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The competence of deep learning for the automation and manufacturing sector has received astonishing attention in recent times. The manufacturing industry has recently experienced a revolutionary advancement despite several issues. One of the limitations for technical progress is the bottleneck encountered due to the enormous increase in data volume for processing, comprising various formats, semantics, qualities and features. Deep learning enables detection of meaningful features that are difficult to perform using traditional methods. The book takes the reader on a technological voyage of the industry 4.0 space. Chapters highlight recent applications of deep learning and the associated challenges and opportunities it presents for automating industrial processes and smart applications. Chapters introduce the reader to a broad range of topics in deep learning and machine learning. Several deep learning techniques used by industrial professionals are covered, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical project methodology. Readers will find information on the value of deep learning in applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. The book also discusses prospective research directions that focus on the theory and practical applications of deep learning in industrial automation. Therefore, the book aims to serve as a comprehensive reference guide for industrial consultants interested in industry 4.0, and as a handbook for beginners in data science and advanced computer science courses.

Blockchain, whether public or private, is capable enough to maintain the integrity of transactions by decentralizing the records for users. Many IoT companies are using blockchain technology to make the world a better-connected place. Businesses and researchers are exploring ways to make this technology increasingly efficient for IoT services. This volume presents the recent advances in these two technologies. Chapters explain the fundamentals of Blockchain and IoT, before explaining how these technologies, when merged together, provide a transparent, reliable, and secure model for data processing by intelligent devices in various domains. Readers will be able to understand how these technologies are making an impact on healthcare, supply chain management and electronic voting, to give a few examples. The 10 peer-reviewed book chapters have been contributed by scholars, researchers, academicians, and engineering professionals, and provide a comprehensive yet easily digestible update on Blockchain on IoT technology.

Augmented intelligence is an alternate approach of artificial intelligence (AI), which emphasizes AI’s assistive role. Augmented intelligence enhances human skills of reasoning in a robotic system or software by simulating expectancy, educational mining, problem solving, recollection, sequencing, and decision-making capabilities. It is based on a combination of techniques such as machine learning, deep learning and cognitive computing. This book explains artificial intelligence models that support assistive processes in different situations. The contributors aim to provide information to a diverse audience with groundbreaking developments in mathematical computing. The book presents 8 chapters on these topics: - Educational data mining in augmented reality virtual learning environment - Brain and computer interfaces - Tree-based tools for chemometric analysis of infrared spectra - Applications of deep learning in medical engineering - Bankruptcy prediction model using an enhanced boosting classifier - Reputation systems for mobile agent security - The crow search algorithm - COVID-19 diagnosis and treatment The contents attempt to integrate various facets of augmented Intelligence, by describing recent research developments and advanced topics of interest to academicians and researchers working on machine learning problems and AI.

Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments is a 2-part book set which presents discoveries, innovative ideas, concepts, practical solutions, and novel applications of Human-Computer Interaction (HCI) and related disciplines such as artificial intelligence, machine learning, data mining, computer vision, and natural language processing. The book provides readers with information about HCI trends which are shaping the future of smart, interconnected urban and industrial environments. This is the second of the two volumes of the edited books. The chapters of this volume cover topics like ERP usability in educational settings, the role of AI in enhancing HCI functionality, usability of local mobile healthcare apps, analyzing the usage of social media apps and a review of HCI systems for disaster management and systems for tracking traffic safety violations. Contributions are authored by experts and scientists in the field of HCI and its interrelated disciplines from 9 different countries – Albania, China, India, Indonesia, Nigeria, Pakistan, Spain, the United Kingdom, and the United States. Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments is an informative reference for scientists, researchers, and developers in both academia and industry who wish to learn, design, implement, and apply these emerging technologies in HCI in different sectors, with the goal of realizing futuristic technology-driven living and functional smart cities and environments.

Machine Learning and Its Application: A Quick Guide for Beginners aims to cover most of the core topics required for study in machine learning curricula included in university and college courses. The textbook introduces readers to central concepts in machine learning and artificial intelligence, which include the types of machine learning algorithms and the statistical knowledge required for devising relevant computer algorithms. The book also covers advanced topics such as deep learning and feature engineering. <p> Key features: <p> - 8 organized chapters on core concepts of machine learning for learners <p> - Accessible text for beginners unfamiliar with complex mathematical concepts <p> - Introductory topics are included, including supervised learning, unsupervised learning, reinforcement learning and predictive statistics <p> - Advanced topics such as deep learning and feature engineering provide additional information <p> - Introduces readers to python programming with examples of code for understanding and practice <p> - Includes a summary of the text and a dedicated section for references <p> Machine Learning and Its Application: A Quick Guide for Beginners is an essential book for students and learners who want to understand the basics of machine learning and equip themselves with the knowledge to write algorithms for intelligent data processing applications.

