Projects | Faris H. Rizk

Projects

Below are the research projects I contributed to, highlighting my experience in these areas. To view the full project publication, click on the project title.

Computer Vision for Detecting Potholes in Asphalt Roads in Real-Time using RC Car

Computer Vision

Computer Vision for Detecting Potholes in Asphalt Roads in Real-Time using RC Car

Developed and trained a Convolutional Neural Network (CNN) based on the AlexNet architecture for real-time detection of potholes in asphalt roads, achieving 92.15% accuracy, 91.38% sensitivity, and an F-score of 96.52%. Optimized the model using high-performance GPUs for deployment in real-time systems.

May 1, 2024

Integrated CNN and Waterwheel Plant Algorithm for Enhanced Traffic Detection

Computer Vision

Integrated CNN and Waterwheel Plant Algorithm for Enhanced Traffic Detection

Led a team in the design and implementation of a hybrid CNN and Waterwheel Plant Algorithm (WWPA) to detect and track vehicles in global traffic data, achieving a model accuracy of 97.28%.'

Jan 1, 2024

Deep Learning Models for Predicting Traffic Patterns in Smart Cities

Deep Learning Models for Predicting Traffic Patterns in Smart Cities

Applied various deep learning architectures to predict and analyze traffic patterns in urban environments, with AlexNet emerging as the best-performing model, achieving an accuracy of 93.18%.

Dec 1, 2023

Machine Learning for Oil Spill Detection via Satellite Imagery

Machine Learning

Machine Learning for Oil Spill Detection via Satellite Imagery

Spearheaded the development of an oil spill detection system using Artificial Neural Networks (ANN), achieving 96.88% accuracy by classifying ocean satellite images.

Apr 1, 2023

Face Mask Detection Using MobileNetV2

Computer Vision

Face Mask Detection Using MobileNetV2

Built a deep learning model using MobileNetV2 to detect face masks, a crucial tool for public safety during the COVID-19 pandemic. Achieved a 97.71% accuracy on the test dataset, demonstrating the model’s robustness and efficiency under varying conditions.

Mar 15, 2023