Machine Learning in Natural Complex Systems PDF Download
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Author: Andre Gruning
Publisher: Frontiers Media SA
ISBN: 2889763692
Category : Science
Languages : en
Pages : 171
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Book Description
Author: Andre Gruning
Publisher: Frontiers Media SA
ISBN: 2889763692
Category : Science
Languages : en
Pages : 171
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Book Description
Author: Lorenza Saitta
Publisher: Springer Science & Business Media
ISBN: 1461470528
Category : Computers
Languages : en
Pages : 488
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Book Description
Abstraction is a fundamental mechanism underlying both human and artificial perception, representation of knowledge, reasoning and learning. This mechanism plays a crucial role in many disciplines, notably Computer Programming, Natural and Artificial Vision, Complex Systems, Artificial Intelligence and Machine Learning, Art, and Cognitive Sciences. This book first provides the reader with an overview of the notions of abstraction proposed in various disciplines by comparing both commonalities and differences. After discussing the characterizing properties of abstraction, a formal model, the KRA model, is presented to capture them. This model makes the notion of abstraction easily applicable by means of the introduction of a set of abstraction operators and abstraction patterns, reusable across different domains and applications. It is the impact of abstraction in Artificial Intelligence, Complex Systems and Machine Learning which creates the core of the book. A general framework, based on the KRA model, is presented, and its pragmatic power is illustrated with three case studies: Model-based diagnosis, Cartographic Generalization, and learning Hierarchical Hidden Markov Models.
Author: Esteban Tlelo-Cuautle
Publisher:
ISBN: 9781032473307
Category : Technology & Engineering
Languages : en
Pages : 0
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Book Description
"This book highlights applications that include machine learning methods to enhance new developments in complex and unmanned systems. The main topics covered under this title include: machine learning, artificial intelligence, cryptography, submarines, drones, security in healthcare, Internet of Things and robotics. This book can be used by graduate students, industrial and academic professionals to revise real case studies in applying machine learning in the areas of modeling, simulation and optimization of complex systems, cryptography, electronics, healthcare, control systems, Internet of Things, security, and unmanned systems such as submarines, drones and robots"--
Author: Fakhteh Ghanbarnejad
Publisher: Springer
ISBN: 3030146839
Category : Science
Languages : en
Pages : 244
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Book Description
This book bridges the gap between advances in the communities of computer science and physics--namely machine learning and statistical physics. It contains diverse but relevant topics in statistical physics, complex systems, network theory, and machine learning. Examples of such topics are: predicting missing links, higher-order generative modeling of networks, inferring network structure by tracking the evolution and dynamics of digital traces, recommender systems, and diffusion processes. The book contains extended versions of high-quality submissions received at the workshop, Dynamics On and Of Complex Networks (doocn.org), together with new invited contributions. The chapters will benefit a diverse community of researchers. The book is suitable for graduate students, postdoctoral researchers and professors of various disciplines including sociology, physics, mathematics, and computer science.
Author: John H. Holland
Publisher: MIT Press
ISBN: 9780262581110
Category : Psychology
Languages : en
Pages : 236
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Book Description
Genetic algorithms are playing an increasingly important role in studies of complex adaptive systems, ranging from adaptive agents in economic theory to the use of machine learning techniques in the design of complex devices such as aircraft turbines and integrated circuits. Adaptation in Natural and Artificial Systems is the book that initiated this field of study, presenting the theoretical foundations and exploring applications. In its most familiar form, adaptation is a biological process, whereby organisms evolve by rearranging genetic material to survive in environments confronting them. In this now classic work, Holland presents a mathematical model that allows for the nonlinearity of such complex interactions. He demonstrates the model's universality by applying it to economics, physiological psychology, game theory, and artificial intelligence and then outlines the way in which this approach modifies the traditional views of mathematical genetics. Initially applying his concepts to simply defined artificial systems with limited numbers of parameters, Holland goes on to explore their use in the study of a wide range of complex, naturally occuring processes, concentrating on systems having multiple factors that interact in nonlinear ways. Along the way he accounts for major effects of coadaptation and coevolution: the emergence of building blocks, or schemata, that are recombined and passed on to succeeding generations to provide, innovations and improvements.
