Research in Fuzziness and Uncertainty (RFU) is an international, peer-reviewed journal devoted to the advancement of theories, methodologies, computational approaches, and applications for representing, analyzing, reasoning with, and making decisions under fuzziness, uncertainty, imprecision, vagueness, incompleteness, ambiguity, and conflicting information.

The journal provides an interdisciplinary forum for researchers working at the interface of fuzzy mathematics, uncertainty modelling, computational intelligence, artificial intelligence, decision sciences, optimization, information fusion, and intelligent systems.

RFU welcomes rigorous theoretical contributions as well as innovative methodological and application-oriented studies. Particular emphasis is placed on research that advances the understanding, modelling, computation, or practical treatment of uncertain and imprecise information.

Topics of Interest

Topics of interest include, but are not limited to, the following areas.

1. Theory and Foundations

RFU welcomes fundamental theoretical and mathematical research related to fuzziness and uncertainty, including:

• Fuzzy set theory and fuzzy systems;
• Type-2 and higher-order fuzzy sets;
• Intuitionistic fuzzy sets and their enhanced versions;
• Picture fuzzy sets, spherical and T-spherical fuzzy sets;
• Hesitant and probabilistic hesitant fuzzy sets;
• Interval-valued and complex-valued fuzzy structures;
• Neutrosophic sets and systems;
• Rough sets and fuzzy-rough models;
• Soft sets and related uncertainty frameworks;
• Hybrid and generalized fuzzy structures;
• Novel representations of uncertain, vague, incomplete, and imprecise information;
• Possibility theory;
• Evidence theory and belief functions;
• Imprecise probabilities and random-set approaches;
• Non-additive measures and integrals;
• Fuzzy relations and fuzzy equations;
• Fuzzy algebraic structures;
• Fuzzy graphs and generalized graph structures;
• Fuzzy topology and fuzzy analysis;
• Interval analysis and interval mathematics;
• Granular computing;
• Approximate reasoning;
• Computing with words;
• Mathematical and logical foundations of uncertainty;
• Measures of uncertainty, fuzziness, information, and entropy;
• Similarity, distance, correlation, divergence, and inclusion measures.

The journal also encourages mathematically rigorous development of new uncertainty frameworks, provided that their theoretical properties, motivation, and relationship with existing models are clearly established.

2. Computational Methods and Intelligent Systems

RFU publishes methodological and computational research for processing and learning from uncertain information, including:

• Fuzzy logic and fuzzy inference systems;
• Fuzzy rule-based systems;
• Fuzzy control and intelligent control;
• Fuzzy clustering and classification;
• Pattern recognition under uncertainty;
• Knowledge representation and reasoning;
• Expert and knowledge-based systems;
• Information granulation;
• Aggregation operators;
• Ordered, prioritized, and generalized aggregation;
• Choquet and Sugeno integrals;
• T-norms and T-conorms;
• Information aggregation and fusion;
• Multi-source and multi-expert information fusion;
• Preference aggregation;
• Consensus modelling;
• Computational intelligence;
• Soft computing;
• Neuro-fuzzy systems;
• Neural and deep learning with uncertainty;
• Uncertainty-aware machine learning;
• Explainable and interpretable intelligent systems;
• Evolutionary computation;
• Swarm intelligence;
• Metaheuristic and bio-inspired computation;
• Hybrid intelligent systems;
• Data-driven uncertainty modelling;
• Intelligent prediction and forecasting;
• Simulation and computational modelling;
• Algorithms and software for fuzzy and uncertain systems.

Machine-learning and artificial-intelligence studies are particularly encouraged when the modelling, quantification, propagation, interpretation, or management of uncertainty constitutes a substantive part of the contribution.

3. Decision, Optimization, and Information Fusion

RFU welcomes theoretical, methodological, and computational advances in decision-making and optimization under uncertainty, including:

• Decision theory under uncertainty;
• Multi-criteria decision-making;
• Multi-objective decision-making;
• Group and multi-expert decision-making;
• Large-scale group decision-making;
• Preference modelling and preference learning;
• Ranking, selection, classification, and prioritization;
• Consensus reaching processes;
• Decision-support systems;
• Intelligent decision analytics;
• Risk and uncertainty assessment;
• Conflict analysis and resolution;
• Fuzzy mathematical programming;
• Uncertain and robust optimization;
• Multi-objective optimization;
• Evolutionary and metaheuristic optimization;
• Optimization under incomplete or imprecise information;
• Hybrid decision and optimization models;
• Data-driven decision support;
• Human-centered and explainable decision systems.

