Artificial Intelligence And Complex Dynamical Systems
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Artificial Intelligence And Complex Dynamical Systems
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Author : Giorgos Tsironis
language : en
Publisher: Springer Nature
Release Date : 2025-03-13
Artificial Intelligence And Complex Dynamical Systems written by Giorgos Tsironis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-13 with Science categories.
This book serves as a comprehensive introduction to nonlinear complex systems through the application of machine learning methods. Artificial intelligence (AI) has affected the foundations of scientific discovery, and can therefore lend itself to developing a better understanding of the unpredictable nature of complex dynamical systems and to predict their future evolution. Utilizing Python code, this book teaches and applies machine learning to topics such as chaotic dynamics and time-series analysis, solitons, breathers, chimeras, nonlinear localization, biomolecular dynamics, and wave propagation in the heart. The consistent integration of methods and models allow for readers to develop a necessary intuition on how to handle complexity through AI. This textbook contains a wealth of expository material, code, and example problems to support and organize academic coursework, allowing the technical nature of these areas of study to become highly accessible. Requiring only a basic background in mathematics and coding in Python, this book is an essential text for a wide array of advanced undergraduate or graduate students in the applied sciences interested in complex systems through the lens of machine learning.
Stochastic Methods For Modeling And Predicting Complex Dynamical Systems
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Author : Nan Chen
language : en
Publisher: Springer Nature
Release Date : 2025-04-12
Stochastic Methods For Modeling And Predicting Complex Dynamical Systems written by Nan Chen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-12 with Mathematics categories.
This Second Edition is an essential guide to understanding, modeling, and predicting complex dynamical systems using new methods with stochastic tools. Expanding upon the original book, the author covers a unique combination of qualitative and quantitative modeling skills, novel efficient computational methods, rigorous mathematical theory, as well as physical intuitions and thinking. The author presents mathematical tools for understanding, modeling, and predicting complex dynamical systems using various suitable stochastic tools. The book provides practical examples and motivations when introducing these tools, merging mathematics, statistics, information theory, computational science, and data science. The author emphasizes the balance between computational efficiency and modeling accuracy while equipping readers with the skills to choose and apply stochastic tools to a wide range of disciplines. This second edition includes updated discussion of combining stochastic models with machine learning and addresses several additional topics, including importance sampling, regression, and maximum likelihood estimate. The author also introduces a new chapter on optimal control.
Machine Learning In Complex Networks
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Author : Thiago Christiano Silva
language : en
Publisher: Springer
Release Date : 2016-01-28
Machine Learning In Complex Networks written by Thiago Christiano Silva and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-28 with Computers categories.
This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is presented, offering necessary background material. In the second part, we describe in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a stochastic particle competition technique for both non-supervised and semi-supervised learning using a stochastic nonlinear dynamical system is described in details. Moreover, an analytical analysis is supplied, which enables one to predict the behavior of the proposed technique. In addition, data reliability issues are explored in semi-supervised learning. Such matter has practical importance and is not often found in the literature. With the goal of validating these techniques for solving real problems, simulations on broadly accepted databases are conducted. Still in this book, we present a hybrid supervised classification technique that combines both low and high orders of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features, while the latter measures the compliance of the test instances with the pattern formation of the data. We show that the high level technique can realize classification according to the semantic meaning of the data. This book intends to combine two widely studied research areas, machine learning and complex networks, which in turn will generate broad interests to scientific community, mainly to computer science and engineering areas.
Artificial Intelligence
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Author : Utku Kose
language : en
Publisher: CRC Press
Release Date : 2024-11-29
Artificial Intelligence written by Utku Kose and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-29 with Computers categories.
This book provides an examination of cutting-edge research and developments in the field of artificial intelligence. It seeks to extend the view in both technical and societal evaluations to ensure a well-defined balance for societal outcomes. It explores hot topics such as generative artificial intelligence, artificial intelligence in law, education, and climate change. Artificial Intelligence: Technical and Societal Advancements seeks to bridge the gap between theory and practical applications of AI by giving readers insight into recent advancements. It offers readers a deep dive into the transformative power of AI for the present and future world. As artificial intelligence continues to revolutionize various sectors, the book discusses applications from healthcare to finance and from entertainment to industrial areas. It discusses the technical aspects of intelligent systems and the effects of these aspects on humans. To this point, this book considers technical advancements while discussing the societal pros and cons in terms of human-machine interaction in critical applications. The authors also stress the importance of deriving policies and predictions about how to make future intelligent systems compatible with humans through a necessary level of human management. Finally, this book provides the opinions and views of researchers and experts (from public/private sector) including educators, lawyers, policymakers, managers, and business-related representatives. The target readers of this book include academicians; researchers; experts; policymakers; educators; and B.S., M.S., and Ph.D. students in the context of target problem fields. It can be used accordingly as a reference source and even supportive material for artificial intelligence-oriented courses.
Artificial Intelligence Of Neuromorphic Systems From Digital Analogue Quantum And Brain Oriented Computing To Hybrid Ai
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Author : Klaus Mainzer
language : en
Publisher: World Scientific
Release Date : 2024-11-15
Artificial Intelligence Of Neuromorphic Systems From Digital Analogue Quantum And Brain Oriented Computing To Hybrid Ai written by Klaus Mainzer and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-15 with Mathematics categories.
