Focus and Scope

Aims and Scope

The Journal of Analytical Uncertainty (JAU) is an international, peer-reviewed, multidisciplinary journal devoted to advancing theoretical, computational, and applied research applied research on randomness and uncertainty in decision-making. The journal provides a platform for researchers, academicians, and practitioners working in the diverse domains of mathematics, statistics, and fuzzy sciences to exchange innovative ideas and findings addressing the complexities of uncertain, imprecise, and vague information.

The JAU aims to promote the integration of analytical modeling, statistical inference, and uncertainty quantification for enhanced decision-making across various fields including economics, finance, engineering, healthcare, environmental studies, and social sciences. The journal welcomes contributions that develop new methods, propose models, or present applications in which uncertainty plays a critical role in decision processes.

Primary Objectives

To foster multidisciplinary research linking mathematical theory, statistical methods, and fuzzy approaches.
To encourage the development of new analytical frameworks for modeling uncertainty in real-world problems.
To promote the application of statistical and fuzzy methodologies in complex systems for improved decision-making.
To serve as a global forum for the dissemination of research addressing uncertainty in economics, healthcare, finance, and related domains.

Scope of the Journal

Topics of interest include, but are not limited to:

Uncertainty modeling using mathematical, statistical, and computational approaches
Decision-making under uncertainty, imprecision, and incomplete information
Fuzzy, intuitionistic fuzzy, and neutrosophic sets and their statistical extensions
Bayesian analysis, stochastic modeling, and reliability analysis
Machine learning and data-driven methods for uncertainty quantification
Artificial intelligence and deep learning models for uncertain data
Statistical learning, predictive analytics, and probabilistic reasoning
Data science methods for uncertain and imprecise information
Optimization, simulation, and intelligent decision-support systems
Uncertainty analysis in economics, healthcare, finance, and engineering systems
Computational and hybrid models integrating fuzzy logic, AI, and statistics
Applications of soft computing and information fusion in uncertain environments