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. <p> 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. <p> 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. <p> Key Features: <p> - Provides an overview of machine learning models and their optimization for intelligent transportation systems in urban areas <p> - Covers classification of traffic behavior <p> - Demonstrates the application of artificial immune system algorithms for traffic prediction <p> - Covers traffic density estimation using deep learning models <p> - Covers Fog and edge computing for intelligent transportation systems <p> - Gives an IoT and Industry 4.0 perspective about intelligent transportation systems to readers <p> - Presents a current perspective on an urban hyperloop system for India <p> 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.

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.

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.

The importance of Artificial Intelligence cannot be over-emphasised in current times, where automation is already an integral part of industrial and business processes. </p> A First Course in Artificial Intelligence is a comprehensive textbook for beginners which covers all the fundamentals of Artificial Intelligence. Seven chapters (divided into thirty-three units) introduce the student to key concepts of the discipline in simple language, including expert system, natural language processing, machine learning, machine learning applications, sensory perceptions (computer vision, tactile perception) and robotics. Each chapter provides information in separate units about relevant history, applications, algorithm and programming with relevant case studies and examples. The simplified approach to the subject enables beginners in computer science who have a basic knowledge of Java programming to easily understand the contents. The text also introduces Python programming language basics, with demonstrations of natural language processing. It also introduces readers to the Waikato Environment for Knowledge Analysis (WEKA), as a tool for machine learning. </p> The book is suitable for students and teachers involved in introductory courses in undergraduate and diploma level courses which have appropriate modules on artificial intelligence.

Artificial Intelligence: Models, Algorithms and Applications presents focused information about applications of artificial intelligence (AI) in different areas to solve complex problems. The book presents 8 chapters that demonstrate AI based systems for vessel tracking, mental health assessment, radiology, instrumentation, business intelligence, education and criminology. The book concludes with a chapter on mathematical models of neural networks. </p> The book serves as an introductory book about AI applications at undergraduate and graduate levels and as a reference for industry professionals working with AI based systems.

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.

This handbook is a concise yet complete guide to fundamental engineering requirements and quality characteristics that users, developers, and marketers of mobile applications should be aware of. It provides detailed definitions and descriptions of eight key software application features that are integral to the overall design and user experience goals, and which may often overlap with certain functionalities. The book explains the essential aspects of these features clearly to novice developers. Readers will also learn about how to optimize the listed features to tailor their applications to the needs of their users. &lt;/P&gt; Key Features: &lt;/P&gt; - Presents detailed information about eight different features which guide mobile application development: capability, reliability, usability, charisma, security, performance, mobility and compatibility &lt;/P&gt; - Reader-friendly, structured layout of each chapter including relevant illustrations and clear language, designed for quick learning &lt;/P&gt; - Focus on both software function and user perception of applications on mobile devices &lt;/P&gt; - Includes a handy appendix with information about mobile learning projects and related work packages &lt;/P&gt; Handbook of Mobile Application Development: A Guide to Selecting the Right Engineering and Quality Features is the ideal learning tool for novice software developers, computer science students, IT enthusiasts and marketers who want to design or develop mobile apps for an optimal user experience.

This Ebook brings together the latest developments and studies of Mobile Ad Hoc Networks (MANETs) and Wireless Sensor Networks (WSNs), which should provide a seedbed for new breakthroughs. It focuses on the most representative topics in MANETs and WSNs, such as energy-efficient routing, backbone construction, multicast/broadcast, resource allocation, data gathering/aggregation, sensor coverage, target tracking, and so on. This Ebook is suitable for scholars, researchers and developers to grasp the most updated work in MANETs and WSNs. Moreover, the Ebook is a valuable companion and comprehensive reference for graduate students who are taking a course in wireless networking. Compared with existing books in the field, this Ebook focuses on general architectures, algorithms and protocols for both MANETs and WSNs, and covers heterogeneous networks where sensors are co-existing with ad hoc nodes.

This Ebook presents state-of-the-art solutions in applications of modern high performance networks. The topics covered in this Ebook include mobile ad-hoc networks, clusters for distance computing, clustering technologies and deployment, emerging wireless sensor network technologies, ultra wideband wireless sensor networks, and smart agriculture with sensor networks. This Ebook stands out from others in the field by bringing together the most diverse applications of networks under one title. It is aimed at academicians, students and working professionals in hospitals, agriculture, government, military, and networking technology fields.

Arduino and Scilab based Projects provides information ranging from the basics to advanced knowledge of Arduino and its interfacing with input/output devices (display devices, actuators, sensors), communication modules (RF modem, Zigbee) and Scilab. It also provides embedded system based on Arduino with simulation, programming and interfacing with Scilab, Arduino interfacing with Scilab with and without Arduino 1.1 packages. Chapters are arranged in an easy-to-understand sequence that enhances the learning experience for readers. Descriptions of real time project prototypes with programming and simulation of Arduino and Scilab.