Name: Hassan Mahmood Khan
Type: User
Company: Concordia University
Bio: Data Science | Machine Learning | MEng Electrical and Computer Engineering
Location: Waterloo, ON, Canada
Blog: https://www.linkedin.com/in/hassan-mahmood-khan/
Hassan Mahmood Khan's Projects
This project aims to predict the income bracket of individuals based on a variety of features, and presents a holistic comparative analysis between multiple machine learning algorithms through hyperparameter optimization on a binary classification problem.
With advanced modeling and visualization techniques, an analytical examination is conducted on a survey comprising candidate feedback across professional occupations. The goal is to unearth pivotal insights through a Power BI dashboard..
Deep convolutional neural networks for brain tumor detection leveraging transfer learning and optimization through fully convolutional neural networks.
Comprehensive revenue analysis using MS Power BI, offering a centralized space for an insightful dashboard, data visualizations, and analytics for informed decison-making.
Hourly energy consumption forecasting using time-series analysis across multiple US states.
A python based data center topology. The project aims to present a comprehensive analysis of the Fat tree topology's performance using Mininet simulator.
This python implemented project primarily makes use of OpenCV & Imutils libraries for object recognition with the aid of machine learning to detect humans in still digital images.
Fault diagnosis of some critical and non-critical faults in electric drives using anomaly detection.
Design and implementation of a Hopfield Network to recognize multiple patterns as an association.
Design and evaluation of an ART network for storing and recognizing alphabets.
An IoT architecture that integrates health sensors, embedded devices, with the lightweight MQTT communication protocol to perform patient health anomaly detection using machine learning and data analytics.
Fully convolutional neural network for semantic segmentation of pet images.
[WACV'24] Separable Self and Mixed Attention Transformers for Efficient Object Tracking
Employs a CNN-based multi-class strawberry variety classification. The predicted label is then used to feed a LLM for information retrieval on that particular strawberry. The entire codebase is shipped as an intuitive web application.
Two-dimensional binary classification problem involving non-convex decision regions. The proposed solution employs a Back Propagation and a Counter Propagation Network to distinguish whether an input pattern belongs to class C1 or C2.
Performing targeted marketing and customized services for credit card customers by employing customer segmentation with clustering analysis.
Application of a convolutional neural network (CNN) to accurately classify urban sounds in a bid to increase pedestrian safety using the UrbanSound8k dataset.
Inter-train Ticketing System. A user-friendly website designed to ensure ease of access for passengers looking for a holiday get-away, business trip.