ReCoin - Restoring our environment and businesses in parallel

Related tags

Text Data & NLPReCoin
Overview

Shashank Ojha, Sabrina Button, Abdellah Ghassel, Joshua Gonzales

"Reduce Reuse Recoin"

Theme Covered:

The themes covered in this project include post pandemic restoration for both the environment, small buisnesses, and personal finance! The app pitched uses an extensivly trained AI system to detect trash and sort it to the proper bin from your smartphone. While using the app, users will be incentivized to use the app and recover the environment through the opportunity to earn points, which will be redeemable in partnering stores.

Problem Statment:

As our actions continue to damage the environment, it is important that we invest in solutions that help restore our community in more sustainable practices. Moreover, an average person creates over 4 pounds of trash a day, and the EPA has found that over 75% of the waste we create are recyclable. As garbage sorting is so niche from town-to-town, students have reportable agreed to the difficulty of accurately sorting garbage, thus causing this significant misplacement of garbage.

Our passion to make our community globally and locally more sustainable has fueled us to use artificial intelligence to develop an app that not only makes sorting garbage as easy as using Snapchat, but also rewards individuals for sorting their garbage properly.

For this reason, we would like to introduce Recoin. This intuitive app allows a person to scan any product and easily find the bin that the trash belongs based off their location. Furthermore, if they attempt to sell their product, or use our app, they will earn points which will be redeemable in partnering stores that advocate for the environment. The more the user uses the app, the more points they receive, resulting in better items to redeem in stores. With this app we will not only help recover the environment, but also increase sales in small businesses which struggled during the pandemic to recover.

About the App:

Incentive Breakdown:

Please note that these expenses are estimated expectations for potential benefit packages but are not defined yet.

We are proposing a $1 discount for participating small businesses when 100 coffee/drink cups are returned to participating restaurants. This will be easy for small companies to uphold financially, while providing a motivation for individuals to use our scanner.

Amazon costs around $0.5 to $2 on packaging, so we are proposing that Amazon provides a $15 gift card per 100 packages returned to Amazon. As the 100 packages can cost from $50 to $200, this incentive will save Amazon resources by 5 to 100 times the amount, while providing positive public perception for reusing.

As recycling plastic for 3D filament is an up-and-coming technology that can revolutionize environment sustainability, we would like to create a system where providing materials for such causes can give the individuals benefits.

Lastly, as metals become more valuable, we hope to provide recyclable metals to companies to reduce their expenses through our platform.

The next steps to this endeavor will be to provide benefits for individuals that provide batteries and electronics with some sort of incentive as well.

User Interface:

#add user stuff!!!!!!!!!!!1

Technological Specifics and Next Steps:

Frontend

----ADDDDDDDDDDDD GRAPHHHHHHHHHHHHHHHHHHHHHHHH____ We used to React.JS to develop components for the webcam footage and capture screen shots. It was also utilized to create the rest of the overall UI design.

Backend

Trash Detection AI:

On Pytorch, we utilized an open-source trash detection AI software and data, to train the trash detection system originally developed by IamAbhinav03. The system utilizes over 2500 images to train, test, and validate the system. To improve the system, we increased the number of epochs to 8 rather than 5 (number of passes the training system has completed) to train it for an additional four hours than required. This allowed the accuracy to increase by 4% more than the original system. We also modified the test train and split amounts to 70%, 10%, and 20% respectively, as more prominent AI studies have found this distribution to receive the best results.

Currently, the system is predicted to have a 94% accuracy, but in the future, we plan on using reinforcement learning in our beta testing to continuously improve our algorithm. Reinforcement learning allows for the data to be more accurate, through learning from user correction. This will allow AI to become more precise as it gains more popularity.

Other Systems:

By using Matbox API and the Google Suite/API, we will be creating maps to find recycling locations and an extensively thorough Recoin currency system that can easily be transferred to real time money for consumers and businesses.

Stakeholders:

After the completion of this project, we intend to continue to pursue the app to improve our communities’ sustainability. After looking at the demographic of interest in our school itself, we know that students will be interested in this app, not only from convenience but also through the reward system. Local cafes and Starbucks already have initiatives to improve public perspective and support the environment (i.e., using paper straws and cups), therefore supporting this new endeavor will be an interest to them. As branding is everything in a business, having a positive public perspective will increase sales.

Amazon:

As Amazon continues to be the leading online marketplace, more packages will continue to be made, which can be detrimental to the world's limited resources. We will be training the UI to track packages that are Amazon based. With such training, we would like to be able to implement a system where the packaging can be sent back to Amazon to be reused for credit. This will allow Amazon to form a more environmentally friendly corporate image, while also saving on resources.

Small Businesses:

As the pandemic has caused a significant decline in small business revenue, we intend to mainly partner with small businesses in this project. The software will also help increase small business sales as by supporting the app, students will be more inclined to go to their store due to a positive public image, and the additive discounts will attract more customers. In the future, we wish to train AI to also detect trash of value (i.e.. Broken smartphones, precious metals), so that consumers can sell it in a bundle to local companies that can benefit from the material (ex: 3D-printing companies that convert used plastic to filament)

Timeline:

The following timeline will be used to ensure that our project will be on the market as soon as possible:

Code Refrences

https://medium.datadriveninvestor.com/deploy-your-pytorch-model-to-production-f69460192217

https://narainsreehith.medium.com/upload-image-video-to-flask-backend-from-react-native-app-expo-app-1aac5653d344

https://pytorch.org/tutorials/beginner/saving_loading_models.html

https://pytorch.org/tutorials/intermediate/flask_rest_api_tutorial.html

https://pytorch.org/get-started/locally/

https://www.kdnuggets.com/2019/03/deploy-pytorch-model-production.html

Refrences for Information

https://www.rubicon.com/blog/trash-reason-statistics-facts/

https://www.dosomething.org/us/facts/11-facts-about-recycling

https://www.forbes.com/sites/forbesagencycouncil/2016/10/31/why-brand-image-matters-more-than-you-think/?sh=6a4b462e10b8

https://www.channelreply.com/blog/view/ebay-amazon-packaging-costs

Owner
sabrina button
First Year Engineering Student at Queen's University (she/her)
sabrina button
A python package to fine-tune transformer-based models for named entity recognition (NER).

nerblackbox A python package to fine-tune transformer-based language models for named entity recognition (NER). Resources Source Code: https://github.

