arXiv ·
A proposed recognition system aims to identify missing persons, deceased individuals, and lost objects during the Hajj and Umrah pilgrimages in Saudi Arabia. The system intends to leverage facial recognition and object identification to manage the large crowds expected in the coming decade, estimated to reach 20 million pilgrims. It will be integrated into the CrowdSensing system for crowd estimation, management, and safety.
KAUST ·
KAUST has launched the KAUST Challenge: Ideas and Solutions for Hajj & Umrah 2020, in partnership with The Makkah Cultural Forum. The challenge aims to catalyze research, innovation, and economic development in Saudi Arabia. The KAUST Challenge will award 1 million SAR in cash and other prizes for ideas to improve the Hajj and Umrah experience and advance efforts to make Makkah a smart city. Why it matters: This initiative connects AI innovation directly to Saudi Arabia's Vision 2030 and the specific needs of religious tourism, a unique application area.
KAUST ·
KAUST hosted the KAUST Ignite ideation challenge with 90+ students from Saudi universities participating. The three-day event partnered with the Ministry of Hajj and Umrah, SWCC, and SAEI, challenging students to address regional and global issues. Participants formed teams to develop solutions for real-world challenges in water, aviation, and the Hajj experience, presenting their ideas to judges. Why it matters: This initiative fosters innovation and entrepreneurship among Saudi students, addressing critical challenges and contributing to Saudi Arabia's economic transformation.
arXiv ·
A research paper proposes a smart waste management system called TUHR for Makkah, Saudi Arabia, leveraging IoT and AI to handle waste accumulation during the annual pilgrimage. The system uses ultrasonic sensors to monitor waste levels and gas detectors to identify harmful substances, alerting authorities when containers are full or hazards are detected. The proposed system aligns with Saudi Vision 2030 by promoting sustainability and improving public health through optimized waste management.
arXiv ·
This paper proposes a smart dome model for mosques that uses AI to control dome movements based on weather conditions and overcrowding. The model utilizes Congested Scene Recognition Network (CSRNet) and fuzzy logic techniques in Python to determine when to open and close the domes to maintain fresh air and sunlight. The goal is to automatically manage dome operation based on real-time data, specifying the duration for which the domes should remain open each hour.
arXiv ·
The QU-NLP team presented their approach to the QIAS 2025 shared task on Islamic Inheritance Reasoning, fine-tuning the Fanar-1-9B model using LoRA and integrating it into a RAG pipeline. Their system achieved an accuracy of 0.858 on the final test, outperforming models like GPT 4.5, LLaMA, and Mistral in zero-shot settings. The system particularly excelled in advanced reasoning, achieving 97.6% accuracy. Why it matters: This demonstrates the effectiveness of domain-specific fine-tuning and retrieval augmentation for Arabic LLMs in complex reasoning tasks, even surpassing frontier models.