Recently, there has been an increase in the number of e-commerce users. This has caused online shopping to become a new and challenging market for e-commerce vendors. Security, inventory management, reliability, and performance of e-commerce websites are a few of the challenges associated with the rising popularity of e-commerce. On a daily basis, millions of e-commerce transactions are taking place. This generates a huge amount of data that can be used to solve the various challenges of e-commerce. Further study on how this data can be used to address these issues is required to propel businesses forward. Empirical Research for Futuristic E-Commerce Systems: Foundations and Applications shares experiences and research outcomes on all aspects of intelligent software solutions such as machine learning, nature-inspired computing, and data science for business-to-consumer (B2C) e-commerce. By looking at the exponential growth of the e-commerce market and its popularity, this book also focuses on the current issues, solutions, and future possibilities in the B2C model of e-commerce. Covering a range of critical topics such as online shopping, supply chain management, and blockchain, this reference work is ideal for academic scientists, data scientists, software developers, business experts, researchers, scholars, practitioners, academicians, instructors, and students.

Recent advancements and innovations in medical image and data processing have led to a need for robust and secure mechanisms to transfer images and signals over the internet and maintain copyright protection. The Handbook of Research on Information Security in Biomedical Signal Processing provides emerging research on security in biomedical data as well as techniques for accurate reading and further processing. While highlighting topics such as image processing, secure access, and watermarking, this publication explores advanced models and algorithms in information security in the modern healthcare system. This publication is a vital resource for academicians, medical professionals, technology developers, researchers, students, and practitioners seeking current research on intelligent techniques in medical data security.

Medical image technologies play a significant role in visualization and interpretation methods in medical diagnosis and practice using decision making, pattern classification, diagnosis, and learning. Progressions in the field of medical imaging lead to interdisciplinary discovery in microscopic image processing and computer-assisted diagnosis systems, and aids physicians in the diagnosis and early detection of diseases. Histopathological Image Analysis in Medical Decision Making provides emerging research exploring the theoretical and practical applications of image technologies and feature extraction procedures within the medical field. Featuring coverage on a broad range of topics such as image classification, digital image analysis, and prediction methods, this book is ideally designed for medical professionals, system engineers, medical students, researchers, and medical practitioners seeking current research on problem-oriented processing techniques in imaging technologies.

The objective of this edited book is to share the outcomes from various research domains to develop efficient, adaptive, and intelligent models to handle the challenges related to decision making. It incorporates the advances in machine intelligent techniques such as data streaming, classification, clustering, pattern matching, feature selection, and deep learning in the decision-making process for several diversified applications such as agriculture, character recognition, landslide susceptibility, recommendation systems, forecasting air quality, healthcare, exchange rate prediction, and image dehazing. It also provides a premier interdisciplinary platform for scientists, researchers, practitioners, and educators to share their thoughts in the context of recent innovations, trends, developments, practical challenges, and advancements in the field of data mining, machine learning, soft computing, and decision science. It also focuses on the usefulness of applied intelligent techniques in the decision-making process in several aspects. To address these objectives, this edited book includes a dozen chapters contributed by authors from around the globe. The authors attempt to solve these complex problems using several intelligent machine-learning techniques. This allows researchers to understand the mechanism needed to harness the decision-making process using machine-learning techniques for their own respective endeavors.

This book covers both theory and applications in the automation of software testing tools and techniques for various types of software (e.g. object-oriented, aspect-oriented, and web-based software). When software fails, it is most often due to lack of proper and thorough testing, an aspect that is even more acute for object-oriented, aspect-oriented, and web-based software. Further, since it is more difficult to test distributed and service-oriented architecture-based applications, there is a pressing need to discuss the latest developments in automated software testing. This book discusses the most relevant issues, models, tools, challenges, and applications in automated software testing. Further, it brings together academic researchers, scientists, and engineers from a wide range of industrial application areas, who present their latest findings and identify future challenges in this fledging research area.

This book discusses the current research and concepts in data science and how these can be addressed using different nature-inspired optimization techniques. Focusing on various data science problems, including classification, clustering, forecasting, and deep learning, it explores how researchers are using nature-inspired optimization techniques to find solutions to these problems in domains such as disease analysis and health care, object recognition, vehicular ad-hoc networking, high-dimensional data analysis, gene expression analysis, microgrids, and deep learning. As such it provides insights and inspiration for researchers to wanting to employ nature-inspired optimization techniques in their own endeavors.

The book discusses machine learning-based decision-making models, and presents intelligent, hybrid and adaptive methods and tools for solving complex learning and decision-making problems under conditions of uncertainty. Featuring contributions from data scientists, practitioners and educators, the book covers a range of topics relating to intelligent systems for decision science, and examines recent innovations, trends, and practical challenges in the field. The book is a valuable resource for academics, students, researchers and professionals wanting to gain insights into decision-making.

