Böcker i Studies in Big Data-serien

What changes have affected the definition of the boundaries of journalism in the last decade? How do technologies influence the boundaries of journalism? Are threats and opportunities identified in those blurred areas of journalism? The aim of this book is to answer these questions and to address, from different perspectives, the redefinition of the boundaries of journalism according to the most recent changes in digital media concerning actors, models, and practices.More than 40 authors from eleven countries contribute to this book, which is structured into six sections to analyze the principles of journalism today, sustainability strategies in the digital context, old and new actors, formats and narratives, adaptation to the mobile scenario and to social platforms, and the changes introduced by artificial intelligence. Undoubtedly, this book is of interest to both academics and professionals, as well as a crucial reference for scholars and students of media and journalism.Chapter 7 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

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.  

The combination of DEA and ratio analysis is introduced as a suitable field for evaluating the performance of DMUs. In this regard, DEA-R is also proposed as a hybrid technique for calculating efficiency, ranking DMUs, and finding efficient faces. Therefore, the relationship between DEA and DEA-R provides a suitable field for researchers in the field of evaluating the performance of DMUs. The audience of this book is not limited to researchers in mathematics fields, but experts and students in industrial engineering and management fields also benefit from the topics of this book.

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.  

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.

This book highlights applied artificial intelligence techniques, tools, and systems to drive strategic advantages, improve operational efficiency, and create added value. The focus is very much on practical applications and how to maximize the value of these technologies. They are being applied across businesses to enhance innovation, improve performance, increase profit, support critical thinking, and ultimately create customer-added value. Whether you are a researcher, manager, or decision-maker, this book provides valuable insights to help you harness the power of AI and big data analytics in your organization. This book attempts to provide answers to the most important questions: Quo Vadis applied artificial intelligence?Quo Vadis cutting-edge business technologies?

This book provides timely studies on multi-view facets of data analytics by covering recent trends in processing and reasoning about data originating from an array of local sources. A multi-view nature of data analytics is encountered when working with a variety of real-world scenarios including clustering, consensus building in decision processes, computer vision, knowledge representation, big data, data streaming, among others.The chapters demonstrate recent pursuits in the methodology, theory, advanced algorithms, and applications of multi-view data analytics and bring new perspectives of data interpretation. The timely book will appeal to a broad readership including both researchers and practitioners interested in gaining exposure to the rapidly growing trend of multi-view data analytics and intelligent systems.

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 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 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.

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 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 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 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.

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 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.

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 is one-stop shop which offers essential information one must know and can implement in real-time business expansions to solve engineering problems in various disciplines. It will also help us to make future predictions and decisions using AI algorithms for engineering problems. Machine learning and optimizing techniques provide strong insights into novice users. In the era of big data, there is a need to deal with data science problems in multidisciplinary perspective. In the real world, data comes from various use cases, and there is a need of source specific data science models. Information is drawn from various platforms, channels, and sectors including web-based media, online business locales, medical services studies, and Internet. To understand the trends in the market, data science can take us through various scenarios. It takes help of artificial intelligence and machine learning techniques to design and optimize the algorithms. Big data modelling and visualization techniques of collected data play a vital role in the field of data science. This book targets the researchers from areas of artificial intelligence, machine learning, data science and big data analytics to look for new techniques in business analytics and applications of artificial intelligence in recent businesses.

This book aims to address emerging challenges in the field of agriculture and natural resource management using the principles and applications of data science (DS). The book is organized in three sections, and it has fourteen chapters dealing with specialized areas.  The chapters are written by experts sharing their experiences very lucidly through case studies, suitable illustrations and tables. The contents have been designed to fulfil the needs of geospatial, data science, agricultural, natural resources and environmental sciences of traditional universities, agricultural universities, technological universities, research institutes and academic colleges worldwide. It will help the planners, policymakers and extension scientists in planning and sustainable management of agriculture and natural resources. The authors believe that with its uniqueness the book is one of the important efforts in the contemporary cyber-physical systems.

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 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 plays a significant role in improvising human life to a great extent. The new applications of soft computing can be regarded as an emerging field in computer science, automatic control engineering, medicine, biology application, natural environmental engineering, and pattern recognition. Now, the exemplar model for soft computing is human brain. The use of various techniques of soft computing is nowadays successfully implemented in many domestic, commercial, and industrial applications due to the low-cost and very high-performance digital processors and also the decline price of the memory chips. This is the main reason behind the wider expansion of soft computing techniques and its application areas. These computing methods also play a significant role in the design and optimization in diverse engineering disciplines. With the influence and the development of the Internet of things (IoT) concept, the need for using soft computing techniques has become more significant than ever. In general, soft computing methods are closely similar to biological processes than traditional techniques, which are mostly based on formal logical systems, such as sentential logic and predicate logic, or rely heavily on computer-aided numerical analysis. Soft computing techniques are anticipated to complement each other. The aim of these techniques is to accept imprecision, uncertainties, and approximations to get a rapid solution. However, recent advancements in representation soft computing algorithms (fuzzy logic,evolutionary computation, machine learning, and probabilistic reasoning) generate a more intelligent and robust system providing a human interpretable, low-cost, approximate solution. Soft computing-based algorithms have demonstrated great performance to a variety of areas including multimedia retrieval, fault tolerance, system modelling, network architecture, Web semantics, big data analytics, time series, biomedical and health informatics, etc. Soft computing approaches such as genetic programming (GP), support vector machine-firefly algorithm (SVM-FFA), artificial neural network (ANN), and support vector machine-wavelet (SVM-Wavelet) have emerged as powerful computational models. These have also shown significant success in dealing with massive data analysis for large number of applications.All the researchers and practitioners will be highly benefited those who are working in field of computer engineering, medicine, biology application, signal processing, and mechanical engineering. This book is a good collection of state-of-the-art approaches for soft computing-based applications to various engineering fields. It is very beneficial for the new researchers and practitioners working in the field to quickly know the best performing methods. They would be able to compare different approaches and can carry forward their research in the most important area of research which has direct impact on betterment of the human life and health.This book is very useful because there is no book in the market which provides a good collection of state-of-the-art methods of soft computing-based models for multimedia retrieval, fault tolerance, system modelling, network architecture, Web semantics, big data analytics, time series, and biomedical and health informatics.

This book discusses fundamentals of Blockchain technology and Industry 4.0. It discusses many applications of Blockchain technology in Industry 4.0, including integration of AI, IoT, and big data with Blockchain for Industry 4.0. It provides cutting-edge research content from researchers, academicians, and other professionals from different background areas to show their state-of-the-art knowledge to use Blockchain in Industry 4.0. The book discusses advantages of Industry 4.0, such as improved productivity, improved efficiency, flexibility, agility, better user experience, and many more, and also entails some challenges too, such as trust, traceability, security, reliability, transparency, etc., for creating an application of Industry 4.0. The book helps graduate, postgraduate, doctoral students, and industrial professionals to implement Blockchain in Industry 4.0.