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.

Additive manufacturing or 3D printing is the construction of an object using CAD models. The technology for additive manufacturing s gaining traction in recent times due to its accessibility for both personal and industrial use, as well as its usefulness in quick prototyping (either of a whole object or its components) and meeting the needs of small commercial workloads. Advances in Additive Manufacturing Processes informs readers about the breadth of 3D printing processes. Chapters cover the basic knowledge of additive techniques and their industrial applications with relevant examples. The improvements in each method and their recent technical achievements with practical experimental data are highlighted. The book presents eight types of 3D printing processes, covering a diverse range of technologies which represent the industry. The contents also cover selected topics of relevance to additive manufacturing which include the formation of defects and factors that affect the mechanical properties of the additively manufactured components. Chapters also provide recent guidelines for adapting additive manufacturing processes to increase productivity and reduce manufacturing cost. Advances in Additive Manufacturing Processes is a handy reference for scholars and professionals involved in manufacturing industries and process engineering as well as hobbyists who are interested in the current state-of-the-art in 3D printing.

Lean Management Solutions for Contemporary Manufacturing Operations: Applications in the automotive industry covers recent techniques aimed at improving manufacturing activities in automotive factories in the time of the fourth industrial revolution. The book informs the reader about some improvements in hard skills (such as technical concepts, new tools, processes, and applied designs), as well as soft skills (strategic planning and the psychology of motivating human resources in manufacturing setups). The book also presents insight for managers who are working with a niche of employees with disabilities with respect to the automotive industry. Topics in the book include: Application of Graph Theory in Workplace Design Applied Design Disability and the 4th Industrial Revolution People Development, Motivation & Results Low Cost Logistics Solutions Agile Methodologies in Manufacturing Projects This book is a concise, informative reference which updates the reader on recent strategies to maximize productivity in the auto manufacturing sector.

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.

This book focuses on cases and studies of interest to mechanical engineers and industrial technicians. The considered applications in this volume are widely used in several industrial fields particularly in the automotive and aviation industries. Readers will understand the theory and techniques which are used in each application covered in each chapter. <p> The book contents include the following topics: <p> Numerical analysis of hydrokinetic turbines <p> Computational fluid dynamics of a CuO based nanofluid in mini-channel cross-sections <p> Orthodontic biomechanics of a NiTi arch wires <p> Reynold’s number effects on fluid flow through Savonius rotors <p> Effect of operating parameters on Zn-Mn alloys deposited from additive-free chloride bath <p> Optical properties and stability of a blue-emitting phosphor (Sr2P2O7:Eu2+) Under UV and VUV excitation <p> Numerical study of the influence of nanofluid type on thermal improvement in a three dimensional mini channel <p> Electrochemical studies and characterization of Zn-Mn coatings deposited in the presence of novel organic additives <p> Prediction of fire and smoke propagation under a range of external conditions <p> Structural design of a 10 kW H-Darrieus wind turbine <p> The presented case studies and development approaches aim to provide the readers, such as graduate students, PhD candidates and professionals with basic and applied information broadly related to mechanical engineering and technology.

This Global Emerging Innovation Summit (GEIS 2021) book summarizes the proceedings of the international summit held on April 9th and 10th, 2021. The meeting was organized by Lovely Professional University (LPU) and supported by ENEA - the National Agency for New Technologies, Energy and Sustainable Economic Development. GEIS-2021 is the most comprehensive conference focused on the diverse facets of the innovations in different technical domains. The goal of this conference is to bring together researchers from academia and industry as well as practitioners in allied fields to share their ideas and solutions related to issues and challenges that rely on the use of technology. The conference highlights the multifaceted nature of innovation in engineering and natural sciences. Attendees are given an opportunity to exchange new ideas to establish research relations to find global partners for future collaboration. Authors from academics, government agencies and industries, contributed several papers which truly make the conference a diverse and unique platform in the region for a productive and interdisciplinary dialogue. This volume features 59 contributions which were selected after review by the editors. Chapters cover emerging developments in a broad range sectors including aviation, transportation, pharmaceutical chemistry, medicine, life sciences, space science, electronics, security, artificial intelligence, communications, energy and much more. This compilation is a handy resource for readers who are seeking updates in the latest trends and innovative developments in technology and engineering.

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.