Fuzzy Analytics Spectrum (FAS) is an international, peer-reviewed scholarly journal dedicated to analytics under uncertainty, with a particular focus on how fuzzy modelling can enhance the interpretation, analysis, prediction, evaluation, and optimization of complex systems.
FAS is positioned at the intersection of fuzzy modelling, data analytics, decision analytics, artificial intelligence, and optimization. The journal focuses on the analytical value of fuzzy approaches and on how uncertain, imprecise, incomplete, or linguistically expressed information can be transformed into meaningful insights and practically useful decisions.
The journal promotes fuzzy analytics as an integrated analytical perspective in which uncertainty modelling forms part of a broader process involving data and information representation, pattern and relationship analysis, prediction, evaluation, optimization, and decision support. Contributions should demonstrate not only how uncertainty is represented, but also what additional analytical insight is obtained through fuzzy modelling and how that insight improves understanding or decision-making.
FAS particularly welcomes research integrating fuzzy approaches with artificial intelligence, machine learning, data-driven modelling, multi-criteria and multi-objective decision analytics, optimization, operations research, simulation, forecasting, and intelligent decision-support systems. Relevant topics also include fuzzy-enhanced data analytics, predictive and prescriptive analytics under uncertainty, fuzzy classification and clustering, information fusion, risk and resilience analytics, explainable and interpretable analytics, and hybrid data-driven and knowledge-driven analytical models.
The journal encourages methodological and computational innovation when it is motivated by an identifiable analytical problem. Studies introducing new fuzzy structures, aggregation mechanisms, distance or similarity measures, weighting procedures, ranking techniques, optimization models, or related methods should clearly explain the analytical need they address and demonstrate their added value. Mathematical novelty alone, without a meaningful analytical contribution, is not the primary focus of the journal.
FAS places particular emphasis on real-world analytics in areas where uncertainty is an inherent component of the problem. Applications may include management and business systems, transportation and logistics, industrial and engineering systems, environmental and waste management, energy and sustainability, supply chains, risk and resilience, smart systems, and other complex socio-technical environments.
The journal welcomes original research articles, methodological studies, computational investigations, application-driven studies, and high-quality review articles that advance the understanding or practice of analytics under uncertainty. Application-oriented research should provide analytical, methodological, or decision-relevant insights that extend beyond the specific case being investigated.
All submitted manuscripts undergo a rigorous double-blind peer-review process to ensure scientific quality, originality, methodological soundness, and relevance to the journal's scope.
