Description
This book belongs to a recognized series dedicated to presenting recent advances and state-of-the-art research in artificial intelligence and its applications within computer science. It aims to provide a comprehensive platform for students, researchers, academics, industry professionals, and policy makers to explore, understand, and disseminate innovative methodologies and solutions based on artificial intelligence techniques. In addition, the book offers a consolidated overview of current scientific contributions and emerging trends reported in the literature.
The book emphasizes original research works and novel developments in multidisciplinary domains, with particular attention to the application of artificial intelligence in computer science, communication systems, and advanced technologies.
This book comprises selected and extended high-quality papers presented at the International Conference on Machine Intelligence and Computer Science Applications (ICMICSA 2025). All contributions were subjected to a rigorous peer-review process and were presented at ICMICSA 2025, held in 2025 in Khouribga-Morocco. The published works reflect both the scientific quality and the diversity of research topics addressed during the conference.
Linear time attention with kernels: A comparison of deterministic feature maps.- Transformer-Based Fundus Screening: Swin-Tiny for Retinal Disease Classification.- A comparative study of balancing genomic data approaches in autism classification.- DeepOmicFusion: A Fusion-Based Deep Learning Framework for Accurate Multiclass Cancer Classification Using Multi-Omics Data.- Deep Learning for Algorithm Selection: 1D CNN for Meta-Heuristic Recommendation in TSP.- Cross-Domain Transfer Learning with Selective Domain Adaptation for Breaking the Cold Start Barrier in Recommendation System.- Modeling Cognitive Flexibility: A Brief Review.- Efficient Potato Disease Detection with Compressed Deep Learning Models.- Advances in Hyperparameter Optimization Algorithms for Robust Facial Recognition.- Discriminative Learning of Copula Densities via Logistic Regression.
