KAUST researchers at the Composite and Heterogeneous Materials Analysis and Simulation Laboratory (COHMAS) are developing new composite materials and computational models. The research focuses on ensuring the stability and service lifetime of composite structures used in aircraft, windmill blades, and industrial pipes. Professor Gilles Lubineau leads the group's work on computational modeling and experimental developments. Why it matters: This research aims to advance the use of composite materials in key sectors by addressing the challenge of long-term reliability, contributing to sustainability goals in energy, transportation and other industries.
Al-Maha Systems, a startup founded by KAUST students, has developed an IoT system for livestock health tracking. The system uses sensors attached to cows to monitor vital data like heart rate and body temperature, transmitting it to a cloud server. The goal is to detect health problems early and optimize breeding times for dairy farms. Why it matters: This innovation can improve efficiency and productivity in Saudi Arabia's dairy industry by leveraging IoT for animal husbandry.
LUMA AI is expanding its presence in Saudi Arabia, establishing its regional headquarters in the Kingdom. The company is partnering with HUMAIN, a Saudi entity, to support the creative industry through AI tools. LUMA AI's technology enables the creation of 3D models from images and videos, catering to the growing demand for digital content in the region. Why it matters: This move signals increasing investment and interest in AI-driven solutions for creative applications within the Saudi Arabian market.
Malaria No More, the Crown Prince Court of Abu Dhabi, and the Reaching the Last Mile program launched the Institute for Malaria and Climate Solutions (IMACS) to combat malaria amidst climate change. Mohamed Bin Zayed University for Artificial Intelligence (MBZUAI) joined as a technical partner, providing research support leveraging AI and data science. The initiative aims to develop and implement AI-driven strategies to address the impact of climate change on malaria transmission. Why it matters: This partnership highlights the UAE's commitment to using AI for global health challenges, particularly in combating climate-sensitive diseases like malaria.
The Symposium on Data Mining and Applications (SDMA 2014) was organized by MEGDAM to foster collaboration among data mining and machine learning researchers in Saudi Arabia, GCC countries, and the Middle East. The symposium covered areas such as statistics, computational intelligence, pattern recognition, databases, Big Data Mining and visualization. Acceptance was based on originality, significance and quality of contribution.
This paper proposes a smart dome system for mosques that uses machine learning to automatically control dome ventilation based on weather conditions and outside temperatures. The system was tested on the Prophet Mosque in Saudi Arabia using K-Nearest Neighbors and Decision Tree algorithms. The Decision Tree algorithm achieved a higher accuracy of 98% compared to 95% for the k-NN algorithm.