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.

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.

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 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.

This book provides a single platform for beginners in systems engineering to start Arduino interface projects with MATLAB®. It covers the basics of the programming with Arduino and Arduino interfacing with MATLAB® (with and without the use or I/O packages) in 3 sections, respectively. Key features: -introduces readers to Arduino IDE, Proteus simulation modeling, Arduino interfaces with display devices, sensor interfaces (both digital and analog), actuators, MATLAB® GUIs, digital read/write systems with I/O interfaces and automation systems. -organized layout for a reader friendly experience -provides detailed circuit diagrams -provides relevant simulation modeling instructions This is an ideal book for engineering students and system designers for learning the basic programming and simulation of Arduino and MATLAB® based real time project prototypes.

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.

This book provides a thorough investigation of the application of chaos theory and fractal analysis to computer vision. The field of chaos theory has been studied in dynamical physical systems, and has been very successful in providing computational models for very complex problems ranging from weather systems to neural pathway signal propagation. Computer vision researchers have derived motivation for their algorithms from biology and physics for many years as witnessed by the optical flow algorithm, the oscillator model underlying graphical cuts and of course neural networks. These algorithms are very helpful for a broad range of computer vision problems like motion segmentation, texture analysis and change detection. The contents of this book include chapters in biological vision systems, foundations of chaos and fractals, behavior of images and image sequences in phase space, mathematical measures for analyzing phase space, applications to pre-attentive vision and applications to post-attentive vision. This book is intended for graduate students, upper division undergraduates, researchers and practitioners in image processing and computer vision. The readers will develop a solid understanding of the concepts of chaos theory and their application to computer vision. Readers will be introduced to a new way of thinking about computer vision problems from the perspective of complex dynamical systems. This new approach will provide them a deeper understanding of the various phenomena present in complex image scenes.

Agent-based Computational Economics using NetLogo explores how researchers can create, use and implement multi-agent computational models in Economics by using NetLogo software platform. Problems of economic science can be solved using multi-agent modelling (MAM). This technique uses a computer model to simulate the actions and interactions of autonomous entities in a network, in order to analyze the effects on the entire economic system. MAM combines elements of game theory, complex systems, emergence and evolutionary programming. The Monte Carlo method is also used in this e-book to introduce random elements. The 11 models presented in this text simulate the simultaneous operations of several agents in an attempt to recreate and predict complex economic phenomena. This e-book explains the topic in a systematic manner, starting with an introduction for readers followed subsequently by methodology and implementation using NetLogo. The volume ends with conclusions based on the results of the experiments presented. The e-book is intended as a concise and vital resource for economists, applied mathematicians, social sciences scientists, systems analysts, operations researchers and numerical analysts