Focuses on the Internet of Healthcare Things and innovative solutions developed for use in the application of healthcare services Discusses artificial intelligence applications, experiments, core concepts, and cutting-edge themes Demonstrates new approaches to analysing medical data and identifying ailments using AI to improve overall quality of life Introduces fundamental concepts for designing the Internet of Healthcare Things solutions Includes pertinent case studies and applications

Since the invention of computers or machines, scientists and researchers are trying very hard to enhance their capabilities to perform various tasks. As a consequence, the capabilities of computers are growing exponentially day by day in terms of diverse working domains, versatile jobs, processing speed, and reduced size. Now, we are in the race to make the computers or machines as intelligent as human beings. Artificial Intelligence (AI) came up as a way of making a computer or computer software think in the similar manner the intelligent humans think. AI is inspired by the study of human brain like how humans think, learn, decide and act while trying to solve a problem. The outcomes of this study are the basis of developing intelligent software and systems or Intelligent Computing (IC). An IC system has the capability of reasoning, learning, problem solving, perception, and linguistic intelligence. The IC systems consist of AI techniques as well as other emerging techniques that make a system intelligent. The use of intelligent computing has been seen in almost every sub-domain of computer science such as networking, software engineering, gaming, natural language processing, computer vision, image processing, data science, robotics, expert systems, and security. Now a days, the use of IC can also be seen for solving various complex problems in diverse domains such as for predicting disease in medical science, predicting land fertility or crop productivity in agriculture science, predicting market growth in economics, weather forecasting and so on. For all these reasons, this book presents the advances in AI techniques, under the umbrella of IC. In this context, the book includes the recent research works have been done in the areas of machine learning, neural networks, deep learning, evolutionary algorithms, genetic algorithms, swarm intelligence, fuzzy systems and so on. This book provides theoretical, algorithmic, simulation, and implementation-based recent research advancements related to the Intelligent Computing.

This work presents recent mathematical methods in the area of optimal control with a particular emphasis on the computational aspects and applications. Optimal control theory concerns the determination of control strategies for complex dynamical systems, in order to optimize some measure of their performance. Started in the 60's under the pressure of the "space race" between the US and the former USSR, the field now has a far wider scope, and embraces a variety of areas ranging from process control to traffic flow optimization, renewable resources exploitation and management of financial markets. These emerging applications require more and more efficient numerical methods for their solution, a very difficult task due the huge number of variables. The chapters of this volume give an up-to-date presentation of several recent methods in this area including fast dynamic programming algorithms, model predictive control and max-plus techniques. This book is addressed to researchers, graduate students and applied scientists working in the area of control problems, differential games and their applications.

The book presents selected, extended and peer reviewed papers from the International Multiconference on System, Automation and Control held Leipzig in 2016. These are complemented with solicited contributions by international experts. This volume is devoted to power electronics in renewable energy systems as well as to hybrid renewable energy systems.

The volume is dedicated to fields related to design, modeling, fundamentals and application of communication systems. Focusing on wireless technology it covers content based video retrieval, B4G wierless communication systems and their application, data fusion and pattern recognition. It also discusses the influences of coding compression and e-mobility on information and communication technology.

Includes case studies illustrating the business processes that underlines the use of big data and health analytics to improve healthcare delivery Discusses AI based smart paradigms for reliable predictions of infectious disease dynamics which can help or prevent disease transmission Highlights the different aspects of using extended reality for diverse healthcare applications and aggregates the current state of research Offers intelligent models of the smart recommender system for personal well-being services and computer-aided drug discovery and design methods Presents novel innovative techniques for extracting user social behavior known as sentiment analysis for healthcare related purposes

The text covers a wide range of topics such as mathematical modeling of crop pest control management, water resources management, impact of anthropogenic activities on atmospheric carbon dioxide concentrations, impact of climate changes on melting of glaciers and polar bear populations, dynamics of slow-fast predator-prey system and spread and control of HIV epidemic. It emphasizes the use of mathematical modeling to investigate the fluid flow problems including the breaking of viscoelastic jet, instability arising in nanofiber, flow in an annulus channel, and thermal instability in nano-fluids in a comprehensive manner. This book will be a readily accessible source of information for the students, researchers and policymakers interested in the application of mathematical and computational modeling techniques to investigate various biological and engineering phenomena. Features Focuses on the current modeling and computational trends to investigate various ecological, epidemiological, and engineering systems. Presents the mathematical modeling of a wide range of ecological and environmental issues including crop pest control management, water resources management, the effect of anthropogenic activities on atmospheric carbon dioxide concentrations, and impact of climate changes on melting of glaciers and polar bear population. Covers a wide range of topics including the breaking of viscoelastic jet, instability arising in nanofiber, flow in an annulus channel, and thermal instability in nano-fluids. Examines evolutionary models i.e., models of time-varying processes. Highlights the recent developments in the analytical methods to investigate the nonlinear dynamical systems. Showcases diversified applications of computational techniques to solve practical biological and engineering problems. The book focuses on the recent research developments in the mathematical modeling and scientific computing of biological and engineering systems. It will serve as an ideal reference text for senior undergraduate, graduate students, and researchers in diverse fields including ecological engineering, environmental engineering, computer engineering, mechanical engineering, mathematics, and fluid dynamics.