Author: Salim Chikhi
Publisher: Springer Nature
ISBN: 3030588610
Category : Technology & Engineering
Languages : en
Pages : 318
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Book Description
This proceedings book gives a new vision and real progress towards more difficult problems resolution. In trying to solve the problems we face every day in the complex world we are living, we are constantly developing artificial systems and increasingly complex middleware. Indeed, the research works contained in this book address a large spread of nowadays topics like IoT architectures, communication and routing protocols, smart systems, software defined networks (SDNs), natural language processing (NLP), social media, health systems, machine intelligence and data science, soft computing and optimization, and software technology. This book, which is a selective collection of research papers accepted by the international program committee of the 6th International Symposium on Modelling and Implementation of Complex Systems (MISC 2020), considers intelligence (CI) more as a way of thinking about problems. It includes a mix of old efficient (Fuzzy, NN, GA) and modern AI techniques (deep learning and CNN). The whole complex systems research community finds in this book an appropriate way to approach problems that have no algorithmic solution and finds many well-formulated technical challenges.
Author: Arindam Dey
Publisher: John Wiley & Sons
ISBN: 1394214111
Category : Computers
Languages : en
Pages : 485
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Book Description
Provides a comprehensive understanding of the latest advancements and practical applications of machine learning techniques. Machine learning (ML), a branch of artificial intelligence, has gained tremendous momentum in recent years, revolutionizing the way we analyze data, make predictions, and solve complex problems. As researchers and practitioners in the field, the editors of this book recognize the importance of disseminating knowledge and fostering collaboration to further advance this dynamic discipline. How Machine Learning is Innovating Today's World is a timely book and presents a diverse collection of 25 chapters that delve into the remarkable ways that ML is transforming various fields and industries. It provides a comprehensive understanding of the practical applications of ML techniques. The wide range of topics include: An analysis of various tokenization techniques and the sequence-to-sequence model in natural language processing explores the evaluation of English language readability using ML models a detailed study of text analysis for information retrieval through natural language processing the application of reinforcement learning approaches to supply chain management the performance analysis of converting algorithms to source code using natural language processing in Java presents an alternate approach to solving differential equations utilizing artificial neural networks with optimization techniques a comparative study of different techniques of text-to-SQL query conversion the classification of livestock diseases using ML algorithms ML in image enhancement techniques the efficient leader selection for inter-cluster flying ad-hoc networks a comprehensive survey of applications powered by GPT-3 and DALL-E recommender systems' domain of application reviews mood detection, emoji generation, and classification using tokenization and CNN variations of the exam scheduling problem using graph coloring the intersection of software engineering and machine learning applications explores ML strategies for indeterminate information systems in complex bipolar neutrosophic environments ML applications in healthcare, in battery management systems, and the rise of AI-generated news videos how to enhance resource management in precision farming through AI-based irrigation optimization. Audience The book will be extremely useful to professionals, post-graduate research scholars, policymakers, corporate managers, and anyone with technical interests looking to understand how machine learning and artificial intelligence can benefit their work.
