Special Stream on Advanced Machine Learning

The stream focuses on modern trends in advanced machine learning, emphasizing theoretical foundations, algorithmic development, and real-world applications. It covers a wide range of approaches rooted in computational intelligence, soft computing, neuromathematics, applied statistics, and optimization techniques.

Special attention is given to novel neural network architectures, hybrid and self-adaptive systems, interpretable and physics-informed models, as well as learning paradigms suited for real-time and data-intensive environments. The stream also highlights developments in distributed and federated learning, generative models, and resource-efficient AI for edge devices.

Topics of interest include, but are not limited to:

• Artificial Neural Networks (ANNs), deep learning, graph and transformer-based models
• Fuzzy systems, neuro-fuzzy and neo-fuzzy systems
• Hybrid and physics-informed learning models
• Online, self-supervised, and reinforcement learning
• Transfer learning and foundation models
• Evolutionary algorithms, particle swarm and other optimization strategies
• Data stream mining, edge AI, and federated learning
• Explainable AI, adversarial learning, and causal inference
• Multimodal learning and spatio-temporal modeling

We welcome contributions that address both methodological advancements and practical implementations in fields such as signal and image processing, control systems, IoT, remote sensing, robotics, and decision support systems.

Submissions should provide a clear formalization of the problem, detail the proposed methodologies, and include comparative analyses with existing approaches where applicable. Empirical validation through computational experiments or real-world applications is highly encouraged to demonstrate the efficacy of the proposed solutions.

Co-Chairmen

• Yevgeniy Bodyanskiy (Dr.Sc., Prof. Professor, Kharkiv National University of Radio Electronics, Kharkiv, Ukraine)
• Nataliia Kussul (Dr.Sc., Prof. Associate Research Professor, University of Maryland, USA)
• Georgi P. Dimitrov (Dr.Sc., Prof. University of Library Studies and Information Technologies, Bulgaria) 
• Dawid Polap (Dr.Sc., Prof., Silesian Unversity of Technology, Poland) (TBC)

IPC Members:

• Iryna Perova (Dr.Sc., Prof., Professor of Biomedical Engineering Department, Professor of System Engineering Department, Kharkiv National University of Radio Electronics, Kharkiv, Ukraine)
• Galina Setlak (Dr.Sc., Prof., Professor Politechniki Rzeszowskiej, Rzeszów, Poland)
• Kristina Vassiljeva (Ph.D., Assoc. Prof. Department of Computer Systems, Tallinn University of Technology, Tallinn, Estonia)
• Igor Aizenberg (Dr.Sc., Prof., Professor and Chair of Computer Science Department, Manhattan College, New York, USA)
• Alina Nechyporenko (Dr.Sc., Prof., Researcher at Technical University of Applied Sciences, Division Molecular Biotechnology and Functional Genomics, Wildau, Germany)
• Anna Vergeles (Ph.D., DataOps Team Lead, Oracle, Kharkiv, Ukraine)
• Valentyna Volkova (Ph.D., Project Leader, Samsung R&D Institute Kyiv, Ukraine)
• Eduard Petlenkov (Ph.D., Prof., Head of the Centre for Intelligent Systems Department of Computer Systems Tallinn University of Technology, Tallinn, Estonia)