Bøger i Studies in Big Data serien

This book presents state-of-the-art research on security and privacy- preserving for IoT and 5G networks and applications. The accepted book chapters covered many themes, including  traceability and tamper detection in IoT enabled waste management networks, secure Healthcare IoT Systems, data transfer accomplished by trustworthy nodes in cognitive radio, DDoS Attack Detection in Vehicular Ad-hoc Network (VANET) for 5G Networks, Mobile Edge-Cloud Computing, biometric authentication systems for IoT applications, and many other applicationsIt aspires to provide a relevant reference for students, researchers, engineers, and professionals working in this particular area or those interested in grasping its diverse facets and exploring the latest advances on security and privacy- preserving for IoT and 5G networks. 

This book presents applications and solutions of Big Data in the GovTech system and recommendations for regulating the institutions of the digital economy and information society for the wide application of Big Data with the use of the institutional approach. In this book, a systematic scientific understanding of GovTech is formed, the central place of Big Data in this system is substantiated, and modern experience in the functioning and development of this system is considered in detail. The contribution of the book to the literature is to bridge the gap between theory and practice of GovTech through a comprehensive study of all its manifestations in the three parts of the book. The first part is devoted to GovTech in the provision of high-tech educational services based on Big Data. The second part reflects state regulation of the economy by industry using Big Data in the GovTech. The third part outlined the digital divide and the experience of overcoming it with the help of GovTech based on Big Data.The practical significance of the book lies in the fact that it offers a holistic practical guide to the development of the GovTech system based on Big Data. The book will be of interest to academic scientists studying GovTech, as it clarified its categorical apparatus and scientific basis. The subjects of management in GovTech form the secondary target audience of this book, which provides them with numerous cases from the experience of modern Russia, as well as applied recommendations for improving the efficiency of the GovTech system based on Big Data. The book is multidisciplinary and is intended for scientists from various fields of science (pedagogy, economics, business, law, management, and ICT).

This book presents a collection of state-of-the-art artificial intelligence and big data analytics approaches to cybersecurity intelligence. It illustrates the latest trends in AI/ML-based strategic defense mechanisms against malware, vulnerabilities, cyber threats, as well as proactive countermeasures. It also introduces other trending technologies, such as blockchain, SDN, and IoT, and discusses their possible impact on improving security. The book discusses the convergence of AI/ML and big data in cybersecurity by providing an overview of theoretical, practical, and simulation concepts of computational intelligence and big data analytics used in different approaches of security. It also displays solutions that will help analyze complex patterns in user data and ultimately improve productivity.This book can be a source for researchers, students, and practitioners interested in the fields of artificial intelligence, cybersecurity, data analytics, and recent trends of networks.

The book explores a new general approach to selecting-and designing-data processing techniques. Symmetry and invariance ideas behind this algebraic approach have been successful in physics, where many new theories are formulated in symmetry terms.The book explains this approach and expands it to new application areas ranging from engineering, medicine, education to social sciences. In many cases, this approach leads to optimal techniques and optimal solutions. That the same data processing techniques help us better analyze wooden structures, lung dysfunctions, and deep learning algorithms is a good indication that these techniques can be used in many other applications as well. The book is recommended to researchers and practitioners who need to select a data processing technique-or who want to design a new technique when the existing techniques do not work. It is also recommended to students who want to learn the state-of-the-art data processing.  

This edited book covers ongoing research in both theory and practical applications of using deep learning for social media data. Social networking platforms are overwhelmed by different contents, and their huge amounts of data have enormous potential to influence business, politics, security, planning and other social aspects. Recently, deep learning techniques have had many successful applications in the AI field. The research presented in this book emerges from the conviction that there is still much progress to be made toward exploiting deep learning in the context of social media data analytics. It includes fifteen chapters, organized into four sections that report on original research in network structure analysis, social media text analysis, user behaviour analysis and social media security analysis. This work could serve as a good reference for researchers, as well as a compilation of innovative ideas and solutions for practitioners interested in applying deep learning techniques to social media data analytics.  

Currently, machine learning is playing a pivotal role in the progress of genomics. The applications of machine learning are helping all to understand the emerging trends and the future scope of genomics. This book provides comprehensive coverage of machine learning applications such as  DNN, CNN, and RNN, for predicting the sequence of DNA and RNA binding proteins, expression of the gene, and splicing control. In addition, the book addresses the effect of multiomics data analysis of cancers using tensor decomposition, machine learning techniques for protein engineering, CNN applications on genomics, challenges of long noncoding RNAs in human disease diagnosis, and how machine learning can be used as a  tool to shape the future of medicine. More importantly, it gives a comparative analysis and validates the outcomes of machine learning methods on genomic data to the functional laboratory tests or by formal clinical assessment. The topics of this book will cater interest to academicians,  practitioners working in the field of functional genomics, and machine learning. Also, this book shall guide comprehensively the graduate, postgraduates, and Ph.D. scholars working in these fields.

