Deadline Date: 01 March 2027
Artificial Intelligence (AI) has emerged as a transformative technology across computational sciences, engineering systems, healthcare, cybersecurity, smart manufacturing, agriculture, and intelligent automation. Recent advances in machine learning, deep learning, generative AI, explainable AI, federated learning, and hybrid intelligent systems have significantly enhanced the capability of computational models to solve complex real-world problems. This special issue aims to bring together researchers, academicians, and industry experts to present cutting-edge research and practical innovations related to the applications of AI in computational modeling, engineering, and scientific domains. The issue will focus on novel methodologies, intelligent algorithms, optimization strategies, data-driven modeling approaches, and interdisciplinary applications that contribute to advancing computational intelligence and engineering sciences. The proposed special issue aligns strongly with the scope of RIMNI by emphasizing computational methods, intelligent systems, simulation techniques, optimization frameworks, and AI-driven solutions for scientific and engineering challenges. The primary objectives of this special issue are:
To explore recent advancements in Artificial Intelligence and Machine Learning for computational modeling and engineering applications.
To encourage interdisciplinary research integrating AI with scientific computing, optimization, and intelligent automation.
To provide a platform for researchers to present innovative AI-driven methodologies and applications.
To highlight emerging trends in explainable AI, trustworthy AI, and hybrid intelligent systems.
To address challenges, opportunities, and future research directions in AI-enabled computational sciences.
The special issue welcomes original research articles, review papers, and case studies in, but not limited to, the following areas:
Artificial Intelligence and Machine Learning
Deep learning architectures and optimization
Explainable and trustworthy AI
Federated and distributed learning
Reinforcement learning and intelligent agents
Hybrid AI and fuzzy systems
Evolutionary computation and swarm intelligence
Generative AI models and applications
Computational Modeling and Intelligent Systems
AI-driven computational modeling
Data-driven simulation and prediction
Intelligent optimization algorithms
Digital twins and smart systems
Computational intelligence in engineering design
AI for numerical analysis and scientific computing
Healthcare and Biomedical Applications
Medical image analysis and diagnosis
AI for disease prediction and healthcare analytics
Biomedical signal processing
Intelligent healthcare monitoring systems
AI-assisted drug discovery and precision medicine
Cybersecurity and Smart Computing
AI-based cyber threat detection
Intrusion detection systems
Secure and trustworthy intelligent systems
AI in blockchain and secure communications
Intelligent anomaly detection
Smart Agriculture and Environmental Applications
AI for crop disease detection and precision agriculture
Intelligent environmental monitoring
Smart irrigation and resource optimization
AI for climate and sustainability applications
Industrial and Emerging Applications
AI in Industry 4.0 and smart manufacturing
Robotics and autonomous systems
Intelligent transportation systems
Edge AI and IoT applications
AI applications in energy systems
Editorial board
