Neelanjan Manna's Projects
A webcam-based object detection model that uses an artificial neural network to label the American Sign Language (ASL) hand sign alphabets. This deep learning model makes use of the Faster-RCNN model architecture to perform the object detection, which is implemented using the Tensorflow framework.
A curated list of edge tools for AI applications
Assisting blind people with the help of image-captioning via a smartphone app. The application makes use of two neural networks, a CNN-based image feature extractor, and an LSTM based sentence generator. The user is able to submit images to the app, which are fed to the CNN feature extractor. The extracted features are then fed to the LSTM network to generate the sentence that describes the image, which is then read aloud to the user.
Repository to hold chaos experiments resource YAML bundles
chaos engineering via kubernetes operator
The bridge between chaos operator and chaos experiment! Lifecycle manager for chaos experiments
Helm charts for the Drone platform on Kubernetes
An AI chess engine developed in Javascript that implements a Mini-Max algorithm to perform an adversarial search with the objective of finding the next best move. To make the process of searching the game tree more efficient, Alpha-Beta pruning is incorporated in the logic. The heuristic function to evaluate the board position is defined as the sum of individual piece cost weighted according to the piece-square table. Finally, there's a leaderboard for those who successfully beat the AI, which is implemented using Firebase Cloud Firestore.
Colorize is a computer vision algorithm that is implemented as a feed-forward pass in a CNN at test time, that has been trained on a million images from the Imagenet dataset. The algorithm is able to fill vibrant and realistic colors to the black and white images to recreate a plausible colorized image. Based on the 2016 research paper by Zhang et al. "Colorful Image Colorization".
A diagnostic AI-enabled mobile app which is able to classify upto 38 different plant diseases ranging for 14 crops and vegetables. The application makes use of the VGG-Net CNN architecture for the purpose of multi-class classification of the images of infected plant leaves. The trained model was then deployed using a Flask backend server, along with a Flutter-based frontend mobile application to interact with the REST API.
A diagnostic AI-enabled mobile app which is able to classify upto 38 different plant diseases ranging for 14 crops and vegetables. The application makes use of the VGG-Net CNN architecture for the purpose of multi-class classification of the images of infected plant leaves. The trained model was then deployed using a Flask backend server, along with a Flutter based frontend mobile application to interact with the REST API.
A one page , two asymmetric column resume template in XeTeX that caters to an undergraduate Computer Science student
A real-time drowsiness detection system for drivers, which alerts the driver if they fall asleep due to fatigue while still driving. The computer vision algorithm used for the implementation uses a trifold approach to detect drowsiness, including the measurement of forward head tilt angle, measurement of eye aspect ratio (to detect closure of eyes) and measurement of mouth aspect ratio (to detect yawning).
Example Go service that uses OPA for API authorization.
An implementation of the Siamese Neural Network for facial recognition using one shot detection, that eliminates the requirement for the Neural Network to be trained each time a new image is added to the database. Trained using a Triplet Loss Function, it may even be used to perform the facial recognition with one training instance image.
A face recognition web app built using Face AI library implemented using tensorflow.js
A Go implementation of the Gap Buffer data structure.
Join the GitHub Graduation Yearbook and "walk the stage" on June 11.
A web app for finding and visualizing the path between a source position and a destination position in a grid using Graph algorithms such as BFS, DFS, Dijkstra, etc.
Simple Demo Block for Harness Chaos Engineering (standalone)
Add-on for KEDA to scale HTTP workloads
A deep convolutional neural network for the detection as well as localization of the area of manipulation in forged images, bearing forgeries of simple as well as complex nature. Further along, the trained model is interfaced with a web application for users to interact with the model in a simple and effective manner, and finally, we also develop a chatbot for further easing the process of interaction with the model and effectively tackling the problem of fake news forwards in popular internet messaging platforms such as WhatsApp.
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