathers', 'Deanna Castano', 'Shitangshu Roy'] | ['1st Place', 'Pandemic - 2nd Place'] | ['adobe', 'amazon-web-services', 'css', 'html', 'javascript', 'jquery', 'laravel', 'lightsail', 'mysql', 'php', 'react', 'wix'] | 7 |
10,529 | https://devpost.com/software/back-to-school | Inspiration
Due to the coronavirus pandemic, school is now online. One problem with having school online is taking attendance as it is tedious and takes quite some time especially for university classes with lots of students
What it does
I made a facial recognition network that identifies who is attending a meeting. The time and the person's name is then inputted to a database where teachers can easily see who came on time to class and who didnt by accessing the data base.
How I built it
I used flask, html, python, ai, open cv, etc.
Challenges I ran into
learning open CV was a challenge
using open CV and flask together was tough as many problems would arise when there was no result
Accomplishments that I'm proud of
First time learning open CV
first time using gray scale
best website in my opinion i ever made
What I learned
learned open CV
javascript forms
boostrap in depth
What's next for Back to School
Deploy it in my school
Make a neural network that's efficient and has a high confidence, so no room for error
Built With
ai
css
css3
flask
html
html5
ml
python
python-package-index
Try it out
github.com | Back to School | Checking attendance in online classes using facial recognition | ['Neeral Bhalgat'] | ['Pandemic - 3rd Place', 'First Overall'] | ['ai', 'css', 'css3', 'flask', 'html', 'html5', 'ml', 'python', 'python-package-index'] | 8 |
10,529 | https://devpost.com/software/diarituar | DIARITAUR: BECOME YOUR BETTER SELF
Inspiration
There was this very interesting project idea posted by the THINK CARE ACT initiative and I decided to expand upon that and convert it into a mobile application. I wanted everyone to have access to a means to work on their mental health.
What it does
Basically you can create a dairy (BEST/WORST) event and evaluate yourself based on characteristics provided and now down how you can use your best self to work on your worst self. It gives you an overall outlook score based on the responses you provided it with
How I built it
I used Ionic, angular to build this mobile application all by myself
Challenges I ran into
A lot of bug fixes and a lot of design issues that went to waste because I did not have enough time to implement them all. Sometimes the bug fixes took me hours to complete. Also a major challenge was actually getting the app to run on my computer. It was giving me a lot of errors it took me the entire day to fix.
Accomplishments that I'm proud of
The entire exercise itself from the example was coded into a solution and stored into a sqlite database. From there I ran specific sqlite queries to get the information and display the results. I am most proud that I finished this app SOLO with no help from others.
What I learned
I learned a lot of neat tricks in the development of this app, but what I learned the most was how to run the app in simulation mode on my actual device as well as a simulator on xcode.
What's next for Diarituar
After fixing a couple of bugs and adding more design features, I plan to create charts like this one below
Built With
android
angular.js
html5
hybrid
ionic
ios
sqlite
typescript
Try it out
github.com | Diarituar: Become Your Better Self | Ever wonder how you can become a better person. WELL, with this mobile application you can record all of your BEST and WORST words/moments and get an overall outlook score that is unique to you. TCA! | ['Apro123 Kapoor'] | ['Think Care Act - 1st Place'] | ['android', 'angular.js', 'html5', 'hybrid', 'ionic', 'ios', 'sqlite', 'typescript'] | 9 |
10,529 | https://devpost.com/software/think-care-act-app | Exercise
Events
Videos
Inspiration
We have used the best and worst exercise and turned it into a beautiful app, HHH's work was really inspiring to make this app really intuitive and easy to use.
What it does
If I have to explain it in one sentence, the app allows you to watch videos, fill up exercises, check for upcoming events and motivate yourself by checking out quotes.
How I built it
I used flutter to build my app.
Challenges I ran into
Integrating videos and coming up with a beautiful and unique UI keeping all the use cases in mind was a big challenge.
Accomplishments that I'm proud of
I am really happy with the way the app turned out and want to see people using it in the future
What I learned
I learned a lot about how to make the app simpler even with having different complexities and working on this app was speciallly motivating as it was for a social cause
What's next for Think Care Act App
Would like to have it used by different people and get necessary feedback
Built With
flutter
Try it out
drive.google.com | Think Care Act App | We have used the best and worst exercise and turned it into a beautiful app | ['Rishav Raj Jain'] | ['Think Care Act - 2nd Place'] | ['flutter'] | 10 |
10,529 | https://devpost.com/software/peer-rate-best-worst-form-web-app | A peer evaluation room
Summary
This is an implementation of the idea #1 of Think Care Act
As the world advances forward, we are becoming ever more connected. Yet many of us feel alone and unclear of our personalities and future. That’s why we created PeerRate. We made it so that you can discover your personality anywhere, at any time. With the only necessity of inputting your name, the user will be given a private room link, where he can then share it with his friends or family members. Once joining the room, the two participants take turns to discuss the topic shown at the top by dragging cards they believe matches them best. There is also a custom tag functionality for more variety of descriptions. Through the interaction between the two, the users will have a better understanding of what others think of themselves and thus having an easier time to improve one self.
What it does
It allows peers to evaluate each other.
User could host a room, give the room code to his friend, and after they all entered that room, they could evaluate each other through a drag and drop UI.
Implementation :
With the usage of HTML5, CSS and JS, we are able to weave the three fundamentals into a modern yet minimalistic user interface. And with the usage of websocket and nodeJS, we brought the once lifeless interface to life, creating the potential to connect and learn about yourself and others at any time.
Built With
css3
html5
javascript
node.js
Try it out
peerrate.net
github.com | Peer Rate (Best Worst Form Web App) | real time application that allows peers to evaluate each other and point out the best and worst of their personalities | ['Devin Han', 'Justin Li'] | ['Contrast Community', 'Think Care Act - 3rd Place'] | ['css3', 'html5', 'javascript', 'node.js'] | 11 |
10,529 | https://devpost.com/software/care-quest-1jwed7 | Inspiration
COVID-19 has been on a rampage all across the globe since the past 5 months. From less than a million cases in February, and currently having more than 10 million cases across the globe. This has caused widespread panic and disorder. This panic is interfering with not only the functioning of already established medical structure, but also allocation of resources which can cost someone's life.
What it does
With increasing reach of technology, relevant information can be extracted from concerned authorities. This information can be used in resource allocation to balance load on various authorities. Providing information can keep people informed about the current scenario and have a calming effect. CareQuest aims to bring order to the chaos caused by COVID-19. To do so, CareQuest services extracts information from its custom network of registered hospitals and government sources which is used in resource allocation and service recommendation with the aim of load balancing.
How we built it
We have used multiple API(s) throughout the project, which in combination with realtime Google Cloud's Firebase gives the user a flawless experience during the pandemic. We have combined multiple API(s) with geolocation services (Google Cloud Map API + Here Maps API) and realtime notification applications (PushBot) to provide extra flexibility to the user. Also, the project is equipped with an AI based bot - CareBot, made using IBM Watson. Furthermore, we have included Firebase Admin package to assign roles to the users with privileges. Last but not the least, we have also include live updates for geo-location in order to achieve contact tracing capabilities for COVID Tracer, which not only updates your COVID status, but even alerts about any infected person around you.
Architecture :
https://github.com/barthwalumang/CareQuest#Architecture
Built with
https://github.com/barthwalumang/CareQuest#built-with
Challenges we ran into
Data Security issues to protect user's geo-position and location history
API stream exchange failure
Accomplishments that we're proud of
AI based bot - Assistant Sigma
Real-time Forms, Database, and Map services
Interactive Dashboard
COVID19 Tracer
Admin Panel
What we learned
Learnt a lot about APIs and Google Cloud services. Also, managed to brush upon our AI and web development skills.
What's next for CareQuest
SMS and mailing services for COVID19 Tracer to make contact tracing more efficient and reliable
Newsletter subscription for the user to keep him / her updated about all the changes around him / her on a daily basis
Inbox feature for CareNet to enable private conversations between hospitals
COVID Community Risk Indicator to be included in COVID19 Tracer to provide realtime updates to alert people about the surfaces and areas to avoid during this pandemic
OpenCV to check if the user is wearing mask or not
Built With
aos
bootstrap
css3
firebase
github
here-map
heroku
html5
ibm-watson
javascript
jquery
mongodb
node.js
pushbot
python
semantic-ui
sendgrid
shell
tesseract
Try it out
care-quest-bitsians.herokuapp.com | Care Quest | CareQuest : a load balancing platform for medical services aimed at bringing order to the chaos caused by COVID-19Services : Dashboard, Plasma Bank, CareNet, Track A Bed, CareBot, COVID Tracker | ['Prashant Jha', 'Barthwal Umang'] | ['GetVirtual'] | ['aos', 'bootstrap', 'css3', 'firebase', 'github', 'here-map', 'heroku', 'html5', 'ibm-watson', 'javascript', 'jquery', 'mongodb', 'node.js', 'pushbot', 'python', 'semantic-ui', 'sendgrid', 'shell', 'tesseract'] | 12 |
10,529 | https://devpost.com/software/codemano | ]
Built With
javascript
node.js | TinderClone(2) | A | ['Justin Li', 'Devin Han'] | [] | ['javascript', 'node.js'] | 13 |
10,529 | https://devpost.com/software/trashbounty | Inspiration
Our street is filled with trash, yet there just aren't enough money for the government to hire people to clean them. This is why we made this web PWA app called trash bounty, and it gives a transparent approach for people with money to place a bounty on a trash site and see it get cleaned.
What it does
User could login, click on a trash site they located, and place a monetary prize on cleaning it up. The data is being saved in Mongodb. Afterward, any other user could see that bounty as a marker, view the prize, and take that bounty by cleaning up the trash site marked on the map and showing proof.
How I built it
We used google map javascript API for the map.
The front end is HTML js css and PWA tech stacks
Backend is node.js, mongoose
background
In the entirety of human history, there has never been a time where we are so advanced as a species. We have altered our atmosphere and have terraformed our planet; not in flowers, but in plastic. With the exponential growth of trash piling in our society every day, we need to act together as one species to combat this challenge. That is why we built the TrashBountify. Through the usage of Node.js, front end stack, google map javascript api and other web technologies, we were able to connect people around the globe toward solving the plastic crisis. Anyone includes you who cares about the environment could directly place bounty on a trash site, and wait for people who can spend the effort to clean it up to clean it up and take the bounty. And if you would like to do something physically to help the environment, you can simply open the app and look for available bounties for trash and litters around your region. Our cycle of placing bounty and awards for trash picking and recycling has an incredible potential in helping the current environmental crisis and cleaning up our littered street and highways. Let us all achieve something great, and step starts with TrashBountify.
http://trashbountify.app
**Please open the link with mobile resolution as it is designed for mobile
Built With
html
mongodb
node.js
Try it out
trashbountify.app
github.com | TrashBountify | A web PWA application that allows people to place a monetary prize on cleaning up a trash site | ['Devin Han', 'Justin Li'] | [] | ['html', 'mongodb', 'node.js'] | 14 |
10,529 | https://devpost.com/software/resolve-f92p73 | home page after signing in
dashboard page
login page
registration page
account page
detail page
forum page
Inspiration
The CAA & the NRC were a set of controversial government Acts that were passed by the Government of India that imposed restrictions on citizenship grants on immigrants from some select countries. Following their approval, protests took place all over the country. The primary means of exposure to the general public was more due to online networks than traditional media where people could directly state their views and opinions to reach their online following. But Instagram and Reddit can only go so far in organising events of such magnitude and thus, the need for a dedicated platform aimed at solving this niche problem of social justice outreach became evident. We recognized this as a growing market of young people wanting a change.
Generation Z has shown itself to be one of the most empathetic generations this world has ever seen and this movement of social justice has opened up opportunities for a platform like Revolve to support this passion and bring a change at a whole new level.
What it does
Revolve is dedicated web platform that empowers people to create new events which are visible on a global wall. This wall displays all the events that have been created by other organizers that the user can pick and choose from. Each event would contain a description, a Google Maps display of the venue for a physical meet up, the date and time, and a dedicated forum section along with a list of all the other users that took part in said event.
What we learned
After speaking to
CAA / NRC and Pride
activists in India, we realized the lack of a centralized platform for organising their events. Communication and outreach were difficult for these groups due to a lack of dedicated means which furthered the ambiguity in their mission statements among their followers. We learned about the chaotic situations that occur when organising such events and brainstormed solutions to tackle such problems.
Having little experience in UI development, we collectively learned Javascript and helped each other in the coding process. This was also our first time working together as a team which was also a fun collaboration.
How we built it
We utilized Python's Django web framework to build the backend and MySQL for the database layer. Most of the frontend was written in Javascript that fed in a JSON API that was created using django-rest-framework. We also used Redis for caching for better performance. Our platform was deployed using Nginx as a reverse proxy and Gunicorn as the HTTP server.
Challenges we ran into
While most of us were proficient in
Python
, some of us were frontend and graphics developers who had never collaborated together. It was a fun experience working as a team. Other technical challenges we faced were of system design and architecture.
Accomplishments that we're proud of
Developing a fully functioning forum allowing users to discuss and share opinions with their equally passionate fellow enthusiasts. Creating a feed for showing all upcoming events that people can sign up for - a similar interface to
change.org
.
What's next for Revolve
For futher outreach of our platform, we're in the process of building dedicated Android and iOS apps using Google's Flutter framework. We also plan on rolling out a set of anonymization features that would protect the privacy of the users.
In the long term, we're also looking at utilizing machine learning to provide better suggestions to our users based on a number of factors such as the content of their comments, their geolocations, the events they've participated in, etc.
Real time chat rooms for different events are also a part of our discussions and may see the light of the day soon.
Built With
django
html
javascript
materialize
mysql
python
Try it out
github.com | Revolve | Revolve is an online platform for passionate people to come together for a cause to organize & execute events that are targetted towards improving the standards and living conditions of this world. | ['Manan Yadav', 'Kaustubh Kale', 'Smeet Mehta', 'Tanishq Patil', 'Amaan Sheikh'] | [] | ['django', 'html', 'javascript', 'materialize', 'mysql', 'python'] | 15 |
10,529 | https://devpost.com/software/it-s-time | Inspiration
The major Black Lives Matter movement rising around the world.
What it does
talks about the protests, injustice, and the solution in a 3 minute video
How I built it
using a video editor and some third party footage
Challenges I ran into
figuring out the video storyboard (how the video would look like)
Accomplishments that I'm proud of
How good the editing and the video was! I I started the project late, but I was able to catch up i a short amount of time and made a great video!
What I learned
all about the movement
What's next for Enough.
I hope to make more content about issues in the future.
Built With
video | It's time. | #BlackLivesMatter #EnoughIsEnough | ['Prisha Pandeya'] | [] | ['video'] | 16 |
10,529 | https://devpost.com/software/biohazard-tracker | Inspiration
The current COVID-19 situation and how it could have been handled better. Spreading information about dangers to the public as soon as possible.
What it does
Retrieves data about various viruses and disasters and gives information about the danger at one convenient website. Data is all from GitHub, and a Google Maps marker displays related statistics at that location. Currently, only SARS-CoV-2 and SARS-CoV have logged data. Ebola and MERS also have tabs with information, but no data is shown yet.
How I built it
Simple website built using Bootstrap and HTML, no extra CSS needed. Javascript was used to handle events and organise the data, and Google's Maps' API helped display it in a readable format.
Challenges I ran into
Issues with parsing CSV data from GitHub repos to JSON. I found a library on the internet and that helped immensely.
Accomplishments that I'm proud of
Collecting a vast amount of useful data and displaying it in a more readable way.
What I learned
How to use Google Maps' API and parse CSV files to JSON.
What's next for Biohazard Tracker
Account registration to save health information, symptom questionnaires, more health risks (viruses, fires and natural disasters), real time data, charity links and medical resources.
Built With
amazon-web-services
bootstrap
github
google-maps
html
javascript
jquery
json
Try it out
github.com
biohazardtracker.net.s3-website-us-west-1.amazonaws.com | Biohazard Tracker | Simple online tool to assess potential health risks. | ['Apsara Fite'] | [] | ['amazon-web-services', 'bootstrap', 'github', 'google-maps', 'html', 'javascript', 'jquery', 'json'] | 17 |
10,529 | https://devpost.com/software/rfgthjk | Inspiration:
The inspiration for this project was really just the current state of our world. Right now our environment has taken a major hit. This current change in the environment has increased heat, drought, and insect outbreaks. There has been a decline in water supplies, reduced agricultural yields, health impacts in cities due to heat, and flooding and erosion in coastal areas. Despite all these clear signs people still choose to ignore the existence of climate change and do not take the proper steps and act on it. We understood the importance of making people aware of the issue from a younger age because that is when people tend to be more open-minded to what is actually going on around them, and every child would want a better planet for themselves and their community.
What it does:
Our project allows for elementary aged students to understand the importance of being environmentally friendly in the form of an engaging game. Students will learn about pollution and its effects on the environment with a fun NPC explaining the importance of recycling and caring for the environment which in turn defeats the garbage monster. Students will first have a discussion with the NPC characters and complete the Pac-man styled mini-game where they collect waste in a maze and recycle the items while running from the garbage monster. After successfully completing the mini-game, Tino, the loveable NPC cat informs the player of the issues in the environment which are caused by pollution, and what students can do to help stop this. After reading the short blurb, students will then be tested on their knowledge through a true or false trivia game, in which they are given the opportunity to re-try the answers to questions as many times as they need. Once they finish this, the game ends, and students will have a greater understanding on the effects of pollution and how they can help.
How we built it:
This game was developed using Game Maker Studio 8.0 using original sprites, as well as sprite sheets and tile sets created by other sources and personalized for the game through both Krita and Photoshop (both are graphic editing software).
Challenges we ran into:
One of the biggest challenges we ran into was managing our time. We developed a full outline of how our game would look and it consisted of various mini-games, challenges, and components covering the different types of pollution. Unfortunately due to being short on time as a result of school, we were not able to input these ideas into our game and only had the time to create the characters and program for two short mini-games. We are satisfied with our finished product, but we would like to be able to continue adding to it so we have more mini-games for students to play, and more they can learn from.
Accomplishments that we're proud of:
• We are proud of having fully functioning game animations of our character running in all directions as that was something we originally found challenging.
• As a result of working on the trivia component of the game, we learned how to create loops within the code which will greatly benefit us in our future projects
• This has been our very first game which we have created in this Top-down RPG format, and we're quite happy with how it came out.
• We're happy with the game compatibility as it can run on window versions as early as 7.
What we learned:
We learned a lot about how to use Game Maker Studio for game development. We learned how to create sprite sheets and tile sets and arrange them, and developed our skills in using graphics editing software.
What's next for EnvironHero :
We plan on adding new mini-games for the three main types of pollution, Land, Water, and Air, as per our original plan, and expanding on the information that the NPC gives to the player so that students will have the opportunity to learn more about helping the environment. We also would like to format our trivia mini-game differently so that it is more interactive and engaging rather than simply having users type in what they believe is true or false. In order to make the game more available for various geographies we would simply have to replace the text with various other languages and give users the option to select their preferred language choice.
Sprite credits:
•
trash items:
https://opengameart.org/content/recycle-items-set4
•
NPC's and main character:
https://solaarnoble.itch.io/free-npcs
•
recycling bin:
https://www.123rf.com/photo_131053209_stock-vector-vector-pixel-art-recycle-bin-isolated-cartoon.html
•
scene 1 tile set
:
https://assetstore.unity.com/packages/2d/characters/tiny-rpg-forest-114685
Music credits:
up-beat music:
https://www.youtube.com/watch?v=Tchb1Q4V-nc
mysterious music:
https://www.youtube.com/watch?v=RI0EGg-ikd0
Built With
gamemaker
krita
photoshop
Try it out
ufile.io | EnvironHero | The hero of the climate crisis | ['Vedha Mereddy', 'Vivian Nguyen'] | [] | ['gamemaker', 'krita', 'photoshop'] | 18 |
10,529 | https://devpost.com/software/ecorank | App screen
Home Screen
Ranking Screen
Shopping Screen
Chatting Screen
Inspiration
How much do you care about saving our planet?
Maybe a little. right?
There can be few reasons. It's boring and kind of annoying when you told to care about it.
Did you know the US has only 4% of its forest left?
Our team decided to make it more enjoyable. So that people can be motivated to act on reducing usage on electricity and water. and eventually participate on saving our planet!
What it does
ECO Rank
is an App that users can post their daily act on saving our planet and get points, chat with the people have same interest on environment and shop eco-friendly products with their points. High rankers who posted lot of their act on saving environment are shown on rank screen.
How we built it
ECO Rank
is built in Java using Android studio. We used parse server to deploy our app and post and query data. At the early stage, we used
link
to make draft of our app.
Challenges we ran into
the Ranking feature was really challenging and we are still working on it. sorting recyclerview by points that user have is the difficult part.
Accomplishments that we're proud of
Live chatting feature on our app is what we are most proud of. It was not easy to get message from other user and display on chatting screen in real time. but when we made it to work, it was really rewarding.
What we learned
We learned to make an app on android studio. and also specific feature like livequery, deploying to parse server. but most important thing we learned from this project was acting on reducing use of water and electricity and doing recycling can be really enjoyable.
What's next for EcoRank
Get Sponsorship by companies So that we can give gifts for the high rankers to motivate to keep their work going!
Advertise our app on media so that many people can join our protect and earn campaign.
Built With
android-studio
figma
java
parse
Try it out
github.com
www.figma.com | ECO Rank | EcoRank is an App that users can post their daily act on saving our planet and get points, chat with the people have same interest on environment and shop eco-friendly products with their points. | ['Won Kyu Jeong', 'Yelin Joh'] | [] | ['android-studio', 'figma', 'java', 'parse'] | 19 |
10,529 | https://devpost.com/software/aztecas-social-justice-project | Inspiration
What inspired us to do this was how people claim police officers to be better people but in this program shows how police can take good relationships with with police officers
How we built it
Challenges we ran into
A challenge we run into is unknown about this hackathon I’m so late and finishing this project in just three days and just how it came out was just amazing
Accomplishments that we're proud of
Me and my partner Eric are very proud of finishing this video and making it look amazing in just three days of knowing of the contest
What we learned
We learned that police officers are are humans just like everybody else and they have a very important role in our community
What's next for Aztecas social justice project
We want to inspire youth to tie good good relationships with the officers because that way they’ll be less juveniles doing illegal things
Built With
imovie | Aztecas social justice project | In this video we explain how police tie up with the community and how the aztecas program is part of this | ['Adrian Madueno'] | [] | ['imovie'] | 20 |
10,529 | https://devpost.com/software/healing-communities | Inspiration
Our group was inspired by the ever-growing need for our communities to step up and take care of each other. We want to give members of our community an immediate way to connect underrepresented and disenfranchised individuals in need to available resources. As police brutality becomes more widely realized, we want to focus on ensuring the safety of our community members by not having to call 911 for everything. It is a way to take a public health view to public safety and begin to heal our communities.
What it does
Healing Communities will allow individuals access to services outside of calling the police and in turn decrease the potential accounts of police brutality in our community. When a passerby sees a situation they may have previously considered calling 911 for, they can instead match the need of an individual to an available resource. In many cases this will serve community members who are underserved and disenfranchised.
This was app was built by Emanuel Navarro using Android studio while using java as the primary language used as the source code.
Challenges we ran into
As far as the illustrations, drawing a representation of a condition or emotional state a person might be in was extremely challenging.
Accomplishments that we're proud of
This is an important service that can and should be put in place! We will continue to do work on this beyond Z Hacks.
What we learned
People should be more kind to one another. There are a lot of us struggling with a variety of problems and we should try to help each other when we can. We need this app in our community!
What's next for Healing Communities
We will continue to work on this project making sure it comes to fruition as we believe strongly that it will be vital moving forward with Healing Communities everywhere.
