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Nikola K. Kasabov

    Neuro fuzzy techniques for intelligent information systems
    Future directions for intelligent systems and information sciences
    Evolving connectionist systems
    Springer handbook of bio-, neuro-informatics
    Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence
    • The focus on spiking neural networks (SNN) highlights their biological inspiration and unique approach to information processing through spike trains. This monograph delves into classical theories and innovative applications, introducing brain-inspired AI (BI-AI) systems. It explores diverse fields, including cognitive brain data, brain-computer interfaces, and multisensory data modeling in various domains. The final chapter discusses future advancements, such as integrating quantum and molecular processing. This research book is aimed at postgraduate students and professionals in multiple disciplines.

      Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence
    • "The Springer Handbook of Bio-/Neuro-Informatics is the first published book in one volume that explains together the basics and the state-of-the-art of two major science disciplines in their interaction and mutual relationship, namely: information sciences, bioinformatics and neuroinformatics...The text contains 62 chapters organized in 12 parts, 6 of them covering topics from information science and bioinformatics, and 6 cover topics from information science and neuroinformatics. Each chapter consists of three main sections: introduction to the subject area, presentation of methods and advanced and future developments."--Back cover

      Springer handbook of bio-, neuro-informatics
    • Evolving connectionist systems

      • 320 pages
      • 12 hours of reading

      Many methods and models have been proposed for solving difficult problems such as prediction, planning and knowledge discovery in application areas such as bioinformatics, speech and image analysis. Most, however, are designed to deal with static processes which will not change over time. Some processes - such as speech, biological information and brain signals - are not static, however, and in these cases different models need to be used which can trace, and adapt to, the changes in the processes in an incremental, on-line mode, and often in real time. This book presents generic computational models and techniques that can be used for the development of evolving, adaptive modelling systems. The models and techniques used are connectionist-based (as the evolving brain is a highly suitable paradigm) and, where possible, existing connectionist models have been used and extended. The first part of the book covers methods and techniques, and the second focuses on applications in bioinformatics, brain study, speech, image, and multimodal systems.

      Evolving connectionist systems
    • This edited volume comprises invited chapters that cover five areas of the current and the future development of intelligent systems and information sciences. Half of the chapters were presented as invited talks at the Workshop „Future Directions for Intelligent Systems and Information Sciences“ held in Dunedin, New Zealand, 22-23 November 1999 after the International Conference on Neuro-Information Processing (lCONIPI ANZIISI ANNES '99) held in Perth, Australia. In order to make this volume useful for researchers and academics in the broad area of information sciences I invited prominent researchers to submit materials and present their view about future paradigms, future trends and directions. Part I contains chapters on adaptive, evolving, learning systems. These are systems that learn in a life-long, on-line mode and in a changing environment. The first chapter, written by the editor, presents briefly the paradigm of Evolving Connectionist Systems (ECOS) and some of their applications. The chapter by Sung-Bae Cho presents the paradigms of artificial life and evolutionary programming in the context of several applications (mobile robots, adaptive agents of the WWW). The following three chapters written by R. Duro, J. Santos and J. A. Becerra (chapter 3), GCoghill . (chapter 4), Y. Maeda (chapter 5) introduce new techniques for building adaptive, learning robots.

      Future directions for intelligent systems and information sciences
    • This volume comprises selected chapters that cover contemporary issues of the development and the application of neuro-fuzzy techniques. Developing and using neural networks, fuzzy logic systems, genetic algorithms and statistical methods as separate techniques, or in their combination, have been research topics in several areas such as mathematics, engineering, computer science, physics, economics and finance. Here the latest results in the fields are presented from both theoretical and practical point of view. The volume has four main parts. Part one presents generic techniques and theoretical issues while part two, three and four deal with practically oriented models, systems and implementations.

      Neuro fuzzy techniques for intelligent information systems