Tomato Leaf Disease Detection:Deep Learning Project

Deep Learning Neural Network Architectures - LeNet and AlexNet, both trained on CIFAR10 and CIFAR100 Datasets

Pistachios are nutritious nuts that are sorted based on the shape of their shell into two categories: Open mouth and Closed mouth. The open-mouth pistachios are higher in price, value, and demand than the closed-mouth pistachios. Because of these differences, it is considerable for production companies to precisely count the number of each kind.

drop out analysis with R and shiny

The primary objective s to develop an accurate and efficient classification model capable of identifying pneumonia cases in patients based on chest X-ray images. Pneumonia is a prevalent and potentially life-threatening respiratory infection. Early detection plays a critical role in timely intervention and effective treatment.

Single (i) Cell R package (iCellR) is an interactive R package to work with high-throughput single cell sequencing technologies (i.e scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST)).

The main aim of this project is to built a predictive model using G Store data to predict the TOTAL REVENUE per customer that helps in better use of marketing budget.

Bidirectional RNNs are used to analyze the sentiment (positive, negative, neutral) of movie reviews. .

Data Science Project: Comparing 3 Deep Learning Methods (CNN, LSTM, and Transfer Learning).

Bayesian Neural Network in PyTorch

A tool for downloading dropout.tv episodes

This project is a real-time traffic sign recognition system built using Python, OpenCV, and a pre-trained CNN model, capable of detecting and recognizing traffic signs from images.

Develop SNN-aware Noise Addition Layers

This repository is associated with the paper "Do Neural Topic Models Really Need Dropout? Analysis of the Effect of Dropout in Topic Modeling", accepted at EACL 2023.

Model to predict bank customer churn

Utilizing advanced Bidirectional LSTM RNN technology, our project focuses on accurately predicting stock market trends. By analyzing historical data, our system learns intricate patterns to provide insightful forecasts. Investors gain a robust tool for informed decision-making in dynamic market conditions. With a streamlined interface, our solution

Leveraging advanced image processing and deep learning, this project classifies plant images using a subset of the Plant Seedlings dataset. The dataset includes diverse plant species captured under varying conditions. This project holds significance within my Master's in Computer Vision at uOttawa (2023).

Imputation method for scRNA-seq based on low-rank approximation