Built With
android-studio
github
java
Try it out
github.com | Healing Communities | Our group created an app for tackling police brutality, homelessness, as well as connecting essential health services to underserved communities. | ['Emanuel Navarro', 'Jenna Contuchio', 'Maureen Sanchez'] | [] | ['android-studio', 'github', 'java'] | 21 |
10,529 | https://devpost.com/software/act-now-1lxu8r | my inspiration was connecting blm and action sports in a short film
using Final Cut Pro my Panasonic camera and lifeflix to get the footage off of the camera
my tape was not working and lagging a lot I fixed it by downloading lifeflix a dv import software
landing some hard tricks for me and figuring out how to use the camera
I learned that using an old camera is very challenging but rewarding
what's next is making more videos, inspiring people to act, getting better and learning my editing software, and informing
Built With
finalcutpro
lifeflix
panasonisdvx100b | think care act now | my idea is I wanted to connect skating and scootering to black lives matter and learn how to use my vintage camcorder at the same time | ['aiden fay'] | [] | ['finalcutpro', 'lifeflix', 'panasonisdvx100b'] | 22 |
10,529 | https://devpost.com/software/fundit-srit95 | fundIt
A platform that democratizes access to capital for small businesses via crowdfunding
Inspiration
Startups founders don't have those connections or profits to get funding and especially in a year full of uncertainties many big investors are scared to invest in small businesses. And not all startups makes million dollars in their beginning years.
Meanwhile, most people are not as rich but want to invest. So we want to build a platform that benefits color businesses (because majority of them are quite small) and Investors both. Startups put their video pitches to help make investor a decision on the startup and investor can make an appointment with the business to know about their future goals before investing.
What it does
fundIt is a an app for small businesses to get crowdfunding by retail investors for equity.
Users can login and authenticate their credentials via Apple/Google/Email
Startups can post data such as PDFs, Images, and Text to supplement their crowdfunding campaign and help investors to make investment decisions
Investors can browse all campaigns via a Tab view
The most unique feature of this platform is the highlighted businesses of the month. Underrepresentation and discrimination is a huge problem in business investments so we want to represent those businesses by having a separate page for them.
Investors can schedule a virtual meeting with the representative of startup that will help investor know about the future plans of the business
Investors can pay as little as $10 for a share in the startup’s equity offered in the crowdfunding campaign
Investors can view their past investments & their total investments on a profile view
Startups can checkout the funds raised from the crowdsourced campaign via Apple/Google Pay to Apple/Google Wallets in a virtual FundIt card
How I built it
Flutter: Dynamic Mobile Applications that runs both on Android and iOS.
Firebase: For authentication
Square: Payment Processing
SQL: For storing the Business and Investor Information
UiPath: For automating the process for investors displaying startups according to their search history
Potential Users
Retail investors - who will be investing in the companies that are listed on our platform
Startups - they sign up for crowdfunding in exchange for equity.
Challenges I ran into
Payment Processing using Square
Automation with UiPath
Making dynamic user interface for startup took some time to apprehend
Accomplishments that I'm proud of
Able to build a working platform with a great team work in such a short time.
What we learned
Learned how to divide tasks as a team and be accountable for it, setting report time
How to do payment processing
What's next for fundIt
We are planning to reach small businesses and small investors who could benefit from each other. Small businesses by getting money and small investors by getting returns on their investment with as little as 10 dollars.
Built With
android
Try it out
github.com | MoneyQ Fundit | A platform that democratizes access to capital for small businesses via crowdfunding | ['Rishav Raj Jain'] | [] | ['android'] | 23 |
10,529 | https://devpost.com/software/kahzum-same-day | Home page for our web-app
Simple shopping cart page for our customers to checkout
Product purchase page customers see
Inspiration
The COVID-19 pandemic has created numerous challenges for everyone, but especially for small businesses. Brick and mortar stores depend on a steady flow of customers coming to their store front. However, the recent lockdowns, occupancy restrictions, and general caution towards getting sick have put many small businesses in a very precarious position. Suddenly, the reliable stream of foot traffic that keeps brick and mortar stores a float has dwindled into almost nothing. In response to this, we have created QRShop, a web app that allows people to shop at physical stores without fear. Using the magic of QR codes, QRShop creates an entirely new shopping experience.
What it does
In the QRShop model, the store owner has their goods displayed outside their store. Next to each item is a QR code. When scanned, the customer is automatically taken to that item's online store page on the QRShop website. From there, the customer can view the product description and the different item variants. The customer can then add that item to their virtual shopping cart, where it will be saved until deleted. When ready to buy, instead of physically taking that item to the register, the customer can simply enter their contact information, and press ‘purchase’. The store owner will get a notification about the customer’s order, and bag the (untouched) items themselves. This ensures to the customer that no one other than store employees have touched the item they intend to buy.
Also, Since all scanned items are saved to the customer’s shopping cart, QRShop allows customers to remember items of interest, and buy them at home. With QRShop, the customer can once again feel safe about shopping at their favorite brick and mortar stores.
How we built it
Our app utilizes the react.js web framework and bulma css for our front end. Using AWS Lambda and DynamoDB, we developed a serverless backend which allows for automatic scaling and also only charges for the duration of each request. We also used AWS’s SNS service to send text messages to both the customer and the business owner. Our app is hosted on AWS Amplify, which allows us to scale easily, and has a powerful framework which can help us expand our apps feature set.
Challenges we ran into
Since it was our first time using AWS, it took a little bit of time learning how to host our web app and our backend. Also, figuring out the database schema was a bit tricky since we wanted to make sure queries could be made efficiently of the data when we start adding features on top.
Accomplishments that we're proud of
Building a web app that can help businesses and also keep people more safer by encouraging a different shopping experience. We hope businesses find our hackathon project useful and try it out for themselves.
What we learned
We learned how to use multiple AWS services like dynamoDB, amplify, etc. to host web apps. It was fun learning how to put all of these technologies together to form our hackathon project.
What's next for QRShop
In the future, we want to develop a portal for business owners to manage their inventory and generate qr codes to attach to their products. Another feature we would like to implement is in-app purchase processing so that the process can be even more seamless. We would like to add accounts to our app as well so customers can view a history of the items that they’ve previously purchased, and the stores they’ve purchased from.
Built With
amazon-dynamodb
amazon-web-services
amplify
bulma
express.js
pwa
qr
react
serverless
Try it out
qr.kahzum.com
github.com
serverless.d29dap18am5m9n.amplifyapp.com | QRShop | Helping small businesses with the power of QR codes and outdoor shopping | ['Farhan Saeed', 'Mason Pierce', 'Alan Brilliant'] | [] | ['amazon-dynamodb', 'amazon-web-services', 'amplify', 'bulma', 'express.js', 'pwa', 'qr', 'react', 'serverless'] | 24 |
10,529 | https://devpost.com/software/fen | Signup Page
Login Page
Location
Home Page
Date and time
Weather forecast
Current season details
Soil Information
Nearest Dam
Suitable Crops
Harmful Effects
Assistant made by AI
Inspiration
The farmers are demanding waivers on farm loans and higher prices for their crops. For decades now, farming in India has been blighted by drought, small plot sizes, a depleting water table, declining productivity, and lack of modernization. Half of its people work on farms, but farming contributes only 15% to India's GDP. Put simply, farms
employ a lot of people
but
produce too little
. Crop failures trigger farm suicides with alarming frequency. This can cause a considerable impact on our
economy
. Therefore, in order to support farmers in their work, our team has created the FEN Assistant.
What it does
FEN is a virtual assistant that has many features. The app needs to be granted access to
location
by the farmer in order to assist them. The app provides accurate values of the
current temperature, weather, pressure, and humidity
. These things need to be taken into consideration and are of utmost importance while farming. It
predicts the season
and accordingly
suggests suitable crops
that can be grown in the current season. This is really important since each type of soil has different properties and can only support some varieties of crops. Moreover, by gaining access to the location, the app informs the farmer of the
nearest dams
for irrigating the crops. Irrigation is the most important agricultural input in a tropical monsoon country like India where rainfall is uncertain. Also, it informs the farmer of the
ill effects or harmful effects of cropping
, which the farmer can take into consideration while farming in order for good cultivation. If the farmer is facing any other problem and needs help, he can always contact our
FEN bot
. All these can aid the farmer for
maximum profit
and a
healthy economy.
How we built it
We built with an android studio and with useful API from the
government
and our survey analysis.
Challenges we ran into
It was difficult to complete the geo-fencing, weather analysis, and crop prediction using the ML technique.
Accomplishments that we’re proud of
Each and every member of our team is proud of what we’ve been able to create in a short-time period. We are also glad to be able to provide a solution to a prevailing problem in our country.
What we learned
We learned how to do the geofencing and KNN algorithm. One of our team members learned to use Figma, which we used for the UI design of the app.
What's next for FEN
Right now, the application is just an MVP. In the future, we plan to make our bot more functional our project and making it work. The FEN bot/chatbot is what we’re planning to integrate into our app in the future. We are also planning to make a device which will tell about the current moisture level in the soil by which we can calculate the amount of water needs to be given to the crops.
Built With
crop
css
html5
java
location-labs-geofence
map
ml
php
sqlite
survey-analytics
xml
Try it out
github.com | FEN | 'Farmer Empowerment Nation' - A personal assistant for every farmer. | ['Kavya Pullanoor', 'JOTHESH S P', 'Mageshwaran R', 'HariOm Dwivedi', 'Prashanth S'] | [] | ['crop', 'css', 'html5', 'java', 'location-labs-geofence', 'map', 'ml', 'php', 'sqlite', 'survey-analytics', 'xml'] | 25 |
10,529 | https://devpost.com/software/pandemic-supply | Home page
"I need" page
"i can supply" page
"see votes" page
Voting on need
Inspiration
Pandemics are a Global problem yet our responses are localized. Beating a pandemic requires international cooperation and resources. When Countries compete with each other for imported supplies, or local communities compete against world trade, in the end, this inefficiency costs lives. I propose a Decentralized Autonomous Supply Chain Database that can match resource needs based on manufacturing equipment, materials, and product availability.
1.Create a global pandemic supply train of resources, supplies, and manufacturing capabilities.
2.A proactive response to outbreaks by using existing models to contain hotspots.
3.Match needs with resources
What it does
The magnitude of the coronavirus pandemic and the scale of resource coordination required has necessitated an international response. Connecting manufacturing with those who are in need and those who can help. Fulfilling the immediate global supply needs through a global supply chain. This platform would allow for a factory in Paris to pivot from perfume to hand sanitizer and matched with a grocery store in rural Arkansas in need of hand sanitizer for their workforce.
Repurposing helps companies to protect their own workforce and serve the greater good. Repurposing also allows companies to keep production lines up and running in times of low demand, generate moderate revenues, and positively impact their reputation.
Basically to create a global resource to respond to a pandemic. A Pandemic supply that offers every country:
1.Access to global, standardized data
2.Access to a global supply chain
3.Contribution to international reserves
4.Futures trading in international markets *
5.A global response to a global crisis through an international cooperative DAO using AI to model, inform, and manage global resources.
How I built it
I got the data from healthdata.org and processed using python and I built the application by connecting AragonUI, AragonDS, and linking it to React which is linked to AragonCLI which in turn is linked to Ethereum.
Challenges we ran into
Everything I did in this project is new to me because I used a lot of new software and figuring out how Blockchain works itself was a great difficulty.
Accomplishments that we're proud of
Passing all the obstacles and successfully running the project.
What I learned
I learned a lot from this project, knowing how to use new software, going out of my comfort zone to create something different and new.
What's next for Pandemic Supply
To be used for a variety of supply chains, but more importantly, to reduce the amount of human bias and mode from the decision chain.
Built With
aragon
dao
ocean
python
react
Try it out
github.com | Pandemic Supply | A Decentralized Autonomous Medical Reserve that manages national medical resources and supplies in a time of crisis. | ['Rethesh Raghav'] | [] | ['aragon', 'dao', 'ocean', 'python', 'react'] | 26 |
10,529 | https://devpost.com/software/shop-to-help | Shop To Help
Services
Donate Here
Daily Blogs to motivate
AR technology products
Problem faced:-
For every poor person, clothing is shelter. But most of them cannot afford even one meal properly then how can they think of quality clothes.
In this pandemic year clothing problems are faced by many poor people around the world and the social distancing is making it difficult for people to go and donate clothes to help them.
Due to this poor people are forced to wear torn clothes. They used to beg for clothes but people don’t give them anything.
So by this project we sincerely hope to help these people by collecting the donation safely and giving them.
Solution:-
Shopagezy is the solution to these problems.
This store is an online platform in which you can go and donate for poor people.
Services it provides are:
~AR Shop:- online shopping with AR view. This allows you to experience traditional shopping while sitting at your home. In this pandemic while following social distancing you can shop by having a real life interaction with the product.
~Donate:- Here you can donate to the poor people. You just need to fill in the details, our agent will come to receive the donation. And those donations will be given to the needy.
~Sell Them:- If you want to sell your clothes which look the same as the new, here you can sell them to the needy at a cheap rate giving both of you the profit.
~Defective:- All the defective yet useful products are here for the sale at a very cheap rate.
~Blogs:- Every wonderful contribution towards the betterment of those little lives are recorded here. These daily blogs will help to motivate you to donate.
How it will help:-
It will help the poor people to get some good clothes without any fear of spreading the virus because of the physical contact.
Our team will make sure to sanitize each and every product before any transactions. Thus with a safe hand.
The one who always wants to help the poor but somehow due to the fear of covid-19 is hesitating to do that can now freely contact us and donate without any worry.
I hope we all help the people by providing some clothes.
Built With
augmented-reality
bootstrap
c#
css3
html5
javascript
Try it out
github.com | Shop to Help | It's far better to donate than accumulate. | ['Tripti Saloni', 'Eksha Singh', 'Anusha Nigam'] | [] | ['augmented-reality', 'bootstrap', 'c#', 'css3', 'html5', 'javascript'] | 27 |
10,529 | https://devpost.com/software/pre-lute | Main page
Languages used for Main Page
Reach Out Screen (To allow users to collaborate to reduce pollution)
Forecasts Screen (Make decisions based on air quality, allergen presence and overall pollution in the air)
Petitions Screen (Take action against pollution)
form for the algorithm
results page
Safety Precaution Page first page
Safety Precaution Page first page
Inspiration
Around 40% of the lakes in America are too polluted for aquatic life, swimming or fishing.Although children make up 10% of the world’s population, over 40% of the global burden of disease falls on them. Environmental factors contribute to more than 3 million children under age five dying every year. Pollution kills over 1 million seabirds and 100 million mammals annually. Recycling and composting alone have avoided 85 million tons of waste to be dumped in 2010. Currently in the world there are over 500 million cars, by 2030 the number will rise to 1 billion, therefore doubling pollution levels. High traffic roads possess more concentrated levels of air pollution therefore people living close to these areas have an increased risk of heart disease, cancer, asthma and bronchitis. Inhaling Air pollution takes away at least 1-2 years of a typical human life. 25% deaths in India and 65% of the deaths in Asia are resultant of air pollution. Over 80 billion aluminium cans are used every year around the world. If you throw away aluminium cans, they can stay in that can form for up to 500 years or more. People aren’t recycling as much as they should, as a result the rainforests are be cut down by approximately 100 acres per minute
On top of this, I being near the Great Lakes and Neeral being in the Bay area, we have both seen not only tremendous amounts of air pollution, but marine pollution as well as pollution in the great freshwater lakes around us. As a result, this inspired us to create this project.
What it does
For the react native app, it connects with the Website Neeral made in order to create a comprehensive solution to this problem.
There are five main sections in the react native app:
The first section is an area where users can collaborate by creating posts in order to reach out to others to meet up and organize events in order to reduce pollution. One example of this could be a passionate environmentalist who is organizing a beach trash pick up and wishes to bring along more people. With the help of this feature, more people would be able to learn about this and participate.
The second section is a petitions section where users have the ability to support local groups or sign a petition in order to enforce change. These petitions include placing pressure on large corporations to reduce carbon emissions and so forth. This allows users to take action effectively.
The third section is the forecasts tab where the users are able to retrieve data regarding various data points in pollution. This includes the ability for the user to obtain heat maps regarding the amount of air quality, pollution and pollen in the air and retrieve recommended procedures for not only the general public but for special case scenarios using apis.
The fourth section is a tips and procedures tab for users to be able to respond to certain situations. They are able to consult this guide and find the situation that matches them in order to find the appropriate action to take. This helps the end user stay calm during situations as such happening in California with dangerously high levels of carbon.
The fifth section is an area where users are able to use Machine Learning in order to figure out whether where they are is in a place of trouble. In many instances, not many know exactly where they are especially when travelling or going somewhere unknown. With the help of Machine Learning, the user is able to place certain information regarding their surroundings and the Algorithm is able to decide whether they are in trouble. The algorithm has 90% accuracy and is quite efficient.
How I built it
For the react native part of the application, I will break it down section by section.
For the first section, I simply used Firebase as a backend which allowed a simple, easy and fast way of retrieving and pushing data to the cloud storage. This allowed me to spend time on other features, and due to my ever growing experience with firebase, this did not take too much time. I simply added a form which pushed data to firebase and when you go to the home page it refreshes and see that the cloud was updated in real time
For the second section, I used native base in order to create my UI and found an assortment of petitions which I then linked and added images from their website in order to create the petitions tab. I then used expo-web-browser, to deep link the website in opening safari to open the link within the app.
For the third section, I used breezometer.com’s pollution api, air quality api, pollen api and heat map apis in order to create an assortment of data points, health recommendations and visual graphics to represent pollution in several ways. The apis also provided me information such as the most common pollutant and protocols for different age groups and people with certain conditions should follow. With this extensive api, there were many endpoints I wanted to add in, but not all were added due to lack of time.
For the fourth section, it is very much similar to the second section as it is an assortment of links, proofread and verified to be truthful sources, in order for the end user to have a procedure to go to for extreme emergencies. As we see horrible things happen, such as the wildfires in California, air quality becomes a serious concern for many and as a result these procedures help the user stay calm and knowledgeable.
For the fifth section, Neeral please write this one since you are the one who created it.
Challenges I ran into
API query bugs was a big issue in formatting back the query and how to map the data back into the UI. It took some time and made us run until the end but we were still able to complete our project and goals.
What's next for PRE-LUTE
We hope to use this in areas where there is commonly much suffering due to the extravagantly large amount of pollution, such as in Delhi where seeing is practically hard due to the amount of pollution. We hope to create a finished product and release it to the app and play stores respectively.
Built With
apis
breezometer
expo.io
firebase
firestore
native-base
react-native
react-navigation
Try it out
github.com | PRE-LUTE | Helping predict, prevent and educate users about Pollution | ['Om Joshi', 'Neeral Bhalgat'] | ['4th place - Credits'] | ['apis', 'breezometer', 'expo.io', 'firebase', 'firestore', 'native-base', 'react-native', 'react-navigation'] | 28 |
10,529 | https://devpost.com/software/covid-19-informational-motion-graphics-video | https://youtu.be/PU3y7uUQcEU
Preface
(This is a solo project.)
I like to edit videos as a hobby, but I usually only work with footage. However, I would like to branch out further and learn new related skills, so for this hackathon I decided to challenge myself by creating motion graphics. A major reason why this project was born was my need to experiment in an unfamiliar media.
Inspiration
All three categories in this hackathon appealed to me, but in the end I chose to make something for the pandemic category. There were many social issues I could have talked about, but I wasn't sure how to visualize and represent those ideas. An educational COVID-19 video, however, would be a concise presentation with orderly facts.
Importance
2020 has been shaped by the pandemic. To stay safe, people need to be informed about the dangers this virus has. In addition, it's imperative that everyone understands why social distancing and masks work against the coronavirus so that these safety measures can be practiced. The benefit my project has is very straightforward: online media can reach a wider audience because a link can simply be shared to anyone who is looking to learn. Regarding accessibility, videos are prevalent in social media and widely available as media to consume.
Process
I started out with some typography to introduce the video, but then switched my attention to the graphics I would want for each scene. I created these images first, then paired them with their text and put together a rough animation path for each scene. Once animation was finished, I would go in and fine tune details like font and color. Finally, I created transitions in between each scene instead of leaving hard cuts.
Challenges and accomplishments
Besides the standard research needed to provide information about COVID-19, I also had to figure out how I could visually convey the facts I presented. Every graphic I made needed to complement its sentences. While some topics were straightforward, such as visualizing two people far apart for social distancing, others were more abstract, like comparing worldwide coronavirus cases with each other. It was definitely a very creative process for me, and was comparable to storyboarding with how I planned out each scene I wanted to animate.
Technically, I had to teach myself a lot of new concepts and software functions in order to create the effects I used in my video. The most difficult part of my video was trying to create a 3D parallax effect with different sized circles. Working in 3D space is much harder for me than working in 2D, and that scene was the only 3D animation I made, along with being the scene I spent the most time on. The 3D positioning also demanded that I learn how camera positioning and points of interest work. Easier 2D effects I taught myself involved masking, plugins, and shape layers.
I had more scenes planned for my video, but the amount I was able to complete within the timeframe still stands on its own as a finished product instead of obviously lacking more content. I'm proud I was able to complete something like this in such a short time, and I feel like this video will be useful for me, outside this hackathon's context, in a potential motion reel or portfolio.
Acknowledgements
Statistics and information cited are from the
WHO
and
CDC
. Program used is After Effects CS6. Music is
"Embrace" by Sappheiros
; all other video assets were created by me.
Built With
aftereffects | COVID-19 motion graphics | A simple motion graphics video presents facts and safety information related to the coronavirus. | ['Michel Nguyen'] | [] | ['aftereffects'] | 29 |
10,529 | https://devpost.com/software/evisecure | Inspiration
My main idea is to stop injustice. Evidence is the main part of any case/investigation and tampering it can falsely accuse an innocent person and ruin their life. There might be thousands of cases in which a corrupt police office destroy evidence and falsely accused someone else, hence there is a need for a reliable, foolproof system to maintain and manage crime records. Incorporating criminal records in a blockchain, authenticity and rigidity of records can be maintained, which also helps to keep the data safe from adversaries. A peer to peer cloud network enables the decentralization of data. It helps prevent unlawful changes in the data.
What it does
My project presents ways in which the authority can maintain the records of criminals efficiently. Authorities (e.g., Law enforcement agencies and courts) will be able to add and access criminal data such as filing an FIR with evidence.
General users (e.g., selected organizations and/or individuals, airports, visa application centers, etc.) can register themselves and will have access to the data so that they can look up criminal records. Proper and timely access to authentic criminal records is essential to enforce the law. The effect of corruption on the law enforcement forces will also decrease, as this will cut off an entire scope of corruption by removing any possibility of tampering with criminal records data.
How I built it
I built it using:
1.Blockchain-Ethereum, Web3, Solidity, IPFS, Truffle
2.Front End (Web DApp)-React JS, Bootstrap
Back End-NodeJS
Database-MongoDB
5.Hosting Services-MLAB (MongoDB),Infura (Blockchain),Metamask.
The police department can upload evidence by creating contracts in the Blockchain which is bridged with Metamask and Web3. Authorities and General Public can request a record and the blockchain platform(Metamask and Web3) will respond to them.
Challenges I ran into
The main challenge I faced is creating a smart contract to upload data to the blockchain and linking it to the webapp.
Accomplishments that I'm proud of
Successfully linking blockchain and web app.
What I learned
This is my first project working with Blockchain. Even though it was tough to create smart contracts and using Ethereum platforms, it was exciting and I learnt quite a lot out of this.
What's next for EviSecure
Expanding in other areas requiring decentralized and trusted security, hence, introducing a universal initiative.
Built With
blockchain
css
html
javascript
react
Try it out
github.com | EviSecure | Evidence management system using Blockchain to prevent unlawful changes in data. | [] | [] | ['blockchain', 'css', 'html', 'javascript', 'react'] | 30 |
10,529 | https://devpost.com/software/wecare-0fjkb9 | Summary: Home Screen of app, which allows you to report your symptoms, check the status of your circle, and get daily personalized tips.
Home Screen of app, which allows you to report your symptoms, check the status of your circle, and get daily personalized tips.
Map Screen of app, which allows you to see hotspots around you and your Care Circle.
Care Circle screen of app, which allows you to health conditions of your loved ones.
Web interface, which can be used to update the symptoms. It is synced with the app.
The problem WeCare solves
As the outbreak of COVID-19 continues to spread throughout the entire world, more stringent containment measures from social distancing to city closure are being put into place, greatly stressing people we care about. To address the outbreak, there have been many ad hoc solutions for symptom tracking (e.g.,
UK app
), contact tracing (e.g.,
PPEP-PT
), and environmental risk dashboards (
covidmap
). However, these fragmented solutions may lead to false risk communication to citizens, while violating the privacy, adding extra layers of pressure to authorities and public health, and are not effective to follow the conditions of our cared ones. Until now, there is no privacy-preserving platform in the world to 1) let us follow the health conditions of our cared ones, 2) use a statistically rigorous live hotspots mapping to visualize current potential risks around localities based on available and important factors (environment, contacts, and symptoms) so the community can stay safer while resuming their normal life, and 3) collect accurate information for policymakers to better plan their limited resources.
