Intelligent Modeling and Decision Analytics (IMDA) is an international, peer-reviewed scholarly journal dedicated to the development and application of intelligent models, analytical methods, and computational approaches for complex decision problems. The journal provides a multidisciplinary platform for research that integrates advanced modeling techniques with data, knowledge, uncertainty, and computational intelligence to support analysis, prediction, optimization, and decision-making.
IMDA is positioned at the intersection of intelligent modeling, decision analytics, artificial intelligence, computational intelligence, operations research, and optimization. The journal particularly encourages research that goes beyond the development of isolated algorithms or mathematical procedures and demonstrates how advanced models and analytical methods contribute to understanding complex systems and improving decision processes.
The journal approaches intelligent modeling as the development of computational, mathematical, data-driven, knowledge-driven, and hybrid models capable of representing, analyzing, learning from, and reasoning about complex systems. This includes models based on artificial intelligence, machine learning, computational intelligence, fuzzy and uncertainty theories, optimization, simulation, and other advanced computational paradigms.
Decision analytics encompasses methods and frameworks that transform data, information, knowledge, and model outputs into meaningful analytical insight and decision support. IMDA therefore welcomes research in multi-criteria and multi-objective decision-making, predictive and prescriptive analytics, optimization, risk and uncertainty analysis, forecasting, intelligent decision-support systems, and related analytical methodologies.
The journal particularly welcomes research integrating artificial intelligence and machine learning with decision analysis, optimization, operations research, fuzzy and uncertainty modeling, simulation, forecasting, and expert knowledge. Relevant topics also include intelligent and hybrid optimization, computational decision models, classification and clustering, pattern recognition, explainable and interpretable artificial intelligence, expert and knowledge-based systems, recommendation systems, information fusion, preference modeling, risk and resilience analytics, and human-centered decision analytics.
A central objective of IMDA is to strengthen the connection between methodological and computational innovation and meaningful decision applications. Studies introducing new models, algorithms, optimization procedures, artificial intelligence methods, uncertainty representations, weighting or ranking techniques, or analytical frameworks should clearly identify the problem they address and demonstrate their methodological, computational, or practical contribution.
IMDA welcomes both theoretical and application-oriented research across complex systems and decision environments. Applications may include management and business, engineering and industrial systems, transportation and logistics, supply chains, energy and sustainability, environmental systems, healthcare, risk and resilience, smart systems, finance, public-sector decision-making, and other areas in which intelligent modeling and analytical decision support provide meaningful value.
The journal welcomes original research articles, methodological studies, computational investigations, application-driven studies, and high-quality review articles that contribute to the development or application of intelligent modeling and decision analytics. Application-oriented research should provide analytical, methodological, computational, 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.
