Spies, Lies, and Algorithms: The History and Future of American Intelligence, Zegart Amy B.

Автор: Rajshree Srivastava, Pradeep Kumar Mallick, Siddha
Название: Computational Intelligence for Machine Learning and Healthcare Informatics
ISBN: 3110647826 ISBN-13(EAN): 9783110647822
Издательство: Walter de Gruyter
Цена: 136310.00 T
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Описание: THE SERIES: INTELLIGENT BIOMEDICAL DATA ANALYSIS
By focusing on the methods and tools for intelligent data analysis, this series aims to narrow the increasing gap between data gathering and data comprehension. Emphasis is also given to the problems resulting from automated data collection in modern hospitals, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring. In medicine, overcoming this gap is crucial since medical decision making needs to be supported by arguments based on existing medical knowledge as well as information, regularities and trends extracted from big data sets.

Автор: Kang Min-wook, Schonfeld Paul
Название: Artificial Intelligence In Highway Location And Alignment Optimization: Applications Of Genetic Algorithms In Searching, Evaluating, And Optimizing Highway Location And Alignments
ISBN: 9813272805 ISBN-13(EAN): 9789813272804
Издательство: World Scientific Publishing
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Цена: 95040.00 T
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Описание: This monograph provides a comprehensive overview of methods for searching, evaluating, and optimizing highway location and alignments using genetic algorithms (GAs), a powerful Artificial Intelligence (AI) technique. It presents a two-level programming structure to deal with the effects of varying highway location on traffic level changes in surrounding road networks within the highway location search and alignment optimization process. In addition, the proposed method evaluates environmental impacts as well as all relevant highway costs associated with its construction, operation, and maintenance. The monograph first covers various search methods, relevant cost functions, constraints, computational efficiency, and solution quality issues arising from optimizing the highway alignment optimization (HAO) problem. It then focuses on applications of a special-purpose GA in the HAO problem where numerous highway alignments are generated and evaluated, and finally the best ones are selected based on costs, traffic impacts, safety, energy, and environmental considerations. A review of other promising optimization methods for the HAO problem is also provided in this monograph.

Автор: Adam Slowik
Название: Swarm Intelligence Algorithms: A Tutorial
ISBN: 1138384496 ISBN-13(EAN): 9781138384491
Издательство: Taylor&Francis
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Цена: 148010.00 T
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Описание: This book can provide the basics for understanding how swarm intelligence algorithms work, and aid readers in programming these algorithms to solve various computational problems. It is useful for students studying nature-based optimization algorithms, and can be a helpful for learning the basics of these algorithms efficiently.

Автор: Adam Slowik
Название: Swarm Intelligence Algorithms: Modifications and Applications
ISBN: 1138391018 ISBN-13(EAN): 9781138391017
Издательство: Taylor&Francis
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Цена: 132710.00 T
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Описание: This book presents 24 swarm algorithms together with their modifications and practical applications. Each chapter is devoted to one algorithm. In addition, each chapter contains a description of selected modifications of the algorithm and shows how it can be used to solve a selected practical problem.

Автор: Shi
Название: Emerging Research On Swarm Intelligence And Algorithm Optimization
ISBN: 1466663286 ISBN-13(EAN): 9781466663282
Издательство: Mare Nostrum (Eurospan)
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Цена: 218990.00 T
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Описание: Throughout time, scientists have looked to nature in order to understand and model solutions for complex real-world problems. In particular, the study of self-organizing entities, such as social insect populations, presents a new opportunity within the field of artificial intelligence.>Emerging Research on Swarm Intelligence and Algorithm Optimization discusses current research analyzing how the collective behavior of decentralized systems in the natural world can be applied to intelligent system design. Discussing the application of swarm principles, optimization techniques, and key algorithms being used in the field, this publication serves as an essential reference for academicians, upper-level students, IT developers, and IT theorists.

Автор: Robert Layton
Название: Algorithms for Automating Open Source Intelligence (OSINT)
ISBN: 0128029161 ISBN-13(EAN): 9780128029169
Издательство: Elsevier Science
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Цена: 33670.00 T
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Описание: . Algorithms for Automating Open Source Intelligence (OSINT) presents information on the gathering of information and extraction of actionable intelligence from openly available sources, including news broadcasts, public repositories, and more recently, social media. As OSINT has applications in crime fighting, state-based intelligence, and social research, this book provides recent advances in text mining, web crawling, and other algorithms that have led to advances in methods that can largely automate this process. The book is beneficial to both practitioners and academic researchers, with discussions of the latest advances in applications, a coherent set of methods and processes for automating OSINT, and interdisciplinary perspectives on the key problems identified within each discipline. Drawing upon years of practical experience and using numerous examples, editors Robert Layton, Paul Watters, and a distinguished list of contributors discuss Evidence Accumulation Strategies for OSINT, Named Entity Resolution in Social Media, Analyzing Social Media Campaigns for Group Size Estimation, Surveys and qualitative techniques in OSINT, and Geospatial reasoning of open data.

Автор: Li, Fanzhang / Zhang, Li / Zhang, Zhao
Название: Dynamic Fuzzy Machine Learning
ISBN: 3110518708 ISBN-13(EAN): 9783110518702
Издательство: Walter de Gruyter
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Цена: 149590.00 T
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Описание: Machine learning is widely used for data analysis. Dynamic fuzzy data are one of the most difficult types of data to analyse in the field of big data, cloud computing, the Internet of Things, and quantum information. At present, the processing of this kind of data is not very mature. The authors carried out more than 20 years of research, and show in this book their most important results. The seven chapters of the book are devoted to key topics such as dynamic fuzzy machine learning models, dynamic fuzzy self-learning subspace algorithms, fuzzy decision tree learning, dynamic concepts based on dynamic fuzzy sets, semi-supervised multi-task learning based on dynamic fuzzy data, dynamic fuzzy hierarchy learning, examination of multi-agent learning model based on dynamic fuzzy logic. This book can be used as a reference book for senior college students and graduate students as well as college teachers and scientific and technical personnel involved in computer science, artificial intelligence, machine learning, automation, data analysis, mathematics, management, cognitive science, and finance. It can be also used as the basis for teaching the principles of dynamic fuzzy learning.