Such a unified solution would help many families who are not able to see each other due to self-quarantine and enable early detection and risk evaluation, which may save many lives, especially for vulnerable groups. These urgent needs would remain for many months given that the quarantine conditions may be in place for the upcoming months, as the outbreak is not reported to occur yet in Africa, the potential arrival of second and third waves, and COVID-19 potential reappearance next year at a smaller scale (like seasonal flu). There is still uncertain information about immunity after being infected and recovered from COVID-19. Therefore, it is of paramount importance to address them using an easy-to-use and privacy-preserving solution that helps individuals, governments, and public health authorities. The closest solution is
COVID Aggregated Risk Evaluation project
, which tries to aggregate environment, contacts, and symptoms into a single risk factor. WeCare takes a different approach and a) visualizes those factors (instead of combining them into a single risk value) for more tangible risk communication and b) incentivizes individuals to regularly check their symptoms and share it with their Care Circle or health authorities.
WeCare Solution
WeCare is a digital platform, both app and website. Both platforms can be used separately, and with freedom of choice towards the user. The app, however, will give users more information and mobile resources throughout the day. Our cross-platform app enables symptom tracking, contact tracing, and environmental risk evaluation (using official data from public health authorities). Individuals can add their family members and friends to a Care Circle and track their health status and get personalized daily updates. In particular, individuals can opt-in to fill a simple questionnaire, supervised by our epidemiologist team member, about their symptoms, comorbidities, and demographic information. The app then tracks their location and informs them of potential hotspots for them and for vulnerable populations over a live map, built using opt-in reports of individuals. This map is accessible on the app and our website. Moreover, symptoms of individuals will be tracked frequently to enable sending a notification to the Care Circle and health authorities once the conditions get more severe. We have also designed a citizen point, where individuals get badges based on their contributions to solving pandemic by daily checkup, staying healthy, avoiding highly risky zones, protecting vulnerable groups, and sharing their anonymous data.
Our contact tracing module follows guidelines of Decentralized Pan-European Privacy-Preserving Proximity Tracing
(PEPP-PT)
, which is an international collaboration of top European universities and research institutes to ensure safety and privacy of individuals.
What we have done during the summer.
We have updated the app-design. New contacts with Brasil, Chile and Singapore. We have also made some translation work with the app. Shared more on social media about the project and also connected to more people on slack and LinkedIn.
We have consolidated the idea and validated it with a customer survey. We then developed a new interface for
website
and changed the python backend to make it compatible with the WeCare app. We have also designed the app prototype and all main functionalities:
Environment: We have developed the notion of hotspots where we have developed a machine learning model that maps the certified number of infected people in a city and the spatial distribution of city population to the approximate number of infected in the neighbourhood of everyone.
Contact tracing: We have developed and successfully tested a privacy-preserving decentralized contact tracing module following the
(PEPP-PT)
, guidelines.
Symptoms tracking: We have developed a symptom tracking module for the app and website.
Care Circle: We have designed and implemented Care Circle where individuals can add significant ones to their circle using an anonymous ID and track their health status and the risk map around their location.
You can change what info you want to share with Care Circle during the crisis.
The app is very easy-to-use with minimal input (less than a minute per day) from the user.
We are proud of the achievements of our team, given the very limited time and all the challenges.
Challenges we ran into
EUvsVirus Hackathon Challenge opened its online borders recently to the global audiences which brought together plenty of people of different expertise and skills. There were challenges that we faced that were very unique, as we faced a variety of communication platforms on top of open-source development tools.
Online Slack workspaces and Zoom meetings and webinars presented challenges in forms of inactive team members, cross-communications, and information bombardment in several separate threads and channels in Slack and online meetings of strangers that are coordinated across different time zones. In developing the website and app for user input data, our next challenge was in preserving the privacy of user information.
In the development of a live map indicating hotspot regions of the COVID-19 real-time dataset, our biggest challenge here was to ensure we do not misrepresent risk and prediction into our live mapping models. We approached Skill Mentor Alise. E, a specialist in epidemiology, who then explained in greater detail that the proper prediction and risk modelling should take into account a large number of factors such as population, epidemiology, and mitigations, etc., and take caution on the information we are presenting to the public. Coupled with the lack of official datasets available for specific municipalities for regions, we based geocoding data mining of user input by area codes cross-compared with available Sweden cities number of fatalities, infected and in intensive care due to COVID-19.
The solution’s impact on the crisis
We believe that WeCare would help many families who can see each other due to self-quarantine and enable early detection and risk evaluation, which may save many lives, especially for vulnerable groups. The ability to check up on their Care Circle and the hotspots around them substantially reduces the stress level and enables a much more effective and safer re-opening of the communities. Also, individuals can have a better understanding of the COVID-19 situation in their local neighbourhood, which is of paramount importance but not available today.
The live hotspot map enables many people of at-risk groups to have their daily walk and exercise, which are essential to improve their immunity system, yet sadly almost impossible today in many countries.
The concept of Care Circle motivates many people to invite a few others to monitor their symptoms on a daily basis (incentivized also through badges and notifications) and take more effective prevention practices.
Thereby, WeCare enables everyone to make important contributions toward addressing the crisis.
Moreover, data sharing would enable a better visual mapping model for public assessment, but also better data collection for the public health authorities and policymakers to make more informed decisions.
The necessities to continue the project
We plan to continue the project and fully develop the app. However, to realize the vision of WeCare we need the followings:
Social acceptance: though being confirmed using a small customer survey, we need more people to use the WeCare app and share their data, to build a better live risk map. We would also appreciate more fine-grained data from the health authorities, including the number of infected cases in small city zones and municipalities.
Public support: a partnership with authorities and potentially being as a part of government services, though not being necessary, to make it more legitimate. This would increase the level of reporting and therefore having a better overview and control of the crisis.
Resources: So far, we are voluntarily (and happily) paying for the costs of the servers. Given that all the services of the app and website would be free, we may need some support to run the services in the long-run.
The value of your solution(s) after the crisis
The quarantine conditions and strict isolation policies may still be in place for upcoming months and year, as the outbreak is not reported to occur yet in Africa, the potential arrival of second and third waves, and possible COVID-19 reappearance next year at a smaller scale (like seasonal flu).
Therefore, we believe that WeCare is a sustainable solution and remains very valuable after the current COVID-19 crisis.
The URL to the prototype
We believe in open science and open-source developments. You can find all the codes and documentation (so far) at our
Website
.
Github repo
.
Other channels.
https://www.facebook.com/wecareteamsweden
https://www.instagram.com/wecare_team
https://www.linkedin.com/company/42699280
https://youtu.be/_4wAGCkwInw
(new app demo 2020-05)
Interview:
https://www.ingenjoren.se/2020/04/29/de-jobbar-pa-fritiden-med-en-svensk-smittspridnings-app
Built With
node.js
python
react
vue.js
Try it out
www.covidmap.se
github.com | WeCare | WeCare is a privacy-preserving app & page that keeps you & your family safer. You can track the health status of your cared ones & use a live hotspot map to start your normal life while staying safer. | [] | ['2nd place', 'Best EUvsVirus Continuation', 'Best Privacy Project'] | ['node.js', 'python', 'react', 'vue.js'] | 31 |
10,529 | https://devpost.com/software/gg-d1oj9v | Inspiration
During my short journey in high school, I've gotten more and more involved in environmental issues. During my research, I came across the perils of synthetic dyes. Though unknown, synthetic dyes place an atrocious effect every single day. In fact, it releases 12 kg of CO2 per month and 144 kg of CO2 per year. Other than these environmental problems, it also causes health problems.
Purpose
To bring awareness of the effects of synthetic dyes and promote natural dyes. With funding and a proper domain, it is even more possible for this change to happen as the production of natural dyes is a little costly due to the availability of fruits, vegetables, etc. and due to the reusability of water. Beyond that, we can have minimal environmental impact and minimal health impact as well.
Cons of Synthetic Dyes
a) Health Problems:
- Constitutes a danger to health on account of their material composition, particularly through toxicological substances or impurities
- Affects a human’s central nervous system, kidney, reproductive system, brain, liver, etc.
- Causes significant health effects like an increase in tumours, carcinogens, dermatitis, and respiratory diseases
Irritates the eyes, skin, mucous membrane, and the upper respiratory tract
b) Environmental Hazards:
- Produces air pollution by releasing particulate matter and dust, chemically oxidizing of nitrogen and sulfur, volatizing organic compounds, and contaminating the air, soil, sediments, surface water and groundwater
- Discharges many chemicals through the polluted water, which results in the death of aquatic life, the ruining of soils and poisoning of drinking water
- Causes foul odor and unwanted sight
- Restricts sunlight penetration in contaminated water bodies and impairs photosynthesis
- Responsible for the eutrophication, recalcitrance, bioaccumulation, and biochemical and chemical oxygen demand
How I built it
HTML
CSS
Dance
Enjoy this short excerpt from Bhala Kanaka Maya by Tygaraja! This Kuchipudi natyam, which is a classical dance from the Indian state of Andhra Pradesh, conveys the nature's many wonders through the perspective of a person filled with awe. Afterwards, animals like the bird, deer, and peacock are shown one by one. However, all dies when nature faces the perils of synthetic dyes.
Built With
css
css3
html
html5 | Natural Dyes vs. Synthetic Dyes | Can't stand the atrocious effects of synthetic dyes any longer? Promote natural dyes with donation! | ['Sahithi Morla'] | [] | ['css', 'css3', 'html', 'html5'] | 32 |
10,529 | https://devpost.com/software/aashray-3izsv7 | .
Built With
html | . | . | [] | [] | ['html'] | 33 |
10,529 | https://devpost.com/software/kawsay | HOME SECTION
ABOUT US
SIGN UP
COVID-19 MAP
Inspiration
Our group was inspired by our own personal experiences in the COVID-19 pandemic. The coronavirus has changed our way of life dramatically. Now, we have to be more careful in public spaces and follow safety guidelines to help protect ourselves and others. Masks have become essential! PPE Kits, gloves, respirators, and face shields have all become necessary tools in this fight against COVID.
We wanted to create a project to help underfunded schools, hospitals, and neighborhoods receive the necessary aid and supplies.
What it does
Kawsay aims to provide a platform that links schools and hospitals with donors that can provide them with COVID-19 essentials such as PPE Kits and masks. Our primary focus is on providing low-income schools, hospitals, and neighborhoods with the supplies necessary to overcome this pandemic.
How we built it
The languages we used to build the website were mostly HTML and CSS. We also used JavaScript and Bootstrap to make the chatbot and animations on our website. We built it using the repl.it IDE.
Challenges we ran into
A lot of us were quite new to coding and so small errors often went unnoticed. Debugging took a while and so did making the website responsive across all devices. However, looking back, we are very satisfied with our improvement as programmers and we feel that we have acquired numerous new skills for future hackathons.
Accomplishments that we're proud of
We are proud of creating a website that is so crucial to the present-case scenario. We believe our website has a huge potential to save and impact lives simply because obtaining the necessary supplies is one of the most important, yet toughest task out there, and Kawsay aims to make it a mere 5-minute job. We are also extremely happy with the aesthetics of the website.
What we learned
As individuals and as a team, we all learned a lot about programming. We learned new commands in HTML, new properties in CSS, and became more familiar with the JS language. We also learned about flask and pyrebase modules in python, using an integrated system which incorporates python, firebase database and html templates.
What's next for Kawsay
Our vision for Kawsay is to expand its services first nationally and then globally. Our ultimate goal is to provide help to as many as possible.
Built With
bootstrap
css
database
easing
firebase
flask
forms
google-maps
html
javascript
jquery
morphext
program-o-chatbot
python
superfish
wow
Try it out
kawsay.zhacks.repl.co
github.com | Kawsay | Ensuring Good Living amidst COVID! | ['Tresa Ignatius', 'Wilder Reyes', 'Anneysha Sarkar', 'Divinaa Raju'] | [] | ['bootstrap', 'css', 'database', 'easing', 'firebase', 'flask', 'forms', 'google-maps', 'html', 'javascript', 'jquery', 'morphext', 'program-o-chatbot', 'python', 'superfish', 'wow'] | 34 |
10,529 | https://devpost.com/software/goodlife-3jiklr | Home
About Us
About Us
About Us
Donate
Donate
Donate
Donate
Donate
Donate
Contact Us
Register
Using VSCode
Using repl.it
Using Notepad++
Using pixlr
Icon
Icon
Icon
Icon
Inspiration
Because of the corona virus, we've all had a lot of extra time on our hands. Looking to be productive and make an impact, we researched a variety of different movements happening in our own communities.
Within our hacking group, we have members who are passionate about climate change, healthcare, and racial equality. We all agreed that these are important challenges that deserve more attention. But how do we, as students, support movements focused on these issues?
We realized that finding reliable sources and organizations can be really difficult for virtually any cause a person might be interested. So, we decided to build a service that would combat this problem.
What it does
GoodLife is a website service that provides users with the unique opportunity to find organizations related to causes that they are passionate about, or discover more topics that they would be interested in supporting.
The GoodLife website is made up of five different web pages: Home, About, Donate, Contact Us, and Register.
1) Home
Our homepage provides an easy-to-use navigation bar to the other pages in our website so that the user can toggle between our different webpages.
2) About
Our about page contains our mission statement along with a list of the core values we fight for at GoodLife. The page also has a written portion called "Your Actions Have Power" which describes some of the most current and up-to-date social problems across the world today.
Finally, we have an automatic image slideshow of pictures representing some of the information in the "Facts to get you motivated" section. These components are also related to the modern issues discussed in "Your Actions Have Power."
3) Donate
The Donate page is the main feature of the GoodLife website. This is where the user can find links to relevant movements about issues they want to support.
To navigate the Donate page, we created an image menu with labelled symbols representing Animal Cruelty, Climate Change, Global Poverty, Human Rights, and Health. Clicking on a symbol will automatically bring the user to that section of the webpage.
For each of the categories in the image menu, we provide a list of related organizations that accept donations along with a quick description of what the organization specializes in. The user can also visit any of the listed organizations to make a donation by clicking the "Donate Now" button.
This page is also color coded with each color representing a different cause category.
4) Contact Us
The contact us page gives the user the option to send a message to the GoodLife team. They simply need to input their name, email, and the message they want to send.
To send a message, hit "send" and you will receive an automatic alert from the browser letting you know if the message was submitted successfully. Hitting "close" will bring you back to the homepage.
5) Register
Finally, our website offers a registration service for users who want to register an account with GoodLife. To create their accounts, users will need to provide a name, email, and password.
To submit your details and register, hit "submit" and you will receive an automatic alert from the browser letting you know if you were registered successfully. Hitting "close" will bring you back to the homepage.
In the future, we hope to further develop our project by giving registered accounts additional perks on our website! Registered accounts in the future might also receive emails about donation or volunteer opportunities for their favorite organizations.
How I built it
For the majority of the hackathon, we used repl.it and VSCode to collaborate on different webpages using HTML, CSS, and JavaScript. After compiling different parts of the website together on VSCode, we made a few more changes in Notepad++ before finalizing everything.
The logo was created in Scratch through vector mode and all other images were created or edited in the pixlr editor.
Challenges I ran into
We overcame a lot of different technical and non technical challenges in this hackathon.
Initially, one of the first challenges we faced was coordinating meetings so we could discuss ideas and assign tasks. The majority of our group was located in India (IST) while only one member was located in the Western United States (PST). Because of this, setting up meetings was difficult because of the very different time zones.
We were able to overcome this problem by meeting early in the morning or late at night during times that worked for everyone. It was really fun to work with the team and we learned a lot about one another through are shared passions for computer science!
A second challenge we faced was combining individual webpages together and maintaining the same styling effects. Some of our webpages were created in different projects and IDEs, so they had their own separate CSS page with different style definitions for key elements like headers, paragraph styles, etc.
We overcame this issue by creating classes and eventually combining all CSS pages into a single CSS file.
Accomplishments that I'm proud of
We are proud of the front-end and UI/UX design! We feel like our color palette is very consistent and helps tie the whole website together! Overall, the website is visually appealing, works as designed, and it's easy to use and understand for both new and returning users.
We are really happy with the way our website turns out and we are also proud to have created a project that serves a purpose and solves an important problem. GoodLife is something that we have already started using ourselves to help each other find trustworthy organizations.
What I learned
We learned a lot about managing our time and how to work effectively in a group even in very different time zones.
Programming wise, we learned a lot from each other! We all had different levels of skill and experience and every one learned a few new commands and tricks for making good, smooth websites. In particular, we learned about CSS shorthand for various styling elements like margins or borders.
What's next for GoodLife
We want to keep updating the About page to make sure information is up-to-date on the latest movements!
And, as stated before, we hope to further develop our project by giving registered accounts additional perks on our website! Registered accounts in the future might also receive emails about donation or volunteer opportunities for their favorite organizations.
------- GoodLife, changing the world a dollar at a time. -------
Built With
css
django
github
html
javascript
notepad++
pixlr
repl
vscode
Try it out
github.com | GoodLife | Your one-stop center for finding movements that matter to you with a compilation of reliable organizations that you can trust and support with your donations! | ['Catherine Rasgaitis', 'Vishrut Grover', 'Samyak kapoor', 'krishna aggarwal'] | [] | ['css', 'django', 'github', 'html', 'javascript', 'notepad++', 'pixlr', 'repl', 'vscode'] | 35 |
10,529 | https://devpost.com/software/yummy-food-during-covid-19 | Pandemic- Hackathon Prompt
Note- PFA PDF document for a business plan and PowerPoint presentation
COVID 19 is not our first health crisis, nor our last. How do we help those at high risk? How do we create vibrant supply chains, fast vaccine testing, better PPE designs, better signage, and solutions for restaurants, stores, etc? How do we help small businesses and performers stay afloat during Shelter-in-Place?
How Yummy Food product fulfills the track for pandemic COVID-19
This company is named Yummy Food, and it is an online food service company aimed to help small local restaurants and grocery stores sell their foods and grocery online by saving their operating cost. Same time the community takes advantage of multicultural food at a reasonable price. This product aims to support the local small supermarket and a small restaurant.
How Yummy Food is helping COVID-19
Yummy Food is designed to help counter these current food issues by offering
online purchases from different sources and businesses.
We will offer meals, food items, and groceries to restaurants, organizers,
food banks, consumers, and other groups in need of food items.
In addition, Yummy Food will seek and work with
farmers and local food producers/distributors to set up contacts to purchase and sell
their food products on our website. We would take care of delivery and such
matters online.
To limit food waste as distributors of food items, we would work
with restaurants, food banks, organizers, and other groups to distribute certain food
items to people in need.
Our Product Name- Yummy Food
Yummy Food Mission Statement-
Providing meals and groceries for those in need. Goals Philosophy to help those in need with our services.
Target Market
Restaurant owners/managers -
people w/ purchasing power for restaurants. General grocers for restaurants. food service organizers - Food Bank organizers general consumers - Individuals, Families, church. government agencies: Social Service heads/employees. Competition FoodService/Bulk Food Warehouse stores - ex: Smart, Costco, Restaurant Depot, Whole Foods Convenient location and services for businesses/consumers, bulk items sales can cut into initial sales and have current customer markets in the local area
General Food Markets - some have contracts with local restaurants. Can provide wholesale prices for both restaurants and consumers.
Products or Services
Yummy Food will be selling two main types of products: meals and groceries. Prepared meals will be offered on the website at different levels of pricing. Purchasing meals online for consumers will range from $10 a simple meal up to $50 for family meals and bulk items. These meals will come in a variety of different cuisines, styles, and food items. These meals would be sourced from different restaurants and food businesses in order to reduce cost and complex processing. Once Yummy Food is up and running at normal operating levels, special discounts and offers can be applied to entice new and returning customers. We hope these meal products will appeal to consumers looking for new dishes and fulfilling meals to help counter the rut during this pandemic; that we, Yummy Food, would be providing a service to entice new and returning customers.
Products for Groceries
As for groceries, these items will be more appealing to restaurants and other foodservice organizations/businesses. These items will be delivered in specialized boxes similar to warehouse storage boxes. Boxes of different fruits, vegetables, grains, spices, and other specialized food items will be priced at different levels depending on their conduct. Pricing will range from $20 for low weight items to up to $100 for larger packages.
All of these items purchased online will be delivered through contactor drivers such as UberEats/Doordash that will deliver meals to consumers and businesses.
Food Donation Plan
To help eliminate food waste and wasted inventory, we can offer donation services for specific food items in danger of being left to waste. For people with little to no income, certain dates will be marked on the store’s calendar offering several food items products at no cost for certain groups of people such as the homeless and those with special needs such as physical or mental ailments. Such events will be advertised on the website and in local flyers and advertisements to help those in need of such services. Items would be marked and be placed outside the warehouse to be picked up or delivered to certain locations for efficient use of such products. By offering such services, Yummy Foods makes ourselves distinct in the current market competition.
Team Members Sally Jain, Shreyansh Jain, Ashok Giri, Jessalyn Wang
How I built it
HTML5, CSS, Java Script
Challenges I ran into
We have a short time to do devlopment..and different time zone with team
What I learned
We are saving local businesses to introduce our Yummy Food Product. We learned due to pandemic how local business is struggling to stand out themselves.
What's next for Yummy Food
We wanted to do develop a marketing plan and wanted to develop an augmented reality feature.
Built With
html5
javascript
Try it out
yummyfood2.sjj3.repl.co
github.com
pdf.ac
pdf.ac | Yummy Food | We are serving multicultural yummy food to our community and same time wanted to help local businesses. | ['Sally Jain', 'Jessalyn Wang', 'Ashok Giri', 'Shreyansh Jain'] | [] | ['html5', 'javascript'] | 36 |
10,529 | https://devpost.com/software/the-change-we-need | Inspiration
Louis Armstrongs quote from 1967 before he performed What a Wonderful world inspired me to create my project.
What it does
How I built it
Challenges I ran into
Accomplishments that I'm proud of
What I learned
What's next for The change we need
Built With
mp4 | The change we need | Our world could be terrible, or it could be great you choose. | ['2024-Phoenix Hardison'] | [] | ['mp4'] | 37 |
10,529 | https://devpost.com/software/project-eject-exterminating-jarring-earth-completely-today | Inspiration
1 billion people are affected due to desertification. 25 million people have escaped their land due to desertification. Desertification is a huge problem in sub-saharan Africa. It costs over 42 million dollars for the government to fix, so the only way to save the earth is to rely on grassroots efforts from people to aid desertification.
What it does
Our web app helps promote grassroots efforts in our community by giving step by step plans to save their land, and allows for people to donate money to others in need. It also gives a climate map where garden growing is most necessary, and it has a blogs page where people can answer any questions or share tips about their project. Lastly, our project gives personalized advice to anyone who needs it.
How I built it
We used machine learning, flask, html, css, and js.
Challenges I ran into
It was extremely tough to finish the project especially when we started 3 days late! Thankfully we were able to finish but it took lots of effort and hardwork to get this project done ASAP especially during school days.
Accomplishments that I'm proud of
I am proud of making a machine learning model that helps people, and making a webapp that has an awesome ui thanks to teammate fara yan!
What I learned
I learned more about css and html and about different machine learning models like SVM and KNN.
What's next for PROJECT EJECT (EXTERMINATING JARRING EARTH COMPLETELY TODAY)
We want to use open CV to allow the user to input a picture of their file and our neural network will automatically find the issues by looking analyzing the image. We also want to make our instructions more detailed and clean.
Built With
css3
flask
html5
javascript
Try it out
github.com
github.com | PROJECT EJECT (EXTERMINATING JARRING EARTH COMPLETELY TODAY) | Promoting eradication of deserted areas in all of Sub-Saharan Africa | ['Neeral Bhalgat', 'Fara Yan'] | [] | ['css3', 'flask', 'html5', 'javascript'] | 38 |
10,529 | https://devpost.com/software/medicom | Doctor's Panel
Fake News Detector
Video Appointments
Anonymous Mental Health Consultation
Doctor's Login
Inspiration
Due the rise of corona virus the healthcare sector seems to face a scarcity in the reach of doctors. This inspired us to create Medicom so that patients could get in contact with medical aid remotely and easily. Also, it prevents people getting from influenced from any kind of fake news on the internet, harming themselves more than the diseases.
