Khadijah Baothman

تخصص الذكاء الاصطناعي · دفعة 2026

Tawazun

Tawazun is an energy grid simulation initiative designed to identify the optimal balance between environmental sustainability and economic fairness in Saudi Arabia. The project models the Kingdom’s grid to determine the ideal mix of conventional and renewable energy sources that reduces carbon emissions while maintaining affordable electricity prices and supporting energy producers. Additionally, the project examines the impacts of carbon emission fees, providing a data-driven assessment of their effects on energy prices, market dynamics, and the transition to a greener economy.

اقرأ التقرير النهائي (PDF)

تخصص الذكاء الاصطناعي · دفعة 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.

اقرأ التقرير النهائي (PDF)

اطّلع عليه في تقرير البرنامج (PDF)

تخصص الذكاء الاصطناعي · دفعة 2023

Auditing Auditor

The main purpose of this project is to classify the output of LLMs or any Language model into Malicious or Benign. We aim to be sure that, all the Large Language models developed in Saudi Arabia fits the seven AI ethics principles which are fairness, privacy & security, humanity, social & environmental benefits, reliability & safety, transparency & explainability, and accountability & responsibility.

اطّلع عليه في تقرير البرنامج (PDF)

تخصص الذكاء الاصطناعي · دفعة 2023

Intelligent Culpability Detection System Using Computer Vision

This project aims to automate the process of determining who is responsible for a car accident in order to reduce traffic congestion. Using computer vision, we analyze the pictures B and determine if there is any damage and how deep it is. In addition, the vehicle's position is determined. Furthermore, using a fault recognition decision-making system, we determine which vehicle is responsible for the accident, as well as the mistake percentage of each vehicle.

اطّلع عليه في تقرير البرنامج (PDF)