Felix Stollenwerk 13 Jul 30, 2022
A Pytorch implementation of "Splitter: Learning Node Representations that Capture Multiple Social Contexts" (WWW 2019).

Splitter ⠀⠀ A PyTorch implementation of Splitter: Learning Node Representations that Capture Multiple Social Contexts (WWW 2019). Abstract Recent inte

Benedek Rozemberczki 201 Nov 09, 2022
BiNE: Bipartite Network Embedding

BiNE: Bipartite Network Embedding This repository contains the demo code of the paper: BiNE: Bipartite Network Embedding. Ming Gao, Leihui Chen, Xiang

leihuichen 214 Nov 24, 2022
RuCLIP tiny (Russian Contrastive Language–Image Pretraining) is a neural network trained to work with different pairs (images, texts).

RuCLIPtiny Zero-shot image classification model for Russian language RuCLIP tiny (Russian Contrastive Language–Image Pretraining) is a neural network

Shahmatov Arseniy 26 Sep 20, 2022
Implementation of N-Grammer, augmenting Transformers with latent n-grams, in Pytorch

N-Grammer - Pytorch Implementation of N-Grammer, augmenting Transformers with latent n-grams, in Pytorch Install $ pip install n-grammer-pytorch Usage

Phil Wang 66 Dec 29, 2022
Code for ACL 2020 paper "Rigid Formats Controlled Text Generation"

SongNet SongNet: SongCi + Song (Lyrics) + Sonnet + etc. @inproceedings{li-etal-2020-rigid, title = "Rigid Formats Controlled Text Generation",

Piji Li 212 Dec 17, 2022
ACL'2021: Learning Dense Representations of Phrases at Scale

DensePhrases DensePhrases is an extractive phrase search tool based on your natural language inputs. From 5 million Wikipedia articles, it can search

Princeton Natural Language Processing 540 Dec 30, 2022
The SVO-Probes Dataset for Verb Understanding

The SVO-Probes Dataset for Verb Understanding This repository contains the SVO-Probes benchmark designed to probe for Subject, Verb, and Object unders

DeepMind 20 Nov 30, 2022
Phrase-Based & Neural Unsupervised Machine Translation

Unsupervised Machine Translation This repository contains the original implementation of the unsupervised PBSMT and NMT models presented in Phrase-Bas

Facebook Research 1.5k Dec 28, 2022
Semi-automated vocabulary generation from semantic vector models

vec2word Semi-automated vocabulary generation from semantic vector models This script generates a list of potential conlang word forms along with asso

9 Nov 25, 2022
A list of NLP(Natural Language Processing) tutorials built on Tensorflow 2.0.

A list of NLP(Natural Language Processing) tutorials built on Tensorflow 2.0.

Won Joon Yoo 335 Jan 04, 2023
Python library for interactive topic model visualization. Port of the R LDAvis package.

pyLDAvis Python library for interactive topic model visualization. This is a port of the fabulous R package by Carson Sievert and Kenny Shirley. pyLDA

Ben Mabey 1.7k Dec 20, 2022
Implementation for paper BLEU: a Method for Automatic Evaluation of Machine Translation

BLEU Score Implementation for paper: BLEU: a Method for Automatic Evaluation of Machine Translation Author: Ba Ngoc from ProtonX BLEU score is a popul

Ngoc Nguyen Ba 6 Oct 07, 2021
Wake: Context-Sensitive Automatic Keyword Extraction Using Word2vec

Wake Wake: Context-Sensitive Automatic Keyword Extraction Using Word2vec Abstract استخراج خودکار کلمات کلیدی متون کوتاه فارسی با استفاده از word2vec ب

Omid Hajipoor 1 Dec 17, 2021
BERT Attention Analysis

BERT Attention Analysis This repository contains code for What Does BERT Look At? An Analysis of BERT's Attention. It includes code for getting attent

Kevin Clark 401 Dec 11, 2022
FB ID CLONER WUTHOT CHECKPOINT, FACEBOOK ID CLONE FROM FILE

* MY SOCIAL MEDIA : Programming And Memes Want to contact Mr. Error ? CONTACT : [ema

Mr. Error 9 Jun 17, 2021
Stack based programming language that compiles to x86_64 assembly or can alternatively be interpreted in Python

lang lang is a simple stack based programming language written in Python. It can

Christoffer Aakre 1 May 30, 2022
Learn meanings behind words is a key element in NLP. This project concentrates on the disambiguation of preposition senses. Therefore, we train a bert-transformer model and surpass the state-of-the-art.

New State-of-the-Art in Preposition Sense Disambiguation Supervisor: Prof. Dr. Alexander Mehler Alexander Henlein Institutions: Goethe University TTLa

Dirk Neuhäuser 4 Apr 06, 2022
Code examples for my Write Better Python Code series on YouTube.

Write Better Python Code This repository contains the code examples used in my Write Better Python Code series published on YouTube: https:/

858 Dec 29, 2022
Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoenc

Venelin Valkov 1.8k Dec 31, 2022