What it does
Medicom has the following features :
Patient-Doctor Community
One-to-One video appointments
Fake News Detector and Original News Source Gatherer
Anonymous Mental Health Consultation
How we built it
We build it upon android systems so as to expand it's reach. We used Firebase as the backend of this project due to simplicity and flexibility of the API. We used the power of neural networks for fake news detection and Vidyo.io for Video conference.
What challenges we faced
It took a lot of careful study and time to implement the video conference api as well as to tweak the fake news detector model.
What's next for Medicom
Some of the future features of Medicom would be as follows :
Expand the app to other systems like web.
Add an online medicine purchase and prescription store.
Fix in person appointments for doctors and patients.
Medical Emergency signal.
Built With
android
firebase
java
tensorflow
Try it out
github.com | Medicom | An app to maximize the reach of doctors. | ['Chinmoy Chakraborty', 'Vishwaas Saxena'] | [] | ['android', 'firebase', 'java', 'tensorflow'] | 39 |
10,529 | https://devpost.com/software/we-are-just-a-verse | I wrote a poem about how we all are just a verse.
We are just a verse
From the smallest particle of dust,
To the biggest Sunburst,
This world is made of both big and little,
But all of us are equal.
This space, this Universe,
From its eternal poem, we are just a verse,
Besides this world we are small
To this sky, we practically don't exist at all.
But this world still gives us a look
Into some pages of its mile-long book.
And possibly, the smallest thing
Might have brought this cosmos into being.
Human life is just a fraction of time
Of this world's lifetime,
But just as we are made from the cosmos,
Existence is also made by us.
But what is time?
Is life just a search for a dime?
Or is it just existing and changing,
In a world that's also ever-evolving?
Let's face this fact,
We all want to be perfect,
In a way, we're perfect and not
If we're giving it a second thought.
Life to appear is a rare sight,
For us to experience a day and a night,
There is a need for perfect balance,
So to create a harmonical Borealis.
So many wrong things could have happened,
That would have brought life to an end,
But still, in this Universe of ours,
We too have survived among these stars.
All the galaxies, planets, moons,
They all have a set of rules,
Rules they must obey,
If in this world they want a role to play.
But our mind doesn't think what the Universe wants,
Instead, it has it's own approach, it's own response,
We all have a Universe in our mind,
Sometimes it's kind, but it doesn't happen all the time.
When people act a bit different than normal,
We sometimes say they're like an animal
But is that quote true?
Or do we need to change it into something new?
Animals attack, hunt,
Sometimes they cooperate, sometimes they confront,
But for them, it's to survive, to live one more day.
To survive, the prey needs the hunter, and the hunter needs the prey.
Without the hunter, there will be too many prey animals,
Which will eat their insects, their plants,
Until there are no more left,
And slowly, all of them will face an end.
But humans don't need to fight,
To live one more day and one more night,
So why do some people always want more when they need,
Letting gratefulness fade behind greed?
As I like to say,
It's because people were made to be loved,
And things were made to be used.
But instead, things are loved,
And people are used.
Built With
music
poetry | We are just a verse | The universe is great and we are a small part of it. We are just a verse. | ['Emma Morea'] | [] | ['music', 'poetry'] | 40 |
10,529 | https://devpost.com/software/back-to-school-jzd47f | Inspiration
the most recent National Center for Education Statistics data reports that out of approximately 19.9 million total college and university students in America, almost 6.9 million of them, close to 35%, are enrolled in some form of distance education for 2019. Undergraduate participation in distance education has grown to 34.5% as of 2018. The rapid increase in online learning is confirmed when one considers the fact that between 2003-2004 only 16% of undergraduates had any participation in distance education. Graduate school has the highest rate of participation in online studies. For the 2018 academic year, a full 39.8% of graduate students were enrolled in some form of distance learning. 73% of students in 2018 studying through private, for-profit institutions enrolled in distance education, 34.1% of students at public institutions the same year took online courses, and 30.4% of students at private non-profit schools enrolled in distance education.
On top of this, I being high schoolers who have the high school burden online, we have sensed the struggle not only for ourselves but for those of other peers who often come to us for help. As a result, this inspired us to create this project.
What it does
For the react native app, it connects with the Website Neeral made in order to create a comprehensive solution to this problem.
There are five main sections in the react native app:
The first section is an area where tutors can make posts for any students to require help to respond to. As a tutor I fill in several fields such as the subjects I wish to tutor, my phone number, email address, name and description and I am able to post a post. Additionally, if I choose to tutor as a volunteer I can enter Free in the costs column as well.
The second section is a help section where students having problems with certain questions can enter them or information they wish to retrieve using Wolfram Simple API. I am able to go to that page and for example enter “Who was George Washington” and a detailed answer and facts in relation to him comes up.
The third section is the courses tabs which users can use in order to find courses to either learn more or seek help via a course in order to learn more or develop that skill further. Here there are a few find pre-selected courses such as Computer Science, Data Science, Calculus 1 for options but instead I will use the custom query input where I can input a course that I might want to find and a list of courses come out of the output using ClassPert Course Search API
The fourth section is a template web app that has been embedding into the react native application, allowing us to create custom community forum page for our app effortlessly. This was created using tribe.so, which created a subdomain link which we then integrated into our app.
The fifth section is a build your own resume section. Users will answer some questions and the program will write a professionally written resume. In the future, I would like to use sentimental analysis to make sure that the tone is as accurate as can be. Each resume comes with 5 sections such as awards, experience, interests, etc. We know writing the perfect resume is tough and must be better than ever due to how many jobs have been displaced due to the pandemic; hence we chose to make the resume builder.
In the future we would also want to make an online notetaker system that tracks what the teacher is saying and writes it down in google docs this way students can refer to what the teacher said. We would also like to use tensorflow to summarize this.
How I built it
For the react native part of the application, I will break it down section by section. For the first section, I simply used Firebase as a backend which allowed a simple, easy and fast way of retrieving and pushing data to the cloud storage. This allowed me to spend time on other features, and due to my ever growing experience with firebase, this did not take too much time. I simply added a form which pushed data to firebase and when you go to the home page it refreshes and see that the cloud was updated in real time
For the second section, I used native base in order to create my UI and used Wolfram Alpha simple api, which takes in the query and then outputs an image with the results. This way the information given is maximized. The UI is again created by me where the icons are from vector-icons this time around.
For the third section, I used classpert, course search api in order to create a query system to output results of courses that are found most appealing to the user. For the user interface, I created the designs myself and I use Material-Paper library to retrieve the icons which are being used throughout the app.
For the fourth section, I used tribe.so in order to create a template forum which I then integrated into the react native app.
For the fifth section, we made a resume builder page that asks the users for answers to certain questions, then prints out a nice formatted resume.
For the 6th section, we made a comments form that users were allowed to write feedback and comments on. They would then be analyzed by our sentimental analysis model, and the feedback would be given to the user along with how strong the words used were.
I used html and css and boostrap for the webpages. We then used flask to connect it all together and pass the inputs from the forms to the resumes. We used javascript for the comment section forms and the resume builder form, and along with this we used python to make our sentimental analysis model. We used an inbuilt dataset which contained numerous reviews.
Challenges I ran into
API query bugs was a big issue in formatting back the query and how to map the data back into the UI. It took some time and made us run until the end but we were still able to complete our project and goals.
What's next for EAS-E
In the future we would also want to make an online notetaker system that tracks what the teacher is saying and writes it down in google docs this way students can refer to what the teacher said. We would also like to use tensorflow to summarize this.
Built With
css
flask
html
python
react | EAS-E | allowing tutors to get jobs, and letting students get extra help | ['Neeral Bhalgat'] | [] | ['css', 'flask', 'html', 'python', 'react'] | 41 |
10,529 | https://devpost.com/software/biz-commerce | Banner of Biz Commerce
Inspiration
Truly the story is very inspiring for me . I am a student and don't have knowledge to this type of stuffs . There is a uncle of mine who told me a few days ago that he is opening a small textile factory so, many un-employed people can get a job in this pandemic . I have a little knowledge in html and CSS he told me to create a website and told me to market this business and more than 150 people got job in 1week . I thought to make a page for this business . So, there is no technical person who will support everyone all day long so, I thought to build a chatbot and got help from all Facebook communities . And finally made a simple one and submitting it .
What it does
It is a Facebook page bot . It takes orders from it's users . Shows which products are available with price . Responds to private replies and shows contact details information about the business .
How I built it
I build it with the help of Manychat a very popular platform . And also used my Facebook page .
Challenges I ran into
It was a lot challenging than ever . I never worked in this platform using this tool . The designs were too much hard and setting up the products was too hard for me as a absolute beginner .
Accomplishments that I'm proud of
I am really proud that I can support small businesses in this pandemic situation and this will help the workers of the factory to get more orders to earn a bit more .
What I learned
The full work actually learning . I learnt how to work with small businesses and I also learnt how teamwork works actually . I learnt planning and a bit of marketing .
What's next for Biz-Commerce
Right now it is just a demo . If it works better for the business I will surely make it more user friendly and design it better that no one could imagine that a chat bot can manage a whole business .
Built With
facebook
facebook-messenger
facebookpage
manychat
messenger
Try it out
m.me
www.jarstextile.com | Biz-Commerce | Really This is the time to move from E-commerce to Biz-commerce. Stay at home, all fashion related content and outfits you need are now at your fingertips . | ['JARS Textile'] | [] | ['facebook', 'facebook-messenger', 'facebookpage', 'manychat', 'messenger'] | 42 |
10,529 | https://devpost.com/software/smart-bot-75mfgo | Smartbot
Inspiration
Assist people conduct self checks and carryout buying and selling of goods and services easily.
What it does
It recognizes key words and give response
How I built it
Built with IBM watson assistance
Challenges I ran into
Implementing in a website or a social media app
Accomplishments that I'm proud of
Finally able to implement.
What I learned
How to work effectively with IBM watson
What's next for Smart bot
Making it accessible through USSD codes offline.
Built With
ibm
ibm-watson
Try it out
web-chat.global.assistant.watson.cloud.ibm.com | Smart bot | A chatbot system that assit individual conduct checks with just some key words, help them contact help centers and also assit them in marketing their goods and sevices | ['Ugochukwu Nnachor'] | [] | ['ibm', 'ibm-watson'] | 43 |
10,529 | https://devpost.com/software/atlas-is-real | Atlas is real
This is the digital artwork that represents what humankind forgets more and more in the 21st century - the fact that all life on our planet depends on the Ocean. From science, we may know that oceans play a key role in regulating Earth's climate, become a place to live for thousands of marine species and provide people with various vital resources. Unfortunately, nowadays the problem of water pollution is the main threat to the Ocean. Atlas is a Greek titan that holds the sky, according to ancient myths. Greeks used to believe that is the reason why the world still exists, and we all would be gone if he didn't do this. The same is happening in the real world - without all things the Ocean gives us, our planet would be already destroyed. The woman represents both the Ocean and Mother Nature, struggling to hold the planet. The planet is already melting, and ocean fish is not looking healthy on the picture - the more people harm the environment, the more difficult it is for her to keep the balance.
In the future, it is possible to use the artwork as the central plot of various posters, banners on environmental issues.
Built With
sketchbook | Atlas is real | Digital artwork on the Environment category | ['Malika Buribayeva'] | [] | ['sketchbook'] | 44 |
10,529 | https://devpost.com/software/msdd-mask-and-social-distancing-detector-with-alarm | What it does
Social Distancing and wearing masks have become the 2 most popular terms in today's life to keep oneself safe from getting infected from the novel coronavirus. Researchers have concluded that these 2 practices are the warriors against COVID. Governments across the nation are rigorously making efforts to make these 2 practices in action. We as a team came up to a real-time solution of detecting distance between humans and detecting persons with no mask covering. This solution was needed as there is a lack of awareness among people about the major precautions which is Mask+Distance. Neither only mask can save, nor only distancing can save you but the aggregation of these two can.
In our project, we have proposed 2 major solutions (i) Mask Covering (ii) Ensuring safe (physical/social) distance between people which is real-time.
Major Functionalities:
(I)Face Covering: The camera detects people with masks and without mask which is differentiated by green and red boxes respectively. On detecting a person without a mask or face covering the system beeps an alert sound.
(II)Social Distancing: Similarly, the system calculates distance among multiple people and highlights boxes of red and green color indicating that the red box is not maintaining safe distance whereas green is.
(III) Alarm: On disobeying any of the precautions an alarm alert is triggered i.e
(i) If a person has not maintained distance and has no face covering
(ii) The person has maintained distance but has no face covering
(iii) The person has a face covered but has not maintained distancing.
Challenges I ran into
The project itself was a great challenge for us as making a whole system which detects face covering and physical distancing is not easy. This challenge was created with the vision of developing a real-time physical distance detection system with face-covering available for public use, to help public health officials all over the world to ensure 2 major precautions.
There were 4 major challenging challenges faced.
i. Collecting Data - No data set of people with masks was available, therefore we scraped the data from the web (Web Scraping) of persons with masks and obtained few images.
ii. Training Data - As web scraped the data only a few images were obtained so for training we have to use a pre-trained model i.e MobilenetV2
iii. Integration of 2 different modules - As mask detection only focuses on face covering and social distancing focuses on 2 distance between persons. On integration we have to work on 2 things simultaneously (1) Mask covering (2) Calculate distance between 2 persons run time.
iv. Calculation of Euclidean distance - This was a new thing for us which took more time comparatively.
What's next for the MSDD [Mask and Social Distancing Detector (With Alarm) ]
This system is useful as it can be implemented on-road cameras/malls/etc. using IoT functionalities and check which areas are disobeying the rules and steps can be taken accordingly and people who have unknowingly broken the rule can be cautioned by an alarm/alert at that time itself.
Built With
cnn
computer-vision
deep-learning
mobilenet
opencv
Try it out
github.com | MSDD [Mask and Social Distancing Detector (With Alarm) ] | Masks on their own will not protect you from COVID'19, practising physical distancing with mask can! Our project ensures these 2 practices with an alarm sound. | ['Vishal Shah', 'Neha Sajnani'] | [] | ['cnn', 'computer-vision', 'deep-learning', 'mobilenet', 'opencv'] | 45 |
10,529 | https://devpost.com/software/shoplocal-sc | The Homepage
The Kitchen section
Sort by "baby"
Inspiration
When one calculates the impact of their life, they measure not in what they achieved for themselves, but what they achieved for others. When Covid-19 hit, I joined GetVirtual to help small business owners. Owners are deeply tied to their businesses -- these are passions but dreams they have put years into achieving. As I was working with my clients, I saw firsthand how little exposure their great products were receiving. From bacon-flavored olive oil to the Homeless Garden Project's baking mix, I knew demand existed for these unique, wholesome goods. This inspired ShopLocal Santa Cruz, a digital marketplace dedicated to the wellbeing and promotion of local small businesses. By aggregating products from the local economy, we aim to reduce search costs and drive revenue to our small business partners.
What it does
The ShopLocal website presents online buyers with a wide variety of uniquely Santa Cruzian products. The buyer places an order accessible through the Shopify API. Our "OrderBot" program then places that order through our small business partners' website using a Selenium browser emulator running in Python. The small business associated with that item receives the order and delivers the item to the buyer. The system requires integration with the small businesses it buys from.
How I built it
The Shopify store is composed of a curated selection of products that embody the unique character of the small business they originated from.
The Python consists of an API call to get new orders and an emulator bot that automatically relays the client’s orders to the original small business' website.
Challenges I ran into
Curating and transferring hundreds of products from the small business' online store to the ShopLocal SC website was time-consuming.
On the backend side, many small business' websites were difficult to order from using a generalized bot due to differences in their cart and checkout interfaces.
Accomplishments that I'm proud of
ShopLocal offers 300+ products from a variety of local small businesses. The order bot is functional for a wide range of products and we are ready to start serving local businesses.
What I learned
We learned that buying online from small businesses is currently a struggle. Many of them have unappealing websites that do not convey the value they provide. When we were searching for products to feature, we were wading through online stores that were poorly designed, difficult to navigate, or lacked key functionality.
We also learned Python has a surprisingly robust system for emulating Chrome browsing activity. This allows a sophisticated enough web driver to proceed through the online checkout process like a real person.
What's next for ShopLocal SC
We will be promoting this project through local news sources as well as the Santa Cruz Works newsletter to get people signed up. Our goal is to have 500 orders placed through the site by the end of the year.
Built With
python
shopify
Try it out
shoplocalsantacruz.com | ShopLocal | An online marketplace presenting the best of local Santa Cruz businesses. | ['Arin Spanner', 'Robert Missirian'] | [] | ['python', 'shopify'] | 46 |
10,529 | https://devpost.com/software/coachally-interactive-virtual-classroom-video-calling-app | Assist feature
CoachAlly Home page
User's can easily seek guidance , report bugs
Seek guidance with in-app screenshot&doodle feature instantly
Video Call
AR Classroom
Broadcast Mode
Inspiration
During these pandemic days, our team too are facing issues while learning through online portals. So our team took a
step forward in resolving the common issues and further improvising it.
What it does
CoachAlly application helps in creating interactive virtual classrooms using the latest technologies like
Augmented Reality
and creates room for the virtual classroom through
high-quality video calling
with a low-latency experience.
Augmented reality in education is surging in popularity in schools worldwide. Through AR, educators are able to improve learning outcomes through increased engagement and interactivity.AR features aspects that enhance the learning of abilities like problem-solving, collaboration, and creation to better prepare students for the future. Teachers can include custom AR objects and pre-recorded lecture videos which help students view course materials at the ease of their home.
Live sessions can be held virtually through the class meet option. We have designed a one-step join meeting keeping in mind of young students. App seeks only the meet code and doesn't collect other credentials thus improvising the privacy of end-user.
We have also integrated an
ASSIST
feature which guides the users step-by-step if they either need a walkthrough on a feature or if they encounter a bug. Our main advantage of this feature allows users can make use of an in-app screenshot feature with a doodle option on board with ease to contact the admin/developer hassle-free.
How I built it
Came across the
Flutter
technology recently and since then was caught up with it. We are
amateurs
and this is our first big step upfront on solving the problem with it.
We have approached our problem with Flutter which makes the app run natively on all platforms. The UI is made with help of google's material UI. The video call runs seamlessly with the help of agora as backend. The feedbacks, assist is done with the help of wiredash which provides instant messages which the end-users provide.
Would thank our sponsor echo-AR which helped us integrate AR seamlessly with our app.
CoachAlly is a light-weight app which is available across various platforms
-
Mobile platforms- IOS, Android
Desktop app-MacOs, Windows, Linux
Web app- Across all browsers
Challenges I ran into
We came across many challenges as this our first big approach using Flutter. We thank the mentors who took the time to help us. Students get insights on concepts& better understanding with AR & am proud to be a part to contribute to the global community.
Accomplishments that I'm proud of
We are very proud of the big leap which we dared to attempt has come out a bug-free working app in a short span of hours.Have learned many skills way from starting of the Hack. We learned to face the challenge by short days to give the best outcome of our app.
What's next for CoachAlly -Interactive Virtual Classroom & Video Calling app
We aim to increase security and add feature-rich contents and make our app more accessible to all age groups.We plan to improvise our app consistently for best end user satisfaction.
Built With
agora
ar
cupertino-ios
dart
echoar
flutter
materialui
Try it out
github.com | CoachAlly -Interactive AR Virtual Classroom & Video Call app | CoachAlly application helps in creating interactive virtual classrooms using the latest technologies like Augmented Reality and creates room for the virtual classroom through high-quality video calls. | ['Sudir Krishnaa RS'] | [] | ['agora', 'ar', 'cupertino-ios', 'dart', 'echoar', 'flutter', 'materialui'] | 47 |
10,529 | https://devpost.com/software/lexconnect-wio92b | Title
Main Screen
Send Text data
Recieve Text data
Inspiration
Let's start with a story
When a stranger asked Lexa if her if she could send a file she did so and that was the beginning of a nightmare. Vulgar calls from multiple numbers at ungodly hours... The constant
dread
when the phone rings.... Like Lexa thousands are
traumatized by cyber bullying
. If only Lexa had shared the file without leaving her digital footprint....
Enter LexConnect
LexConnect is an open-source tool developed by Hrishikesh P with the vision of
NO STRINGS ATTACHED
data transfer. LexConnect transmits data using audio which makes it easy and safe to share it with strangers as it
leaves no digital footprint
if used wisely.
No more fear
of the stranger getting your phone no: or email id or even taking the risk of allowing them into your LAN. As of now,
text
and
music
can be transmitted.
LexConnect is also a
data-broadcasting
app which allows you to easily send data to a room full of strangers.
Motto
The motto of LexConnect is to help
reduce the risk of cyber-bullying
and
increase women safety
.
LexConnect also aims to make
data-broadcasting
easier.
Creativity Factor
LexConnect is the
ONLY
app of it's kind. As it uses sound as a medium it is compatible with all electronic devices having a microphone and speaker. This also makes plausible digital footprints like phone no:, email id, LAN etc. unnecessary. Thus, transmissions can be 100% anonymous with no way of tracing the source back to the sender.
What's next for LexConnect
Being open-source I hope LexConnect will be able to transfer more kinds of data. I myself shall work on
predictive audio-based image compression
in the coming days. The goal is to make LexConnect the Xender of the audio world.
Tech Stack used
Tech stack comprises of React, JS, & plain old HTML.
How it works !
Data is first encoded using a Rot cipher and then converted to respective audio signals which is then compressed using Fast Fourier transforms. These signals are then transmitted via the sender's speaker. The signals are captured via the receiver's microphone and are decompressed and the encoded text is obtained which is then decoded using an inverse Rot cipher.
Challenges I ran into
Where should I start ;) This is my first React project. I spent ours trying to figure out the enmity between github pages and React routes :(
I had a lot of trouble figuring out the
optimum data-transfer method
that could be implemented in a day! I spent a lot of time figuring out a way to make the audio unique but finally realized that a better algorithm would be to make the data encoded and the audio ordinary. This makes the app accessible irrespective of the speaker and microphone specs at the same time making the data secure.
Accomplishments I'm proud of
Building something that helps in making the world a
safer place for women
and making an effort to reduce cyber-bullying these are the 2 rewards I have gained during this hackathon. Nothing compares to helping others and
that is a reward in itself
.
What I learned
I have
tasted the joy of social service
. This was my first React project and I learned the beauty of the Virtual DOM. The renders are so satisfying !!!
Built With
javascript
react
Try it out
github.com | LexConnect | Prevent cyber-harassment of women using anonymous data transfer | ['Hrishikesh P'] | ['Best Anti-Cyberbullying Project'] | ['javascript', 'react'] | 48 |
10,529 | https://devpost.com/software/agriculture-supply-chain-monitor-using-satellite-data | Inspiration
Today a farmer cannot have a digital overlook of his or her production.
Right now it is difficult to know the demand of their crops.
They do not have an exact overlook of the needs of the land and therefore cannot maximize the crop production.
What it does
Farmer be informed in real time of the need of his product within the local community.
Effective way to merge the supply and demand of the needs of any local community.
Since many farmers might not be used to digital tools and therefor not digitally literate.we developed a tools to be user friendly so that it ensures the farmers can benefit and use the solution over a long period of time.
The digital product is user friendly that involves no rejection by the end user
How I built it
We are providing centralized system of supply and demand where the farmer and grocery stores can enrol and fill hp the stock of product as needed by the demand from the community
satellite based data driven application
complete control of the soil for crops growth,
informed suggestions of the soil fertility and usability
Challenges I ran into
Managing huge data to store and process
Accomplishments that I'm proud of
Successful product which contribute in make society better
What I learned
Satellite data
Microsoft Azure
What's next for Agriculture & Supply Chain Monitor using Satellite data
get the customer
Built With
android
python
satellite
Try it out
github.com | Agriculture & Supply Chain Monitor using Satellite data | A Reliable tool for Farmers and local communities.Designing Distribution within Sustainable Food Systems | ['Mahabir Gupta'] | [] | ['android', 'python', 'satellite'] | 49 |
10,529 | https://devpost.com/software/smart-masks | Smart Mask Design
Our Problem Statement is that: With the ever-growing number of Covid-19 cases from the past 8 months, and several lockdowns issued by governments internationally, the human race has to define the New Normal in the Post-Covid era and start to re-open offices and schools.
However, the underlying issue is that people don't feel safe yet to let their loved ones out of their homes. And this is completely justified, because of the weakly regulated ‘prevention techniques’, of wearing a mask at all times, and to maintain 6 feet distance from others in public places.
