Monica Bianchini – författare

Artificial Intelligence for Future Generation Robotics offers a vision for potential future robotics applications for AI technologies. Each chapter includes theory and mathematics to stimulate novel research directions based on the state-of-the-art in AI and smart robotics. Organized by application into ten chapters, this book offers a practical tool for researchers and engineers looking for new avenues and use-cases that combine AI with smart robotics. As we witness exponential growth in automation and the rapid advancement of underpinning technologies, such as ubiquitous computing, sensing, intelligent data processing, mobile computing and context aware applications, this book is an ideal resource for future innovation.

Brings AI and smart robotics into imaginative, technically-informed dialogue Integrates fundamentals with real-world applications Presents potential applications for AI in smart robotics by use-case Gives detailed theory and mathematical calculations for each application Stimulates new thinking and research in applying AI to robotics

Tremendous advances in all disciplines including engineering, science, health care, business, avionics, management, and so on, can also be attributed to the development of artificial intelligence paradigms. In fact, researchers are always interested in desi- ing machines which can mimic the human behaviour in a limited way. Therefore, the study of neural information processing paradigms have generated great interest among researchers, in that machine learning, borrowing features from human intelligence and applying them as algorithms in a computer friendly way, involves not only Mathem- ics and Computer Science but also Biology, Psychology, Cognition and Philosophy (among many other disciplines). Generally speaking, computers are fundamentally well-suited for performing au- matic computations, based on fixed, programmed rules, i.e. in facing efficiently and reliably monotonous tasks, often extremely time-consuming from a human point of view. Nevertheless, unlike humans, computers have troubles in understanding specific situations, and adapting to new working environments. Artificial intelligence and, in particular, machine learning techniques aim at improving computers behaviour in tackling such complex tasks. On the other hand, humans have an interesting approach to problem-solving, based on abstract thought, high-level deliberative reasoning and pattern recognition. Artificial intelligence can help us understanding this process by recreating it, then potentially enabling us to enhance it beyond our current capabilities.

Tremendous advances in all disciplines including engineering, science, health care, business, avionics, management, and so on, can also be attributed to the development of artificial intelligence paradigms. In fact, researchers are always interested in desi- ing machines which can mimic the human behaviour in a limited way. Therefore, the study of neural information processing paradigms have generated great interest among researchers, in that machine learning, borrowing features from human intelligence and applying them as algorithms in a computer friendly way, involves not only Mathem- ics and Computer Science but also Biology, Psychology, Cognition and Philosophy (among many other disciplines). Generally speaking, computers are fundamentally well-suited for performing au- matic computations, based on fixed, programmed rules, i.e. in facing efficiently and reliably monotonous tasks, often extremely time-consuming from a human point of view. Nevertheless, unlike humans, computers have troubles in understanding specific situations, and adapting to new working environments. Artificial intelligence and, in particular, machine learning techniques aim at improving computers behaviour in tackling such complex tasks. On the other hand, humans have an interesting approach to problem-solving, based on abstract thought, high-level deliberative reasoning and pattern recognition. Artificial intelligence can help us understanding this process by recreating it, then potentially enabling us to enhance it beyond our current capabilities.

This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include: Deep architectures Recurrent, recursive, and graph neural networks Cellular neural networks Bayesian networks Approximation capabilities of neural networks Semi-supervised learning Statistical relational learning Kernel methods for structured data Multiple classifier systems Self organisation and modal learning Applications to content-based image retrieval, text mining in large document collections, and bioinformatics  This book is thought particularly for graduate students, researchers and practitioners, willing to deepen their knowledge on more advanced connectionist models and related learning paradigms.

This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include: Deep architectures Recurrent, recursive, and graph neural networks Cellular neural networks Bayesian networks Approximation capabilities of neural networks Semi-supervised learning Statistical relational learning Kernel methods for structured data Multiple classifier systems Self organisation and modal learning Applications to content-based image retrieval, text mining in large document collections, and bioinformatics  This book is thought particularly for graduate students, researchers and practitioners, willing to deepen their knowledge on more advanced connectionist models and related learning paradigms.

This book covers all core technologies like neural networks, fuzzy systems, and evolutionary computation and their applications in the systems. Computationally intelligent system is a new concept for advanced information processing. The computationally intelligent system highly relies on numerical information supplied by manufacturers unlike AI.

This book covers all core technologies like neural networks, fuzzy systems, and evolutionary computation and their applications in the systems. Computationally intelligent system is a new concept for advanced information processing. The computationally intelligent system highly relies on numerical information supplied by manufacturers unlike AI.

We are, therefore, putting an effort to write this edited book on the future applications of machine learning on robotics where several applications have been included in separate chapters. This book will provide the future vision on the unexplored areas ofapplications of Robotics using machine learning.

We are, therefore, putting an effort to write this edited book on the future applications of machine learning on robotics where several applications have been included in separate chapters. This book will provide the future vision on the unexplored areas ofapplications of Robotics using machine learning.

This book gathers selected high-quality research papers presented at International Conference on Advanced Computing and Intelligent Technologies (ICACIT 2021) held at NCR New Delhi, India, during March 20–21, 2021, jointly organized by Galgotias University, India, and Department of Information Engineering and Mathematics Università Di Siena, Italy. It discusses emerging topics pertaining to advanced computing, intelligent technologies, and networks including AI and machine learning, data mining, big data analytics, high-performance computing network performance analysis, Internet of things networks, wireless sensor networks, and others. The book offers a valuable asset for researchers from both academia and industries involved in advanced studies.

This book gathers selected high-quality research papers presented at International Conference on Advanced Computing and Intelligent Technologies (ICACIT 2022), held at BIHER Chennai India, during March 12–13, 2022, jointly organized by Institute of Higher Education and Research Chennai 600073, Indira Gandhi National Tribal University, Regional Campus Manipur, India, and Department of Information Engineering and Mathematics Università Di Siena, Italy. It discusses emerging topics pertaining to advanced computing, intelligent technologies and networks including AI and machine learning, data mining, big data analytics, high performance computing network performance analysis, Internet of things networks, wireless sensor networks, and others. The book offers a valuable asset for researchers from both academia and industries involved in advanced studies.

This book gathers selected high-quality research papers presented at International Conference on Advanced Computing and Intelligent Technologies (ICACIT 2022), held at BIHER Chennai India, during March 12–13, 2022, jointly organized by Institute of Higher Education and Research Chennai 600073, Indira Gandhi National Tribal University, Regional Campus Manipur, India, and Department of Information Engineering and Mathematics Università Di Siena, Italy. It discusses emerging topics pertaining to advanced computing, intelligent technologies and networks including AI and machine learning, data mining, big data analytics, high performance computing network performance analysis, Internet of things networks, wireless sensor networks, and others. The book offers a valuable asset for researchers from both academia and industries involved in advanced studies.

This book gathers selected high-quality research papers presented at International Conference on Advanced Computing and Intelligent Technologies (ICACIT 2024), which is jointly organized by Università degli Studi di Siena, Italy and ADSRS Education and Research during December 13 – 14, 2024.