This book focuses on new and original research ideas and findings in three broad areas: computing, analytics, and networking and their potential applications in the various domains of engineering - an emerging, interdisciplinary area in which a wide range of theories and methodologies are being investigated and developed to tackle complex and challenging real-world problems. The book also features keynote presentations and papers from the International Conference on Computing Analytics and Networking (ICCAN 2019), which offers an open forum for scientists, researchers and technocrats in academia and industry from around the globe to present and share state-of-the-art concepts, prototypes, and innovative research ideas in diverse fields. Providing inspiration for postgraduate students and young researchers working in the field of computer science & engineering, the book also discusses hardware technologies and future communication technologies, making it useful for those in the field of electronics.

The objective of this edited book is to share the outcomes from various research domains to develop efficient, adaptive, and intelligent models to handle the challenges related to decision making. It incorporates the advances in machine intelligent techniques such as data streaming, classification, clustering, pattern matching, feature selection, and deep learning in the decision-making process for several diversified applications such as agriculture, character recognition, landslide susceptibility, recommendation systems, forecasting air quality, healthcare, exchange rate prediction, and image dehazing. It also provides a premier interdisciplinary platform for scientists, researchers, practitioners, and educators to share their thoughts in the context of recent innovations, trends, developments, practical challenges, and advancements in the field of data mining, machine learning, soft computing, and decision science. It also focuses on the usefulness of applied intelligent techniques in the decision-making process in several aspects. To address these objectives, this edited book includes a dozen chapters contributed by authors from around the globe. The authors attempt to solve these complex problems using several intelligent machine-learning techniques. This allows researchers to understand the mechanism needed to harness the decision-making process using machine-learning techniques for their own respective endeavors.

The book discusses machine learning-based decision-making models, and presents intelligent, hybrid and adaptive methods and tools for solving complex learning and decision-making problems under conditions of uncertainty. Featuring contributions from data scientists, practitioners and educators, the book covers a range of topics relating to intelligent systems for decision science, and examines recent innovations, trends, and practical challenges in the field. The book is a valuable resource for academics, students, researchers and professionals wanting to gain insights into decision-making.

This book covers both theory and applications in the automation of software testing tools and techniques for various types of software (e.g. object-oriented, aspect-oriented, and web-based software). When software fails, it is most often due to lack of proper and thorough testing, an aspect that is even more acute for object-oriented, aspect-oriented, and web-based software. Further, since it is more difficult to test distributed and service-oriented architecture-based applications, there is a pressing need to discuss the latest developments in automated software testing. This book discusses the most relevant issues, models, tools, challenges, and applications in automated software testing. Further, it brings together academic researchers, scientists, and engineers from a wide range of industrial application areas, who present their latest findings and identify future challenges in this fledging research area.

This book discusses the current research and concepts in data science and how these can be addressed using different nature-inspired optimization techniques. Focusing on various data science problems, including classification, clustering, forecasting, and deep learning, it explores how researchers are using nature-inspired optimization techniques to find solutions to these problems in domains such as disease analysis and health care, object recognition, vehicular ad-hoc networking, high-dimensional data analysis, gene expression analysis, microgrids, and deep learning. As such it provides insights and inspiration for researchers to wanting to employ nature-inspired optimization techniques in their own endeavors.

This book discusses harnessing the real power of cloud computing in optimization problems, presenting state-of-the-art computing paradigms, advances in applications, and challenges concerning both the theories and applications of cloud computing in optimization with a focus on diverse fields like the Internet of Things, fog-assisted cloud computing, and big data. In real life, many problems - ranging from social science to engineering sciences - can be identified as complex optimization problems. Very often these are intractable, and as a result researchers from industry as well as the academic community are concentrating their efforts on developing methods of addressing them. Further, the cloud computing paradigm plays a vital role in many areas of interest, like resource allocation, scheduling, energy management, virtualization, and security, and these areas are intertwined with many optimization problems. Using illustrations and figures, this book offers students and researchers a clear overview of the concepts and practices of cloud computing and its use in numerous complex optimization problems.

This book introduces the latest research findings in cloud, edge, fog, and mist computing and their applications in various fields using geospatial data. It solves a number of problems of cloud computing and big data, such as scheduling, security issues using different techniques, which researchers from industry and academia have been attempting to solve in virtual environments. Some of these problems are of an intractable nature and so efficient technologies like fog, edge and mist computing play an important role in addressing these issues. By exploring emerging advances in cloud computing and big data analytics and their engineering applications, the book enables researchers to understand the mechanisms needed to implement cloud, edge, fog, and mist computing in their own endeavours, and motivates them to examine their own research findings and developments.

The book focuses to foster new and original research ideas and results in three broad areas: computing, analytics, and networking with its prospective applications in the various interdisciplinary domains of engineering. This is an exciting and emerging interdisciplinary area in which a wide range of theory and methodologies are being investigated and developed to tackle complex and challenging real world problems. It also provides insights into the International Conference on Computing Analytics and Networking (ICCAN 2017) which is a premier international open forum for scientists, researchers and technocrats in academia as well as in industries from different parts of the world to present, interact, and exchange the state of art of concepts, prototypes, innovative research ideas in several diversified fields. The book includes invited keynote papers and paper presentations from both academia and industry to initiate and ignite our young minds in the meadow of momentous research and thereby enrich their existing knowledge. The book aims at postgraduate students and researchers working in the discipline of Computer Science & Engineering. It will be also useful for the researchers working in the domain of electronics as it contains some hardware technologies and forthcoming communication technologies.