Therefore, we decided to go on a journey to use technology to create the New Normal, in order for schools and offices to re-open as soon as possible. And we are doing this by releasing the concept of the ‘SMART MASK’. We plan to be B2B and B2C providers, but more about that later.
With our product, we are targeting two industries. The primary target industry is the Biotechnology Industry, in the BioPharma Market (with a Compound Annual Growth of 7.2% through 2020 to 2026), and the secondary target industry is Pharmaceutical industry, in the surgical mask market (with a CAGR of 8.38% through 2020 - 2024). We came to this conclusion as we plan to have a brand image of a technology company before having that of just any other mask company in the consumer’s eyes, and this is what makes us different.
The Smart Mask is a mask with IoT breathing sensors, which notifies you or your family if you are not wearing a mask in public areas. With the help of a team of Biotechnologist and IoT specialists, we can design these sensors, to record the location of your mask and your mobile application. Also the IoT sensors will record the breath of the person wearing the mask. So, if no one is breathing into the sensor, even if it is taken along in the public place, the person will be notified to wear it as well.
The mask asks for calendar and location access, and with that the user can be reminded to keep the mask nearby the night before a calendar event outside the house, and notify if the mask is forgotten at home while out of the house.The Smart Mask will come in a range of different designs. We plan to have designs with a variety of colours and also those that are cultural in nature.
In the B2C Model, Users have control over making and joining groups, with their families and loved ones, and can choose to share location and their mask’s location with them. So a parent can know if their child is wearing his/her mask in school or forgot it on the school bus.
In the B2B Model, While the businesses can’t track their employees after job, however they will be notified if two or more users have not worn masks or maintained social distancing for prolonged periods.
With the Smart Masks, reaching the ‘New Normal’ will come to a reality more than ever. This would mean re-opening shopping malls, schools, offices, public parks, if Smart Masks are used extensively.
In order to be aware of our strengths and weaknesses, I would like to share our SWOT Analysis. Our Strengths are that we have a strong vision for our product, we are learning developers, enrolled in the best universities in Australia and India for AI. Also being in the Gen-Z we both know the right ways to market this product to the youth and how to effectively use marketing growth channels to scale.
Also, we have many opportunities that will benefit our business, like having the right timing to launch the Smart Masks (as we are in the midst of a pandemic). We have no direct competition yet, as there are no such products available on the market, but also have a large customer base which is growing in hundreds of thousands on a daily basis (as Covid 19 unfortunately infects communities at a time). To add, we could also add machine learning and artificial intelligence features in the near future. All in all, the growth potential for the industry and scalability of our product both are promising.
However, like every other business, we do have weaknesses, as we have low financial funding, and do not have an experienced team of IoT and BioTech specialists. Further on, our biggest threat is the possibility of large monopoly conglomerates who would also release similar products. But still, the market is big enough for a startup like us to survive the competition until we scale significantly, and being the first comer to the industry will surely reward us with the loyal customers.
We are targeting to first sell the product in Indonesia, due to its rising cases of Covid 19, and so are benchmarking the data with the Smartwatch market in Indonesia. 2.5% of Indonesians have Smartwatches, out of which 23% of them wear it for fitness purposes (which means that 1.495 million people are ready to spend about a $100 on a tech-lifestyle device in Indonesia).
If the Smart Mask manages to attract even a mere 2% of those 1.495 million people to buy our Masks, and 10% of them to have it on a subscription model, then about 180,000 Indonesians would use our product.
To conclude, if our product gets a steady increase in the influx of customers for a year after the first MVP (of a working product), as shown in the table, then we can hit 3.8 million dollars in revenue, with a 2.16 million dollar cost… leaving us with a profit in the first year of 1.71 million dollars. And the only investment we need is the right resources, to gather the right team.
Built With
figma
html
Try it out
www.figma.com
docs.google.com | Smart Masks | As an attempt to minimise effects of Covid, we present 'Smart Masks' which connect with your Mobile Phone and notifies on correct your way of wearing the mask, and when you forget your mask at home. | ['Rahul Mawa'] | [] | ['figma', 'html'] | 50 |
10,529 | https://devpost.com/software/don8-donation-made-great | DON8 Banner
Roadmap
Screenshot of home page
Inspiration
Getting a coffee first thing in the morning is kind of essential for us, and most of you people too, i suppose. We (Aşkın and Görkem) would meet up everyday morning and get our coffees before anything, we used to drink at this Local coffee joint, the employees knew about us and we were kind of friends, so was the homeless person living around the coffee joint, Mehmet. We always gave him the change from our drinks. He told us he was getting his breakfast with the little amount of Money we gave him and we were just happy for him.
One day it popped in our mind: “It’d be cool if there was an app that donated a part of our Money when we buy food via that app, I’d use it.” And now here we are!
What problem DON8 solves?
The poverty rates around the World are continiously increasing each year. People are aware that there are people not as lucky as us in life. And people are aware that it is our responsibility to help those in need. But most people don’t tend to. We, Don8, are making it easy for people. Making donation just as simple as buying coffee.
Our Vision
People stepping out and accomplishing from “I am aware of that.”
What it does
Don8 is a non-profit, transparent-to-user app. Making people get discounts on selected restaurants. And upon every purchase, user gets a free entry to a giveaway. And a small part of the purchase gets donated to the charity. As you use the app for your purchases, your tier increases. You might even see your name on the leaderboard!
How we built it
We built the DON8 with Dart programming language. We made an cross-platform app with Flutter framework with widget-style front-end. In backend, we use also Firebase (Firestore, Cloud Storage and Authentication). Currently we created an android app but we're thinking about IOS in future.
Challenges we ran into
The Hackathon started at 2 AM (GMT +3) for us, and at 4:30AM (IST) for our UI/UX designer, we had troubles before our meetings because one of our members lived in another timezone. It was hard for us to work continuously after a whole day. We had nearly no sleep and consumed tons of coffee to stay up and focused. Taking care of 3 timezones was by far the biggest challenge we ran into.
Accomplishments that we're proud of
We met and worked with new team members with DON8, communicated and interacted on Discord chat and calls.
What We learned
We learned how can we organize our team to work together and interacted, managing the time, new programming technologies, and keeping under pressure.
What's next for DON8?
We want to make DON8 with not just an app. We are decided to make it real and worked! After this hackathon, we will:
switch the app from prototype to real completely.
create an another app for restaurants / cafes.
set meetings and interviews to be united about donation with cafes & restaurants.
Test DON8 now!
Credentials
E-mail:
demo@demo.com
Password: don8demo
Built With
dart
firebase
flutter
Try it out
github.com
1drv.ms | DON8 - Donation Made Great! | DON8 is a non-profit platform based on food & drink purchases. Drink or eat, get a discount, also DON8! | ['Aanya P', 'Askin Kadir Cekim', 'Görkem Eyler', 'Burak Osman Yaldız'] | [] | ['dart', 'firebase', 'flutter'] | 51 |
10,529 | https://devpost.com/software/beeyond | Logo
Inspiration
COVID has made it difficult for small businesses and self-employed people to thrive and expand their audiences, and our teammates have practically seen this, seeing local businesses shut down and self-employed people become troubled on how secure their source of income is. We decided to target such businesspeople and create a platform specific to them. Small businesses or self-employed people often struggle with effective advertising in the face of larger competitors with more money, time, and resources. Our platform provides
free marketing
by focusing on
connecting
local businesses and self-employed people to consumers all with a simple search, even in the midst of COVID.
What it does
With this in mind, we created Beeyond, a website that connects
small businesses
and
self-employed people
to consumers through search. Unlike other social medias and similar sites, we really focus on the “small”, providing free advertising for sellers who may not have the time or budget to effectively do it themselves in a competitive way. Beeyond also helps consumers more easily find and support local businesspeople. Beeyond provides free marketing to businesses and helps consumers find local people who can help with their needs, even in the midst of COVID.
How I built it
We used
React.js
for the UI,
Node.js
for the server,
IBM Countant database
and
IBM Watson Assistant
for the chat bot.
Challenges I ran into
We used technologies that some of us never used, so we had to navigate and learn them
Accomplishments that I'm proud of
We built a really nice looking site searching a really great and critical purpose all from scratch!
What I learned
We learned about frontend development
What's next for Beeyond
We plan on fixing bugs and getting the platform up to the public. Also we have the ability for businesses to contact other businesses through email but we would like to further this interaction by creating a separate page just for business owners where they can chat and find projects to do with other creators. For that we may want to design a login page where users and businesses can create accounts. We also wish to create a way to filter out businesses that are large that may have signed up, or businesses that became large and give less priority to them while maintaining credibility based on customer reviews.
Built With
css3
html5
ibm
ibm-cloudant-database
ibm-watson
node.js
react.js
semantic-ui
Try it out
github.com | Beeyond | You can't buy happiness. But you can buy local. | ['Abi Patchaiyappan', 'Jenny Zhao', 'Grace Kejie Zhou', 'Mythili Karra'] | ['Honorable Mention', 'Best Business Project', 'Best Local Community Project'] | ['css3', 'html5', 'ibm', 'ibm-cloudant-database', 'ibm-watson', 'node.js', 'react.js', 'semantic-ui'] | 52 |
10,529 | https://devpost.com/software/reducing-elders-loneliness-and-social-isolation-nationwide | Inspiration: I have felt lonely and been socially isolated during different times of my life and can relate both to the pain of loneliness and social isolation and the joy of connection through shared activities.
What it does: Reduces elders' loneliness and social isolation nationwide by creating a web page on every county's website with links to senior programs and activities.
How I built it: We have the approval to have a webpage on the Monterey County website from the Advisory Council to the Monterey County Area Agency on Aging. We are currently in the research and recruitment phase of the project.
Challenges I ran into: Time. I am a student and intern at CSUMB and am involved in community leadership roles. I also discovered there were more senior programs than I realized and many needed publicity through website development and social media.
Accomplishments that I'm proud of: Graduating from Cabrillo College with an AA in Health Science/Community Health. Going back to school for a BA at CSUMB in Collaborative Health and Human Services with concentrations in Public Administration/Nonprofit Management and (Macro)Social Work. Developing the idea for this social entrepreneurship.
What I learned: I learned that some senior programs are run by different sources. Senior Centers (city-owned or nonprofits with membership fees), CHISPA affordable housing for seniors, the Parks and Recreation Centers of different cities, a county Area Agency on Aging, a local nonprofit (Alliance on Aging) that serves seniors throughout a county, assisted living or senior living facilities, and other organizations that support senior programs. Many of these different sources welcomed help with creating or developing their websites to publicize their programs, services, and activities. Also, Monterey County, like many, most, or perhaps all, other counties in the U.S. did not have a central webpage that lists all the senior programs with links to their programs, services, and activities. If we had that central webpage in every county, locals would know where to go to do what they would like with other older adults and traveling seniors could do activities with local seniors, and making friends who could visit them also. This would be a wonderful way to build up social networks and reduce loneliness and social isolation.
What's next for Reducing Elders' Loneliness and Social Isolation Nationwide: Build a team and start a social entrepreneurship. Apply for grants, beginning with having a winning project for the new startup at Z hackathon
Built With
collaboration
community
service-learning
students
teamwork | Reducing Elders' Loneliness and Social Isolation Nationwide | Build a team for during and after COVID19 to reduce elders' loneliness and social isolation nationwide by creating a web page on every county's website with links to senior programs and activities. | ['Kathybelle Barlow'] | [] | ['collaboration', 'community', 'service-learning', 'students', 'teamwork'] | 53 |
10,529 | https://devpost.com/software/love-is-love-20cmsf | Love is Love Poster
Inspiration
Education is continuously becoming more diverse in its student population. In today's heated political environment, some populations are fighting to maintain their basic human rights. This issue is not exclusive to adults; it is an issue that spreads from adulthood to our youngest populations. Young students fight to have themselves understood as they try to understand themselves. They struggle to be identified the way they want to be identified. Oftentimes, students in these situations face discrimination and harassment among their peers, family, friends, and beyond. According to the Trevor Project, LGBTQ youth struggle with suicide at much higher rates than their heterosexual classmates, and they are more likely to be rejected from their families (
https://www.thetrevorproject.org/resources/preventing-suicide/facts-about-suicide/
). While there is a lot of work yet to do on these issues, our youth need and deserve to have themselves understood by their peers and the adults in their lives.
What it does
This poster is the first of many posters that could be made to promote LGBTQ rights and to create an inclusive environment to students in today's schools, beyond being inclusive to only their cultural backgrounds. As a school counselor in training, I would promote these poster and others in my office and lessons, as possible, to promote these issues. It can be made as part of a lesson plan for school counselors, educators, etc. as a visual supplement to their diversity, anti-discrimination, anti-bullying, etc. lesson plans. Posters such as these can be displayed during important LGBTQ history months and dates. Using digital imaging programs, such as Photoshop, it can be edited and built upon for personalizing purposes.
How I built it
This is a mixed media poster on 9 inch by 10 inch canvas board. The rainbow background was made by watercolor paints, tilted to create the watercolor run at the bottom. The Earth was drawn in pencil before being painted over in acrylic paint; the people, hearts, and lettering was done in acrylic paint, too. I chose to settle with non-distinguishable gendered people so the viewer of the poster can impose their own gender onto each individual. After letting it dry overnight, I used a Canon printer to scan the image onto my laptop, where I used Adobe to convert it from a PDF file to a JPG file.
Challenges I ran into
My biggest challenge was to decide what social justice issue I wanted to discuss. As a member of the LGBTQ community living in a very unstable political atmosphere, I chose to promote inclusion and equality for all genders and sexuality. As an artist, I wanted to do this in visual form. As I am still learning digital art, I preferred to do this in paint. I am in a masters program for school counseling, so I chose something I could put into use when I am in the schools.
Accomplishments that I'm proud of
I am proud of finding a way to bring art into a Hack-a-Thon. I am proud to display my amateur talent in painting, as I am much better in graphite, in a way that can advocate for a community I am apart of.
What I learned
I learned that I could incorporate this into future lesson plans, and that there is many ways to advocate for a community, such as in art.
What's next for Love is Love
This is the beginning of a series of posters and advocacy art to address issues in the LGBTQ community and promote awareness, anti-discrimination, and anti-bullying. It is targeted to today's youth but can be expanded to the general population.
Built With
acrylic
paint
watercolor | Love is Love | Promoting Inclusive Education, Embracing Diversity | ['Tiffany Arnett'] | [] | ['acrylic', 'paint', 'watercolor'] | 54 |
10,529 | https://devpost.com/software/chefpost | Our Logo
Add a recipe page
Recipes Page
What it does
Our app is a destination for food bloggers and chefs and their step towards global fame! They can expand their businesses out of
just
the restaurants. The timely leaderboards compete them with the rest of the chefs in the world and they can
create
their own recipes along with other collaborators in the app itself, leading to new innovations in the world of cooking.
How we built it
We built the app completely using Flutter and Firebase, with the editor being Android Studio. Firebase Dependencies used were, Firebase Firestore, Firebase Storage and Firebase Authentication. Libraries used were image_picker, flutter_launcher_icons, google_sign_in, font_awesome_flutter, flutter_spinkit, cached_network_image, shared_preferences.
Challenges we ran into
Submission was itself a challenge as this had been our biggest and most complex code/app we had ever managed. But the biggest challenge we faced was creating a repository in GitHub. It was our first time on GitHub and we were all immature kids in GitHub. Luckily, we got some help through the mini-event hosted by MLH LocalHost. We also faced some major bugs at the last hour which (nearly) crashed our app. But we managed somehow.
Accomplishments that we're proud of
Happy about having successfully completed and submitted our most complex project yet. And we are now finally a
bit
experienced in GitHub, but we need to go far still. Learnt pressure handling again, as we had to manage a last hour scare.
What we learned
As previously mentioned, got a
bit
of an experience working in GitHub and polished our skills again in Flutter and HTML-CSS-JS. Finally got the "real" coding experience as we managed to successfully pull off our biggest project yet. And of course! Debugging!
What's next for ChefPost
Testing our app and website in the real world and working on enhanced User Interface and fixing other minor bugs.
Built With
android-studio
css3
firebase
flutter
html5
javascript
Try it out
docs.google.com
github.com
chefpost.tech
drive.google.com
youtu.be | ChefPost | Your One-Stop Destination for Culinary Fame | ['Agrim A.', 'Amit Maheshwari', 'Siddharth Agrawal'] | [] | ['android-studio', 'css3', 'firebase', 'flutter', 'html5', 'javascript'] | 55 |
10,530 | https://devpost.com/software/project-sigma-submission-nlp | Please see GitHub below for latest documentation and code.
Built With
machine-learning
roberta
tensorflow
Try it out
github.com | Project Sigma - NLP Tweet classification | Text classification for postive or negative tweets | ['Stanley Zheng'] | ['Division Sigma: First Place Overall'] | ['machine-learning', 'roberta', 'tensorflow'] | 0 |
10,530 | https://devpost.com/software/tfuse | Inspiration
Sentiment analysis uses natural language processing to detect the polarity (positive, neutral, negative) of a text, which can be used to draw conclusions about a population. It is a common challenge that programmers have solved for years and is used by companies around the world to gauge how people feel about certain topics such as the stock market. We are all passionate about Python and machine learning, so we put our skills to the test to gain more experience with this area and have some fun!
What it does
tFUSE is a model coded in Python that uses machine learning to perform sentiment analysis on a random selection of tweets to find if tweets carried positive or negative sentiment. Tweets underwent preprocessing, which included lowercasing, regex, stopword removal, normalization, and stemming.
How we built it
Libraries
It was decided that Tensorflow was to be used to construct the model, utilizing additional Python data science modules such as Pandas and Numpy.
Preprocessing
Due to the nature of tweets and other fast text message based communication, it was imperative that preprocessing was necessary for effective natural language processing (NLP) and deep learning.
The most fundamental and perhaps most effective approach is lowercasing all the text data. Although this technique is more useful with sparse instances of words in smaller datasets, lowercasing still proved beneficial by improving validation accuracy by approximately 1% to 3%.
Tweets often contain expressions that may not contribute to the overall sentiment, such as user handles (@janedoe), hashtags (#ignitionhacks2020), and links (
www.ignitionhacks.org
). These expressions often contribute to greater noise in the dataset since many require additional context, such as understanding a user’s history or reading what is on the linked webpage, to provide a substantive sentiment relation. It is important to note that, in this dataset, it was found that hashtags were beneficial for understanding sentiment, at least by an empirical measure of the accuracy metric. A possible hypothesis is that certain emotions were associated with these tags, and could in fact be used for sentiment analysis on its own. Regex was used to clean up the data by removing handles and links, as well as punctuation and numbers.
To further reduce noise and improve sentiment analysis performance, stopword removal, normalization, and stemming was used. The English language contains many short and common words that do not give additional context for NLP, such as ‘a’ or ‘the’. These stopwords were removed by tokenizing the sentences and checking for matches against a list provided by NLTK. To keep normalization times low, a simple NFKD (Normalization Form Compatibility Decomposition) unicode normalization was implemented to remove special characters. Afterwards, the Porter stemming algorithm reduced words to their root form, allowing for more consistent sentence encoding.
Perhaps the most crucial portion of the algorithm’s preprocessing is text enrichment using techniques such as word and sentence embedding.
Encoding
Most NLP algorithms utilize a basic tokenization and a pretrained embedding layer such as ‘keras.embedding’. In order to improve upon this and create a model that could be effective on different types of text data, word vectorization and sentence encoding was tested. Both methods convert strings of text into vectors of floats based on semantic similarity, given by techniques such as cosine or euclidean distance. This is superior to conventional embedding layers as it gives a measure of semantic similarity between different words in the case of word embedding, as well as word-sentence context in the case of sentence embedding.
A disadvantage of using word embedding is losing context and thus being susceptible to spelling mistakes, expressions that require multiple words, abbreviations, and words with similar meanings. After testing, sentence encoding proved to be a better approach, and consistently provided close to a 2% increase in validation accuracy compared to the same model with default keras embedding. However, it must be noted that these encodings came at the cost of long run times and there are also averaging techniques to create contextual relationships between word vectors. Nonetheless, it is important to focus on the real strength of sentence encoding, which is providing associations between words such as “Ignition Hacks” and “Hackathon”, as well as greater flexibility for the model to handle new data or train on other languages.
Model Type
The large amount of training data, as well as the lack of sentiment clarity in many tweets (as manually observed), led us to use deep learning models, with a larger number of deep learning layers. This would allow the model to learn more subtle patterns within the data, and make full use of the dataset.
Train-Test Split
Due to there only being one feature, the tweet itself, it was difficult to collect information about the dataset without using natural language processing tools. It was, however, noted that in the training data, there was an exact 50-50 split between tweets which were labeled to be of positive sentiment and tweets of negative sentiment. Therefore, it was decided that the split between training and validation data would be done at random, with approximately 15% of the data set to be validation data and the remaining 85% to be training data.
Modelling
A variety of models were created and tested on the training data, such as one with only dense and dropout layers, a convolutional neural network, a bidirectional long short-term memory (LSTM) network, and a gated recurrent unit (GRU) neural network. These were all trained for 5 to 40 epochs, depending on the time it took for them to finish each epoch, and each of their layers were tinkered with, such as by adding dense layers and dropout layers, and changing parameters such as the number of nodes in a layer or the activation function used. These neural networks were tried with the Keras tokenizer, as well as with the encoded data which was created with the Universal Sentence Encoder.
Challenges we ran into
We ran into difficulties when encoding the contestant judging data because of two computer and Colaboratory crashes, which delayed our project for hours. Fortunately, the Ignition Hacks team (Grace) gave us a more flexible submission time so we could finish our encoding.
In addition, we could not implement all the preprocessing that we wanted and some of our models did not yield the desired results. We were also limited to optimization techniques from the TensorFlow API and the time constraints of the hackathon. While pre-trained models and other transfer-learning approaches from robust NLP models already had optimized pre-training hyperparameters tuning approaches, they require immense resources to train (with runtimes well beyond the scope of this hackathon) or result in using pretrained weights and biases that have already exhausted large external datasets outside of the hackathon and will deliver preeminent results by simply applying the model on the given dataset.
Accomplishments that we're proud of
We are proud of the model we accomplished, which has model and preprocessing features that we all had to learn about during the hackathon. In addition, all our group members have little to none experience competing in hackathons, but we were able to work well together remotely to create a project we are proud of.
What we learned
We all learned more about Python and machine learning as well as how to use other resources like Colaboratory to test our code. We tested many models and layers, which gave us all a better understanding of TensorFlow. In addition, we learned how to use preprocessing to get more accurate results.
What's next for tFUSE
In order to possibly receive more accurate predictions, an ensemble of different models could be used. They would be able to make predictions on each test tweet, and the results could be averaged to find a hopefully better estimate of the tweet sentiment by reducing the effects of individual models overfitting. A limitation which we faced, however, is that the short timespan of the hackathon did not allow for adequate training of some models, and this step would require additional computational resources and time to do.
When looking at the training graphs for many of the models, the validation accuracy appeared to level off, while the training accuracy continued to increase. This is classic overfitting, and was manually solved in our case; however, this could also have been solved more efficiently using early stopping, which can be done using the tf.keras.callbacks module to save past models in a H5 file before reverting to the model which was least likely to be significantly overfitted.
Built With
colaboratory
machine-learning
natural-language-processing
nltk
python
tensorflow
use
Try it out
github.com | tFUSE | Used Python and TensorFlow to train a model that conducts sentiment analysis on tweets and give them a rating from 0 (negative) to 1 (positive). | ['Dylan Xiao', 'Simhon Chourasia', 'Andrew Z L', 'David Hua'] | ['Division Sigma: Second Place Overall'] | ['colaboratory', 'machine-learning', 'natural-language-processing', 'nltk', 'python', 'tensorflow', 'use'] | 1 |
10,530 | https://devpost.com/software/sigma-nlp | Neural Network Architecture
Training Logistics
Submission for IgnitionHacks Sigma Division
View model.ipynb and submission.csv in Github
Inspiration
The inspiration behind our project stems from a desire to learn about neural network architectures. We took on the classic problem statement of sentiment analysis and hoped to gain new skills and professional assets from the experience. The research we performed invoked a deeper understanding of neuron layers and we were inspired to innovate upon conventional network architectures.
What it does
The application analyzes positive and negative sentiment in text. The text is preprocessed and tokenized before being passed into our neural network. The neural network analyzes the sequences and searches for patterns using the weights from the training process before finally determining how positive/negative a piece of text is.
