Generative AI and Soft Computing for Adaptive Intelligent Systems

Submission Deadline: 30 June 2027 View: 179 Submit to Special Issue

Guest Editor(s)

Dr. Luong Vuong Nguyen

Email: vuongnl3@fe.edu.vn

Affiliation: Faculty of Artificial Intelligence, FPT University, Danang, Vietnam

Homepage:

Research Interests: data mining, machine learning, deep learning, ambient intelligence, recommendation system, intelligent system, logical reasoning, LLMs

Assoc. Prof. Thi-Thu-Hong Phan

Email: hongptt11@fe.edu.vn

Affiliation: Faculty of Artificial Intelligence, FPT University, Danang, Vietnam

Homepage:

Research Interests: time series processing and forecasting, computer vision, machine learning/deep learning, and application of AI to solving problems in agriculture

Summary

Recent advances in generative AI, large language models, and machine learning have significantly expanded the capabilities of intelligent systems in knowledge processing, decision-making, and human–machine interaction. However, intelligent systems operating in dynamic and complex environments still face challenges in adaptability, reliability, interpretability, and handling heterogeneous data. These challenges have created a growing need for adaptive learning and soft computing approaches that can enable intelligent systems to continuously learn, reason, and make effective decisions under changing conditions.

This Special Issue explores recent advances in generative AI, large language models, machine learning, and soft computing for developing adaptive, reliable, explainable, and intelligent systems. It covers emerging topics such as continual and multimodal learning, graph-based AI, recommendation systems, federated learning, and evolutionary optimization, as well as practical applications in cybersecurity, IoT, smart environments, multimedia, and intelligent decision support.

Potential topics could include, but are not limited to:
· Generative AI and large language models for intelligent systems
· Adaptive and continual learning
· Multimodal and contrastive learning
· Graph neural networks and knowledge-enhanced AI
· Explainable and trustworthy AI
· Intelligent recommendation and personalization
· Federated and distributed learning
· Evolutionary and swarm-based optimization
· Intelligent information processing and decision support
· Applications in cybersecurity, IoT, smart environments, and multimedia systems

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