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Multidisciplinary Approaches to Neural Computing

Author: Anna Esposito

Publisher: Springer

Published: 2017-08-28

Total Pages: 388

ISBN-13: 331956904X

This book presents a collection of contributions in the field of Artificial Neural Networks (ANNs). The themes addressed are multidisciplinary in nature, and closely connected in their ultimate aim to identify features from dynamic realistic signal exchanges and invariant machine representations that can be exploited to improve the quality of life of their end users. Mathematical tools like ANNs are currently exploited in many scientific domains because of their solid theoretical background and effectiveness in providing solutions to many demanding tasks such as appropriately processing (both for extracting features and recognizing) mono- and bi-dimensional dynamic signals, solving strong nonlinearities in the data and providing general solutions for deep and fully connected architectures. Given the multidisciplinary nature of their use and the interdisciplinary characterization of the problems they are applied to – which range from medicine to psychology, industrial and social robotics, computer vision, and signal processing (among many others) – ANNs may provide a basis for redefining the concept of information processing. These reflections are supported by theoretical models and applications presented in the chapters of this book. This book is of primary importance for: (a) the academic research community, (b) the ICT market, (c) PhD students and early-stage researchers, (d) schools, hospitals, rehabilitation and assisted-living centers, and (e) representatives of multimedia industries and standardization bodies.

An Information-Theoretic Approach to Neural Computing

Author: Gustavo Deco

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 265

ISBN-13: 1461240166

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A detailed formulation of neural networks from the information-theoretic viewpoint. The authors show how this perspective provides new insights into the design theory of neural networks. In particular they demonstrate how these methods may be applied to the topics of supervised and unsupervised learning, including feature extraction, linear and non-linear independent component analysis, and Boltzmann machines. Readers are assumed to have a basic understanding of neural networks, but all the relevant concepts from information theory are carefully introduced and explained. Consequently, readers from varied scientific disciplines, notably cognitive scientists, engineers, physicists, statisticians, and computer scientists, will find this an extremely valuable introduction to this topic.

Combining Artificial Neural Nets

Author: Amanda J.C. Sharkey

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 300

ISBN-13: 1447107934

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This volume, written by leading researchers, presents methods of combining neural nets to improve their performance. The techniques include ensemble-based approaches, where a variety of methods are used to create a set of different nets trained on the same task, and modular approaches, where a task is decomposed into simpler problems. The techniques are also accompanied by an evaluation of their relative effectiveness and their application to a variety of problems.

Handbook of Neural Computation

Author: Pijush Samui

Publisher: Academic Press

Published: 2017-07-18

Total Pages: 658

ISBN-13: 0128113197

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Handbook of Neural Computation explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image and speech recognition, natural language processing and risk analysis. Edited by renowned authorities in this field, this work is comprised of articles from reputable industry and academic scholars and experts from around the world. Each contributor presents a specific research issue with its recent and future trends. As the demand rises in the engineering and medical industries for neural networks and other machine learning methods to solve different types of operations, such as data prediction, classification of images, analysis of big data, and intelligent decision-making, this book provides readers with the latest, cutting-edge research in one comprehensive text. Features high-quality research articles on multivariate adaptive regression splines, the minimax probability machine, and more Discusses machine learning techniques, including classification, clustering, regression, web mining, information retrieval and natural language processing Covers supervised, unsupervised, reinforced, ensemble, and nature-inspired learning methods

Advanced Methods in Neural Computing

Author: Philip D. Wasserman

Publisher: Van Nostrand Reinhold Company

Published: 1993

Total Pages: 280

ISBN-13:

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This is the engineer's guide to artificial neural networks, the advanced computing innovation which is posed to sweep into the world of business and industry. The author presents the basic principles and advanced concepts by means of high-performance paradigms which function effectively in real-world situations.

