Cycle AI: Using Artificial Intelligence to Classify Recyclables

Title: Cycle AI: Using Artificial Intelligence to Classify Recyclables
Group: Michael Fourie, James Gleave, Luis Mangubat, Jean Yaacoub
Course: This project was for QHacks and The Mayor’s Innovation Challenge

Description: Cycle AI is a mobile application that utilizes the power of artificial intelligence to classify recyclables, organics, hazardous waste, as well as regular waste allowing users to properly dispose of their waste. The original model built during QHacks utilized a standard convolutional neural network that could classify only one piece of waste in an image. After receiving some constructive feedback from the City of Kingston judges, the team created an entirely new more advanced convolutional neural network that has the ability to isolate and mask images. This means that users can now take a photo of as much waste as they would like, and Cycle would be able to classify each piece of waste in that photo. This entire project was written in Python, using Kivy for the front-end, and TensorFlow, Keras, and OpenCV for the back-end. Cycle has a built-in achievement system, granting users rewards such as using the app consistently, scanning every category of recyclables, and reaching 100 total scans. This is to incentivize users to use the app more often, furthering their knowledge of proper recycling habits.

Watch the group’s pitch for the Mayor’s Innovation Challenge (starts at 9:50).

More information can be found on the Cycle AI Devpost page.




Title: WaveNotes
Group: Rithik Bhatia, Berge Yaacoubian, Sammy Moss, Spencer Neal, Daniel Pang
Course: CISC 325

Description: WaveNotes is a note taking application that lets the user record the audio as well as type their notes. When a class is over, they can use the app to review the notes by previewing the audio of their notes and see what they typed at what time using a timeline. They can then find out when they did not type anything and fill up missing sections of notes.

Download WaveNotes (Mac OS X only)

Educ[alcul]ate – iOS Academics Tracker

Title: Educ[alcul]ate – iOS Academics Tracker
Author: Jordan Belinsky

Description: Educ[alcul]ate is an iOS app I created following a Python-based proof of concept. The app is designed to help students, of all education levels, keep track of their courses and assignment marks. Included in the app are two calculators and a class tracker.

The weighted average calculator allows for calculating marks based on individual weightings or section weights. The final exam calculator allows for calculating what exam mark is needed for a desired final mark, and what final mark will come from a desired exam mark.

The classes section allows students to create, edit and organize their individual classes. Within each class, students are able to add the titles and corresponding marks of assignments, and keep track of them throughout the term. This can be incredibly useful for schools or classes which don’t use an online grading system.

Throughout this school year, I have been working on a complete rewrite using Apple’s new SwiftUI framework. Support for native dark mode, GPA calculation, and cloud saving are going to be included with the new update, along with a completely reworked user interface. Version 1.1 is available on the App Store as of now, with Version 2.0 likely releasing Summer 2020.

I encourage you to give the app a try if you want to have an easy method of tracking your success through school, or check out the source code if you wish to learn more about how the app works.

Download on the App Store Fork on GitHub