Integrated approaches combining uncertainty modelling, artificial intelligence, optimization, information fusion, and decision analytics are particularly encouraged when the integration is scientifically motivated and produces a clear methodological or practical advancement.

Application Areas

RFU encourages innovative applications of theories and methods for fuzziness and uncertainty across science, engineering, technology, management, and interdisciplinary domains.

Application areas include, but are not limited to:

• Engineering systems and engineering design;
• Intelligent control and automation;
• Robotics and autonomous systems;
• Manufacturing and Industry 4.0/5.0;
• Energy and power systems;
• Renewable and sustainable energy;
• Environmental systems and climate-related decision support;
• Sustainability and sustainable development;
• Smart cities and intelligent infrastructure;
• Transportation and mobility;
• Logistics and supply-chain management;
• Operations and production management;
• Agriculture and precision agriculture;
• Autonomous and intelligent agricultural systems;
• Healthcare and medical decision support;
• Biomedical and health informatics;
• Finance, banking, and economics;
• Financial risk and credit assessment;
• Business intelligence and management;
• Project and strategic management;
• Risk, safety, and reliability analysis;
• Information systems;
• Cybersecurity and information security;
• Internet of Things and sensor systems;
• Big-data and data-analytics applications;
• Image, signal, and pattern analysis;
• Natural-resource and environmental management;
• Disaster and emergency management;
• Education and learning systems;
• Social and behavioral decision problems;
• Public-policy and socio-economic decision support.

Applications in other areas are welcome when the treatment of fuzziness or uncertainty is scientifically meaningful and constitutes an integral part of the research contribution.

Research Contribution and Relevance

RFU places particular emphasis on manuscripts that make a clear theoretical, methodological, computational, or application-driven scientific contribution to the understanding or treatment of fuzziness and uncertainty.

The journal particularly encourages research that:

• develops new theories, models, mathematical structures, or uncertainty representations;
• establishes new theoretical properties or meaningful connections among existing uncertainty frameworks;
• proposes innovative algorithms, computational methods, or intelligent systems;
• substantially advances existing fuzzy, uncertainty, decision, optimization, or information-fusion methodologies;
• develops scientifically justified hybrid or integrated approaches;
• provides rigorous analytical, computational, experimental, or comparative validation;
• presents meaningful real-world applications that generate methodological or domain-specific insights;
• investigates robustness, sensitivity, uncertainty propagation, reliability, or stability where appropriate;
• provides reproducible computational implementations, algorithms, datasets, or software where applicable; or
• establishes new interdisciplinary links between uncertainty modelling and emerging scientific or technological problems.

Application-oriented manuscripts should demonstrate substantive scientific novelty or methodological advancement beyond the routine use of an established technique on a different dataset or case study.

Similarly, manuscripts based primarily on introducing a new fuzzy or generalized uncertainty representation should clearly demonstrate its mathematical motivation, distinctive properties, advantages, and theoretical or practical significance rather than relying only on terminology changes or straightforward extensions of existing structures.

Hybrid decision-making studies should provide a clear scientific rationale for integrating the selected methods and should demonstrate an advancement beyond the simple combination or sequential application of established techniques.

Types of Contributions

RFU welcomes:

• Original research articles;
• Theoretical and methodological papers;
• Computational and algorithmic studies;
• Application-oriented research with substantive scientific novelty;
• Review and survey articles;
• Systematic reviews and critical state-of-the-art studies;
• Comparative and benchmarking studies;
• Reproducible computational research;
• Software and algorithm papers, where appropriate;
• Interdisciplinary studies addressing significant uncertainty-related research problems.

Through this scope, Research in Fuzziness and Uncertainty (RFU) seeks to provide an international platform connecting the mathematical foundations of fuzziness and uncertainty with modern computational intelligence, artificial intelligence, decision science, optimization, and meaningful real-world applications.