2017 Spring (105-2) -- Machine Learning
An Image captioning web application combines the power of React.js for front-end, Flask and Node.js for back-end, utilizing the MERN stack. Users can upload images and instantly receive automatic captions. Authenticated users have access to extra features like translating captions and text-to-speech functionality.
Predicts COVID-19 from PA view of X-ray when submitted on the website
Project in several phases to ultimately built an app that translate from human to cats and cats to human using deep learning algorithm
AI-Powered Smart Farming & Marketplace System
Cough It is an android app that leverages deep learning and acoustics to diagnose COVID-19 infection from cough audio samples.
A web application for classifying clothing photos uploaded by users. It makes use of a deep learning model which is deployed using FastAPI. Achieved 85% accuracy in classifying images.
A web-based solution utilizing a robust tensorflow model for precise traffic condition classification made in ReactJs and FastAPI for backend.
A food image classifier that uses convolutional neural networks.
Character recognition CNN with server based interface.
Django Wrapper for MNIST dataset
The project takes as captured image as input and predicted the accurate scene using Convolutional neural network.
Lung Cancer Prediction Model: Leverage the power of deep learning with this TensorFlow-based project. Trained on a dataset of lung X-Ray images, the model accurately predicts cancer cases. Easily integrate and utilize the model for early detection. #HealthTech #MachineLearning
A modern application that can classify images for you and will store the classification history.
With a help smart phone camera get access to a web app that will help to identify the ripe and unripe bananas, its just a tech demonstrator for easy sorting of fruits easily.
A simple demonstration for CNN models.
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