DIGITIZATION OF HEALTHCARE DATA USING BLOCKCHAIN The book gives a detailed description of the integration of blockchain technology for Electronic Health Records and provides the research challenges to consider in various disciplines such as supply chain, drug discovery, and data management. The aim of the book is to investigate the concepts of blockchain technology and its association with the recent development and advancements in the medical field. Moreover, it focuses on the integration of workflow strategies like NLP, and AI which could be adopted for boosting the clinical documentation and electronic healthcare records (EHR) usage by bringing down the physician EHR data entry. Also, the book covers the usage of smart contracts for securing patient records. Digitization of Healthcare Data Using Blockchain presents the practical implementations that deal with developing a web framework for building highly usable healthcare applications, a simple blockchain-powered EHR system. Audience Researchers in information technology, artificial intelligence, electronics engineering, medical informatics, as well as policymakers and healthcare providers and management systems.

How the Blockchain Technology is Changing the Landscape of HealthCare KEY FEATURES ● Includes detailed implementations of the blockchain technology in the real world to support evidence-based practices. ● Includes patient satisfaction, medical device installation, and Covid-19 prediction use cases. DESCRIPTION Before deciding whether or not a new piece of technology has credibility, institutions that specialize in the health sciences demand to see extensive documentation of it being used in clinical practice and published in academic journals. This book gives convincing facts to educate readers on this technology's advantages and limitations for advancing healthcare. This book discusses the deployment and use of blockchain technologies in real-world scientific, biomedical, and data applications. Applying the unique possibilities inherent to distributed ledger systems, the book reveals significant developments in health science research and development. Each chapter reveals the present applications of blockchain in drug development, drug and device tracking, real-world data collection, and more significant patient interaction. These all are utilized to open chances to further health science research. From the views of pharmaceutical executives, biotechnology startups, regulatory agencies, ethical review boards, and blockchain developers, this paradigm shift is investigated. After reading this book, the reader will understand the possibilities for enhancing and facilitating data use in health science research. WHAT YOU WILL LEARN ● Employ blockchain in a supervised environment and maintain data integrity and transparency. ● Connect distant data sources to encourage virtual trials. ● Connect different data sources to make big data analytics and visualizations elastic and real-time. ● Create crowdsourcing and data challenges without compromising IP. ● Disseminate blockchain education to the next generation of health science users. WHO THIS BOOK IS FOR This book will interest academicians, blockchain consultants, machine learning instructors, and anyone working toward utilizing blockchain technology in the health science sector. TABLE OF CONTENTS 1. Internet of Medical Things-Blockchain Integration 2. Barriers and Benefits of Blockchain Adoption in the Healthcare System 3. Patient Engagement in Healthcare Using Technology 4. Distributed Ledger and Transaction processing 5. Medical Device Implementation in Blockchain 6. Predictive–Based Solution for COVID-19 7. Optimization of Blockchain Technology for Patient Satisfaction 8. A Cogitative Analysis in Healthcare 9. An Analysis of Overview of Blockchain 10. End-user Computing Using Blockchain Technology

The year 2022 marks the 100th birth anniversary of Kathleen Hylda Valerie Booth, who wrote the first assembly language and designed the assembler and auto code for the first computer systems at Birkbeck College, University of London. She helped design three different machines including the ARC (Automatic Relay Calculator), SEC (Simple Electronic Computer), and APE(X). School of Computer Science and Engineering, under the aegis of Lovely Professional University, pays homage to this great programmer of all times by hosting “BOOTH100”—6th International Conference on Computing Sciences.

IoT Based Data Analytics for the Healthcare Industry: Techniques and Applications explores recent advances in the analysis of healthcare industry data through IoT data analytics. The book covers the analysis of ubiquitous data generated by the healthcare industry, from a wide range of sources, including patients, doctors, hospitals, and health insurance companies. The book provides AI solutions and support for healthcare industry end-users who need to analyze and manipulate this vast amount of data. These solutions feature deep learning and a wide range of intelligent methods, including simulated annealing, tabu search, genetic algorithm, ant colony optimization, and particle swarm optimization. The book also explores challenges, opportunities, and future research directions, and discusses the data collection and pre-processing stages, challenges and issues in data collection, data handling, and data collection set-up. Healthcare industry data or streaming data generated by ubiquitous sensors cocooned into the IoT requires advanced analytics to transform data into information. With advances in computing power, communications, and techniques for data acquisition, the need for advanced data analytics is in high demand. - Provides state-of-art methods and current trends in data analytics for the healthcare industry - Addresses the top concerns in the healthcare industry using IoT and data analytics, and machine learning and deep learning techniques - Discusses several potential AI techniques developed using IoT for the healthcare industry - Explores challenges, opportunities, and future research directions, and discusses the data collection and pre-processing stages

Multiple-criteria decision making, including multiple rule-based decision making, multiple-objective decision making, and multiple-attribute decision making, is used by clinical decision makers to analyze healthcare issues from various perspectives. In practical healthcare cases, semi-structured and unstructured decision-making issues involve multiple criteria that may conflict with each other. Thus, the use of multiple-criteria decision making is a promising source of practical solutions for such problems. AI-Enabled Multiple-Criteria Decision-Making Approaches for Healthcare Management investigates the contributions of practical multiple-criteria decision analysis applications and cases for healthcare management. The book also considers the best practices and tactics for utilizing multiple-criteria decision making to ensure the technology is utilized appropriately. Covering key topics such as fuzzy data, augmented reality, blockchain, and data transmission, this reference work is ideal for computer scientists, healthcare professionals, nurses, policymakers, researchers, scholars, academicians, practitioners, educators, and students.