The aim of this book is to present and analyse theoretical advances and also emerging practical applications of swarm and evolutionary intelligence. It comprises nine chapters. Chapter 1 provides a theoretical introduction of the computational optimization techniques regarding the gradient-based methods such as steepest descent, conjugate gradient, newton and quasi-Newton methods and also the non-gradient methods such as genetic algorithm and swarm intelligence algorithms. Chapter 2, discusses evolutionary computation techniques and genetic algorithm. Swarm intelligence theory and particle swarm optimization algorithm are reviewed in Chapter 3. Also, several variations of particle swarm optimization algorithm are analysed and explained such as Geometric PSO, PSO with mutation, Chaotic PSO with mutation, multi-objective PSO and Quantum mechanics - based PSO algorithm. Chapter 4 deals with two essential colony bio-inspired algorithms: Ant colony optimization (ACO) and Artificial bee colony (ABC). Chapter 5, presents and analyses Cuckoo search and Bat swarm algorithms and their latest variations. In chapter 6, several other metaheuristic algorithms are discussed such as: Firefly algorithm (FA), Harmony search (HS), Cat swarm optimization (CSO) and their improved algorithm modifications. The latest Bio-Inspired Swarm Algorithms are discussed in chapter 7, such as: Grey Wolf Optimization (GWO) Algorithm, Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA) and other algorithm variations such as binary and chaotic versions. Chapter 8 presents machine learning applications of swarm and evolutionary algorithms. Illustrative real-world examples are presented with real datasets regarding neural network optimization and feature selection, using: genetic algorithm, Geometric PSO, Chaotic Harmony Search, Chaotic Cuckoo Search, and Evolutionary Algorithm and also crime forecasting using swarm optimized SVM. In chapter 9, applications of swarm intelligence on deep long short-term memory (LSTM) networks and Deep Convolutional Neural Networks (CNNs) are discussed, including LSTM hyperparameter tuning and Covid19 diagnosis from chest X-Ray images. The aim of the book is to present and discuss several state-of-theart swarm intelligence and evolutionary algorithms together with their variances and also several illustrative applications on machine learning and deep learning.

Gives a holistic approach to machine learning and data science applications, from design to deployment and quality assurance, as an overarching cyclical process; Bridges machine learning and software engineering to build a shared set of best practices useful to both academia and the industry; Discusses deployment options for different types of models and data to help practitioners reason and make informed choices. Emphasizes the role of coding standards and software architecture alongside statistical rigor to implement reproducible and scalable machine learning models Key Features: A complete guide to software engineering for machine learning and data science applications, from choosing the right hardware to analysing algorithms and designing scalable architectures. Surveys the state of the art of the software and frameworks used to build and run machine learning applications, comparing and contrasting their trade-offs. Comes with a complete case study in natural language understanding which illustrates the principles and the tools covered in the book. Code available from GitHub. Provides a multi-disciplinary view of how traditional software learning practices can be integrated with the workflows of domain experts and the unique characteristics of software in which data play a central role.

Artificial Intelligence and Deep Learning for Computer Network: Management and Analysis aims to systematically collect quality research spanning AI, ML, and deep learning (DL) applications to diverse sub-topics of computer networks, communications, and security, under a single cover. It also aspires to provide more insights on the applicability of the theoretical similitudes, otherwise a rarity in many such books.

Features:

A diverse collection of important and cutting-edge topics covered in a single volume

Several chapters on cybersecurity, an extremely active research area

Recent research results from leading researchers and some pointers to future advancements in methodology

Detailed experimental results obtained from standard data sets

This book serves as a valuable reference book for students, researchers, and practitioners who wish to study and get acquainted with the application of cutting-edge AI, ML, and DL techniques to network management and cyber security.