How we built it
The neural network was built in Google Colab using the Tensorflow libraries for neural networks. Preprocessing was done through a Python script and Regex libraries. The thought process behind our network architecture is outlined below:
Our Model Architecture
Embedding - Converts words into vectors in transformed space to represent the data more meaningfully as opposed to a 'Bag of Words' where words are mapped to an ID.
Dropout - The dropout layer randomly sets inputs to 0, this helps introduce some randomness to prevent overfitting.
LSTM - Long Short-Term Memory is a layer specialized for taking into account the context from a sequence. This helps with tasks such as NLP, as language is very context dependant.
GlobalAveragePooling1D - Pooling layers are used to reduce the number of parameters in a model. This helps avoid
overfitting to the training data. We are performing binary classification so it is only 1-dimensional.
Dense - Dense layers represent a layer of neurons that is fully connected to the previous layer. The weights are adjusted during training as with all the other layers.
Challenges we ran into
One of the issues we faced was creating a concise framework for sharing our code. We encountered issues with sharing on Github because of the large file size of our trained model. The common solution of using Github LFS helped us work around these issues, but raised some challenges of their own.
Github LFS has limited storage and bandwidth each month which eventually forced us to recreate our repo after we had maxed out the monthly limits. The experience taught us to be more conservative and precise with our usage of commits, as well as how to appropriately structure local save files to reduce storage usage.
We also faced challenges with inconsistent Tokenization, as we were fine-tuning our preprocessing script while the model was training. This led to inconsistencies with the training data and the cleaned data, but despite the adversity, we were able to salvage key elements and end up with a result that we are satisfied with.
Accomplishments that we're proud of
We're proud of the presentation of our code. We believe that our concise code and commenting make it easily accessible to a wide array of audiences. Also, we are proud that we were able to apply new-found knowledge and techniques to fine-tune our algorithm.
What we learned
The project taught us skills such as utilizing regex and a variety of data structures (NumPy, pandas, TensorFlow).
We learned a lot about the inner-workings of neural networks and have gained a more in-depth understanding of Tensorflow libraries. A skill we picked up was how to work with data pipelines. Initially, the large dataset gave us problems as we attempted to load it into the RAM for training. However, by setting up a data pipeline through Tensorflow Datasets we were able to pass our data in batches, preventing our RAM from being used up and streamlining the training in the process.
What's next for Sigma NLP
Our hope with Sigma NLP is to create an interface for sentiment analysis that can be utilized by everyday users. Creating a user-friendly online experience is one of the sectors that we can see Sigma NLP helping to advance. Implementations could include creating filters against negative posts/tweets on social media, in a similar manner to parental controls.
In the future, we hope to increase the accuracy of Sigma NLP by utilizing industry-standard preprocessing techniques and further innovating upon the neural network architecture.
Built With
python
regex
tensorflow
Try it out
github.com | Sigma NLP | Precise and Streamlined NN-based Sentiment Analyzer | ['Eric Shim', 'Ronald You', 'Kenneth Ruan'] | ['Division Sigma: Third Place Overall'] | ['python', 'regex', 'tensorflow'] | 2 |
10,530 | https://devpost.com/software/natural-language-processing-with-neural-networks | Inspiration
I am pursuing a career in ML to create something new and different
What it does
It takes a statement and classifies it into whether it is a negative sentence or a positive one
How I built it
We used tensorflow libraries to build it.We made a recurrent neural network with LSTM(Long Short-Term Memory) for training.
Challenges I ran into
Our runtime was killed off many times .Once when we had almost reached 90% accuracy
Accomplishments that I'm proud of
we overcame the challenges and submitted our work
What I learned
Never give up
What's next for Natural Language Processing with Neural Networks
We will try to increase accuracy and also optimize it
Built With
colab
keras
machine-learning
python
tensorflow
Try it out
github.com
drive.google.com | Natural Language Processing with Recurrent Neural Networks | Sentiment Analysis with recurrent neural networks. We have used machine learning to predict the sentiment of a given sentence. | ['Ayaan Mustafa', 'Syon Pratap', 'Aakarsh Gupta'] | ['Division Sigma: First Place Accuracy'] | ['colab', 'keras', 'machine-learning', 'python', 'tensorflow'] | 3 |
10,530 | https://devpost.com/software/sentiment-analyzer-jqhfda | What it does
Submission for the 2020 Ignition Hacks Sigma Division. We created an AI model that predicts the sentiment of a given sentence, classifying it as positive (represented with a 1) or negative (represented with a 0). The code was written in Python using the scikit-learn machine learning library and the Natural Language Toolkit.
Methodology
The given data was first read into a Pandas Dataframe within a Google Colab Notebook environment. The following data cleaning techniques were implemented and tested in our pre-processing step. Each technique was run several times and usability was determined by averaging F1 scores. To standardize each method, dataframe size and model were set constant each time. Furthermore, the data was split into 20% for testing and 80% for training. See specific implementations in the
submissions_extras.ipynb
file in the repository.
String Processing
Attempts to remove stopwords and lemmatize names and other words were unsuccessful as the F1 scores decreased slightly. However punctuation removal improved the results.
Cleaning
Stopwords
Removing stopwords from the sentences rendered the model a bit more inaccurate
Lemmatization of names
Changing the names in the text (identified by an ‘@’ sign preceding the name) to all be the same name resulted in a slight loss in accuracy
Lemmatization with part-of-speech tagging
After implementing NLTK.wordnet’s lemmatization functions, we observed a noticeable decrease in accuracy for the model
Removal of punctuation
Removing punctuation lead to a very slight increase in accuracy
Since punctuation removal improved F1 scores, we solely implemented this technique.
Vectorizer
We tested CountVectorizer and TfidfVectorizer with different parameters and different classifiers to see which combination would yield the greatest accuracy. Since the focus is on accuracy and not speed, we easily opted for the TfidfVectorizer.
Classifier
The following classifiers were implemented and evaluated using the Sci-Kit Learn library:
Neural Network
Decision Tree
Logistic Regression
Support Vector Machine
Stochastic Gradient Descent
Logistic Regression yielded the greatest averaged F1 scores under constant data size, punctuation removal, vectorization, and train-test size.
We then used GridSearchCV to find the optimal parameters for each classifier and reevaluated their usability.
Built With
google-colab
jupyter-notebook
nltk
pickle
pycharm
python
scikit-learn
Try it out
github.com | Sentiment Analyzer | The smartest sentiment analyzer ever. AI meets emotion. | ['George Liu', 'David Chen', 'David Wang', 'Michael Yang'] | ['Division Sigma: Second Place Accuracy'] | ['google-colab', 'jupyter-notebook', 'nltk', 'pickle', 'pycharm', 'python', 'scikit-learn'] | 4 |
10,530 | https://devpost.com/software/ignitionhacks_sentiments | Inspiration
One application for sentiment analysis is to recognize social media content that is inappropriate or fake and removing it. This is a very relevant problem in an era where fake news and inappropriate content is prevalent. I always wondered how this was possible, and realized that in this hackathon we are solving a very similar but simpler problem were we evaluate the sentiment of tweets. This was my inspiration while building this project.
What it does
My model predicts the sentiment(good or bad) of tweets.
How I built it
I tried 2 popular methods used in sentiment analysis which are logistic regression and a neural network. My focus through out the project was to pre-process and clean data as the model is only as good as the data that its fed!
Challenges I ran into
Some challenges that I ran into are running out of RAM on collab!
Accomplishments that I'm proud of
I am proud that I have tried 2 different models and verified for myself which model works better and find the reasoning behind why this is the case. I am also proud that I did this by myself, but I will probably work in a group next time!
What I learned
I learned the neural networks work better with larger datasets. As well I learned many NLP pre-processing techniques like lemmatization and using the Spacy library.
What's next for Sentiment Analysis!
I'm planning on further building on this model to use it on larger pieces of text and use it to detect fake news.
Built With
jupyter-notebook
python
pytoch
spacy
Try it out
github.com | IgnitionHacks_Sentiments | “A machine learning model is only as good as the data it is fed”. I'm sure you have heard that before right? My main focus was to pre-processing and cleaning my data to ensure a great model! | ['ruqhia frozaan'] | ['Division Sigma: Most Creative'] | ['jupyter-notebook', 'python', 'pytoch', 'spacy'] | 5 |
10,530 | https://devpost.com/software/sentiment-analysis-using-supervised-deep-learning-model | training and validation accuracy of LSTM model
result from 10 epochs
Log scaled graph that shows the change of word count after preprocessing
LSTM model architecture with embedded layers
number of negative and positive sentiments to access the need to of augumentation
Inspiration
Learning about how a machine processes data was always a question that I had since childhood. But due to a lack of knowledge and resources, I could not discover this part of technology. I had a rough idea about machine learning before enrolling in Ignition Hacks. I thought that this competition would increase my tech stack and help me discover more about machine learning and deep learning. Soon machine learning will play a vital role in computer science, and having it as a skill will be helpful for me to face the competitive world.
What it does
Sentimental Analysis is a program that interprets the sentence given by the user and tells us if that sentence is positive or negative. To solve that sentence, it uses Pre-Processing to remove the inconsistencies. Then machine learning model gets trained by the data set provided and predicts the most likely outcome.
How I built it
Pre-Processing
Before applying machine learning algorithms to the data, we need to make sure that the information is free from ambiguity
and noises
For example:" Hello Adam!! Are you feeling good today??"
In this sentence, punctuation like! And? It does not tell us about the sentiments of the sentence, but it creates ambiguity in our program; hence these must be removed.
Another category of words is
stopwords
as "say," "me," etc., do not play any role in deciding sentiments; thus, they are removed.
Pre-Processing is essential as it will help reduce unnecessary data and clean our data to reduce inconsistencies. To convert all the lines into their processed form, I used a function that used bs4 to remove the HTML tags and contractions to replace contractions in the string text.
Creating tokenizer and embedding layers
After pre-processing the data then, we tokenized the data by using an inbuilt function in Keras called the tokenizers. Words are called tokens, and splitting text into tokens is called tokenization. These tokens help understand the context or develop the NLP model. The tokenization allows interpreting the meaning of the text by analyzing the sequence of the words.
For example, the text "It is raining" can be tokenized into 'It,' 'is,' and 'raining.'
Embedding data
The sole purpose of embedding data is to convert the low dimensional data (our original information) into high dimensional data. Our machine learning models are more efficient when we use high-dimensional vectors.
Creation of neural network using Keras
We have to build a neural network that will process all the data collected and predict the output.
LSTM has been used to build the model. LSTMs are a special kind of RNN, capable of learning long-term dependencies.
Challenges I ran into
I faced some issues while making the program, but I researched and learned from the problems to overcome them.
When I was testing the data file, I realized that the data file was large, and Google collab showed a Runtime Error. So I switched to the desktop version of visual studio code, which solved the problem. While I was using a universal sentence encoder on visual studio code, I learned that Google had not released the Windows version of TensorFlow_text, so I changed the encoder to do the same.
Accomplishments that I'm proud of
I am delighted that I could learn and complete a completely new project in two days.
What I learned
I learned about machine learning algorithms. I learned about supervised and Unsupervised learning and how to train a machine learning model. This project gave me insight into NLPs and their application in various technology parts.
What's next for Sentiment analysis using the Supervised Deep Learning model
I would explore new models like ensemble stacking methods to improve accuracy. The model uses neural networks, and I want to try NN variants like CNN1D BLSTM and other time series, NLP models, e.g., Hidden Markov Models, for better prediction. TF and glove.6B sentence encoders were a bit slow for 600,000 tuples, so I want to try them on distributed computing like Hadoop for faster pre-processing
Built With
bs4
contractions
corpus
deeplearning
encoding
glove.6b
inflects
keras
lstm
machine-learning
math
matplotlib
natural-language-processing
neural-networks
nltk
numpy
pandas
pre-processing
python
scipy
seaborn
sklearn
tensorflow
Try it out
github.com | Sentiment analysis using Supervised Deep Learning model | Created a model for sentiment analysis using deep neural networks(LSTM) and tensorflow universal sentence encoder. | ['Harsh Poddar'] | ['Division Sigma: Best Commenting and Project Description'] | ['bs4', 'contractions', 'corpus', 'deeplearning', 'encoding', 'glove.6b', 'inflects', 'keras', 'lstm', 'machine-learning', 'math', 'matplotlib', 'natural-language-processing', 'neural-networks', 'nltk', 'numpy', 'pandas', 'pre-processing', 'python', 'scipy', 'seaborn', 'sklearn', 'tensorflow'] | 6 |
10,530 | https://devpost.com/software/fixercise | Landing Page
Achievements Page
Deck Creation Page
An Example Deck Configuration
An Example workout card
A successfully finished workout deck
Website Flowchart that I used as a reference
Inspiration
I was inspired to create fixercise because I've gotten out of shape recently and wanted a workout experience that is more bite sized and go-at-your-own-pace. I also thought about how competing with worldwide users for a space on a leaderboard would definitely be a good motivator for me and many others. I also just really liked the idea of making an 80s themed website/video
What it does
Fixercise is a social media platform that allows users to compete in workouts with others. User's can create a new deck of cards based off of what muscle group they're targeting, along with what difficulty they want their experience to be at. When a new deck is created, users are presented with each card and upon completion of them, are rewarded points that can help them level up and earn achievements. User's can view their spot in the leaderboard and what level they're at in the respective pages.
How I built it
For the entire core of Fixercise I used asp.net core. Users accounts were controlled and managed with Identity core and the entity framework. I extended the initial class to make user binding strings a part of user accounts. The binding string is used to connect a user to a separate database that keeps their current scores. The most algorithmic-ally daunting task was web scraping in an efficient algorithmic manner. My algorithm ended up utilizing string to string dictionaries holding every aspect of the card. SignalR websockets were used to create an environment where get or post requests aren't needed to retrieve the next card in a deck.
Challenges I ran into
Working with asp.net identity core was very daunting, as this is only my 2nd time properly using it. Extending users worked better than the first time, but eventually I was stuck at an authentication error. Several headaches and google searches later, I found out that I forgot to add a single
Services.UseAuthentication()
in my startup class. Another challenge was the webscraping tuning, to me personally it's a really tedious task to manipulate nodes and find out which nodes will actually give me the result I needed.
Accomplishments that I'm proud of
I am proud of how far I've gotten in my understanding of asp.net's architecture and how to use identity users more properly. In the past, although there have been improvements, my understanding of how I should use classes and routing properly have been minimal in the past. This time however, my focal shift from solely algorithm to a more UI based standpoint really made my application's front and back end work more hand in hand.
What I learned
I learnt a ton of helpful tips about UI work, such as centering a button and how margins and div hierarchy work, I also am extremely grateful for how much I learnt about patience and debugging, as the two huge time wasting bugs were simply typos or forgetting a line of code.
What's next for Fixercise
In the future, Fixercise will definitely need some UI reworks, but for the most part I'd work on migrating databases to a cloud based service, along with deployment to Azure for real life usage.
Built With
asp.net
aspnetcore
bootstrap
c#
css3
html5
razor
Try it out
github.com | Fixercise | A card based workout curation system that rewards users in a competitive 80s themed platform | ['Braden Everson'] | ['Division Delta: Best Overall Junior Project'] | ['asp.net', 'aspnetcore', 'bootstrap', 'c#', 'css3', 'html5', 'razor'] | 7 |
10,530 | https://devpost.com/software/peer-tvmdx7 | Peerify's home page.
Critique the responses others have written.
View the responses others have written for discussion.
Final standings! Words with strong correlation to low/high ratings are shown on the right.
A Division Delta project
Inspiration
Studying online has turned education into a solitary experience. As a team of soon-to-be University students, we understand the value of study groups and collaboration. Thus, we were inspired to created Peerify – a game-based learning platform that turns study sessions into an exciting social experience.
What it does
We are all too familiar with grinding through AP FRQs and writing prompts with our classmates. The process of peer review and collaboration often brings out the best ideas from all parties, and makes a strenuous task enjoyable.
Our website facilitates this process online by allowing students to join online lobbies, create prompts, and review each other's responses in a style akin to online party games. Rooms are created with a unique id, which can be easily shared so people can join. Students have the freedom to ask any question, ranging from writing prompts to chemistry or geography problems. Communication is encouraged each round, and a point system is used to reward those with the best answers.
Following each round, we use machine learning to generate insights that allow students make the most of their experience. The feedback is analyzed and key words and topics are displayed as a word map, highlighting the subject areas that should be reviewed. This data can help educators plan future lessons and contribute to our dataset which guides future games.
How we built it
We built the frontend using React and used Node.js for the backend, using websockets to interact between the two. We hosted our backend using Digital Ocean, so we could each join a game from each of our computers.
We created a model using gensim's Word2Vec with the data of 300000 Amazon reviews. Based on these word vectors we were able to train a random forest classifier through sklearn and predict whether certain words had a stronger correlation with low ratings or high ratings. We started with this base dataset but as more rounds are played, the model can be updated every once in a while (let's say, every 500 user reviews).
Challenges we ran into
We had trouble initially implementing python machine learning script into our Node.js web app, but we figured it out in the end. We also went through quite a few iterations of our UI, but we are quite happy with the end result.
Accomplishments that we're proud of
We were proud of creating a project that not only had a robust website, but also integrated data science and machine learning to offer a unique solution to students. Being able to create a project that solves a problem that we often encounter as students was also incredibly fulfilling.
What we learned
We all learned a lot about web development and the theme of this hackathon also challenged us to build a machine learning model which was also integrated to the backend.
What's next for Peerify
As this project scales, we hope to continuously improve our AI model by using relevant data from users that interact with our website. We also hope to improve the user flow and round system depending on future user feedback.
Built With
digitalocean
gensim
machine-learning
node.js
python
react
scikit-learn
websockets
Try it out
github.com
github.com | Peerify | Turning online study groups into social experiences. | ['Jason Xiong', 'Andrew Xue', 'William Li'] | ['Division Delta: Best Overall Project'] | ['digitalocean', 'gensim', 'machine-learning', 'node.js', 'python', 'react', 'scikit-learn', 'websockets'] | 8 |
10,530 | https://devpost.com/software/s-a-grader | Your previous essays, the Main Menu
Essay submission box
What it does
An essay grader that is built to rate essays and provide a variety of stats on your essay.
How we built it
We heavily modified an old essay rating artificial intelligence model from the The Hewlett Foundation: Automated Essay Scoring competition to suit our needs. This included changing the scoring system so it became a percentage grade (wrecking havoc on the Kappa score in the process), upgrading the model using GloVe, retraining the model, and adding more functionality in a Semantic Similarity and Grammatical/Spelling Accuracy finder.
We used Django to create a website where you can submit essays to get them analyzed, as well as view past essays and their scores as well.
Challenges we ran into
Originally meant to be solely an SAT grader, we expanded it into the full range of essays since it was too difficult to train three separate AIs to look through the 3 categories of the SAT. Also, there wasn't nearly enough training data.
We were going to use solely html, js, and css to create the website portion, but halfway through we realized it would be extremely difficult for us to implement the neural net on that. Instead we ended up having to learn Django from scratch in order to successfully implement our code!
Accomplishments that we're proud of
Successfully implementing Semantic Similarity
Successfully implementing an AI model to score essays with relatively good accuracy
Creating a website from Django, which we had little-no experience in prior
What's next for S. A. Grader
Making the model run faster both on its own (by changing the model to learn off GloVe) and in the browser.
Beautifying the website further.
Making it so that you can share essays and scores online.
More accurate predictions as currently it's a bit harsh with grading.
Built With
django
numpy
pip
python
Try it out
github.com | S. A. Grader | Grading essays using neural networks! | ['Aaron Zhou', 'George Wang', 'Sharn Singh', 'Christian Choi'] | ['Division Delta: Best Program'] | ['django', 'numpy', 'pip', 'python'] | 9 |
10,530 | https://devpost.com/software/safe-at-school | Inspiration
We were inspired to create this program after reflecting on how different our university experience will be as a result of COVID-19. Understanding the importance of face-to-face interaction for young children, we decided to create a tool that helps preserve the possibility of in-person education.
What it does
Safe at School teaches elementary students about public health guidelines through a choose-your-own-adventure style game. Safe at School begins with a fun, personalized greeting and accessibility features such as a voiceover for each page. Players are placed in typical school day scenarios and are asked to make safe decisions. Each decision relates to a Centre for Disease Control (CDC) guideline, outlining the importance of social distancing, hand washing and sanitizing, and limiting the items one touches.
How we built it
Under the hood, Safe at School was built with HTML and JavaScript, and CSS was used for the styling of the website. The website’s graphics were made with Adobe Photoshop using several royalty-free images. Safe at School also incorporates the SpeechSynthesis interface, a part of WebSpeech API.
Challenges we ran into
A sample challenge that we ran into was getting the checkbox table in the first question to function properly in different cases, and getting the back buttons and alerts on each page to display nicely with the background.
Accomplishments that we're proud of
We’re super proud of the many bugs that we fixed! At times, it felt like we were squashing one bug after another with alerts not showing up, mistakes in logic operators, and styling issues. However, after getting some rest and regrouping, we managed to work through all the bugs in our program. We’re especially proud of Question 1 in our game, the table checklist activity, as we ran multiple test cases to ensure that all alerts were functioning as they should.
What we learned
Through this project, we learned to never underestimate the complexity of checkboxes and to NEVER assume “des” is short for “description”. We also learned how to apply Bootstrap to manage the alerts on our game.
What's next for Safe at School
A feature that we’d like to implement in the future is support for more browser as the voiceover feature is currently available through the Google Chrome browser.
Built With
css
html
javascript
photoshop
webspeechapi
Try it out
github.com
IgnitionHacks.tabithawong.repl.co | Safe at School | **IgnitionHacks Division Delta** Safe at School is an interactive game that teaches elementary school students how to follow public health guidelines for COVID-19 at school. | ['Bonnie Peng', 'Tabitha Wong'] | ['Division Delta: Best Presentation'] | ['css', 'html', 'javascript', 'photoshop', 'webspeechapi'] | 10 |
10,530 | https://devpost.com/software/ar-labs | Our process in blender (which is an application we used for our project)
AR Labs in action!
Inspiration:
We were disappointed by how many students (including us) will not be able to participate in "hands on" activities this upcoming school year. This is what inspired us.
What it does:
It allows students to participate in VR 'hands on' activities such as science experiments.
How we built it:
The first thing we did was we took 3D models of beakers and spoons off of a website called "Sketchfab". The second thing we did was we imported the 3D models into a program called "Blender". After, we added shadows and we tried to add colors to the 3D models which didn't work out. Lastly, we animated the models and we transferred it into EchoAR.
Challenges we ran into:
Making the 3D models (we tried to make them but it didn't work out so we got the models off of Sketchfab)
Adding colour and texture to the 3D models
Finding the right software to use for our project
Accomplishments that we are proud of:
We managed to finish our project in 2 days!
What we learned:
We learned how to make VR/AR applications
We gained experience
What's next for AR Labs:
In the future we plan to add more “hands on’ activities that will not only include science experiments but also VR geography classes. In addition we plan to implement haptic to AR labs, which will make it an even more immersive experience where you can feel the texture or heat of the reaction.
Furthermore, with our current knowledge, we haven’t progressed far enough to add texture, colour and advanced animations into our 3D model. Our hopes for this app is that in the next coming years, we can show exactly what happens to the chemical reaction instead of just shaking the beaker like we did in the demo. We believe that if we were to develop this, we can have an AR model to show what is happening to the molecules at a molecular level.
Built With
blender
echoar
Try it out
github.com | AR Labs (Division Delta) | AR Labs is a AR/VR program that allows students to participate in “hands on” activities such as science experiments. | ['Brandon Tai', 'archie shou', 'John Hu', 'Ricky Qin'] | ['EchoAR Prize'] | ['blender', 'echoar'] | 11 |
10,530 | https://devpost.com/software/hand_gesture_recognition_ai | Hand_Gesture_Recognition_AI
Problem
Estimates have shown that the population of native ASL speakers falls between 250,000 and 500,000, but there have always been a shortage of education in ASL in U.S schools and colleges due to the lack of certified teachers. As a result, many Deaf and Hard-of-hearing students face many challenges in the classroom and everyday life: facing challenges with hearing aids and lip reading, social concerns, language deficiency, lack of support and empathy from others. Seeing this problem, we have decided to build this hand-gesture recognition A.I. to assist the learning of sign language for everyone.
