It is known that trust is of the utmost importance in human interactions, and blockchain technology establishes a new type of foundation for financial and political confidence. This new kind of trust is based on cryptographic techniques and distributed in digital networks. In an uncertain world where it is difficult to tell what is real or fake, decentralized organizational networks may prove to be particularly competitive given that this new ""distributed trust"" endows them with an unusual functional autonomy, namely guaranteeing the authenticity, confidentiality, and integrity of the processed data. Besides the direct sharing of information enabled by blockchain, transactions can now also take place with newfound trust and ways to safely manage personal data. It is important to look at these implications, particularly in sectors such as business and healthcare. Political and Economic Implications of Blockchain Technology in Business and Healthcare provides relevant theoretical frameworks on the political and economic impact of blockchain technology, which is thought to be able to redesign human interactions concerning transactions. Specifically, it will give ideas, concepts, and instruments considered relevant to advance the knowledge about ""cryptoeconomics"" and decentralized governance. The chapters will also provide several insights on business applications of this digital innovation, particularly in the healthcare sector, and will explore the ethical impact of the new ""distributed trust"" paradigm resulting from the surge of such a disruptive technology. This book is essential for students and researchers in social and life sciences, professionals and policymakers working in the fields of public and business administration, healthcare workers and researchers, academicians, and students interested in blockchain technology and the political and economic impacts in the industry.

Though traditionally information systems have been centralized, these systems are now distributed over the web. This requires a re-investigation into the way information systems are modeled and designed. Because of this new function, critical problems, including security, never-fail systems, and quality of service have begun to emerge. Novel Approaches to Information Systems Design is an essential publication that explores the most recent, cutting-edge research in information systems and exposes the reader to emerging but relatively mature models and techniques in the area. Highlighting a wide range of topics such as big data, business intelligence, and energy efficiency, this publication is ideally designed for managers, administrators, system developers, information system engineers, researchers, academicians, and graduate-level students seeking coverage on critical components of information systems.

Increased use of artificial intelligence (AI) is being deployed in many hospitals and healthcare settings to help improve health care service delivery. Machine learning (ML) and deep learning (DL) tools can help guide physicians with tasks such as diagnosis and detection of diseases and assisting with medical decision making. This edited book outlines novel applications of AI in e-healthcare. It includes various real-time/offline applications and case studies in the field of e-Healthcare, such as image recognition tools for assisting with tuberculosis diagnosis from x-ray data, ML tools for cancer disease prediction, and visualisation techniques for predicting the outbreak and spread of Covid-19. Heterogenous recurrent convolution neural networks for risk prediction in electronic healthcare record datasets are also reviewed. Suitable for an audience of computer scientists and healthcare engineers, the main objective of this book is to demonstrate effective use of AI in healthcare by describing and promoting innovative case studies and finding the scope for improvement across healthcare services.

This updated compendium provides the linear algebra background necessary to understand and develop linear algebra applications in data mining and machine learning.Basic knowledge and advanced new topics (spectral theory, singular values, decomposition techniques for matrices, tensors and multidimensional arrays) are presented together with several applications of linear algebra (k-means clustering, biplots, least square approximations, dimensionality reduction techniques, tensors and multidimensional arrays).The useful reference text includes more than 600 exercises and supplements, many with completed solutions and MATLAB applications.The volume benefits professionals, academics, researchers and graduate students in the fields of pattern recognition/image analysis, AI, machine learning and databases.

With the rise of mobile and wireless technologies, more sustainable networks are necessary to support such communications. These next generation networks can now be utilized to strengthen the growing era of the Internet of Things. Powering the Internet of Things With 5G Networks is a comprehensive reference source for the latest scholarly research on the progression and design of fifth generation networks and their role in supporting the Internet of Things. Including a range of perspectives on topics such as privacy and security, large scale monitoring, and scalable architectures, this book is ideally designed for technology developers, academics, researchers, and practitioners interested in the convergence of the Internet of Things and 5G networks. Topics Covered The many academic areas covered in this publication include, but are not limited to: Autonomous Computing Big Data Management Cyber Threats Disruptive technologies Large Scale Monitoring Privacy and security Scalable Architectures Wireless sensor networks