This book focuses on contemporary technologies and research in computational intelligence that has reached the practical level and is now accessible in preclinical and clinical settings. This book's principal objective is to thoroughly understand significant technological breakthroughs and research results in predictive modeling in healthcare imaging and data analysis. Machine learning and deep learning could be used to fully automate the diagnosis and prognosis of patients in medical fields. The healthcare industry's emphasis has evolved from a clinical-centric to a patient-centric model. However, it is still facing several technical, computational, and ethical challenges. Big data analytics in health care is becoming a revolution in technical as well as societal well-being viewpoints.  Moreover, in this age of big data, there is increased access to massive amounts of regularly gathered data from the healthcare industry that has necessitated the development of predictive models and automated solutions for the early identification of critical and chronic illnesses. The book contains high-quality, original work that will assist readers in realizing novel applications and contexts for deep learning architectures and algorithms, making it an indispensable reference guide for academic researchers, professionals, industrial software engineers, and innovative model developers in healthcare industry.

The book provides an insight into the practical applications and theoretical foundation of data science. The book discusses new ways of embracing agile approaches to various facets of data science, including machine learning and artificial intelligence, data mining, data visualization, and communication. The book includes contributions from academia and industry experts detailing the shortfalls of current tools and techniques used and generating the blueprint of the new technologies. The topics covered in the book range from theoretical and foundational research, platforms, methods, applications, and tools in data science. The chapters in the book add a social, geographical, and temporal dimension to data science research. The papers included are application-oriented that prepare and use data in discovery research. This book will provide researchers and practitioners with a detailed snapshot of current progress in data science. Moreover, it will stimulate new study, research, and the development of new applications. 

This book gives a comprehensive view of graph theory in informational retrieval (IR) and natural language processing(NLP). This book provides number of graph techniques for IR and NLP applications with examples. It also provides understanding of graph theory basics, graph algorithms and networks using graph. The book is divided into three parts and contains nine chapters. The first part gives graph theory basics and graph networks, and the second part provides basics of IR with graph-based information retrieval. The third part covers IR and NLP recent and emerging applications with case studies using graph theory. This book is unique in its way as it provides a strong foundation to a beginner in applying mathematical structure graph for IR and NLP applications. All technical details that include tools and technologies used for graph algorithms and implementation in Information Retrieval and Natural Language Processing with its future scope are explained in a clear and organized format.

This book is focused on AI-empowered knowledge management to improve processes, implementation of technology for providing easy access to knowledge and the impact of knowledge management to promote the platform for generation of new knowledge through continuous learning. The book discusses process of knowledge management which includes entirety of the creation, distribution, and maintenance of knowledge to achieve organizational objectives. It also covers knowledge management tools which enable and enhance knowledge creation, codification, and transfer within business firms thereby reducing the burden of work and allowing application of resources and effective usage towards practical tasks. An immense growth of artificial intelligence in business organizations has occurred and AI-empowered knowledge management practice is leading towards growth and development of the organization.

This book presents the latest findings in the areas of digital ecosystem for innovation in agriculture. The book is organized into two sections with thirteen chapters dealing with specialized areas. It provides the reader with an overview of the frameworks and technologies involved in the digitalization of agriculture, as well as the data processing methods, decision-making processes, and innovative services/applications for enabling digital transformations in agriculture. The chapters are written by experts sharing their experiences in lucid language through case studies, suitable illustrations, and tables. The contents have been designed to fulfill the needs of geospatial, data science, agricultural, and environmental sciences of universities, agricultural universities, technological universities, research institutes, and academic colleges worldwide. It helps the planners, policymakers, and extension scientists plan and sustainably manage agriculture and natural resources.

This book broadly covers the given spectrum of disciplines in Computational Life Sciences, transforming it into a strong helping hand for teachers, students, practitioners and researchers. In Life Sciences, problem-solving and data analysis often depend on biological expertise combined with technical skills in order to generate, manage and efficiently analyse big data. These technical skills can easily be enhanced by good theoretical foundations, developed from well-chosen practical examples and inspiring new strategies. This is the innovative approach of Computational Life Sciences-Data Engineering and Data Mining for Life Sciences: We present basic concepts, advanced topics and emerging technologies, introduce algorithm design and programming principles, address data mining and knowledge discovery as well as applications arising from real projects. Chapters are largely independent and often flanked by illustrative examples and practical advise.

