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Imtinan Alghamdi

After KAUST Academy

Imtinan works as a Structured Solutions Officer in Global Markets at The Saudi National Bank through its Rowad Program, joined in November 2024. Imtinan graduated from King Saud University with first class honors in August 2024 and joined the KAUST Academy Summer AI program at Oxford in 2024.

Summer projects

Artificial Intelligence Specialization · Cohort 2024

Accelerated Post-Disaster Recovery: An AI-Powered System for Damage Assessment and Reconstruction Prioritization

This project introduces an innovative AI-driven framework to revolutionize post-disaster recovery efforts. Leveraging UAV imagery and advanced AI techniques, our system rapidly assesses damage to buildings and infrastructure using an Attention U-Net model for image segmentation. Unsupervised learning then clusters affected areas based on damage severity, building importance, and distance, utilizing both segmentation results and Google Maps metadata. A reinforcement learning model, employing PPO and DQN algorithms, optimizes resource allocation and prioritizes reconstruction efforts, aiming to minimize total recovery time. This integrated approach promises faster, more accurate damage assessment and data-driven decision-making, significantly reducing the time needed to restore affected communities and setting a new standard for efficient disaster response strategies.

See it in the program report (PDF)

Artificial Intelligence Specialization · Cohort 2023

Detecting Inappropriate Content in Short Videos for Children

The rise of user-generated content on platforms like TikTok and YouTube Shorts has raised concerns about inappropriate content reaching young audiences. These platforms rely on AI-driven recommendation systems to show tailored content. However, these systems might unintentionally suggest unsuitable material. Our project proposes a solution that combines the InceptionV3 CNN-RNN model for video analysis and the BERT model for understanding text context. The goal is to accurately identify and address inappropriate content in videos and their text descriptions. The dataset consists of 2,255 videos from YouTube Shorts and TikTok. After trying various approaches, the modified InceptionV3 model showed the best results, achieving an accuracy of 84.48%. This is a significant improvement over previous methods.

See it in the program report (PDF)

With KAUST Academy

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