Submission Deadline: 31 July 2026 View: 585 Submit to Special Issue
Prof. Shyi-Chyi Cheng
Email: csc@mail.ntou.edu.tw
Affiliation: Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, 20224, Taiwan
Research Interests: computer Vision, machine learning, big data analytics, AIoT systems
Associate Professor Naomi A. Ubina
Email: naomi.a.ubina@isu.edu.ph
Affiliation: College of Computing Studies, Information and Communications Technology, Isabela State University, Isabela, 3309, Philippines
Research Interests: computer vision, artificial intelligence, intelligent systems
The design and functionality of intelligent systems in engineering, science, and technology have been significantly transformed by the rapid development of deep learning and computer vision. Automation in robotics, healthcare, food, agriculture, aquaculture, transportation, and manufacturing has significantly transformed the design and operation of intelligent systems. The ability to model, simulate, and interpret complex visual and sensory data has opened new frontiers. However, even with these significant developments, many challenges remain when these algorithms are integrated into modelling and simulation frameworks, particularly in engineering and scientific applications.
The goal of this special issue is to present the recent methodological advances, interdisciplinary applications, and innovative ideas at the intersection of deep learning, computer vision, and computational modeling. It will provide opportunities for novel research that bridges theory and practice, emphasizing explainability, robustness, and real-world deployment in intelligent systems.
The scope of the special issue encompasses, but is not restricted to:
· Deep learning architectures and algorithms for computer vision
· Vision-based intelligent systems and autonomous systems
· Data-driven modelling approaches
· Generative and multimodal vision models in dynamic environments
· Explainable and trustworthy AI in visual modelling
· Applications in robotics, medical imaging, smart manufacturing, and remote sensing
· Edge and federated learning for vision-enabled systems
· Digital twins and simulation-driven visual intelligence
· Explore future direction and new developments (e.g., self-supervised methods, generative modelling, explainable AI)
Login
Register
Submit a Paper
Propose a Special lssue