This book discusses harnessing the real power of cloud computing in optimization problems, presenting state-of-the-art computing paradigms, advances in applications, and challenges concerning both the theories and applications of cloud computing in optimization with a focus on diverse fields like the Internet of Things, fog-assisted cloud computing, and big data. In real life, many problems - ranging from social science to engineering sciences - can be identified as complex optimization problems. Very often these are intractable, and as a result researchers from industry as well as the academic community are concentrating their efforts on developing methods of addressing them. Further, the cloud computing paradigm plays a vital role in many areas of interest, like resource allocation, scheduling, energy management, virtualization, and security, and these areas are intertwined with many optimization problems. Using illustrations and figures, this book offers students and researchers a clear overview of the concepts and practices of cloud computing and its use in numerous complex optimization problems.

This book discusses applications of computational intelligence in sensor networks. Consisting of twenty chapters, it addresses topics ranging from small-scale data processing to big data processing realized through sensor nodes with the help of computational approaches. Advances in sensor technology and computer networks have enabled sensor networks to evolve from small systems of large sensors to large nets of miniature sensors, from wired communications to wireless communications, and from static to dynamic network topology. In spite of these technological advances, sensor networks still face the challenges of communicating and processing large amounts of imprecise and partial data in resource-constrained environments. Further, optimal deployment of sensors in an environment is also seen as an intractable problem. On the other hand, computational intelligence techniques like neural networks, evolutionary computation, swarm intelligence, and fuzzy systems are gaining popularity in solving intractable problems in various disciplines including sensor networks. The contributions combine the best attributes of these two distinct fields, offering readers a comprehensive overview of the emerging research areas and presenting first-hand experience of a variety of computational intelligence approaches in sensor networks.

This book discusses applications of computational intelligence in sensor networks. Consisting of twenty chapters, it addresses topics ranging from small-scale data processing to big data processing realized through sensor nodes with the help of computational approaches. Advances in sensor technology and computer networks have enabled sensor networks to evolve from small systems of large sensors to large nets of miniature sensors, from wired communications to wireless communications, and from static to dynamic network topology. In spite of these technological advances, sensor networks still face the challenges of communicating and processing large amounts of imprecise and partial data in resource-constrained environments. Further, optimal deployment of sensors in an environment is also seen as an intractable problem. On the other hand, computational intelligence techniques like neural networks, evolutionary computation, swarm intelligence, and fuzzy systems are gaining popularity in solving intractable problems in various disciplines including sensor networks. The contributions combine the best attributes of these two distinct fields, offering readers a comprehensive overview of the emerging research areas and presenting first-hand experience of a variety of computational intelligence approaches in sensor networks.

Recently, there has been an increase in the number of e-commerce users. This has caused online shopping to become a new and challenging market for e-commerce vendors. Security, inventory management, reliability, and performance of e-commerce websites are a few of the challenges associated with the rising popularity of e-commerce. On a daily basis, millions of e-commerce transactions are taking place. This generates a huge amount of data that can be used to solve the various challenges of e-commerce. Further study on how this data can be used to address these issues is required to propel businesses forward. Empirical Research for Futuristic E-Commerce Systems: Foundations and Applications shares experiences and research outcomes on all aspects of intelligent software solutions such as machine learning, nature-inspired computing, and data science for business-to-consumer (B2C) e-commerce. By looking at the exponential growth of the e-commerce market and its popularity, this book also focuses on the current issues, solutions, and future possibilities in the B2C model of e-commerce. Covering a range of critical topics such as online shopping, supply chain management, and blockchain, this reference work is ideal for academic scientists, data scientists, software developers, business experts, researchers, scholars, practitioners, academicians, instructors, and students.

Real-Time Data Analytics for Large-Scale Sensor Data covers the theory and applications of hardware platforms and architectures, the development of software methods, techniques and tools, applications, governance and adoption strategies for the use of massive sensor data in real-time data analytics. It presents the leading-edge research in the field and identifies future challenges in this fledging research area. The book captures the essence of real-time IoT based solutions that require a multidisciplinary approach for catering to on-the-fly processing, including methods for high performance stream processing, adaptively streaming adjustment, uncertainty handling, latency handling, and more.

Big Data Analytics for Intelligent Healthcare Management covers both the theory and application of hardware platforms and architectures, the development of software methods, techniques and tools, applications and governance, and adoption strategies for the use of big data in healthcare and clinical research. The book provides the latest research findings on the use of big data analytics with statistical and machine learning techniques that analyze huge amounts of real-time healthcare data.

Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear processing, which aids in feature extraction and learning in supervised and unsupervised ways, including classification and pattern analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning algorithms extract high level complex data and process these complex sets to relatively simpler ideas formulated in the preceding level of the hierarchy. The authors of this book focus on suitable data analytics methods to solve complex real world problems such as medical image recognition, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinoformatics, audio recognition, drug design, and medical image analysis.