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Abdulelah Khalaf

After KAUST Academy

Abdulelah graduated in Information Systems from King Abdulaziz University in 2025 and works at Nagarro. Abdulelah was a teaching assistant for KAUST Academy's Advanced AI program, won the Sanah Hackathon, and completed the SDAIA and University of Oxford AI Application Engineering Bootcamp.

Summer projects

Artificial Intelligence Specialization · Cohort 2025

Event-Recognition and Highlight Clipping

This project aims to enhance the sports viewing experience by identifying and extracting key moments from match videos. The system detects six events goals, penalties, fouls, shots on target, red and yellow cards, and direct free kicks. For each detected event, it retrieves short highlight clips, allowing viewers to instantly rewatch pivotal moments. Alongside these clips, it integrates a dynamic tracking mini-map that providesreal-time positional tracking of players and the ball, offering a tactical overview ofhow the play unfolded. This combination of precise highlight extraction andvisualized tracking delivers an immersive and insightful way to analyze and enjoysports content.

Read the final report (PDF)

See it in the program report (PDF)

Artificial Intelligence Specialization · Cohort 2024

Detection of Brain Disease Using Advanced Deep Learning Model

An AI-powered website has been developed to provide diagnostic services for brain diseases using MRI scans, focusing on Tumor Detection, Alzheimer’s Disease Diagnosis, and Multiple Sclerosis Detection. The platform enables users to upload brain images for immediate analysis. The Tumor Detection module employs the Efficiency Net86 model to classify MRI images into glioma, meningioma, pituitary tumors, or no tumor. Alzheimer’s Disease module, utilizing a modified AlexNet, categorizes images into five stages, from Non-Demented to Severe Demented, with an estimation of disease progression. The Multiple Sclerosis module utilizes a modified AlexNet to identify brain lesions and determine whether the scan is positive or negative for multiple sclerosis. The integration of advanced models and a robust dataset ensures precise, reliable diagnostics, aiding in the early detection and management of brain diseases.

Read the final report (PDF)

See it in the program report (PDF)

With KAUST Academy

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