This book discusses the advances of artificial intelligence and data sciences in climate change and provides the power of the climate data that is used as inputs to artificial intelligence systems. It is a good resource for researchers and professionals who work in the field of data sciences, artificial intelligence, and climate change applications.

This book proposes a comprehensive overview of the state-of-the-art research work on multimedia analysis in IoT applications. This is a third volume by editors which provides theoretical and practical approach in the area of multimedia and IOT applications and performance analysis. Further, multimedia communication, deep learning models to multimedia data, and the new (IOT) approaches are also covered. It addresses the complete functional framework in the area of multimedia data, IoT, and smart computing techniques. It bridges the gap between multimedia concepts and solutions by providing the current IOT frameworks, their applications in multimedia analysis, the strengths and limitations of the existing methods, and the future directions in multimedia IOT analytics.

The authors of this book tried to make these experiences available to those interested, considering the experience of several years of training, research, and implementation of projects in the supply chain performance evaluation field.This book intends to identify the current performance and competitive position of that supply chain compared to other supply chains by presenting and reviewing the techniques and models for measuring the efficiency and performance of the supply chain. Determining the performance of a supply chain is a good description of the status quo (what is). Determining the performance of a supply chain is useful for describing the past and present of supply chain processes, and on the other hand, it can be used to set performance goals and initiate the improvement process. To realize this, a strategic framework or model is needed to be able to extract indicators related to the efficiency of the supply chain and design the appropriate model.

This book redefines the essence of the information society and the digital economy, offering a new approach to their management and organization based on big data. The novelty of the new approach is that it ensures the use of the advanced technological capabilities of the Fourth Industrial Revolution to accelerate socio-economic development. The success of the new approach is based on progressive social institutions and advanced big data technology.Theoretical issues, methodological developments, and the author¿s applied recommendations are consistently presented in forty chapters distributed in five sections. The book contains cases that reveal the practical experience of the Eurasian Economic Union (EAEU). The intended readership of the book is scientists. The book is interesting and useful for them because it presents an innovative model of information society and digital economy development driven by big data.

The use of data to guide action is growing. Even the public uses data to guide everyday decisions! How do we develop data acumen across a broad range of fields and varying levels of expertise? How do we foster the development of effective data translators? This book explores these questions, presenting an interdisciplinary collection of edited contributions across fields such as education, health sciences, natural sciences, politics, economics, business and management studies, social sciences, and humanities. Authors illustrate how to use data within a discipline, including visualization and analysis, translating and communicating results, and pedagogical considerations. This book is of interest to scholars and anyone looking to understand the use of data science across disciplines. It is ideal in a course for non-data science majors exploring how data translation occurs in various contexts and for professionals looking to engage in roles requiring data translation.

This book is divided into three parts. The first part discusses the Metaverse's basics, development, and optional applications such as 3D virtual dressing room-based user-friendly Metaverse, the use of Metaverse in the healthcare and environment sectors as well as the ethics of the Metaverse and digital virtual environments. Part two presents some chapters that discuss emerging technologies in the Metaverse world including IoT, digital twining, and artificial intelligence and shows its impact on climate change. The third part contains chapters discussing cybersecurity in the Metaverse including blockchain technology opportunities and applications and the threat of the digital humanities in the Metaverse. The book is suitable for students and academics aiming to build up their background on the Future of the Metaverse in the Virtual Era and Physical World.

This book provides a quick but insightful introduction to Bayesian tracking and particle filtering for a person who has some background in probability and statistics and wishes to learn the basics of single-target tracking. It also introduces the reader to multiple target tracking by presenting useful approximate methods that are easy to implement compared to full-blown multiple target trackers.The book presents the basic concepts of Bayesian inference and demonstrates the power of the Bayesian method through numerous applications of particle filters to tracking and smoothing problems. It emphasizes target motion models that incorporate knowledge about the target¿s behavior in a natural fashion rather than assumptions made for mathematical convenience.The background provided by this book allows a person to quickly become a productive member of a project team using Bayesian filtering and to develop new methods and techniques for problems the team may face.