Second-Order Methods for Neural Networks

Author: Adrian J. Shepherd

Publisher:

Published: 2014-01-15

Total Pages: 160

ISBN-13: 9781447109549

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Neural Computing - an Introduction

Author: R Beale

Publisher: CRC Press

Published: 2017-06-29

Total Pages:

ISBN-13: 9781138413092

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Neural computing is one of the most interesting and rapidly growing areas of research, attracting researchers from a wide variety of scientific disciplines. Starting from the basics, Neural Computing covers all the major approaches, putting each in perspective in terms of their capabilities, advantages, and disadvantages. The book also highlights the applications of each approach and explores the relationships among models developed and between the brain and its function. A comprehensive and comprehensible introduction to the subject, this book is ideal for undergraduates in computer science, physicists, communications engineers, workers involved in artificial intelligence, biologists, psychologists, and physiologists.

Neural Approaches to Dynamics of Signal Exchanges

Author: Anna Esposito

Publisher: Springer Nature

Published: 2019-09-18

Total Pages: 525

ISBN-13: 9811389500

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The book presents research that contributes to the development of intelligent dialog systems to simplify diverse aspects of everyday life, such as medical diagnosis and entertainment. Covering major thematic areas: machine learning and artificial neural networks; algorithms and models; and social and biometric data for applications in human–computer interfaces, it discusses processing of audio-visual signals for the detection of user-perceived states, the latest scientific discoveries in processing verbal (lexicon, syntax, and pragmatics), auditory (voice, intonation, vocal expressions) and visual signals (gestures, body language, facial expressions), as well as algorithms for detecting communication disorders, remote health-status monitoring, sentiment and affect analysis, social behaviors and engagement. Further, it examines neural and machine learning algorithms for the implementation of advanced telecommunication systems, communication with people with special needs, emotion modulation by computer contents, advanced sensors for tracking changes in real-life and automatic systems, as well as the development of advanced human–computer interfaces. The book does not focus on solving a particular problem, but instead describes the results of research that has positive effects in different fields and applications.

Multidisciplinary Approaches in AI, Creativity, Innovation, and Green Collaboration

Author: Fields, Ziska

Publisher: IGI Global

Published: 2023-05-01

Total Pages: 425

ISBN-13: 1668463687

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Creativity must be turned into innovation that adds value and leads to strategic action. Innovation is often associated with Silicon Valley, expensive research and development departments, and expensive commercialization that primarily benefits a small portion of the worlds population. A small portion of the worlds population working together to solve wicked green problems is not enough either. Green creativity and eco-innovation are necessary to help solve green problems by making products and services available and affordable to the masses. Multidisciplinary Approaches in AI, Creativity, Innovation, and Green Collaboration focuses on the importance of green creativity, eco-innovation, and collaboration to create a more sustainable world. It builds on the available literature and joint expertise in the field of management while providing further research opportunities in this dynamic field. Covering topics such as eco-leadership, green marketing, and social responsibility communication, this premier reference source is a comprehensive and timely resource for government officials, decision makers, business leaders and executives, students and educators of higher education, librarians, researchers, and academicians.

Computational Intelligence for Semantic Knowledge Management

Author: Giovanni Acampora

Publisher: Springer

Published: 2019-07-11

Total Pages: 135

ISBN-13: 3030237605

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This book provides a comprehensive overview of computational intelligence methods for semantic knowledge management. Contrary to popular belief, the methods for semantic management of information were created several decades ago, long before the birth of the Internet. In fact, it was back in 1945 when Vannevar Bush introduced the idea for the first protohypertext: the MEMEX (MEMory + indEX) machine. In the years that followed, Bush’s idea influenced the development of early hypertext systems until, in the 1980s, Tim Berners Lee developed the idea of the World Wide Web (WWW) as it is known today. From then on, there was an exponential growth in research and industrial activities related to the semantic management of the information and its exploitation in different application domains, such as healthcare, e-learning and energy management. However, semantics methods are not yet able to address some of the problems that naturally characterize knowledge management, such as the vagueness and uncertainty of information. This book reveals how computational intelligence methodologies, due to their natural inclination to deal with imprecision and partial truth, are opening new positive scenarios for designing innovative semantic knowledge management architectures.