Jithin Sasikumar's Projects
The project is a concoction of research (audio signal processing, keyword spotting, ASR), development (audio data processing, deep neural network training, evaluation) and deployment (building model artifacts, web app development, docker, cloud PaaS) by integrating CI/CD pipelines with automated tests and releases.
This research mini-project trains an expandable image classification system for place categorization which solves the closet-set limitation of convnets. The state-of-the-art Places365 convnet is trained using Places365 dataset with one vs all random forest classifier that outputs place labels.
This research work focuses on comparing the existing approaches to explain the decisions of models trained using time-series data and proposing the best-fit method that generates explanations for a deep neural network. The proposed approach is used specifically for explaining LSTM networks for anomaly detection task in time-series data (satellite telemetry data).
In this task, I developed code for my own facial recognition library using Eigen faces and OpenCV (i.e.) by using API or libraries and without any available APIs or libraries. Eigenvectors have many applications which are not limited to obtaining surface normals from a set of point clouds.
A collection of guidelines for writing file names used in web projects.
I developed "Intelligent Bot [iBot]" which is a programmed application that performs an automated task in a conversational format using supervised learning [ML Paradigm] and Natural Language Processing [NLP] along with NLU. It was programmed using C# and .NET libraries and designed using Microsoft Visual Studio, Bot Builder SDK, Bot Connector, Bot Emulator. NLP was done using LUIS to make the bot more interactive and natural along with computer vision like face detection, caption detection, emotion detection.
In this exercise, I implemented my own Naive Bayes classifier that can be used for predicting the stability of object placements on a table. Statistical measures such as classification error, accuracy, precision, recall values, confidence interval are also determined for the ML classifier model developed. Thus Classifier performance is reported.
Implemented Pattern Recognition using templatematching() - OpenCV
Developed a regression model to predict the amount of Co2 emission from the automobiles using the Machine Learning algorithms such as Linear Regression and Polynomial Regression in Python
This project promulgates an automated end-to-end ML pipeline that trains a biLSTM network for sentiment analysis, experiment tracking, benchmarking by model testing and evaluation, model transitioning to production followed by deployment into cloud instance via CI/CD
This project is an amalgamation of research (federated training and comparison with normal training), development (data preprocessing, model training etc.) and deployment (model serving). It creates a pipeline that trains models using federated learning and deploys them using tensorflow serving and docker
Universal Media Player is a win32 application that was developed and deployed using C#, XAML as part of .Net Framework. It performs as a real time music or video player application with some advanced features like drag and drop , casting to other devices etc.