This book provides state-of-the-art coverage of deep learning applications in image analysis. The book demonstrates various deep learning algorithms that can offer practical solutions for various image-related problems; also how these algorithms are used by scientists and scholars in industry and academia. This includes autoencoder and deep convolutional generative adversarial network in improving classification performance of Bangla handwritten characters, dealing with deep learning-based approaches using feature selection methods for automatic diagnosis of covid-19 disease from x-ray images, imbalance image data sets of classification, image captioning using deep transfer learning, developing a vehicle over speed detection system, creating an intelligent system for video-based proximity analysis, building a melanoma cancer detection system using deep learning, plant diseases classification using AlexNet, dealing with hyperspectral images using deep learning, chest x-ray image classification of pneumonia disease using efficient net and inceptionv3.The book also addresses the difficulty of implementing deep learning in terms of computation time and the complexity of reasoning and modelling different types of data where information is currently encoded. Each chapter has the application of various new or existing deep learning models such as Deep Neural Network (DNN) and Deep Convolutional Neural Networks (DCNN). The detailed utilization of deep learning packages that are available in MATLAB, Python and R programming environments have also been discussed, therefore, the readers will get to know about the practical implementation of deep learning as well. The content of this book is presented in a simple and lucid style for professionals, nonprofessionals, scientists, and students interested in the research area of deep learning applications in image analysis.

The book examines the current state of mHealth and Human-Centered Design (HCD) initiatives toward health, care, and well-being. The present surge in interest in improving people's quality of life is creating new prospects for the development of innovative design solutions aimed at enhancing living conditions. The combination of emerging user needs and opportunities provided by recent innovative mHealth technologies enables research institutions, stakeholders, and academia to design new solutions to promote well-being, health, and care, thereby improving the quality of life of people of all ages. The book analyzes and discusses the most innovative services, products, and systems in the healthcare field. This strategy is in line with the concept of ambient assisted living or enhanced living environment, which focuses on the comfort and health of specific categories of users.This book covers several topics highlighting the importance of involving end-users in the design of innovative solutions in digital health care, and design considerations of mobile healthcare applications.Furthermore, the covered topics are described in their current applications in relevant fields focusing on the design of smart solutions, such as biomonitoring systems, activity recognition tools, smart living environments, physical autonomy, and virtual assistance.This editorial project is addressed to academics, designers, engineers, and practitioners in health care who want to promote cooperation between academia, stakeholders, and research institutions.

This book presents ample, richly illustrated account on results and experience from a project, dealing with the analysis of data concerning behavior patterns on the Web. The advertising on the Web is dealt with, and the ultimate issue is to assess the share of the artificial, automated activity (ads fraud), as opposed to the genuine human activity.After a comprehensive introductory part, a full-fledged report is provided from a wide range of analytic and design efforts, oriented at: the representation of the Web behavior patterns, formation and selection of telling variables, structuring of the populations of behavior patterns, including the use of clustering, classification of these patterns, and devising most effective and efficient techniques to separate the artificial from the genuine traffic.A series of important and useful conclusions is drawn, concerning both the nature of the observed phenomenon, and hence the characteristics of the respective datasets, and theappropriateness of the methodological approaches tried out and devised. Some of these observations and conclusions, both related to data and to methods employed, provide a new insight and are sometimes surprising. The book provides also a rich bibliography on the main problem approached and on the various methodologies tried out.

This book illustrates how the advanced technology developed for smart cities requires increasing interaction with citizens to motivate and incentive them. Megacities' needs have been encouraging for the creation of smart cities in which the needs of inhabitants are collected using virtualization and digitalization systems. On the other hand, machine learning algorithms have been implemented to provide better solutions for diverse areas in smart cities, such as transportation and health. Besides, conventional electric grids have transformed into smart grids, improving energy quality. Gamification, serious games, machine learning, dynamic interfaces, and social networks are some elements integrated holistically to provide novel solutions to design and develop smart cities. Also, this book presents in a friendly way the concept of social devices that are incorporated into smart homes and buildings. This book is used to understand and design smart cities where citizens are strongly interconnected so the demand response time can be reduced.

The volume presents research works on developing Artificial Intelligence based algorithms and methodologies for making social good that too to a notable one. The book discusses latest findings on efficient technological solutions of e-governance and other areas of life from the leading researchers in the field. The prime focus is on solving socio-economic technical problems using state-of-the-art research findings like fuzzy computing, evolutionary and hybrid frameworks, neuro computing, etc., along with other AI based computation platforms. The topics covered include solution frameworks using Artificial Intelligence based models in application areas like agriculture and rural development, road accident, travel and tourism, solid waste management, rural medical care, crowd sourced election monitoring system, ragging, rape and other abuses, cyber criminals and cyber bullying, disaster management, social good, etc. The book offers a valuable resource for all undergraduate, postgraduate students and researchers interested in exploring solution frameworks for social good problems using artificial intelligence.