Table of Contents

Deep Learning in traffic management: Deep traffic analysis of secure DNS

MINAL MOHARIR, NIKITHA SRIKANTH, NEEL BHANDARI AND RISHA DASSI

Machine Learning based Approach for Detecting Beacon Forgeries in Wi-Fi NetworksROHIT JAYSANKAR, VAMSHI SUNKU MOHAN, AND SRIRAM SANKARAN

Reinforcement learning-based approach towards switch migration for load balancing in SDN

ABHA KUMARI, SHUBHAM GUPTA, JOYDEEP CHANDRA, AND ASHOK SINGH SAIRAM

Green Corridor over a Narrow Lane: Supporting High Priority Message Delivery through NB-IoT

RAJA KARMAKAR, SAMIRAN CHATTOPADHYAY, AND SANDIP CHAKRABORTY

Vulnerabilities Detection in Cyber Security using Deep Learning based Information Security and Event Management

KOTHANDARAMAN D, S SHIVA PRASAD, AND P SIVASANKAR

Detection and Localization of Double Compressed Forged Regions in JPEG Images using DCT Coefficients and Deep Learning based CNN

JAMIMAMUL BAKAS, AND RUCHIRA NASKAR

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This book investigates the disagreement behavior analysis problems for signed networks in the presence of both cooperative and antagonistic interactions among agents. Owing to the existing antagonistic interactions, signed networks exhibit a variety of disagreement behaviors subject to different topology conditions, especially in comparison with commonly considered unsigned networks involving only cooperative interactions among agents. Since signed networks are generally adopted to describe the dynamics of some practical network systems, they have attracted much attention in many areas, such as biology, sociology, economics, and politics. By focusing on agents with the first-order linear dynamics, the book establishes the systematic behavior analysis frameworks for signed networks, under which diverse disagreement behaviors have been disclosed, including both convergence and fluctuation behaviors, regardless of static or dynamic network topologies. In particular, a class of dynamic signed networks has been introduced, together with the associated dynamic distributed controller design and disagreement behavior analysis of agents. This book is intended for undergraduate and graduate students, engineers, and researchers who are interested in control of network systems, multi-agent systems, social networks, and so on.

Agricultural automation is the emerging technologies which heavily rely on computer-integrated management and advanced control systems. The tedious farming tasks had been taken over by agricultural machines in last century, in new millennium, computer-aided systems, automation, and robotics has been applied to precisely manage agricultural production system. With agricultural automation technologies, sustainable agriculture is being developed based on efficient use of land, increased conservation of water, fertilizer and energy resources. The agricultural automation technologies refer to related areas in sensing & perception, reasoning & learning, data communication, and task planning & execution. Since the literature on this diverse subject is widely scattered, it is necessary to review current status and capture the future challenges through a comprehensive monograph. In this book we focus on agricultural automation and provide critical reviews of advanced control technologies, their merits and limitations, application areas and research opportunities for further development. This collection thus serves as an authoritative treatise that can help researchers, engineers, educators, and students in the field of sensing, control, and automation technologies for production agriculture.

Covers technologies considered to explore, predict, operation and maintenance of renewable energy sources Aids design and use of renewable energy sources including application of artificial intelligence in a real-time environment Includes IoT, Cloud Computing, Big data, Smart Grid and different optimization techniques for resource forecasting, installation, operation, and optimization of energy generation Discusses the principle of integration/hybridization of renewable energy sources along with their optimization based on energy requirement. Reviews concept and challenges involved in the implementation of smart grid.

Completely revised including six new chapters, this new edition presents a more comprehensive knowledge of issues facing developers of complex products and process management. It includes more tools for implementing a Systems Engineering approach to minimize the risks of delays and cost overruns and helps create the right product for its customers. Designing Complex Products with Systems Engineering Processes and Techniques, Second Edition highlights how to increase customer satisfaction, quality, safety, and usability to meet program timings and budgets using a Systems Engineering approach. It provides decision-making considerations and models for creating sustainable product design and describes many techniques and tools used in product development and the product life-cycle orientation. The book also offers techniques used in Design for Manufacturing, Design for Assembly, and product evaluation methods for verification and validation testing. Many new examples, case studies, six new chapters, and updated program and data charts held on our website are offered. The book targets practicing engineers, engineering management personnel, product designers, product planners, product and program managers in all industrialized and developing countries. In addition the book is also useful to undergraduate, graduate students, and faculty in engineering, product design, and product project and program management.

