Elsevier Online Library e-Books

A volume in Hybrid Computational Intelligence for Pattern Analysis Book • 2021

Edited by: Vincenzo Piuri, Sandeep Raj, ... Rajshree Srivastava

About this book: Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more. In recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models.

Author:Dioneia Motta Monte-Serrat and Carlo Cattani

About this book:The Natural Language for Artificial Intelligence presents the biological and logical structure typical of human language in its dynamic mediating process between reality and the human mind. The book explains linguistic functioning in the dynamic process of human cognition when forming meaning. After that, an approach to artificial intelligence (AI) is outlined, which works with a more restricted concept of natural language that leads to flaws and ambiguities. Subsequently, the characteristics of natural language and patterns of how it behaves in different branches of science are revealed to indicate ways to improve the development of AI in specific fields of science. A brief description of the universal structure of language is also presented as an algorithmic model to be followed in the development of AI. Since AI aims to imitate the process of the human mind, the book shows how the cross-fertilization between natural language and AI should be done using the logical-axiomatic structure of natural language adjusted to the logical-mathematical processes of the machine.

Author: Zhongzhi Shi

About this book: Intelligence Science: Leading the Age of Intelligence covers the emerging scientific research on the theory and technology of intelligence, bringing together disciplines such as neuroscience, cognitive science, and artificial intelligence to study the nature of intelligence, the functional simulation of intelligent behavior, and the development of new intelligent technologies. The book presents this complex, interdisciplinary area of study in an accessible volume, introducing foundational concepts and methods, and presenting the latest trends and developments. Chapters cover the Foundations of neurophysiology, Neural computing, Mind models, Perceptual intelligence, Language cognition, Learning, Memory, Thought, Intellectual development and cognitive structure, Emotion and affect, and more. This volume synthesizes a very rich and complex area of research, with an aim of stimulating new lines of enquiry.

Edited by: Mohammad S. Obaidat and Petros Nicopolitidis

About this book: Smart Cities and Homes: Key Enabling Technologies explores the fundamental principles and concepts of the key enabling technologies for smart cities and homes, disseminating the latest research and development efforts in the field through the use of numerous case studies and examples. Smart cities use digital technologies embedded across all their functions to enhance the wellbeing of citizens. Cities that utilize these technologies report enhancements in power efficiency, water use, traffic congestion, environmental protection, pollution reduction, senior citizens care, public safety and security, literacy rates, and more. This book brings together the most important breakthroughs and advances in a coherent fashion, highlighting the interconnections between the works in different areas of computing, exploring both new and emerging computer networking systems and other computing technologies, such as wireless sensor networks, vehicle ad hoc networks, smart girds, cloud computing, and data analytics and their roles in creating environmentally friendly, secure, and prosperous cities and homes. Intended for researchers and practitioners, the book discusses the pervasive and cooperative computing technologies that will perform a central role for handling the challenges of urbanization and demographic change.

Edited by: Tim Menzies, Laurie Williams and Thomas Zimmermann

About this book: Perspectives on Data Science for Software Engineering presents the best practices of seasoned data miners in software engineering. The idea for this book was created during the 2014 conference at Dagstuhl, an invitation-only gathering of leading computer scientists who meet to identify and discuss cutting-edge informatics topics. At the 2014 conference, the concept of how to transfer the knowledge of experts from seasoned software engineers and data scientists to newcomers in the field highlighted many discussions. While there are many books covering data mining and software engineering basics, they present only the fundamentals and lack the perspective that comes from real-world experience. This book offers unique insights into the wisdom of the community’s leaders gathered to share hard-won lessons from the trenches. Ideas are presented in digestible chapters designed to be applicable across many domains. Topics included cover data collection, data sharing, data mining, and how to utilize these techniques in successful software projects. Newcomers to software engineering data science will learn the tips and tricks of the trade, while more experienced data scientists will benefit from war stories that show what traps to avoid.

Author: Leigh Metcalf and William Casey

About this book:Cybersecurity and Applied Mathematics explores the mathematical concepts necessary for effective cybersecurity research and practice, taking an applied approach for practitioners and students entering the field. This book covers methods of statistical exploratory data analysis and visualization as a type of model for driving decisions, also discussing key topics, such as graph theory, topological complexes, and persistent homology. Defending the Internet is a complex effort, but applying the right techniques from mathematics can make this task more manageable. This book is essential reading for creating useful and replicable methods for analyzing data.

Authors: Zhaohui Wu, Shuiguang Deng and Jian Wu

About this book:Service computing is a cross-disciplinary field that covers science and technology, and represents a promising direction for distributed computing and software development methodologies. It aims to bridge the gap between business services and IT services by supporting the whole lifecycle of services innovation. Over the last ten years applications in industry and academic research have produced considerable progress and success Service Computing: Concept, Method and Technology presents the concept of service computing and a proposed reference architecture for service computing research before proceeding to introduce two underlying technologies: Web services and service-oriented architecture. It also presents the authors’ latest research findings on hot topics such as service discovery, recommendation, composition, verification, service trust, dynamic configuration and big data service. Some new models and methods are proposed including three service discovery methods based on semantics and skyline technologies, two service recommendation methods using graph mining and QoS prediction, two service composition methods with graph planning and one service verification method using π calculus and so on. Moreover, this book introduces JTang, an underlying platform supporting service computing, which is a product of the authors’ last ten years of research and development.

Edited by: John Sammons

About this book: Digital Forensics: Threatscape and Best Practices surveys the problems and challenges confronting digital forensic professionals today, including massive data sets and everchanging technology. This book provides a coherent overview of the threatscape in a broad range of topics, providing practitioners and students alike with a comprehensive, coherent overview of the threat landscape and what can be done to manage and prepare for it. Digital Forensics: Threatscape and Best Practices delivers you with incisive analysis and best practices from a panel of expert authors, led by John Sammons, bestselling author of The Basics of Digital Forensics.

Authors: Timothy J. Shimeall and Jonathan M. Spring

About this book: Most introductory texts provide a technology-based survey of methods and techniques that leaves the reader without a clear understanding of the interrelationships between methods and techniques. By providing a strategy-based introduction, the reader is given a clear understanding of how to provide overlapping defenses for critical information. This understanding provides a basis for engineering and risk-management decisions in the defense of information. Information security is a rapidly growing field, with a projected need for thousands of professionals within the next decade in the government sector alone. It is also a field that has changed in the last decade from a largely theory-based discipline to an experience-based discipline. This shift in the field has left several of the classic texts with a strongly dated feel.

Authors: Ian H. Witten, Eibe Frank, ... Christopher J. Paly

About this book: Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research. Please visit the book companion website at https://www.cs.waikato.ac.nz/~ml/weka/book.html. It contains Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.