Aim & Scope
With this product, we aim to:
Bridge the gap in education for Deaf and Hard-of-hearing students
Help address the shortage of instructors and interpreters in sign languages.
Provide resources in learning sign language for all students.
Code Explanation
The first cell is importing the libraries
The next 6 cells are to import the information from training and testing CSV file on google drive of the pixels of different hand gesture images
Then it organizes the data and prepares it for training
Then some more libraries were imported and the neural network is created
Data training
The AI predicts
Future
We believe that with this product, we could help to better the educational experience for everyone, especially the hearing-impaired. We believe that it would become a convenient, helpful, and easy-to-use tool for everyone.
*Also created by Fujia Wang who couldn't join devpost for some reason.
Built With
colab
github
python
Try it out
github.com | Hand_Gesture_Recognition_AI | An AI which interprets letters to sign language using machine learning. | ['Reet Bhamra', 'Kathleen Nguyen', 'Andrew Ye', 'Fujia Wang'] | [] | ['colab', 'github', 'python'] | 12 |
10,530 | https://devpost.com/software/meet-ar | Goal:
Create an AR room with video conferencing features
Where we got:
link
What was used:
PeerJS was used to make video conferencing possible
HTML/SCSS was used for the frontend
echoAR was going to be used for the AR capablities
I know this readme looks bad:
But we kept working on the project and the video instead of here
Built With
echoar
html
javascript
peerjs
scss
Try it out
github.com | Meet AR | AR video conferencing room | ['Adrian Lobo', 'Damian Musk', 'Isac Portillo', 'Eric Zhu'] | [] | ['echoar', 'html', 'javascript', 'peerjs', 'scss'] | 13 |
10,530 | https://devpost.com/software/ignition-hacks-sigma-code-ofht96 | hey guys
this a project sentiment analysis made by my team and me
we use sklearn nltk to make the machine learn
my teammate was Nirbhay Sharma
this was basically for the hackathon
please leave ur comment down there
Built With
jupyter
Try it out
github.com | Ignition-Hacks-Sigma-Code | This is the code for the ignition hacks sigma division problem | ['shubhojeet brahmachari'] | [] | ['jupyter'] | 14 |
10,530 | https://devpost.com/software/youtube-tracker | Inspiration
During quarantine, it is easy feel like there is lots of time use, and waste a lot on youtube
What it does
Tracks time spent on youtube to raise awareness about the time people waste every day
How I built it
Used TypeScript and Javascript, and integrated it with the Chrome API to turn it into a simple chrome extension
Challenges I ran into
Working with ChromeAPI and a new testing environment was confusing, and we ran into lots of trouble regarding chrome extension permissions
Accomplishments that I'm proud of
Adapted to the new development environment and learned to use TypeScript
What I learned
A lot about Javascript, TypeScript and chrome extensions
What's next for Youtube Tracker
Add functionalities and server side code to truly optimize tracking non-work-related videos
Built With
chrome
javascript
typescript
Try it out
github.com | Youtube Tracker | Optimized youtube time tracker for tracking videos unrelated to productivity | ['Ethan Lim', 'Jaden Pereira'] | [] | ['chrome', 'javascript', 'typescript'] | 15 |
10,530 | https://devpost.com/software/ignitionhacks2020-hpkjd5 | IgnitionHacks2020 - Division Sigma
Built by Johnny Chen and Ritchie Yu
See: SigmaRNN.py and contestant_judgement.csv
dorime
Built With
python
Try it out
github.com | Division Sigma - IgnitionHacks2020 | Submission for Division Sigma | ['Johnny Chen', 'Ritchie Yu'] | [] | ['python'] | 16 |
10,530 | https://devpost.com/software/scheduler-14a53n | Login Screen
Selection Screen
Create Assignment
View
Inspiration
The inspiration behind this application comes from the idea of procrastination and the need for more parental involvement in their child's academics. Parents should be able to oversee the tasks completed and not completed by their child, and should be notified. This vision for this application included ways where parents had admin functionality; they could remove task, approve task completion, and get notified when a task has been overdue. However, our group was unable to implement some of the functionality.
What it does
This application is a time-management app that has the ability to create, and store tasks, in separate files, created by the user. The user can view all the ongoing tasks or choose to remove the ones that have been completed.
How I built it
This program was built using Java 8 and javafx.
Challenges I ran into
Our group ran into several challenges. We tried to get a notification system, however, we were unable to set up a usable timer. Another problem was implemented functionality to send auto emails to the admin upon completion of a certain task or the failure of doing so.
Accomplishments that I'm proud of
Being able to construct a list of tasks and corresponding due dates, and being able to read and write to files in order to accomplish user history.
What I learned
-javafx
-how to create Buttons, TextField, TableView, & file I/O
What's next for Scheduler
Future prospects for this app include functionality for parents. This will allow for more interaction of parent with their child's academic journey. The parent will act as an admin, monitoring the accomplishment of tasks.
Built With
java
Try it out
github.com | Scheduler | A secure, time-management tool for students | ['Amma2003 Vora', 'Carter Tam', 'Grace Zhou'] | [] | ['java'] | 17 |
10,530 | https://devpost.com/software/gettingaiisentimental | GettingAIISentimental
What the Project Does:
Our program is an AI model which is designed to take a set of data and determine whether the sentences within the collection, demonstrate a positive or negative sentiment with great accuracy. Our program in particular is unique due to our "hybrid algorithm", in which we combined K Nearest Neighbors, and Support Vector Machine algorithms together. With each method being more accurate than the last, we were able to obtain precise results with great efficiency.
How We Built Our Model:
Using Python, our team built the model through Google Colab in which we were able to write and execute code through our browser and make modifications over a multitude of devices. We have used Sci-Kit Learn, MatPlotLib, Pandas, and various other libraries to amplify the accuracy of our program.
How Is Our Program Useful and Where Can It Be Applied:
Sentiment analysis is especially widespread in terms of its possibilities. It can largely be implemented in the customer service field, as well as predictive analytics. Our AI model can be used to gain insights into how customers feel about a product, and also help us identify and understand how business brand reputations develop.
How Users Can Get Started With the Project:
Our program can run on any platform that supports python and all that is required is to type in the authorization code given for the data set.
Challenges We Faced:
Working with large quantities of data proved to be a challenge, which we eventually overcame with dividing the procedure of data cleaning into smaller components.
Combining the two different methods of sentiment analysis to form our hybrid model tended to be very complex and convoluted especially as our team members did not already have previous experience with AI, but the effort and struggle eventually paid off when we developed our final finished product.
Another major challenge we had to overcome was dividing tasks among our team members and establishing when we would spend time on our project and when we would be resting. This was the first time any of our team members have done a hackathon and due to our inexperience there tended to be a level of disorganization, but despite that we were able to pull through, and now we’re all ready for hackathons in the future.
Built With
google-colab
matplotlib
pandas
python
sci-kit-learn
Try it out
github.com | Getting AII Sentimental | A hybrid algorithm for sentiment analysis | ['Vedha Mereddy', 'Jude Thibeault', 'Kiersten Schmidt', 'Adrian Lloyd'] | [] | ['google-colab', 'matplotlib', 'pandas', 'python', 'sci-kit-learn'] | 18 |
10,530 | https://devpost.com/software/islamic-knowledge | Inspiration
Love of Islam and lack of knowledge about Islam in my society
What it does
It teaches people about Islam
How we built it
Using HTML
Challenges we ran into
It was hard to connect to the database due to network issues and other issues
Accomplishments that we're proud of
The idea and the information we collected as well as what we learned
What we learned
PHP and MySQL
What's next for Islamic Knowledge
To finish the website, add more information and publishing it
Built With
html
mysql
php
Try it out
github.com | Islamic Knowledge | Our idea was to educate people about Islam so that they will have a better view about the beauty of Islam. | ['Mariam Abada', 'Sarah Abada'] | [] | ['html', 'mysql', 'php'] | 19 |
10,530 | https://devpost.com/software/ignitionhacksdivisiondelta | IgnitionHacksDivisionDelta
Division Delta submission Vruti Soni and Sukhleen Bhogal
Both Ritti and Vruti are seniors and in the process of applying to colleges. They found that the research process of colleges can be very tedious and time consuming. Especially during this pandemic, it has become extremely difficult to understand the college environment, let alone enter it. We founded a website that is able to gather the research of these colleges, their lifestyle, virtual tours dates, and even reviews of these colleges from other students which in the end helps the student apply. We call it College Spark! This website makes it a place where the students are able to find and pick their perfect colleges to apply with the research available all in one site. Therefore, through reaching this Hackathon from AI club, Ritti and Vruti were able to create College Spark as a solution to the stresses caused by trying to figure out which college is right for not just them, but their fellow seniors!
Our Video
https://youtu.be/Sc55jjZ0KAs
Our website url:
http://98.114.195.75:5000
If the url is not working, then you may simply download the contents of the repository and open the index.html file in your browser and view the project from there.
Built With
css
html
javascript
python
Try it out
github.com | IgnitionHacksDivisionDelta | Division Delta submission Vruti Soni and Sukhleen Bhogal: College Sparks, where instead of colleges judging you, you judge colleges! | ['Sukhleen Bhogal', 'Vruti Soni'] | [] | ['css', 'html', 'javascript', 'python'] | 20 |
10,530 | https://devpost.com/software/ignitionhacks2020divsigma | Inspiration
PAIN
What it does
PAIN
How we built it
Built it using a neural network
Challenges we ran into
PAIN
Accomplishments that we're proud of
PAIN
What we learned
PAIN
What's next for IgnitionHacks2020DivSigma
PAIN
Built With
jupyter-notebook
Try it out
github.com | IgnitionHacks2020DivSigma | Division Sigma - Jupyter Notebook that pertains to sentiment analysis using a TF ML model | ['Karthika Thiruvallur', 'Raghav Thiruvallur', 'Baladithya Balamurugan', 'Rohit Ganti'] | [] | ['jupyter-notebook'] | 21 |
10,530 | https://devpost.com/software/learntoread | Inspiration
Giving back to the community, specifically kids. Our goal is to have kids helping kids; peer-to-peer learning.
What it does
Users can snap a picture of the text they are reading. Next they can upload the image to the LearnToRead web application. Our application then processes the image and prompts the user to highlight specific words that they are unfamiliar with. LearnToRead helps the user pronounce and understand the word by saying the word out loud.
How I built it
This application was built using Python, Tesseract text-to-speech application, HTML and CSS
What's next for LearnToRead
The next steps for this application include making it accessible via mobile devices.
Try it out
github.com | LearnToRead | Web application to help children learn to read through image-to-speech technology | ['Inaya Rajwani', 'Akshara Debnath', 'Hasan Majid', 'Vanessa Lobo'] | [] | [] | 22 |
10,530 | https://devpost.com/software/education-in-the-21st-century-y6r5c3 | Education-in-the-21st-century
Built With
pygame
python
Try it out
github.com | Education-in-the-21st-century | education | ['Abhishek Kakolla', 'J W'] | [] | ['pygame', 'python'] | 23 |
10,530 | https://devpost.com/software/sentiment-analysis-modg61 | Preprocessing is a vital part of machine learning to get clean data. Data cleansing improves data quality and improves productivity by leaving only significant and meaningful data. In this implementation for statistical analysis, phrases such as tags and hyperlinks were removed from the text as they rarely signified sentiment. Various machine learning algorithms were used for sentiment analysis on social media status updates and compared based on the evaluation metric. Initially, I tried deep learning techniques such as XGBoost and LSTM. Deep learning is one of the most advanced and recent ML methods that are powerful because of their hidden layers. However, both did not significantly improve accuracy while being computationally expensive to train. I finally tried Logistic Regression which improved accuracy and was able to train in a reasonable amount of time. Logistic regression is a simple yet efficient machine learning algorithm for sentiment analysis.
Built With
python
Try it out
github.com | Sentiment Analysis | I performed sentiment analysis on social media status updates using Logistic Regression. | ['R Lee'] | [] | ['python'] | 24 |
10,530 | https://devpost.com/software/ignition_hacks_2020_sigma | In this code we used sklearn’s svm to create an AI to determine whether a given statement was positive or negative. Beginning with a csv file containing the user, ID, Review and its sentiment. With this, we stripped it of irrelevant data (including Users and IDs), preprocessed our reviews (which includes filtering sentences to only contain relevant and using bag of words to transfer words into binary). Applying this preprocessed data, we used the “Sklearn SVM” model (using an RBF kernel) to record trends and create a working AI. This AI was tested with an accuracy test and an F1_score (the F1_score was not in the main code but was used during development). Finally, we used our new AI on the attached “Judgement Data” to predict the sentiment of real world messages. This project was composed of members: Anusha Shekhar, Michelle Pansa, Robert Saab, Ryan Awad.
Roughwork repo:
https://github.com/Ryan-Awad/Ignition-Hacks-2020-Division-Sigma.git
Built With
python
Try it out
github.com | Ignition Hacks 2020 Division Sigma | This project was composed of members: Anusha Shekhar, Michelle Pansa, Robert Saab, Ryan Awad | ['Anusha Shekhar', 'Michelle Pansa', 'Ryan Awad', 'Robbot-syntax-error'] | [] | ['python'] | 25 |
10,530 | https://devpost.com/software/ignition-hacks-2020_anikap_ishitaw | Inspiration:
Our inspiration was to learn as much as possible about Machine learning and AI through this hackathon in order to develop a deeper interest within this field. From here, we're hoping to expand our knowledge, since this was our very first time working with machine learning and AI.
Challenges we ran to:
The fact that we came into the competition with a very rudimentary knowledge of Python made it a very steep learning curve.
Accomplishments:
We were really proud of the fact that given our very limited knowledge, we were able to produce a product with some of the aspects required for this hackathon. Given more time and a deeper understanding, we are confident that we would be able to produce a more sophisticated final product.
Built With
numpy
pandas
python
seaborn
textblob
Try it out
github.com | Ignition Hacks 2020_AnikaP_IshitaW | We attempted a sentiment analyzer from scratch using Python, and our knowledge of neural networks and machine learning. | ['Ishita Wadhwa'] | [] | ['numpy', 'pandas', 'python', 'seaborn', 'textblob'] | 26 |
10,530 | https://devpost.com/software/computernewbies | Inspiration
What it does
How we built it
Challenges we ran into
We were computer dummies that learned python in 2 days to attempt to build an AI model
Accomplishments that we're proud of
What we learned
We learned that there are many concepts in python that we must first learn before creating a Sentiment Analysis
What's next for ComputerNewbies
Try more hackathons
Built With
python
Try it out
github.com | Computer Newbies | We have no idea what we are doing | ['Joshua Ronquillo', 'Jose Jesus II Abejo'] | [] | ['python'] | 27 |
10,530 | https://devpost.com/software/hangman-3cqlnd | Inspiration
educational hangman that incorporates echoar
What it does
How I built it
Challenges I ran into
Accomplishments that I'm proud of
What I learned
more time needed
What's next for Hangman
Try it out
github.com | Hangman | Hangman that incorporates EchoAR | ['Mia Chen', 'Carolyn Pyun'] | [] | [] | 28 |
10,530 | https://devpost.com/software/student-planner-division-delta | Inspiration
I am personally a very unorganized person who goes with the flow. I wanted to create something that could organize the day of people who are similar to me.
What it does
Currently this demo is only able to scroll through menus and has usable text boxes that unfortunately do not carry information in the way that was intended. This planner is intended to have a fully functioning system in which the user can input their own schedule and an AI would give pointers on how the user could improve their daily tasks written down in the planner. This is not what the demo does currently, and is more of a simple showcase of what that intended app might look like.
How I built it
I used unity 2D and their UI system in order to create the demo. I used basic C# along with visual studio to code my components.
Challenges I ran into
One of the biggest challenges I ran into was transferring variable info between unity scenes. I wanted this demo to have the user be able to type in their own schedule, store that information, and relay it to the next scene. However I was unable to ultimately do so due to time constrictions and my own limited computer programming knowledge.
Accomplishments that I'm proud of
I'm proud of how I learned how to utilize GitHub to aid in the creation process of my demo. I am also proud that I learned some of Unity's UI system in the time given, and overall proud that I was able to submit somewhat of a project to this hackathon.
What I learned
I learned some of the functions in Unity's UI system, most prominently the buttons and how to use C# script to swap scenes when they are pressed. I learned a lot about Github and how it is used in the computer science field. I learned some of the complications that can come with coding, such as the simple transfer of text, and how it really had me stuck.
What's next for Student Planner (Division Delta)
In the future I may look into cleaning up the demo and creating a functioning text box system that can successfully carry data from one unity scene to the next.
Built With
c#
unity
visual-studio
Try it out
github.com | Student Planner (Division Delta) | A demo idea for a student planner in the 21st century. | ['Ethan Huynh'] | [] | ['c#', 'unity', 'visual-studio'] | 29 |
10,530 | https://devpost.com/software/educate-hut | Educate Hut
About Us
With our dedication to sharing knowledge, Educate Hut has educated and inspired countless people inside and outside the classroom. We have organized many events, that serve as learning opportunities for students. Get in touch to find out more about this engaging educating platform.
Our Goal
We are a student-run organization that is working to provide future generations of students with assistance in their school life. The core features present in our assistant is the alarm function and the shutdown system.
Problem
Students struggling to learn in this complicated environment. Online school has made it difficult for students to grasp methods. Students cannot find a lot of events that are safe and knowledgeable.
Solution
We provide a virtual assistant, Nicole, who is able to assist you in problems relating to common subjects taught in school. Online events that provide a knowledge enriching experience for students are also included: Coding Workshop and post-secondary Discussion.
Built With
audacity
github
java
priemere-pro
python
Try it out
github.com
docs.google.com
creator.voiceflow.com | Educate Hut - Division Delta | Our product helps individuals learn during these unprecedented times through a virtual assistant, Nicole. Nicole answers your quick questions and provides you with resources if that is necessary. | ['Virendra Jethra', 'Amitoz Jatana', 'Vasu Sukhija', 'Vinay Reddy'] | [] | ['audacity', 'github', 'java', 'priemere-pro', 'python'] | 30 |
10,530 | https://devpost.com/software/ignition-hacks-delta | Book Suggester User Interface
Modules
Main Script
Ignition-Hacks-Delta
Book Suggester 2000
Built With
python
Try it out
github.com | Nova, The Book Suggester | Provide book suggestions based on your borrow history | ['Alexander Li'] | [] | ['python'] | 31 |
10,530 | https://devpost.com/software/kennethzhangignitionhackssigma | Ignition Hacks Sigma By: Kenneth Zhang
I submitted two different programs using different methods and algorithms to classify and predict the sentiment of sentences.
The first program submitted used traditional methods to classify and predict the sentiment of sentences whereas the second program submitted used a Sequential Model
to predict and classify sentiment values of the sentences. In this brief README file, I will go over the important segments of code and my thinking behind why I did it the way I did. If you have any further questions please email me at
kzhang138@gmail.com
.
Ignition Hacks Sigma Problem Solution 1 By: Kenneth Zhang
Dataset Visualization and Pre-Processing
Firstly, I visualized the data provided to me.
From the plot generated we can see that the distribution of sentiment values (0s and 1s) are even.
import pandas as pd
data = pd.read_csv('training_data.csv')
data.head()
data.info()
data.Sentiment.value_counts()
Sentiment_count=data.groupby('Sentiment').count()
plt.bar(Sentiment_count.index.values, Sentiment_count['Text'])
plt.xlabel('Review Sentiments')
plt.ylabel('Number of Text Inputs')
plt.xticks([0,1])
plt.show()
Next, I extracted the features from the text provided.
I converted the text into a matrix which visualizes the occurence of words in an individual text sample.
A matrix doc is created, and includes the number of times a word occurs in a given text sample.
I then proceeded to split the data which is ideal for when I will assess the performance of the overall model.
Generally, data is split into a training and test set.
Using Sklearn's most popular train_test_split function, we can easily pass three necessary parameters: features, target, and test_set size.
The count vectorizer converted the text in the text column into a vector of token counts.
from sklearn.feature_extraction.text import CountVectorizer
from nltk.tokenize import RegexpTokenizer
token = RegexpTokenizer(r'[a-zA-Z0-9]+')
cv = CountVectorizer(lowercase=True,stop_words='english',ngram_range = (1,1),tokenizer = token.tokenize)
text_counts= cv.fit_transform(data['Text'])
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
text_counts, data['Sentiment'], test_size=0.3, random_state=1)
Building the Overall Model
Different algorithms in the Naive Baynes common group can be utilized in this case.
I tried many different algorithms such as the Bernoulli, Gaussian, and Complement Naive Baynes algorithm.
Additionally, I attempted to use the RandomForestClassifer to classify and predict the sentiments for the sentences, but proved to achieve a lower accuracy.
In the end, I was deciding between the Gaussian, Complement, and Multinomial Naive Baynes Algorithms.
from sklearn.naive_bayes import MultinomialNB
from sklearn import metrics
clf = MultinomialNB().fit(X_train, y_train)
predicted = clf.predict(X_test)
print("MultinomialNB Accuracy:",metrics.accuracy_score(y_test, predicted))
print(predicted)
clf.intercept_
clf.predict(X_train)
In my solution, I used the Multinomial Naive Baynes algorithm, but it can be interchangeable with the Gaussian, Bernoulli, and Complement Algorithms.
Although they are all Naive Baynes algorithms, they do have differences and are optimized for different scenarios.
The Multinomial Naive Baynes Classifier assumes that features of the text are drawn from Multinomial Distribution.
Scikit Learn has made it very easy to use the functions to implement the Multinomial Naive Baynes Classifier and can be accessed simply by using sklearn.naive_baynes.GaussianNB.
The MultinomialNB module is first imported then used to fit the X_train and y_train values.
But, I must fit the model first before implementing the .predict method.
Generating our Features
I proceeded to split the data for the text classification model, again, splitting the data into the same three parameters mentioned in previous sections.
I will use the TFidVectorizer to extract those features from the text samples.
from sklearn.feature_extraction.text import TfidfVectorizer
tf=TfidfVectorizer()
text_tf = tf.fit_transform(data['Text'])
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
text_tf, data['Sentiment'], test_size=0.3, random_state=123)
I will continue to use the MultinomialNB but other Naive Baynes algorithms are interchangeable depending on the dataset provided.
The program was executed and produced an accuracy of approx. 81.2% at the time of testing.
from sklearn.naive_bayes import MultinomialNB
from sklearn import metrics
clf = MultinomialNB().fit(X_train, y_train)
predicted= clf.predict(X_test)
print("MultinomialNB Accuracy:", metrics.accuracy_score(y_test, predicted))
Alternatively, we can use the Random Forest Classifier, but it did not achieve an accuracy as high as the Multinomial or Bernoulli NB Classifiers.
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification
clf = RandomForestClassifier(max_depth=2, random_state=0)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print(clf.predict(X_test))
from sklearn import metrics
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))
Ignition Hacks Sigma Problem Solution 2 By: Kenneth Zhang
Building a Recurrent Neural Network for Text Classification
After importing the necessary modules to pre-process and construct the model, I began vectorizing and tokenizing the text.
To improve the accuracy of the overall RNN, I attempted to fiter the data of any 'neutral' text, meaning text that did not show negativity or positivity.
This resulted in only valid text and words remained in the dataset therefore improving the overall accuracy.
trainingData = pd.read_csv('training_data.csv')
trainingData = trainingData[['Text','Sentiment']]
trainingData = trainingData[trainingData.Sentiment != "Neutral"]
trainingData['Text'] = trainingData['Text'].apply(lambda x: x.lower())
trainingData['Text'] = trainingData['Text'].apply((lambda x: re.sub('[^a-zA-z0-9\s]','',x)))
print(trainingData[ trainingData['Sentiment'] == 'Positive'].size)
print(trainingData[ trainingData['Sentiment'] == 'Negative'].size)
for idx,row in trainingData.iterrows():
row[0] = row[0].replace('rt',' ')
max_features = 2000
tok = Tokenizer(num_words=max_features, split=' ')
tok.fit_on_texts(trainingData['Text'].values)
X = tok.texts_to_sequences(trainingData['Text'].values)
X = pad_sequences(X)
Next, I constructed the Sequential Model.