New technologies and computing methodologies are now used to address the existing issues of urban traffic systems. The development of computational intelligence methods such as machine learning and deep learning, enables engineers to find innovative solutions to guide traffic in order to reduce transportation and mobility problems in urban areas. &lt;p&gt; This volume, Computational Intelligence for Sustainable Transportation and Mobility, presents several computing models for intelligent transportation systems, which may hold the key to achieving sustainable development goals by optimizing traffic flow and minimizing associated risks. The book begins with the basic computational Intelligence techniques for traffic systems and explains its applications in vehicular traffic prediction, model optimization, behavior analysis, traffic density estimation, and more. The main objectives of this book are to present novel techniques developed, new technologies and computational intelligence for sustainable mobility and transportation solutions, as well as giving an understanding of some Industry 4.0 trends. &lt;p&gt; Readers will learn how to apply computational intelligence techniques such as multiagent systems (MAS), whale optimization, artificial Intelligence (AI), deep neural networks (DNNs) so that they can to develop algorithms, models, and approaches for sustainable transportation operations. &lt;p&gt; Key Features: &lt;p&gt; - Provides an overview of machine learning models and their optimization for intelligent transportation systems in urban areas &lt;p&gt; - Covers classification of traffic behavior &lt;p&gt; - Demonstrates the application of artificial immune system algorithms for traffic prediction &lt;p&gt; - Covers traffic density estimation using deep learning models &lt;p&gt; - Covers Fog and edge computing for intelligent transportation systems &lt;p&gt; - Gives an IoT and Industry 4.0 perspective about intelligent transportation systems to readers &lt;p&gt; - Presents a current perspective on an urban hyperloop system for India &lt;p&gt; This volume is essential reading for scholars and professionals involved in courses and training programs in the field of transportation, computer science, data science and applied machine learning.

This book provides an ideal foundation for readers to understand the application of artificial intelligence (AI) and machine learning (ML) techniques to expert systems in the healthcare sector. It starts with an introduction to the topic and presents chapters which progressively explain decision-making theory that helps solve problems which have multiple criteria that can affect the outcome of a decision. Key aspects of the subject such as machine learning in healthcare, prediction techniques, mathematical models and classification of healthcare problems are included along with chapters which delve in to advanced topics on data science (deep-learning, artificial neural networks, etc.) and practical examples (influenza epidemiology and retinoblastoma treatment analysis). Key Features: - Introduces readers to the basics of AI and ML in expert systems for healthcare - Focuses on a problem solving approach to the topic - Provides information on relevant decision-making theory and data science used in the healthcare industry - Includes practical applications of AI and ML for advanced readers - Includes bibliographic references for further reading The reference is an accessible source of knowledge on multi-criteria decision-support systems in healthcare for medical consultants, healthcare policy makers, researchers in the field of medical biotechnology, oncology and pharmaceutical research and development.

A variety of computing techniques have been developed in recent times in combination with emerging technologies. Such techniques, coupled with an increase in computing power, has given credence to an information based paradigm in many fields (also termed as informatics). Informatics computing has evolved into complex structures of heterogeneous methods involving multiple data processing applications. Research on new technologies also brings new tools to use along with continuous improvements in existing tools. This reference presents contributions that cover emerging computing techniques and their implementation in computer science, informatics and engineering, as well as other important topics that are often discussed in the modern computing environment. Chapters in this book are contributed by researchers, academicians and industry experts and inform readers about current computer technologies and applications. The topics covered in the book include, online privacy, internet gaming disorder, epidemiological modelling (including COVID-19), computer security and malware detection, document sentiment analysis, and project management. This book is an interesting update on new trends in computing techniques and applications for readers interested in the latest developments in computer science.

How to Design Optimization Algorithms by Applying Natural Behavioral Patterns is a guide book that introduces readers to optimization algorithms based on natural language processing. Readers will learn about the basic concept of optimization, optimization algorithm fundamentals and the methods employed to formulate natural ideas and behaviors into algorithms. Readers will learn how to create their own algorithm from the information provided in the text. The book is a simple reference to students and programming enthusiasts who are interested in learning about optimization and the process of designing algorithms designed to mimic natural phenomena.

Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments is a 2-part book set which presents discoveries, innovative ideas, concepts, practical solutions, and novel applications of Human-Computer Interaction (HCI) and related disciplines such as artificial intelligence, machine learning, data mining, computer vision, and natural language processing. The book provides readers with information about HCI trends which are shaping the future of smart, interconnected urban and industrial environments. Contributions are authored by experts and scientists in the field of HCI and its interrelated disciplines from 8 different countries – Chile, China, Croatia, India, Iran, Malaysia, Peru, and South Korea. The chapters of this volume present novel and state of the art research works conducted at the intersection of HCI aimed at developing trust, increasing user acceptance, augmenting user performance, and fostering human-technology partnerships. Chapters cover usability testing in digital healthcare systems, user experience testing of handicapped children and assistive technologies for visually impaired users and a gamified user experience design for learning. The volume also presents a review of twitter usability testing among Indian users, along with specific cases of arthritis diagnostic systems, meteorological draught analysis and the role of EUPS in improving GUI design to improve the user experience. Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments is an informative reference for scientists, researchers, and developers in both academia and industry who wish to learn, design, implement, and apply these emerging technologies in HCI in different sectors, with the goal of realizing futuristic technology-driven living and functional smart cities and environments.