Автор: Zhang Tsinghua University Press Liyi
Название: Blind Equalization in Neural Networks
ISBN: 3110449625 ISBN-13(EAN): 9783110449624
Издательство: Walter de Gruyter
Цена: 123910.00 T
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Описание: The book begins with an introduction of blind equalization theory and its application in neural networks, then discusses the algorithms in recurrent networks, fuzzy networks and other frequently-studied neural networks. Each algorithm is accompanied by derivation, modeling and simulation, making the book an essential reference for electrical engineers, computer intelligence researchers and neural scientists.

Автор: Xia Lirong
Название: Learning and Decision-Making from Rank Data
ISBN: 1681734400 ISBN-13(EAN): 9781681734408
Издательство: Mare Nostrum (Eurospan)
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Цена: 61910.00 T
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Описание: The ubiquitous challenge of learning and decision-making from rank data arises in situations where intelligent systems collect preference and behavior data from humans, learn from the data, and then use the data to help humans make efficient, effective, and timely decisions. Often, such data are represented by rankings. This book surveys some recent progress toward addressing the challenge from the considerations of statistics, computation, and socio-economics. We will cover classical statistical models for rank data, including random utility models, distance-based models, and mixture models. We will discuss and compare classical and state of-the-art algorithms, such as algorithms based on Minorize-Majorization (MM), Expectation-Maximization (EM), Generalized Method-of-Moments (GMM), rank breaking, and tensor decomposition. We will also introduce principled Bayesian preference elicitation frameworks for collecting rank data. Finally, we will examine socio-economic aspects of statistically desirable decision-making mechanisms, such as Bayesian estimators. This book can be useful in three ways: (1) for theoreticians in statistics and machine learning to better understand the considerations and caveats of learning from rank data, compared to learning from other types of data, especially cardinal data; (2) for practitioners to apply algorithms covered by the book for sampling, learning, and aggregation; and (3) as a textbook for graduate students or advanced undergraduate students to learn about the field. This book requires that the reader has basic knowledge in probability, statistics, and algorithms. Knowledge in social choice would also help but is not required.

Автор: Judson, Philip
Название: Knowledge-based expert systems in chemistry
ISBN: 1788014715 ISBN-13(EAN): 9781788014717
Издательство: Royal Society of Chemistry
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Цена: 209790.00 T
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Описание: This new edition has been thoroughly revised and updated to reflect the advances in using knowledge-based expert systems for chemistry.

Автор: Xia Lirong
Название: Learning and Decision-Making from Rank Data
ISBN: 1681734427 ISBN-13(EAN): 9781681734422
Издательство: Mare Nostrum (Eurospan)
Рейтинг:
Цена: 82230.00 T
Наличие на складе: Невозможна поставка.
Описание: The ubiquitous challenge of learning and decision-making from rank data arises in situations where intelligent systems collect preference and behavior data from humans, learn from the data, and then use the data to help humans make efficient, effective, and timely decisions. Often, such data are represented by rankings. This book surveys some recent progress toward addressing the challenge from the considerations of statistics, computation, and socio-economics. We will cover classical statistical models for rank data, including random utility models, distance-based models, and mixture models. We will discuss and compare classical and state of-the-art algorithms, such as algorithms based on Minorize-Majorization (MM), Expectation-Maximization (EM), Generalized Method-of-Moments (GMM), rank breaking, and tensor decomposition. We will also introduce principled Bayesian preference elicitation frameworks for collecting rank data. Finally, we will examine socio-economic aspects of statistically desirable decision-making mechanisms, such as Bayesian estimators. This book can be useful in three ways: (1) for theoreticians in statistics and machine learning to better understand the considerations and caveats of learning from rank data, compared to learning from other types of data, especially cardinal data; (2) for practitioners to apply algorithms covered by the book for sampling, learning, and aggregation; and (3) as a textbook for graduate students or advanced undergraduate students to learn about the field. This book requires that the reader has basic knowledge in probability, statistics, and algorithms. Knowledge in social choice would also help but is not required.

Автор: Michael Stanley, Jongmin Lee
Название: Sensor Analysis for the Internet of Things
ISBN: 1681732890 ISBN-13(EAN): 9781681732893
Издательство: Mare Nostrum (Eurospan)
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Цена: 82230.00 T
Наличие на складе: Невозможна поставка.
Описание: While it may be attractive to view sensors as simple transducers which convert physical quantities into electrical signals, the truth of the matter is more complex. The engineer should have a proper understanding of the physics involved in the conversion process, including interactions with other measurable quantities. A deep understanding of these interactions can be leveraged to apply sensor fusion techniques to minimize noise and/or extract additional information from sensor signals.Advances in microcontroller and MEMS manufacturing, along with improved internet connectivity, have enabled cost-effective wearable and Internet of Things sensor applications. At the same time, machine learning techniques have gone mainstream, so that those same applications can now be more intelligent than ever before. This book explores these topics in the context of a small set of sensor types.We provide some basic understanding of sensor operation for accelerometers, magnetometers, gyroscopes, and pressure sensors. We show how information from these can be fused to provide estimates of orientation. Then we explore the topics of machine learning and sensor data analytics.