The Sequential Model consisted of Embedding, Spatial Dropout, Dense, and Long Short Term Memory layer(s).
model = Sequential()
model.add(Embedding(max_features, embed_dim, input_length = X.shape[1]))
model.add(SpatialDropout1D(0.4))
model.add(LSTM(lstm_out, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(2,activation='softmax'))
model.compile(loss = 'categorical_crossentropy', optimizer='adam',metrics = ['accuracy'])
print(model.summary())
I trained the model with 7 epochs, but I wanted to use 10.
model.fit(X_train, Y_train, epochs = 7, batch_size=batch_size, verbose = 2)
After extracting a validation set, we can measure the overall score and accuracy of the model.
validation_size = 1500
X_validate = X_test[-validation_size:]
Y_validate = Y_test[-validation_size:]
X_test = X_test[:-validation_size]
Y_test = Y_test[:-validation_size]
score,acc = model.evaluate(X_test, Y_test, verbose = 2, batch_size = batch_size)
print("score: %.2f" % (score))
print("acc: %.2f" % (acc))
The Neural Network had a significantly higher accuracy than the traditional Naive Baynes classifiers.
Built With
python
Try it out
github.com | Ignition Hacks 2020 Kenneth Zhang Sigma Submission | Code and description for Kenneth Zhang's solution to Sigma Problem | ['Kenneth Zhang'] | [] | ['python'] | 32 |
10,530 | https://devpost.com/software/division-sigma-distilbert-model-trial | Our Story
We wanted to use a different NLP model we hadn't before and Bert seemed like a great choice as it is overall one of the best NLP models that we were slightly familiar with. However, our model turned out to have a training time of about 300 hours. Which we tried to troubleshoot however didn't succeed in. Then we changed the model to DistilBERT which was working much better. Our training time was around 4 hours. It was about to finish training the first epoch but then it crashed. We tried to fix this however it only got worse. In the end, we tried our best even though we didn't succeed on this one model. We were still able to learn a lot. Queue Coldplay:
[Verse 1: Chris Martin]
When you try your best, but you don't succeed
When you get what you want, but not what you need
When you feel so tired, but you can't sleep
Stuck in reverse
When the tears come streaming down your face
When you lose something you can't replace
When you love someone, but it goes to waste
Could it be worse?
[Chorus: Chris & Will, Chris]
Lights will guide you home
And ignite your bones
And I will try to fix you
[Verse 2: Chris Martin]
And high up above or down below
When you're too in love to let it go
But if you never try, you'll never know
Just what you're worth
[Chorus: Chris & Will, Chris]
Lights will guide you home
And ignite your bones
And I will try to fix you
Thank you for the opportunity!!!!
Built With
python
tensorflow
Try it out
github.com | Div Sigma DistilBERT Model Trial- Mayank David Matthew Adam | When you try your best, but you don't succeed When you get what you want, but not what you need When you feel so tired, but you cant sleep Stuck in reverse When the tears come streaming down your face | ['Matthew Oliveira', 'Mayank Mehra', 'Adam Holan', 'dg9278 Guo'] | [] | ['python', 'tensorflow'] | 33 |
10,530 | https://devpost.com/software/sentiment-ckwevh | What I learned
Coding in Python
Machine Learning concept
Try it out
github.com | Sentiment | AI determines positive and negative emotions in text | ['Mincy Yang'] | [] | [] | 34 |
10,530 | https://devpost.com/software/garbage-wr4ybl | Inspiration
Career cruising
## What it does
How we built it
Challenges we ran into
Accomplishments that we're proud of
What we learned
What's next for School Pathway Planner
Built With
python
Try it out
github.com | School Pathway Planner | An idea that makes future pathway planning less stressful | ['Hassam Sulehria', 'Muhammad Zaeem Khalid', 'Rahul Doguparty', 'Marjan Ahmed'] | [] | ['python'] | 35 |
10,530 | https://devpost.com/software/division_sigma | division_sigma
Determining if a given sentence is positive or negative. Submitted to Ignition Hacks.
How I built It
In order to detect whether a given sentence is either positive or negative, I decided to create a sequential neural network using Keras. I chose to work with a classification model.
I started off with uploading the training data onto my notebook. To filter out noisy data such as words that start with @, quotes, and special characters, I changed the data in the training set. I did the same with the contestant judging file. In tokenization, I converted the sentences into token, which is also known as text segmentation.
The next step was to prepare and train my model. Like mentioned before, I made my model as sequential. I setted the layers, and I was ready to train. Epochs as 20 and batch size as 32, my model got an accuracy of 79%.
Lastly, I created the submitting csv file with the id and results.
Challenges
My biggest challenge was that I was not completely familiar with building a classification model. I have come across neural networks before, but it wasn’t my best suit. I spent a lot of time researching and getting myself more comfortable with understanding neural networks, and there were multiple times where I was stumped. The only way I could continue to work through was to research and learn.
A minor challenge was trying to higher the accuracy level, but that was partially solved by raising the epochs number in the model.
What's next
What I have created in the two days was a very basic approach to solving this problem. What I can do next is learning a more advanced preprocessing technique, such as data normalization and standardization. I would also find a different way to higher the accuracy, not just relying on the epoch.
Built With
jupyter-notebook
Try it out
github.com | division_sigma | Determining if a given sentence is positive or negative. | ['Sungmin Kim'] | [] | ['jupyter-notebook'] | 36 |
10,530 | https://devpost.com/software/ignitionhacks2020-85jm1a | IgnitionHacks2020
This code uses the data in the contestant_judgment.csv file, cleans it (removing all punctuation, URLs, and making everything lowercase), then uses the nltk sentiment library to figure out how positive, negative, and neutral a sentence is (in percents) and compares these values to figure out if the phrase is overall more positive or negative. It then stores it in the results.csv file.
IMPORTANT: We were unable to figure out how to add the training_data.csv and contestant_judgment.csv to this repository as the files were too large, even when compressed. If you have those downloaded separately, this should still work.
Members: Selena Zhang, Michelle Saltoun
Built With
python
Try it out
github.com | IgnitionHacks2020 | Enjoy! | ['Michelle Saltoun'] | [] | ['python'] | 37 |
10,530 | https://devpost.com/software/ignition-hacks-division-sigma-entry | Ignition-Hacks-Division-Sigma-Entry
Sentiment analysis of tweets using natural language processing
My team: Kevin Liu, Josh Li, Justin Lo, Brian Chan
Built With
jupyter-notebook
Try it out
github.com | Ignition-Hacks-Division-Sigma-Entry | Sentiment analysis of tweets using natural language processing | ['Brian Chan', 'Joshua Li', 'Heresay Lo', 'Kevin Liu'] | [] | ['jupyter-notebook'] | 38 |
10,530 | https://devpost.com/software/sentimental-ai | The code tells you if a given review is positive or negative. We coded this in python with the natural language toolkit and sci-kit learn machine learning library. The biggest challenge is that our team is new to python and AI and we encountered many new commands, hence much time was for learning and debugging.
Built With
natural-language-processing
nltk
numpy
pandas
python
scikit-learn
Try it out
github.com | Sentimental Sentance AI | predict sentiment of a sentance | ['Qwertu Meng', 'Sean Wang'] | [] | ['natural-language-processing', 'nltk', 'numpy', 'pandas', 'python', 'scikit-learn'] | 39 |
10,530 | https://devpost.com/software/the-sentimentalizer | Inspiration
Our inspiration for this project stemmed through the theme of the project. We were given a demo program and were asked to predict the sentiments of a given data set of tweets. The name was inspired by the great Safwan.
What it does
Our project helps to determine whether the tweet's are either positive( indicated by a 1) or negative (indicated by a 0). When given the data to our machine, it can predict whether the machine is positive or negative.
How I built it
We were able to build this program by using the given demo which helped to train and test the machine. We used python skleaner and pandas to help create this. Also, textblob was used.
Challenges I ran into
Honestly this entire project was challenging, especially since we both had no knowledge of AI or machine learning before this. The demo and resources given helped to understand many aspects of this project. Also, getting an accuracy higher than 78% was really difficult and we were having trouble getting a better accuracy.
Accomplishments that I'm proud of
An accomplishment I'm proud of is being able to create this entire program. Doing this project with no prior knowledge was extremely difficult and I'm proud to be able to say that I completed this.
Built With
pandas
python
skleaner
Try it out
github.com | The Sentimentalizer | This project is able to accurately predict whether tweets are positive or negative, using an AI program. | ['Yasmin Modarai', 'Safwan Hasan'] | [] | ['pandas', 'python', 'skleaner'] | 40 |
10,530 | https://devpost.com/software/sentiment-analyzer-xni9gj | Inspiration
Every company has varied products and services and their sentiment cannot be accurately predicted based on a generalized model. Therefore we create one to exactly fit their needs
What it does
It trains an AI model based on a predefined database to predict sentiment of inputs.
How I built it
It was built on google colab using nltk, sklearn and pandas library
Challenges I ran into
Optimizing the code for highest accuracy and lowest run time possible
Accomplishments that I'm proud of
I am proud of creating a project that has potential of development for a real world application
What I learned
I learned data cleaning and basic AI model development
What's next for Sentiment Analyzer
It can be made better by effective data cleaning and removal of ineffective stop words
sidenote
The files were sent over via mail and uploaded to github as well.
Built With
google-colab
nltk
pandas
python
sklearn
Try it out
github.com
github.com | Sentiment Analyzer | Every company with reviews needs to know the type of reviews they are getting. Our AI model trained by a predefined dataset(that you can set) is perfect for accurate reviews.) | ['Aaditya Yadav', 'Ikshita Yadav'] | [] | ['google-colab', 'nltk', 'pandas', 'python', 'sklearn'] | 41 |
10,530 | https://devpost.com/software/mathwiz | The code behind the app
The output
Inspiration
We personally have younger siblings at home who we see struggle with math occasionally. Which is why we thought creating a program that provides mathematical support for elementary students would be beneficial to their understanding.
What it does
When using the application, the user chooses a category from one of five subtopics in the grade 8 curriculum. After selecting an option, the user is provided a link to a youtube tutorial, for this specific topic and a practice question. If the user enters a correct answer, then they get a positive message and if they answer incorrectly, they receive a detailed explanation on how to solve the question.
How we built it
We built it using Python and google colab. In Python, we used a while loop and many if statements in order for the program to function properly.
Challenges we ran into
We had trouble limiting the curriculum to some topics and programming how the user will input the answer. This is why, we need to clarify the question to make it known to the user which units to enter and account for the various ways the units can be entered.
Accomplishments that we're proud of
We are proud that we took upon a programming language that we are not familiar with since we have primarily worked with Java, however, we decided to take upon a challenge. We are proud to create a resource that aids students in their academics.
What we learned
We built upon our collaboration and critical thinking skills as the code required several aspects of Java to be used together.
What's next for MathWiz
We hope to incorporate resources for more grades and a wider variety of practice questions for students. We also hope to make this an application available to students.
Built With
canva
google-collab
imovie
python
Try it out
colab.research.google.com | MathWiz | Conquer your fear of Math | ['Ashley Pulickeel', 'Poorvi P', 'druthi P'] | [] | ['canva', 'google-collab', 'imovie', 'python'] | 42 |
10,530 | https://devpost.com/software/student-performance-estimator | Estimating a student's first period grade based on his other 5 attributes.
Inspiration
For any high school student, it is more than usual to encounter some sort of stress before exams. However, it'd probably make you feel better if you can get a general estimation of your score, as well as your classmates' score, before the exam. Thus, the primary purpose of this program is to estimate one's final exam grade, based on how they previously performed, how many hours they study per day, etc.
What it does
The general purpose of this program is to estimate 1 out of 6 academic attributes of a student (first period grade, second period grade, final grade, study time, number of failures, number of absences) based on his other 5 attributes, as well as the general trend in his class.
The program does it by reading an csv file which contains the various attributes of a group of students, and building 5 different models (degree 1 - 5) using the polynomial regression algorithm. After the user enters their desired attribute, the program will ask for the remaining 5 attributes, and return the estimated result from each of the five models, as well as their corresponding R-Square Error values for the user to reference.
The program stores the five models each time after training. It'll reuse the models if the user's next desired attribute is the same as the previous one. Or else, it'll retrain the models, dumping the previously stored one.
The example data set, student-mat.csv, is an student performance data set from Paulo Cortez, University of Minho, Guimarães, Portugal. This data approach student achievement in secondary education of two Portuguese schools, which contains 33 different attributes over 349 students. However, this program only utilizes 6 of the attributes (G1 for first period grade, G2 for second period grade, G3 for final grade, studytime, failures, absences). The example data set can be found at
https://archive.ics.uci.edu/ml/datasets/Student+Performance
Challenges I ran into
It was hard at first to get familiar with the libraries. However, after watching a few tutorials, I'm astonished by how powerful these libraries are.
Accomplishments that I'm proud of
I've only started learning python AI this weekend, and this isn't too bad as my first project on it.
What I learned
Polynomials regression, many useful python libraries, as well as a ton of fun!
What's next for Student Performance Estimator
I would include more parameters to better estimate the attributes. For instance, how many hours students spend on entertainments per day, how many hours they sleep, etc.
Built With
numpy
pandas
pickle
python
sklearn
Try it out
github.com | Student Performance Estimator | The general purpose of this program is to estimate a student's first period grade, second period grade, final grade, study time, number of failures, number of absences based on his other info. | ['Steven Chen'] | [] | ['numpy', 'pandas', 'pickle', 'python', 'sklearn'] | 43 |
10,530 | https://devpost.com/software/seweasy | An example pattern created through SewEasy
The main page of the web app
SewEasy's mission
SewEasy's goals
Submitted to Division Delta
Inspiration
Quarantine has forced many young people to get creative and find new hobbies, such as sewing. However, sewing has a steep learning curve and it's often difficult to find easy to sew patterns that fit. When attempting to draft your own custom pattern, the math became complex and annoying. The idea to automate this process created SewEasy.
What it does
Our program outputs patterns for sewing and measurements based on the user inputted measurements. They can also select the specific garment they wish to sew. At the moment, users can choose between creating a tank top, pants, skirt, face mask or sweater. SewEasy will output a labelled diagram for users to follow. This allows people to easily recreate the pattern themselves with measurements tailored to fit themselves.
How we built it
We created SewEasy using React and Javascript. We also used css to style our pages and various packages to polish our project.
Accomplishments that we're proud of
We are proud to have learned more about effectively changing states in React. We are also proud to have learned and used javascript and have successfully built a working application.
What we learned
By creating SewEasy, we became more familiar with coding in React and using various packages with npm to create our final project. As well, we learned more about hosting options on GitHub pages or Heroku.
What's next for SewEasy
In the future, we would like to add more and more patterns so people can have more options of what they can make. We also would allow users to download a printable version of the pattern.
Built With
css
html5
javascript
react
Try it out
github.com
sewingapp.herokuapp.com | SewEasy | A webapp generating sewing patterns tailored to your measurements | ['Annie Chen', 'Annie Sun'] | [] | ['css', 'html5', 'javascript', 'react'] | 44 |
10,530 | https://devpost.com/software/prepsci | Inspiration
Amidst the global pandemic, it is getting increasingly more difficult for students to find study groups, educational support, and more specifically, tutors. We knew that now more than ever, a service that allows students and other users to utilize common resources around them was needed. PrepSci is a platform based on the fundamentals of the sharing economy, which also allows any person to participate in the community, generating jobs and opportunities for all involved. This is something that we thought could be very beneficial to many that suffer from unemployment and lack of educational support due to COVID-19.
What it does
PrepSci is a mobile/desktop platform that matches tutees with tutors based on subject specificity, price range, location, and other factors. Based on the sharing economy, PrepSci is a space where anyone with a smartphone can get involved, provide educational support, and earn a bit of cash. The algorithm is designed to find a tutor based on the interests selected by the tutee and provide them with their top matches. All tutors that sign up with this app will be required to show proof of experience and/or any certifications, which will publicly be disclosed on the app to aid in helping tutees find the right tutor. Tutees are able to find the perfect tutor that is credible and tailored to their needs and schedules.
How we built it
We started PrepSci with an app prototype, to visualize how we wanted the app to look like and any features we wanted to showcase. This allowed us to see the goal we had in mind, and cater to any other ideas as the next steps. After visualizing, we began to create the backend code via Python. We made classes for the tutors and tutees and began hard coding the instances to see how the database would look like in the future. Our next steps are to integrate all of this code using the Google Cloud API called App Engine/
Challenges we ran into
Given the vast potential of our project in areas of optimal matching criteria, group study sessions, location optimization, subject intensity, our team had an extensive discussion at the start of the design process to determine the best approach. The discussion was extensive and resulted in a slower start to the design process, however, it helped us lay out a tangible pathway to follow and helped us efficiently overcome many hurdles before they arose to become major issues.
Accomplishments that we're proud of
PrepSci is something all of our teammates felt deeply about, as it connected with issues that we were facing personally. Everyone in the PrepSci team will attend university in the fall, where we are exposed to new learning styles and changes in educational support due to COVID-19. Having an academic community is vital to the success of a student, and so is having ample job opportunities in such devastating cases of unemployment. PrepSci offers a chance for students to connect with those around them and creates paid opportunities for those looking for work. We are really proud of PrepSci as we truly believe it could make a difference to so many people amidst the pandemic.
What we learned
Through the process of developing PrepSci, we have gotten yet another chance to appreciate the power of our skills and apply them to the real world to get a sense of its true impact. The opportunity to build both the back-end engine and develop the business strategy for the project gave us an opportunity to appreciate the business model and develop our model based on the user interest. Furthermore, the team experience where every individual brings in a unique skill set formed a dynamic team that resembles project implementation in the real world where individuals from all different fields come together to find the optimal solution through exceptional communication and teamwork.
What's next for PrepSci
After its launch, PrepSci is intended to increase its scope by incorporating several different features. PrepSci intends to include additional options for tutees to choose from to help tailor their needs more effectively. These options are designed to find a tutor that perfectly incorporates the student’s values, interests, and needs in their teachings. The PrepSci team is also currently conducting research in order to incorporate cryptocurrency to aid with payments and transfers as well as chatbots to help users and expedite the process. We hope to partner with LinkedIn in the near future for user login and credibility as well.
Built With
proto.io
prototype-geoip
python
Try it out
github.com | PrepSci | PrepSci is a mobile/desktop platform that matches tutees with tutors based on subject specificity, price range, location and other factors. Within the sharing economy, PrepSci uses common resources. | ['Fatima Jangda', 'Ali Meshkat', 'Yawar Ashraf', 'Eeman Salman'] | [] | ['proto.io', 'prototype-geoip', 'python'] | 45 |
10,530 | https://devpost.com/software/stars-pedia | Stars-pedia
Under the tremendous impact of the current Covid-19 pandemic, online education has become more indispensable than ever. Stars-pedia is an educational website designed for people of all ages who are interested in astronomy and astrology. It provides users with great experiences of learning more information about the solar system and zodiac signs while having fun with an intelligent quiz system and an online fortune teller. Made with a wide array of elements from html/css in the front-end and javascript in the back-end.
Built With
css
html
javascript
Try it out
github.com | Stars-pedia | An astronomy & astrology - based educational website with an engaging GUI and enticing user-interactive components | ['Catherine Wu', 'Xianglei (Abigail) Xu', 'Joe Wang', 'Steven Liu'] | [] | ['css', 'html', 'javascript'] | 46 |
10,530 | https://devpost.com/software/sentiment-analysis-gw50ao | Submission for Ignition Hacks 2020, Division Sigma ( ∑ ).
Inspiration
The prompt for this division of the hackathon was to build an artificial intelligence model for predicting the sentiment of a dataset of tweets. With a stronger statistics background, we decided to extend that knowledge in our approach.
What it does
Our program determines whether a piece of text has positive (1) or negative (0) sentiment.
How we built it
We used Google Colaboratory to collaborate on a jupyter notebook running remotely.
Our algorithm starts off by fetching the data remotely from a GitHub page, as Colab recycles the files periodically. Downloading it to the local environment and reading it using Pandas, next we tokenize and cleanse the data.
We remove hyperlinks, and call a function
cleanse()
, which will determine whether or not to take into consideration the current word based on stop words, whether it is another user mention (starts with @), remove punctuation and symbols, and if none of the above, will lemmatize it, and return either the lemmatized word, or the original (with punctuation and symbols removed).
After our preprocessing, we train our Naive Bayes algorithm (with a Gaussian distribution). Our algorithm implements Bayes Theorem in probability and statistics, using the probability that a word is of one sentiment to determine the sentiment of a given sentence.
In our testing, the training data's processing (in the section labelled
# == Preprocessing
) takes around 23-25 minutes to build.
Challenges we ran into
We could not think of an algorithm that had the right balance of efficiency and accuracy, and experimented with quite a few algorithms. After finding an algorithm of choice, we had a lot of trouble optimizing both the speed, memory, and accuracy.
We also ran into difficulties working simultaneously on Google Colab as we could not work on it simultaneously without overriding (and erasing) one another's work.
Accomplishments that we're proud of
We are proud of learning how to use Google Colaboratory in the matter of two days. We are also proud of learning so much about machine learning and how sentiment analysis works. Finally, we are proud of a completed machine learning project for our first hackathon about machine learning.
What we learned
We learned how to work with Google Colaboratory and some machine learning algorithms and ideas such as Naive Bayes, neural networks, and vectorization.
Having been the first time we've used machine learning, we spent most of our first day watching tutorials and grasping new AI concepts, and started to focus in on NLP techniques. Lemmatization, morphological segmentation, part-of-speech tagging, and tokenization were some of the cleaning methods we learned and implemented.
What's next for Sentiment Analysis
Improving the accuracy of the analysis. While about 80% accuracy is nice, we seek to refine the algorithm even more to increase its accuracy.
Improving the efficiency of the algorithm.
https://colab.research.google.com/drive/1tX4ZdAjeaLOAoPX5-Wtz1pPz-j5kgomQ?usp=sharing
Built With
jupyter-notebook
nltk
pandas
python
sklearn
textblob
Try it out
colab.research.google.com
github.com | Sentiment Analysis | AI solution to determine sentiment in a tweet using Bayes' algorithm | ['Justin Lu', 'Matthew Li', 'Celeste L', 'Justin Zhu'] | [] | ['jupyter-notebook', 'nltk', 'pandas', 'python', 'sklearn', 'textblob'] | 47 |
10,530 | https://devpost.com/software/sentiment-analyzer-oaz6ht | Inspiration & what it does
This project was created for Ignition Hacks 2020. The machine learning algorithm analyzes text from a CSV file for positive and negative sentiments.
How we built it
1. Data Cleaning
~ Remove HTML ampersand codes (&, etc.) and punctuation ~ Remove @twitterhandles ~ Remove stopwords (unnecessary words) ~ Make one with all lowercase (can be commented out to test) ~ Separate the text into individual words and two word phrases, as a list ~ Lemmatize
2. Feature Extraction
Use count vectorizer to select features from the bag of words generated
3. Model Selection
Test multiple models, including Logistic Regression, Random Forest, Artificial Neural Network (ANN)
Challenges
One challenge that our team had was the memory capacity of our computers and Google Colab. Our laptops crashed while attempting to run some of the algorithms. Also, some of us used Jupyter Lab rather than Colab, so it took a long time to push the notebooks to GitHub and download them onto another teammate's laptop.
Accomplishments that we're proud of
We were able to manage the work efficiently and collaborated well, despite the challenges and time restraints. We ran multiple variations of the data and models, which proved beneficial to discovering the optimal accuracy but also detrimental to our computer memory. Coming into the hackathon, we each had our areas of specialty and were able to use that to our advantage while coding the Sentiment Analyzer.
What we learned
We all learned a lot about machine learning and natural language processing. The workshops we attended and the research we conducted as a team certainly contributed to our increase in knowledge of writing machine learning algorithms. The Ignition Hacks community (communicating by discord server) also provided tips and useful information.
What's next for Sentiment Analyzer
In the future, we would like to improve our algorithm to optimize accuracy rates. This would come in the form of a bigger dataset and many more variations of data cleaning, like including punctuation or emojis. Also, we could use different models that we hadn't tested yet, such as Naive Bayes or k-Means.
Built With
csv
github
google-colab
jupyter-lab
nltk
numpy
pandas
python
sklearn
tensorflow
Try it out
github.com | Sentiment Analyzer (IgnitionHacks 2020) | NLP and ML to analyze text for positive or negative sentiment | ['Anna Yang', 'Jenny Yang', 'Ayush Raj'] | [] | ['csv', 'github', 'google-colab', 'jupyter-lab', 'nltk', 'numpy', 'pandas', 'python', 'sklearn', 'tensorflow'] | 48 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.