This book argues for neuromorphic systems as a technology of the future, which are oriented towards the energy efficiency of natural brains. Energy efficiency is a dramatic claim in times of environmental and climate challenges which should consider the sustainability goals of the United Nations (UN). Mathematically, neuromorphic computing is connected to analogue ('real') computing, which theoretically overcomes the limits of digital Turing computability. Therefore, the book also considers material sciences and engineering sciences which start to realize neuromorphic computing in hardware. Other mathematical formalisms such as quantum mechanics also open up new solutions (e.g., quantum computing) beyond the limits of digital Turing computability. These research fields are no longer merely of theoretical interest, they promise increasing innovation power of market interest. Nevertheless, neuromorphic computing is connected with deep logical, mathematical, and epistemic questions. Does it open new avenues to Artificial General Intelligence (AGI)? All these tendencies of research and innovation demonstrate that we need more integrated research in the foundations of logic, mathematics, physics, engineering sciences, cognitive science, and philosophy. The book is a plea for this kind of research.
Multi Chaos Fractal And Multi Fractional Artificial Intelligence Of Different Complex Systems
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Author : Yeliz Karaca
language : en
Publisher: Academic Press
Release Date : 2022-06-22
Multi Chaos Fractal And Multi Fractional Artificial Intelligence Of Different Complex Systems written by Yeliz Karaca and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-22 with Science categories.
Multi-Chaos, Fractal and Multi-Fractional Artificial Intelligence of Different Complex Systems addresses different uncertain processes inherent in the complex systems, attempting to provide global and robust optimized solutions distinctively through multifarious methods, technical analyses, modeling, optimization processes, numerical simulations, case studies as well as applications including theoretical aspects of complexity. Foregrounding Multi-chaos, Fractal and Multi-fractional in the era of Artificial Intelligence (AI), the edited book deals with multi- chaos, fractal, multifractional, fractional calculus, fractional operators, quantum, wavelet, entropy-based applications, artificial intelligence, mathematics-informed and data driven processes aside from the means of modelling, and simulations for the solution of multifaceted problems characterized by nonlinearity, non-regularity and self-similarity, frequently encountered in different complex systems. The fundamental interacting components underlying complexity, complexity thinking, processes and theory along with computational processes and technologies, with machine learning as the core component of AI demonstrate the enabling of complex data to augment some critical human skills. Appealing to an interdisciplinary network of scientists and researchers to disseminate the theory and application in medicine, neurology, mathematics, physics, biology, chemistry, information theory, engineering, computer science, social sciences and other far-reaching domains, the overarching aim is to empower out-of-the-box thinking through multifarious methods, directed towards paradoxical situations, uncertain processes, chaotic, transient and nonlinear dynamics of complex systems. - Constructs and presents a multifarious approach for critical decision-making processes embodying paradoxes and uncertainty. - Includes a combination of theory and applications with regard to multi-chaos, fractal and multi-fractional as well as AI of different complex systems and many-body systems. - Provides readers with a bridge between application of advanced computational mathematical methods and AI based on comprehensive analyses and broad theories.
Artificial Intelligence Methodology Systems And Applications
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Author : Petia Koprinkova-Hristova
language : en
Publisher: Springer Nature
Release Date : 2025-01-31
Artificial Intelligence Methodology Systems And Applications written by Petia Koprinkova-Hristova and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-31 with Computers categories.
This book constitutes the refereed proceedings of the 19th International Conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2024, held in Varna, Bulgaria, during September 18–20, 2024. The 18 revised full papers presented in this book were carefully reviewed and selected from 23 submissions. They cover a wide range of topics in AI and its applications: natural language processing, sentiment analyses, image processing, optimization, reinforcement learning, from deep ANNs to spike timing NNs, applications in economics, medicine and process control.
Ai 2022 Advances In Artificial Intelligence
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Author : Haris Aziz
language : en
Publisher: Springer Nature
Release Date : 2022-12-02
Ai 2022 Advances In Artificial Intelligence written by Haris Aziz and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-02 with Computers categories.
This book constitutes the refereed proceedings of the 35th Australasian Joint Conference on Artificial Intelligence, AI 2022, which took place in Perth, WA, Australia, in December 5–8, 2022. The 56 full papers included in this book were carefully reviewed and selected from 90 submissions. They were organized in topical sections as follows: Computer Vision; Deep Learning; Ethical/Explainable AI; Genetic Algorithms; Knowledge Representation and NLP; Machine Learning; Medical AI; Optimization; and Reinforcement Learning.
The Hidden Pattern
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Author : Ben Goertzel
language : en
Publisher: Universal-Publishers
Release Date : 2006
The Hidden Pattern written by Ben Goertzel and has been published by Universal-Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Computers categories.
The Hidden Pattern presents a novel philosophy of mind, intended to form a coherent conceptual framework within which it is possible to understand the diverse aspects of mind and intelligence in a unified way. The central concept of the philosophy presented is the concept of "pattern" minds and the world they live in and co-create are viewed as patterned systems of patterns, evolving over time, and various aspects of subjective experience and individual and social intelligence are analyzed in detail in this light. Many of the ideas presented are motivated by recent research in artificial intelligence and cognitive science, and the author's own AI research is discussed in moderate detail in one chapter. However, the scope of the book is broader than this, incorporating insights from sources as diverse as Vedantic philosophy, psychedelic psychotherapy, Nietzschean and Peircean metaphysics and quantum theory. One of the unique aspects of the patternist approach is the way it seamlessly fuses the mechanistic, engineering-oriented approach to intelligence and the introspective, experiential approach to intelligence.
Probabilistic Graphical Models
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Author : Daphne Koller
language : en
Publisher: MIT Press
Release Date : 2009-07-31
Probabilistic Graphical Models written by Daphne Koller and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-07-31 with Computers categories.
A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions. Most tasks require a person or an automated system to reason—to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.