Deep-Learning-Workshop
The repository contains python codes, which I have developed as the facilitator of the two consecutive Deep Learning Workshops (I and II) for the master's students of computer science, University of Windsor.
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About
Assistant Professor, School of Computer Science
Evidence-backed skills
Repositories
Driving_Style_Prediction
This repository contains driving behavior prediction and performance analysis using Deep Artificial Neural Networks and Decision Tree Classifier. The tree also saved in a PDF file for visual demonstration using "graphviz". The analysis has been performed on Aggressive Driving Datasets from the following Kaggle repository: https://www.kaggle.com/veeralakrishna/aggressive-driving-data
Algorithm
This repository contains solutions of some well know algorithms using Java programming language
ReportAnalytics---Editied-Version
This is collaborative project on building report analytics software.
Image_Classification_Using_CNN
This repository contains code and datasets and implementation of Canadian Medicinal Plants Classification using Convolution Neural Network (CNN) and Transfer Learning with the help of InceptionV3 library. The datasets has been collected from the internet images. The datasets collection and the implementation has been done as an assignment of Artificial Neural Network Course. Thanks to my teammates effort to collect the data, prepare the presentation and help in writing the report.
FakeNewsDetetction
Fake news detection using deep learning techniques.
DiseaseProgrationModel
Agent based simulation model to propagate the spread of infectious diseases considering social distancing and hospital capacity as the two key parameters.
Artificial-Neural-Networks-Problem-Solving
This repository contains solutions of some problems solved by me as a part of course work assignments.
Grounded-Segment-Anything
Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything
ML-foundations
Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science