Control Systems Design Guide has helped thousands of engineers to improve machine performance. This fourth edition of the practical guide has been updated with cutting-edge control design scenarios, models and simulations enabling apps from battlebots to solar collectors. This useful reference enhances coverage of practical applications via the inclusion of new control system models, troubleshooting tips, and expanded coverage of complex systems requirements, such as increased speed, precision and remote capabilities, bridging the gap between the complex, math-heavy control theory taught in formal courses, and the efficient implementation required in real industry settings. George Ellis is Director of Technology Planning and Chief Engineer of Servo Systems at Kollmorgen Corporation, a leading provider of motion systems and components for original equipment manufacturers (OEMs) around the globe. He has designed an applied motion control systems professionally for over 30 years He has written two well-respected books with Academic Press, Observers in Control Systems and Control System Design Guide, now in its fourth edition. He has contributed articles on the application of controls to numerous magazines, including Machine Design, Control Engineering, Motion Systems Design, Power Control and Intelligent Motion, and Electronic Design News.

China Satellite Navigation Conference (CSNC 2021) Proceedings presents selected research papers from CSNC 2021 held during 22nd-25th May, 2021 in Nanchang, China. These papers discuss the technologies and applications of the Global Navigation Satellite System (GNSS), and the latest progress made in the China BeiDou System (BDS) especially. They are divided into 10 topics to match the corresponding sessions in CSNC2021 which broadly covered key topics in GNSS. Readers can learn about the BDS and keep abreast of the latest advances in GNSS techniques and applications.

This book investigates new important applications of the Spatial Grasp Technology (SGT) allowing us to effectively simulate and manage large distributed dynamic systems on semantic and holistic levels. This patented technology, developed for decades and in different countries, is based on a completely different philosophy and model allowing us to directly operate in united distributed physical and virtual spaces and provide system solutions much simpler and more compact than under other approaches. The described applications include basic operations suitable for solving many network-related problems, simulation of such mysterious concept as consciousness so important for the design of advanced intelligent systems, modelling the spread of viruses and distribution of antivirus vaccine, and also implementation of the latest decision-centric and mosaic-based organizational concepts important for modern defence and industrial systems. The described technology version with its Spatial Grasp Language can be implemented even within university environments, with communicating language interpreter copies, potentially numbering millions to billions, easily embedded into any existing systems, including Internet, thus converting the whole world into a powerful symbiotic simulation management engine. The book is oriented on system scientists, application programmers, industry managers, and also university students interested in advanced M.Sc. and Ph.D. projects related to distributed system management.

This book focuses on universal nonlinear dynamics model of mesoscale eddies. The results of this book are not only the direct-type applications of pure mathematical limit cycle theory and fractal theory in practice but also the classic combination of nonlinear dynamic systems in mathematics and the physical oceanography. The universal model and experimental verification not only verify the relevant results that are obtained by Euler's form but also, more importantly, are consistent with observational numerical statistics. Due to the universality of the model, the consequences of the system are richer and more complete. The comprehensive and systematic mathematical modeling of mesoscale eddies is one of the major features of the book, which is particularly suited for readers who are interested to learn fractal analysis and prediction in physical oceanography. The book benefits researchers, engineers, and graduate students in the fields of mesoscale eddies, fractal, chaos, and other applications, etc.

Computational Intelligence in Image and Video Processing presents introduction and state-of-the-art adaptations of computational intelligence techniques and their usefulness in image and video enhancement, classification, retrieval, forensics and captioning. It covers an amalgamation of such techniques in diverse applications of image and video processing. Features: A systematic overview of state-of-the-art technology in computational intelligence techniques for image and video processing Advanced evolutionary and nature inspired approaches to solve optimization problems in the image and video processing domain. Outcomes of Recent research and some pointers to future advancements in image and video processing and intelligent solutions using computational intelligence techniques. Code snippets of the computational intelligence algorithm/techniques used in image and video processing This book is primarily aimed at advanced undergraduates, graduates and researchers in computer science and information technology. Engineers and industry professionals will also find this book useful.