DESCRIPTION Users expect search to be simple: They enter a few terms and expect perfectly-organized, relevant results instantly. But behind this simple user experience, complex machinery is at work. Whether using Elasticsearch, Solr, or another search technology, the solution is never one size fits all. Returning the right search results requires conveying domain knowledge and business rules in the search engine's data structures, text analytics, and results ranking capabilities. Relevant Search demystifies relevance work. Using Elasticsearch, it tells how to return engaging search results to users, helping readers understand and leverage the internals of Lucene-based search engines. The book walks through several real-world problems using a cohesive philosophy that combines text analysis, query building, and score shaping to express business ranking rules to the search engine. It outlines how to guide the engineering process by monitoring search user behavior and shifting the enterprise to a search-first culture focused on humans, not computers. It also shows how the search engine provides a deeply pluggable platform for integrating search ranking with machine learning, ontologies, personalization, domain-specific expertise, and other enriching sources. KEY FEATURES Highly relevant, concrete, hands-on guide Digs deep into search engine technology Contains essential tools, tips, and strategies for building engaging search engines AUDIENCE For readers who can code moderately complex tasks. ABOUT THE TECHNOLOGY Lucene is the underlying technology that backs both Elasticsearch and Solr. Dominant search engines are based upon Lucene and since Lucene itself is based upon the strong foundation of Information Retrieval research, the book will be applicable to almost any search technology available now or in the foreseeable future.

The book is aimed at providing an understanding of basic principles and practices of Biostatistics. Attempt has been made to give a comprehensive inside into the basic concepts of biostatistics i.e. sound easy to read, relevant and useful to students and practitioners of biostatistics. The book includes the design of clinical trials and epidemiological studies collection of data, research methods, clinical measurement and sample size, calculations for various study designs. This makes it a handbook of biostatistics for students and researchers for agriculture, veterinary, life sciences, medicine, public health, biological and social sciences.

Artificial neural network research is one of the new directions for new generation computers. Current research suggests that open box artificial higher order neural networks (HONNs) play an important role in this new direction. HONNs will challenge traditional artificial neural network products and change the research methodology that people are currently using in control and recognition areas for the control signal generating, pattern recognition, nonlinear recognition, classification, and prediction. Since HONNs are open box models, they can be easily accepted and used by individuals working in information science, information technology, management, economics, and business fields. Emerging Capabilities and Applications of Artificial Higher Order Neural Networks contains innovative research on how to use HONNs in control and recognition areas and explains why HONNs can approximate any nonlinear data to any degree of accuracy, their ease of use, and how they can have better nonlinear data recognition accuracy than SAS nonlinear procedures. Featuring coverage on a broad range of topics such as nonlinear regression, pattern recognition, and data prediction, this book is ideally designed for data analysists, IT specialists, engineers, researchers, academics, students, and professionals working in the fields of economics, business, modeling, simulation, control, recognition, computer science, and engineering research.

There is not a single industry which will not be transformed by machine learning and Internet of Things (IoT). IoT and machine learning have altogether changed the technological scenario by letting the user monitor and control things based on the prediction made by machine learning algorithms. There has been substantial progress in the usage of platforms, technologies and applications that are based on these technologies. These breakthrough technologies affect not just the software perspective of the industry, but they cut across areas like smart cities, smart healthcare, smart retail, smart monitoring, control, and others. Because of these "game changers," governments, along with top companies around the world, are investing heavily in its research and development. Keeping pace with the latest trends, endless research, and new developments is paramount to innovate systems that are not only user-friendly but also speak to the growing needs and demands of society. This volume is focused on saving energy at different levels of design and automation including the concept of machine learning automation and prediction modeling. It also deals with the design and analysis for IoT-enabled systems including energy saving aspects at different level of operation. The editors and contributors also cover the fundamental concepts of IoT and machine learning, including the latest research, technological developments, and practical applications. Valuable as a learning tool for beginners in this area as well as a daily reference for engineers and scientists working in the area of IoT and machine technology, this is a must-have for any library.

In the modern era each new innovation poses its own special ethical dilemma. How can human society adapt to these new forms of expression, commerce, government, citizenship, and learning while holding onto its ethical and moral principles? The Changing Scope of Technoethics in Contemporary Society is a critical scholarly resource that examines the existing intellectual platform within the field of technoethics. Featuring coverage on a broad range of topics such as ethical perspectives on internet safety, technoscience, and ethical hacking communication, this book is geared towards academicians, researchers, and students seeking current research on domains of technoethics.