Dr. Mejdl Safran is a passionate researcher and educator in the field of artificial intelligence, with a focus on deep learning and its applications in various domains. He is currently an Associate Professor of Computer Science at King Saud University, where he has been a faculty member since 2008. He obtained his bachelor’s degree in computer science from King Saud University in 2007, his master’s degree in computer science from Southern Illinois University Carbondale in 2013, and his doctoral degree in computer science from the same university in 2018. His doctoral dissertation was on developing efficient learning-based recommendation algorithms for top-N tasks and top-N workers in large-scale crowdsourcing systems. He has published more than 20 articles in peer-reviewed journals and conference proceedings, such as ACM Transactions on Information Systems, Applied Computing and Informatics, Mathematics, Sustainability, International Journal of Digital Earth, IEEE Access, Biomedicine, Sensors, IEEE International Conference on Cluster, IEEE International Conference on Computer and Information Science, International Conference on Database Systems for Advanced Applications, and International Conference on Computational Science and Computational Intelligence. He has been leading grant projects in the fields of AI in medical imaging and AI in smart farming. His current research interests include developing novel deep learning methods for image processing, pattern recognition, natural language processing, and predictive analytics, as well as modeling and analyzing user behavior and interest in online platforms. He has been working as an AI consultant for several national and international agencies since 2018.
Cloud computing has demonstrated its effectiveness in handling complex data that requires substantial computational power, immediate responsiveness, and ample storage capacity.
Leaf diseases are a global threat to crop production and food preservation. Detecting these diseases is crucial for effective management. We introduce LeafDoc-Net, a robust, lightweight transfer-…