Computational Intelligence, Machine Learning and Causal Inference for Data-Driven Modelling of Complex Engineering, Business and Health Systems
Special Issue Title: Computational Intelligence, Machine Learning and Causal Inference for Data-Driven Modelling of Complex Engineering, Business and Health Systems
Author: Assoc. Prof. Nikolay Netoff, PhD
Department of Statistics and Econometrics, Faculty of Economics and Business Administration, Sofia University "St. Kliment Ohridski", Sofia, Bulgaria
E-mail: nnetoff@feb.uni-sofia.bg
Aim and Scope: The rapid growth of data availability and computational power has transformed the modelling, analysis, and optimization of complex systems across engineering, business, healthcare, and social domains. Modern systems are increasingly characterized by high dimensionality, uncertainty, nonlinear interactions, heterogeneous data sources, and dynamic behavior, demanding advanced intelligent methodologies capable of extracting actionable knowledge and supporting robust decision-making.
This Special Issue aims to bring together researchers, practitioners, and industry experts working on cutting-edge developments in computational intelligence, machine learning, deep learning, causal inference, and hybrid intelligent systems for data-driven modelling and analytics. Particular emphasis is placed on methodologies that combine predictive intelligence with explainability, interpretability, causality, robustness, and real-world applicability.
The Special Issue welcomes original research articles, review papers, case studies, and interdisciplinary contributions addressing theoretical advances, methodological innovations, and practical applications in complex engineering, business, financial, industrial, environmental, and health-related systems.
The issue also seeks contributions exploring the integration of artificial intelligence with optimization, uncertainty quantification, digital twins, cyber-physical systems, Internet of Things (IoT), blockchain technologies, and intelligent decision-support frameworks.
Topics of Interest:
Potential topics include, but are not limited to:
Computational Intelligence and AI
- Computational intelligence techniques for complex systems
- Artificial intelligence for predictive modelling
- Hybrid intelligent systems
- Evolutionary computation and genetic algorithms
- Swarm intelligence and bio-inspired optimization
- Fuzzy systems and neuro-fuzzy modelling
- Reinforcement learning and adaptive systems
- Explainable and trustworthy AI
- Physics-informed machine learning
Machine Learning and Data Analytics
- Supervised and unsupervised learning
- Deep learning architectures and applications
- Transfer learning and federated learning
- Graph neural networks and graph analytics
- Time-series forecasting and sequential modelling
- Big data analytics and high-dimensional data processing
- Multimodal and heterogeneous data fusion
- Edge AI and distributed intelligence
- Automated machine learning (AutoML)
Causal Inference and Explainability
- Causal machine learning
- Bayesian causal networks
- Counterfactual reasoning and inference
- Causal discovery in complex systems
- Interpretable machine learning
- Uncertainty-aware intelligent systems
- Fairness, bias mitigation, and ethical AI
- Robustness and reliability of AI models
Engineering Applications
- Intelligent control and automation systems
- Smart manufacturing and Industry 4.0
- Digital twins and cyber-physical systems
- Intelligent transportation and autonomous systems
- Power systems and smart grids
- Renewable energy forecasting and optimization
- Structural health monitoring
- Signal and image processing
- Robotics and unmanned systems
- Internet of Things (IoT) applications
Business and Financial Systems
- AI for financial engineering and fintech
- Business intelligence and decision support systems
- Predictive analytics in economics and management
- Risk assessment and fraud detection
- Supply chain optimization
- Blockchain analytics and decentralized systems
- Market forecasting and algorithmic trading
- Consumer behavior modelling
Health and Biomedical Systems
- AI-driven healthcare analytics
- Medical diagnosis and clinical decision support
- Biomedical signal and image analysis
- Precision medicine and personalized healthcare
- Epidemiological modelling and public health analytics
- Intelligent wearable systems and remote monitoring
- AI applications in veterinary medicine
- Healthcare digital twins and smart hospitals
Emerging and Interdisciplinary Topics
- Quantum machine learning
- Human-centered AI systems
- Sustainable AI and green computing
- Intelligent cybersecurity systems
- AI for climate and environmental systems
- Generative AI for scientific modelling
- Large language models for engineering and healthcare
- Multi-agent intelligent systems
- AI-driven scientific discovery
Submission Types:
- Original research papers
- Survey and review articles
- Industrial case studies
- Application-oriented studies
- Short communications and technical notes