SACM - Jordan
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9658
Browse
2 results
Search Results
Item Restricted Harnessing Machine Learning and Deep Learning for Analyzing Electrical Load Patterns to Identify Energy Loss(Saudi Digital Library, 2025) Alabbas, Mashhour Sadun Abdulkarim; Albatah, MohammadMeeting the challenges of energy requirements, consumption patterns, and the push for sustainability makes energy management in contemporary agriculture critically important. This study aims to devise a holistic model for energy efficiency in agricultural contexts by integrating modern computer vision methodologies for field boundary extraction together with anomaly detection techniques. To achieve the accurate segmentation of agricultural fields from satellite imagery, high-resolution imagery is processed using the YOLOv8 object detection model. The subsequently generated field feature datasets enable the smart grid data to serve as a basis for the anomaly detection process using the Isolation Forest algorithm. The methodology follows a multi-stage pipeline: data collection, preprocessing, augmentation, model training, fine-tuning, and evaluation. To validate accurate and reliable field boundary detection, evaluation metrics precision, recall, and mAP (mean Average Precision) are computed and analyzed. Subsequently, energy consumption data are processed for anomaly detection, enabling the identification of irregular and potentially inefficient consumption patterns. The findings indicate that YOLOv8 has a very high detection accuracy with an mAP score over 90%. Furthermore, the Isolation Forest algorithm has shown improved F1 scores over traditional approaches in detecting anomalies in energy consumption. This integrated method provides an automated and scalable solution in precision agriculture which allows users to monitor cultivation conditions and minimize energy consumption, thereby enhancing the energy efficiency and the overall decision-making framework. The study advances the convergence of artificial intelligence, remote sensing, and intelligent energy management systems, offering a basis for developing technological innovations that promote sustainablility in agriculture.22 0Item Restricted The Role of Artificial Intelligence in Strengthening Cyber Defense Mechanisms: Opportunities and Challenges(University of Bedfordshire, 2024) Alanazi, Mohammed; Garner, LeeThis study explores the role of Artificial Intelligence (AI) in strengthening cyber defense mechanisms, focusing on the opportunities and challenges it presents. In recent years, AI has shown potential in enhancing threat detection, response efficiency, and proactive cybersecurity measures. The study examines various AI applications in cyber defense, including machine learning for real-time threat identification and natural language processing for analyzing large-scale data patterns. While AI provides significant advantages in mitigating cyber threats, challenges such as model interpretability, ethical concerns, and vulnerability to adversarial attacks persist. The findings contribute to cybersecurity by highlighting both the promising capabilities and limitations of AI in this domain, suggesting future research directions to address these challenges.38 0