About the Journal
Applied Expert Systems and Knowledge Management (AESKM) is an international, peer-reviewed journal dedicated to the advancement and application of artificial intelligence, knowledge engineering, and advanced decision-making paradigms across industry, government, and academia. The journal welcomes original research articles, review articles, short communications, perspectives, and technical case studies.
Aims
Applied Expert Systems and Knowledge Management (AESKM) is a premier, peer-reviewed international open-access journal dedicated to the advancement and practical application of artificial intelligence, knowledge engineering, and advanced decision-making paradigms.
The primary mission of AESKM is to bridge foundational theoretical breakthroughs in AI, such as, granular computing, approximate reasoning, and fuzzy sets with the engineering of robust, scalable decision-support systems across industry, government, and academia. The journal serves as a global forum for academic researchers, engineers, and industry practitioners to exchange pioneering findings concerning the architectural design, algorithmic development, empirical testing, and strategic management of expert and intelligent systems.
To foster a comprehensive and transparent scientific dialogue, the journal operates under the following structural pillars:
• No Article Length Restrictions: AESKM has no restrictions regarding the maximum length of papers, encouraging authors to publish their theoretical and experimental results in as much detail as possible.
• Reproducibility and Rigor: Full mathematical, experimental, and methodological details must be provided so that the results can be fully replicated by the scientific community.
• Supplementary Materials: Authors are highly encouraged to deposit software, source code, datasets, and associated electronic files as supplementary materials to facilitate validation and replication studies.
AESKM publishes several manuscript types to accommodate diverse research contributions:
• Original Research Articles: Rigorous treatises presenting pioneering methodologies, empirical validations, or theoretical advancements.
• Review and Survey Articles: Critical, comprehensive evaluations of existing literature, technological trends, and future research directions.
• Short Communications: Concise papers presenting preliminary findings or significant isolated breakthroughs requiring rapid dissemination.
• Perspectives and Viewpoints: Forward-looking commentaries on emerging paradigms, methodologies, and technical challenges.
• Case Studies / Empirical Evaluations: Methodologically sound validations of cutting-edge technologies within real-world deployments.
Scope
The scope of AESKM encompasses the core methodologies of soft computing, expert systems, and knowledge extraction, their interactions, and their translation into real-world application domains. Specific areas of interest include, but are not limited to, the following core tracks, methodologies, and industrial sectors:
Core Methodologies & Techniques
• Knowledge Engineering & Extraction
• Fuzzy Computing & Soft Computing
• Decision-Making Paradigms
• Machine & Deep Learning
• Bio-Inspired & Evolutionary Computing
Specific Thematic Tracks
• Knowledge Engineering, Fuzzy Systems, and Expert Systems
• Large Language Models and Generative AI
• Computer Vision and Machine Perception
• Intelligent Healthcare and Health Computing
• Information Security, Privacy, and Trust
Industrial and Real-World Application Areas
AESKM emphasizes the translational impact of AI. The journal welcomes submissions applying soft computing and expert systems to:
• Robotics
• Process automation
• Predictive maintenance
• Quality control
• Intelligent manufacturing systems.
• Quantitative finance
• Computational auditing
• Automated stock trading
• Risk assessment
• Strategic marketing
• Econometrics
• Supply chain optimization
• Logistics
• Human capital optimization
• Crisis mitigation
• Telecommunications
• Network management
• Power electronics
• Smart energy grids.
• Autonomous reasoning
• Autonomous systems
• System identification
• Modeling, and fault diagnosis
• Emerging Technologies
• Big Data analytics
• IoT
• Edge computing
• Extended Reality (XR)
• Metaverse
• Digital Twins.
• Agricultural machinery
• Precision agriculture
• Environmental system modeling
• Explainable AI (XAI) for improving model transparency
• Ethical considerations
• Bias mitigation
• Societal impacts of deploying AI-driven systems.
