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
This repository has a implementations of data augmentation for NLP for Japanese.

daaja This repository has a implementations of data augmentation for NLP for Japanese: EDA: Easy Data Augmentation Techniques for Boosting Performance

Koga Kobayashi 60 Nov 11, 2022
Every Google, Azure & IBM text to speech voice for free

TTS-Grabber Quick thing i made about a year ago to download any text with any tts voice, over 630 voices to choose from currently. It will split the i

16 Dec 07, 2022
Tool to check whether a GCP bucket is public or not.

Tool to check publicly accessible GCP bucket. Blog https://justm0rph3u5.medium.com/gcp-inspector-auditing-publicly-exposed-gcp-bucket-ac6cad55618c Wha

DIVYANSHU SHUKLA 7 Nov 24, 2022
Framework for fine-tuning pretrained transformers for Named-Entity Recognition (NER) tasks

NERDA Not only is NERDA a mesmerizing muppet-like character. NERDA is also a python package, that offers a slick easy-to-use interface for fine-tuning

Ekstra Bladet 141 Dec 30, 2022
KoBART model on huggingface transformers

KoBART-Transformers SKT에서 공개한 KoBART를 편리하게 사용할 수 있게 transformers로 포팅하였습니다. Install (Optional) BartModel과 PreTrainedTokenizerFast를 이용하면 설치하실 필요 없습니다. p

Hyunwoong Ko 58 Dec 07, 2022
This is the 25 + 1 year anniversary version of the 1995 Rachford-Rice contest

Rachford-Rice Contest This is the 25 + 1 year anniversary version of the 1995 Rachford-Rice contest. Can you solve the Rachford-Rice problem for all t

13 Sep 20, 2022
Wind Speed Prediction using LSTMs in PyTorch

Implementation of Deep-Forecast using PyTorch Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting Adapted from original implementation Setu

Onur Kaplan 151 Dec 14, 2022
TTS is a library for advanced Text-to-Speech generation.

TTS is a library for advanced Text-to-Speech generation. It's built on the latest research, was designed to achieve the best trade-off among ease-of-training, speed and quality. TTS comes with pretra

Mozilla 6.5k Jan 08, 2023
ThinkTwice: A Two-Stage Method for Long-Text Machine Reading Comprehension

ThinkTwice ThinkTwice is a retriever-reader architecture for solving long-text machine reading comprehension. It is based on the paper: ThinkTwice: A

Walle 4 Aug 06, 2021
Différents programmes créant une interface graphique a l'aide de Tkinter pour simplifier la vie des étudiants.

GP211-Grand-Projet Ce repertoire contient tout les programmes nécessaires au bon fonctionnement de notre projet-logiciel. Cette interface graphique es

1 Dec 21, 2021
Just Another Telegram Ai Chat Bot Written In Python With Pyrogram.

OkaeriChatBot Just another Telegram AI chat bot written in Python using Pyrogram. Requirements Python 3.7 or higher.

Wahyusaputra 2 Dec 23, 2021
LewusBot - Twitch ChatBot built in python with twitchio library

LewusBot Twitch ChatBot built in python with twitchio library. Uses twitch/leagu

Lewus 25 Dec 04, 2022
FactSumm: Factual Consistency Scorer for Abstractive Summarization

FactSumm: Factual Consistency Scorer for Abstractive Summarization FactSumm is a toolkit that scores Factualy Consistency for Abstract Summarization W

devfon 83 Jan 09, 2023
pkuseg多领域中文分词工具; The pkuseg toolkit for multi-domain Chinese word segmentation

pkuseg:一个多领域中文分词工具包 (English Version) pkuseg 是基于论文[Luo et. al, 2019]的工具包。其简单易用,支持细分领域分词,有效提升了分词准确度。 目录 主要亮点 编译和安装 各类分词工具包的性能对比 使用方式 论文引用 作者 常见问题及解答 主要

LancoPKU 6k Dec 29, 2022
Python module (C extension and plain python) implementing Aho-Corasick algorithm

pyahocorasick pyahocorasick is a fast and memory efficient library for exact or approximate multi-pattern string search meaning that you can find mult

Wojciech Muła 763 Dec 27, 2022
Blackstone is a spaCy model and library for processing long-form, unstructured legal text

Blackstone Blackstone is a spaCy model and library for processing long-form, unstructured legal text. Blackstone is an experimental research project f

ICLR&D 579 Jan 08, 2023
Machine learning models from Singapore's NLP research community

SG-NLP Machine learning models from Singapore's natural language processing (NLP) research community. sgnlp is a Python package that allows you to eas

AI Singapore | AI Makerspace 21 Dec 17, 2022
Implementation of TTS with combination of Tacotron2 and HiFi-GAN

Tacotron2-HiFiGAN-master Implementation of TTS with combination of Tacotron2 and HiFi-GAN for Mandarin TTS. Inference In order to inference, we need t

SunLu Z 7 Nov 11, 2022
Data and evaluation code for the paper WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER (EMNLP 2021).

Data and evaluation code for the paper WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER. @inproceedings{tedes

Babelscape 40 Dec 11, 2022
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning

GenSen Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning Sandeep Subramanian, Adam Trischler, Yoshua B

Maluuba Inc. 309 Oct 19, 2022