Projects

typing, computer, man

2023

Birmingham City University

Master's in Artificial Intelligence

Modules: Machine Learning (Merit), Applied Artificial Intelligence (1st), Deep Learning (2:1), Data Visualisation (2:1), Impact of AI (1st)
Project: Implemented U-Net and Duck-Net deep learning architectures for precise medical image segmentation using Python and compared both convolutional neural networks CNNs on CVC-ClinicDB dataset available through Kaggle. Yielded a Dice Coefficient of 0.91 with slight modifications to the original Duck-Net architecture (1st)

2023

Birmingham City University

Master's in Artificial Intelligence

Modules: Machine Learning (Merit), Applied Artificial Intelligence (1st), Deep Learning (2:1), Data Visualisation (2:1), Impact of AI (1st)
Project: Implemented U-Net and Duck-Net deep learning architectures for precise medical image segmentation using Python and compared both convolutional neural networks CNNs on CVC-ClinicDB dataset available through Kaggle. Yielded a Dice Coefficient of 0.91 with slight modifications to the original Duck-Net architecture (1st)

2019

National University of Science and Technology (NUST)

Bachelors of Engineering in Electrical Engineering

Modules: Object Oriented Programming (1st), Data Structures and Algorithms (1st), Digital Signal Processing (1st), Digital Logic Design (1st), Microprocessor Systems (1st), Embedded System Design (1st).
Project: Automated Identification of Abnormal EEG (Enhanced the accuracy of the chronoNET algorithm for identifying abnormal EEG patterns using deep learning techniques. Contributed to advancements in EEG analysis using Deep Neural Networks)

2019

National University of Science and Technology (NUST)

Bachelors of Engineering in Electrical Engineering

Modules: Object Oriented Programming (1st), Data Structures and Algorithms (1st), Digital Signal Processing (1st), Digital Logic Design (1st), Microprocessor Systems (1st), Embedded System Design (1st).
Project: Automated Identification of Abnormal EEG (Enhanced the accuracy of the chronoNET algorithm for identifying abnormal EEG patterns using deep learning techniques. Contributed to advancements in EEG analysis using Deep Neural Networks)

2019

National University of Science and Technology (NUST)

Bachelors of Engineering in Electrical Engineering

Modules: Object Oriented Programming (1st), Data Structures and Algorithms (1st), Digital Signal Processing (1st), Digital Logic Design (1st), Microprocessor Systems (1st), Embedded System Design (1st).
Project: Automated Identification of Abnormal EEG (Enhanced the accuracy of the chronoNET algorithm for identifying abnormal EEG patterns using deep learning techniques. Contributed to advancements in EEG analysis using Deep Neural Networks)