The continuous evolution of decision sciences is driven by developments in mathematics and statistics that provide a rigorous foundation for modeling, analysis, and optimization under uncertainty. In today’s data-driven and complex environment, decision-making problems increasingly rely on analytical models that integrate stochastic processes, optimization techniques, and statistical inference. Mathematical frameworks play a crucial role in designing algorithms, predicting outcomes, and managing risks, while statistical methodologies ensure the reliability and interpretability of data-based conclusions. This special issue aims to bring together innovative research that advances theoretical, computational, and applied aspects of mathematics and statistics with direct implications for decision-making across diverse fields, including engineering, economics, healthcare, management, and information sciences. Submissions are encouraged that contribute to both the methodological depth and practical relevance of decision-oriented modeling and computation.
The potential topics of this special issue include, but are not limited to:
- Statistical and mathematical modeling for intelligent decision-making systems
- Bayesian inference, stochastic modeling, and uncertainty analysis in decision processes
- Optimization and metaheuristic algorithms for multi-criteria and fuzzy decision environments
- Computational and numerical approaches for intelligent systems and control problems
- Approximation theory, functional analysis, and advanced numerical schemes applied to decision sciences
- Machine learning, deep learning, and AI-based techniques with statistical and mathematical foundations
- Mathematical modeling and simulation in industrial engineering, economics, and management sciences
- Robust, stochastic, and hybrid optimization methods for large-scale and complex decision systems
- Time series forecasting, econometrics, and predictive analytics using intelligent algorithms
- High-dimensional, fuzzy, and probabilistic data analysis for knowledge-based decision support
- Reliability modeling, uncertainty quantification, and risk assessment in engineering and management
- Integration of mathematical programming, fuzzy logic, and statistical learning for intelligent systems
- Applications of mathematics, statistics, and artificial intelligence in healthcare, energy, environment, and smart technologies
Submission Deadline: September 30, 2026
Guest Editors:
Prof. Dr. Dilber Uzun Ozsahin
University of Sharjah, Sharjah, United Arab Emirates
Google Scholar: https://scholar.google.com/citations?user=GpPfg4wAAAAJ&hl=en
Assoc. Prof. Dr. Hijaz Ahmad
Near East University, Nicosia/TRNC, Turkey
Google Scholar: https://scholar.google.com/citations?hl=en&user=JjwnbsoAAAAJ