Author: Kohei Nakajima
Publisher: Springer
ISBN: 9789811316869
Category : Computers
Languages : en
Pages : 458
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Book Description
This book is the first comprehensive book about reservoir computing (RC). RC is a powerful and broadly applicable computational framework based on recurrent neural networks. Its advantages lie in small training data set requirements, fast training, inherent memory and high flexibility for various hardware implementations. It originated from computational neuroscience and machine learning but has, in recent years, spread dramatically, and has been introduced into a wide variety of fields, including complex systems science, physics, material science, biological science, quantum machine learning, optical communication systems, and robotics. Reviewing the current state of the art and providing a concise guide to the field, this book introduces readers to its basic concepts, theory, techniques, physical implementations and applications. The book is sub-structured into two major parts: theory and physical implementations. Both parts consist of a compilation of chapters, authored by leading experts in their respective fields. The first part is devoted to theoretical developments of RC, extending the framework from the conventional recurrent neural network context to a more general dynamical systems context. With this broadened perspective, RC is not restricted to the area of machine learning but is being connected to a much wider class of systems. The second part of the book focuses on the utilization of physical dynamical systems as reservoirs, a framework referred to as physical reservoir computing. A variety of physical systems and substrates have already been suggested and used for the implementation of reservoir computing. Among these physical systems which cover a wide range of spatial and temporal scales, are mechanical and optical systems, nanomaterials, spintronics, and quantum many body systems. This book offers a valuable resource for researchers (Ph.D. students and experts alike) and practitioners working in the field of machine learning, artificial intelligence, robotics, neuromorphic computing, complex systems, and physics.
Author: Xiong Luo
Publisher: Mdpi AG
ISBN: 9783036590592
Category :
Languages : en
Pages : 0
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Book Description
This reprint contains 15 articles from the Special Issue of the MDPI journal Processes on "Machine Learning-Assisted Intelligent Processing and Optimization of Complex Systems". These articles focus on the most recent developments in intelligent optimization methods and their applications in various test cases. The reprint covers various topics, including distributed multiagent modeling, metaheuristic algorithms, multisource data fusion, mobile computing and mobile sensing, machine learning-based intelligent processing for modeling complex manufacturing systems, and data-driven intelligent modeling. Focusing on the abovementioned subjects, this reprint can be useful for researchers interested in intelligent optimization techniques and their applications in the fields of artificial intelligence and machine learning. We believe that this reprint will encourage the convergence between many communities.
Author: Kevin Allen Andrew
Publisher:
ISBN:
Category : Champlain, Lake, Watershed
Languages : en
Pages : 0
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Book Description
Agent-based models are becoming increasingly useful in studying the behavior of real-world complex multi-agent systems; however, one of the outstanding challenges in the modeling of coupled natural and human systems is the dearth of techniques for creating agents that are able to learn from their past failures and successes, as well as compounded environmental and social uncertainties. This research has been focused on the integration of traditional agent-based modeling with machine learning methodologies for modeling agent decision-making and its recursive impacts on economic, environmental, and societal outcomes, feeding into the dynamic co-evolution of the coupled natural and human system state variables within simulated worlds, resulting in the development of two models incorporating and exploring the use of deep reinforcement machine learning as a driver for decision-policy making in agent-based models. The first of these models is a model of agricultural land use and the adoptionof agricultural best-management practices by farmers in response to ecological and economic scenarios as a result of municipal regulation and variance in the occurrence of extreme weather events. The primary study area used for the model is a region of the Missiquoi Bay Area of Lake Champlain in Vermont, containing 480 farmer agents corresponding to agricultural land parcels within the region. A parameter sweep and sensitivity analysis on model hyperparameters was conducted to explore the effects of changes to agent calibration and training on agent decision-making and model performance. The second model expands upon the scope of the first, including foresteragents and commercial and residential urban agents within a larger region of the Lake Champlain Basin of Vermont. Additionally, the impacts of agent decision-making take place on the simulated landscape, resulting in gradual land cover change over time. Land cover data from the United States Geological Survey's National Land Cover Database was used for initial parameterization, calibration, and training of the model (years 2001, 2006) and model testing (year 2011). Results suggest that with appropriate scoping and hyperparameter selection,the integration of deep reinforcement machine learning techniques into the development of agent-based models can increase predictive accuracy in the modeling of real-world phenomena; however, these gains must be weighed against the increased technical complexity of such a model and the associated risk of introducing model error.
