The paper introduces a web-based expert system called RCSES for civil service regulations in Saudi Arabia. The system covers 17 regulations and utilizes XML for knowledge representation and ASP.net for rule-based inference. RCSES was validated by domain experts and technical users, and compared favorably to other web-based expert systems.
This study compares AI uptake in the UAE and Kuwait, analyzing how constitutional, collective-choice, and operational rules shape AI implementation and its impact on citizen centricity and public value creation. It finds that the UAE's concentrated authority and pro-innovation environment enable scaling AI initiatives, while Kuwait's dispersed governance and cautious approach limit progress despite similar resources. The research highlights the importance of vertical rule coherence over wealth in determining AI's public-value yield.
This paper introduces the AI Pentad model, comprising humans/organizations, algorithms, data, computing, and energy, as a framework for AI regulation. It also presents the CHARME²D Model to link the AI Pentad with regulatory enablers like registration, monitoring, and enforcement. The paper assesses AI regulatory efforts in the EU, China, UAE, UK, and US using the CHARME²D model, highlighting strengths and weaknesses.
This paper introduces a novel two-step method for predicting urban expansion using time-series satellite imagery. The approach combines semantic image segmentation with a CNN-LSTM model to learn temporal features. Experiments on satellite images from Riyadh, Jeddah, and Dammam in Saudi Arabia demonstrate improved performance compared to existing methods based on Mean Square Error, Root Mean Square Error, Peak Signal to Noise Ratio, Structural Similarity Index, and overall classification accuracy.
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.
The paper introduces ILION, a deterministic execution gate designed to ensure the safety of autonomous AI agents by classifying proposed actions as either BLOCK or ALLOW. ILION uses a five-component cascade architecture that operates without statistical training, API dependencies, or labeled data. Evaluation against existing text-safety infrastructures demonstrates ILION's superior performance in preventing unauthorized actions, achieving an F1 score of 0.8515 with sub-millisecond latency.
This paper focuses on analyzing surveys of women entrepreneurs in the UAE using machine learning techniques. The goal is to extract relevant insights from the data to understand the current landscape and predict future trends. The study aims to support better business decisions related to women in entrepreneurship.