Download Free Multidisciplinary Approaches To Neural Computing Book in PDF and EPUB Free Download. You can read online Multidisciplinary Approaches To Neural Computing and write the review.

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.
This book describes new theories and applications of artificial neural networks, with a special focus on neural computation, cognitive science and machine learning. It discusses cutting-edge research at the intersection between different fields, from topics such as cognition and behavior, motivation and emotions, to neurocomputing, deep learning, classification and clustering. Further topics include signal processing methods, robotics and neurobionics, and computer vision alike. The book includes selected papers from the XIX International Conference on Neuroinformatics, held on October 2-6, 2017, in Moscow, Russia.
Applications of Computational Intelligence in Multi-Disciplinary Research provides the readers with a comprehensive handbook for applying the powerful principles, concepts, and algorithms of computational intelligence to a wide spectrum of research cases. The book covers the main approaches used in computational intelligence, including fuzzy logic, neural networks, evolutionary computation, learning theory, and probabilistic methods, all of which can be collectively viewed as soft computing. Other key approaches included are swarm intelligence and artificial immune systems. These approaches provide researchers with powerful tools for analysis and problem-solving when data is incomplete and when the problem under consideration is too complex for standard mathematics and the crisp logic approach of Boolean computing. - Provides an overview of the key methods of computational intelligence, including fuzzy logic, neural networks, evolutionary computation, learning theory, and probabilistic methods - Includes case studies and real-world examples of computational intelligence applied in a variety of research topics, including bioinformatics, biomedical engineering, big data analytics, information security, signal processing, machine learning, nanotechnology, and optimization techniques - Presents a thorough technical explanation on how computational intelligence is applied that is suitable for a wide range of multidisciplinary and interdisciplinary research
In a world where the pace of technological advancement continues to accelerate, the imperative to ensure sustainable development has never been more pressing to address the same, the 1st International Conference on Multidisciplinary Approaches for Sustainable Development in Science & Technology (MASDST - 2024), took place at Manipal University Jaipur, Rajasthan, India, from 28th to 29th March 2024. Embracing the spirit of innovation and collaboration, this conference marks a significant milestone in the pursuit of sustainable solutions for our global challenges.
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 world’s population. A small portion of the world’s 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.
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.
With the advancement of sensorial media, objects, and technologies, multimedia can play a significant role in smart healthcare by offering better insight of heterogeneous healthcare multimedia content to support affordable and quality patient care. While researchers and the scientific community have been making advances in the study of multimedia tools and healthcare services individually, very little attention has been given to developing cost effective and affordable smart healthcare services. Multimedia-based smart healthcare has the potential to revolutionize many aspects of our society; however, many technical challenges must be addressed before this potential can be realized. Using Multimedia Systems, Tools, and Technologies for Smart Healthcare Services includes high-quality research on the recent advances in various aspects of intelligent interactive multimedia technologies in healthcare services and, more specifically, in the state-of-the-art approaches, methodologies, and systems in the design, development, deployment, and innovative use of multimedia systems, tools, and technologies for providing insights into smart healthcare service demands. Covering topics such as genetic algorithms, automatic classification of diseases, and structural equation modeling, this premier reference source is an essential resource for hospital administrators, medical professionals, health IT specialists, hospital technicians, students and faculty of higher education, researchers, and academicians.
The book presents the latest developments in intelligent communication networks based on applicability from various domains of artificial intelligence and machine learning including channel modeling, model-based structure, channel prediction, and signal detection. It further explains important topics such as vehicular mobility modeling, human-centric network applications, security and privacy in social networks, and trust-based intelligent transportation systems. This book: Presents a model-based approach to constructing an effective network by using state-of-the-art artificial intelligent techniques. Discusses the theoretical and practical applications of channel prediction and signal detection. Introduces the fundamental concepts and application of vehicular networks in conjunction with artificial intelligence. Explores wireless communication network techniques enabled by human-centric applications, designed, and developed with artificial intelligence characteristics. Highlights the challenges in designing and developing an effective and intelligent communication network that can be applied in different domains of human activities for finding sustainable solutions. It is primarily written for senior undergraduate, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer engineering, and information technology.
For any research field to have a lasting impact, there must be a firm theoretical foundation. Neural networks research is no exception. Some of the founda tional concepts, established several decades ago, led to the early promise of developing machines exhibiting intelligence. The motivation for studying such machines comes from the fact that the brain is far more efficient in visual processing and speech recognition than existing computers. Undoubtedly, neu robiological systems employ very different computational principles. The study of artificial neural networks aims at understanding these computational prin ciples and applying them in the solutions of engineering problems. Due to the recent advances in both device technology and computational science, we are currently witnessing an explosive growth in the studies of neural networks and their applications. It may take many years before we have a complete understanding about the mechanisms of neural systems. Before this ultimate goal can be achieved, an swers are needed to important fundamental questions such as (a) what can neu ral networks do that traditional computing techniques cannot, (b) how does the complexity of the network for an application relate to the complexity of that problem, and (c) how much training data are required for the resulting network to learn properly? Everyone working in the field has attempted to answer these questions, but general solutions remain elusive. However, encouraging progress in studying specific neural models has been made by researchers from various disciplines.