Discusses the efficiency measurement of online education Presents the environmental impact of online education Offers a parametric evaluation and categorization of online learning systems Covers big data ecosystems in cloud computing Provides analytical methods to find solutions for big data challenges

This book contains all refereed papers accepted during the fourth asia-pacific edition & twelve edition - which were merged this year - of the CSD&M conference that took place in Beijing, People's Republic of China by 2021. Mastering complex systems requires an integrated understanding of industrial practices as well as sophisticated theoretical techniques and tools. This explains the creation of an annual go-between European and Asian forum dedicated to academic researchers & industrial actors working on complex industrial systems architecting, modeling & engineering. These proceedings cover the most recent trends in the emerging field of complex systems, both from an academic and professional perspective. A special focus was put this year on "Digital Transformation in Complex Systems Engineering". CESAM Community The CSD&M series of conferences are organized under the guidance of CESAM Community, managed by CESAMES. CESAM Community aims in organizing the sharing of good practices in systems architecting and model-based systems engineering (MBSE) and certifying the level of knowledge and proficiency in this field through the CESAM certification. The CESAM systems architecting & model-based systems engineering (MBSE) certification is especially currently the most disseminated professional certification in the world in this domain through more than 1,000 real complex system development projects on which it was operationally deployed and around 10,000 engineers who were trained on the CESAM framework at international level.

The book may have the potential to fill a gap for researchers who will work in this field and those who need information by considering agriculture and digital technologies together for the needs of the digital age we are in today. Agriculture is a necessary industry for survival - it has become necessary to use the opportunities offered by technology in agricultural production. The reader will be able to see what the latest technologies used as technology in agriculture are and what kind of work has been done. It addresses agricultural needs and the issues showing how various technologies work and their components. It features information that the user can apply in the field. Topics are presented in a simple manner and comprehensive. The author has through hundreds of books and articles - literature on this topic remains scattered. Although most of his publications on Researchgate are in Turkish, the number of reads has exceeded 55,000 - these publications are on subject matter that will appear in this book - there should be lots of interest from both farmers and technology developers. Multidisciplinary - agriculture, agricultural engineering, computer engineering, electrical engineering, mechanical engineering, software engineering

Since Computational Intelligence is a latest technological aspect, the book is likely to be adopted in almost all leading Universities. This book aims to provide state-of-art research in the context of Computational Intelligence related with Healthcare its applications, challenges and management and it would promote how optimization or intelligent techniques envisage the role of Artificial Intelligence-Machine/Deep Learning (AI-ML/DL) in Healthcare.

Computer vision-based crack-like object detection has many useful applications, such as pavement surface inspection, underground pipeline inspection, bridge cracking monitoring, railway track assessment, etc. However, in most contexts, cracks appear as thin, irregular long-narrow objects, and often are buried into complex, textured background with high diversity which make the crack detection very challenging. During the past a few years, the deep learning technique has achieved great success and has been utilized for solving a variety of object detection problems. However, using deep learning for accurate crack localization is non-trivial. This book discusses crack-like object detection problem in a comprehensive way. It starts by discussing traditional image processing approaches for solving this problem, and then introduces deep learning-based methods. The book provides a comprehensive review of object detection problems and focuses on the most challenging problem, crack-like object detection, to dig deep into the deep learning method. It includes examples of real-world problems, which are easy to understand and could be a good tutorial for introducing computer vision and machine learning.

Advances in high spatial resolution mapping capabilities and the new rules established by the Federal Aviation Administration in the United States for the operation of Small Unmanned Aircraft Systems (sUAS) have provided new opportunities to acquire aerial data at a lower cost and more safely versus other methods. A similar opening of the skies for sUAS applications is being allowed in countries across the world. Also, sUAS can access hazardous or inaccessible areas during disaster events and provide rapid response when needed. Applications of Small Unmanned Aircraft systems: Best Practices and Case Studies is the first book that brings together the best practices of sUAS applied to a broad range of issues in high spatial resolution mapping projects. Very few sUAS pilots have the knowledge of how the collected imagery is processed into value added mapping products that have commercial and/or academic import. Since the field of sUAS applications is just a few years old, this book covers the need for a compendium of case studies to guide the planning, data collection, and most importantly data processing and map error issues, with the range of sensors available to the user community. Written by experienced academics and professionals, this book serves as a guide on how to formulate sUAS based projects, from choice of a sUAS, flight planning for a particular application, sensors and data acquisition, data processing software, mapping software and use of the high spatial resolution maps produced for particular types of geospatial modeling. Features: Focus on sUAS based data acquisition and processing into map products Broad range of case studies by highly experienced academics Practical guidance on sUAS hardware, sensors, and software utilized Compilation of workflow insights from expert professors and professionals Relevant to academia, government, and industry Positional and thematic map accuracy, UAS curriculum development and workflow replicability issues This book would be an excellent text for upper-level undergraduate to graduate level sUAS mapping application courses. It is also invaluable as a reference for educators designing sUAS based curriculum as well as for potential sUAS users to assess the scope of mapping projects that can be done with this technology.