• UA

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    Briefly:

    • Models, methods, and technologies for the development of distributed artificial intelligence systems

    • Agents and multi-agent systems

    • Research and development of ontology-based methods and models for knowledge representation in distributed intelligent systems

    • Intelligent search and information processing in distributed environments and the Internet

    • Intelligent decision support systems

    • Computational intelligence: adaptive neuro-fuzzy systems

    • Distributed systems

    • Semantic Web, Semantic Technologies, and Web 4.0

    • Intelligent Web Mining

    • Big Data

    • Distributed Artificial Intelligence

    • Machine Learning

    • Data Mining in Cyber Security Systems

    • Database and Knowledge Base Management

    • Internet-based systems for electronic and mobile commerce

    • Cognitive Systems and Technologies

    Detailed:

    • Artificial Intelligence
    • Machine Learning
    • Big Data
    • Data Mining in Cyber Security Systems
    • Cognitive Systems and Technologies
    • Agents and Multi-Agent Systems (especially in decentralized AI, autonomous systems, and reinforcement learning)
    • Computational intelligence: adaptive neuro-fuzzy systems
    • Intelligent decision support systems
    • Distributed systems (especially cloud-native AI, federated learning, edge AI)
    • Semantic Web, Semantic Technologies, and Web 4.0
    • Internet-based systems for electronic and mobile commerce (more an application domain than a core AI research trend.)
    • Database and Knowledge Base Management now often embedded within broader data engineering and ML infrastructure.
    • Ontology-based methods and models for knowledge representation
    • Important in explainable AI (XAI), knowledge graphs, and symbolic AI, though overshadowed by data-driven deep learning.
    • Intelligent Web Mining
    • Replaced in many contexts by terms like AI-powered data analytics, web-scale ML, or NLP for web content.
    • Database and Knowledge Base Management
    • Now often embedded within broader data engineering and ML infrastructure.
    • Internet-based systems for electronic and mobile commerce (more an application domain than a core AI research trend).