Internet of things (IoT) applications employed for healthcare generate a huge amount of data that needs to be analyzed to produce the expected reports. To accomplish this task, a cloud-based analytical solution is ideal in order to generate faster reports in comparison to the traditional way. Given the current state of the world in which every day IoT devices are developed to provide healthcare solutions, it is essential to consider the mechanisms used to collect and analyze the data to provide thorough reports. Integrating AI in IoT Analytics on the Cloud for Healthcare Applications applies artificial intelligence (AI) in edge analytics for healthcare applications, analyzes the impact of tools and techniques in edge analytics for healthcare, and discusses security solutions for edge analytics in healthcare IoT. Covering topics such as data analytics and next generation healthcare systems, it is ideal for researchers, academicians, technologists, IT specialists, data scientists, healthcare industries, IoT developers, data security analysts, educators, and students.

The integration of artificial intelligence (AI), quantum computing, and semiconductor technology offers improved innovation to redefine computational power and capabilities. As AI drives advances in machine learning and data processing, quantum computing revolutionizes problem-solving with its ability to handle complex calculations at improved speeds. Advancements in semiconductor technology push the limits of processing efficiency and miniaturization. Continued exploration on this convergence may accelerate breakthroughs in various fields such as cryptography, material science, and healthcare. Integration of AI, Quantum Computing, and Semiconductor Technology explores the intersection of artificial intelligence (AI) and semiconductor technology within the context of quantum computing. It offers a comprehensive analysis of the current advancements, challenges, and potential applications resulting from this convergence. This book covers topics such as cyber security, healthcare monitoring, and machine learning, and is a useful resource for computer engineers, energy scientists, business owners, healthcare administrators, environmental scientists, academicians, and researchers.

This book discusses applications of blockchain in healthcare sector. The security of confidential and sensitive data is of utmost importance in healthcare industry. The introduction of blockchain methods in an effective manner will bring secure transactions in a peer-to-peer network. The book also covers gaps of the current available books/literature available for use cases of Distributed Ledger Technology (DLT) in healthcare. The information and applications discussed in the book are immensely helpful for researchers, database professionals, and practitioners. The book also discusses protocols, standards, and government regulations which are very useful for policymakers. /div /div /div /div /div /div /div /div /div /div /div /div /div /div /div /div /div /div /div /div /div /div /div

This book constitutes the proceedings of the 9th International Conference on Big Data Analytics, BDA 2021, which took place virtually during December 7–9, 2021. The 15 full papers and 1 short paper included in this volume were carefully reviewed and selected from 60 submissions. They were organized in topical sections as follows: Data science: systems; data science: architectures; big data analytics in healthcare support systems, information interchange of web data resources; and business analytics.

METAHEURISTICS for MACHINE LEARNING The book unlocks the power of nature-inspired optimization in machine learning and presents a comprehensive guide to cutting-edge algorithms, interdisciplinary insights, and real-world applications. The field of metaheuristic optimization algorithms is experiencing rapid growth, both in academic research and industrial applications. These nature-inspired algorithms, which draw on phenomena like evolution, swarm behavior, and neural systems, have shown remarkable efficiency in solving complex optimization problems. With advancements in machine learning and artificial intelligence, the application of metaheuristic optimization techniques has expanded, demonstrating significant potential in optimizing machine learning models, hyperparameter tuning, and feature selection, among other use-cases. In the industrial landscape, these techniques are becoming indispensable for solving real-world problems in sectors ranging from healthcare to cybersecurity and sustainability. Businesses are incorporating metaheuristic optimization into machine learning workflows to improve decision-making, automate processes, and enhance system performance. As the boundaries of what is computationally possible continue to expand, the integration of metaheuristic optimization and machine learning represents a pioneering frontier in computational intelligence, making this book a timely resource for anyone involved in this interdisciplinary field. Metaheuristics for Machine Learning: Algorithms and Applications serves as a comprehensive guide to the intersection of nature-inspired optimization and machine learning. Authored by leading experts, this book seamlessly integrates insights from computer science, biology, and mathematics to offer a panoramic view of the latest advancements in metaheuristic algorithms. You’ll find detailed yet accessible discussions of algorithmic theory alongside real-world case studies that demonstrate their practical applications in machine learning optimization. Perfect for researchers, practitioners, and students, this book provides cutting-edge content with a focus on applicability and interdisciplinary knowledge. Whether you aim to optimize complex systems, delve into neural networks, or enhance predictive modeling, this book arms you with the tools and understanding you need to tackle challenges efficiently. Equip yourself with this essential resource and navigate the ever-evolving landscape of machine learning and optimization with confidence. Audience The book is aimed at a broad audience encompassing researchers, practitioners, and students in the fields of computer science, data science, engineering, and mathematics. The detailed but accessible content makes it a must-have for both academia and industry professionals interested in the optimization aspects of machine learning algorithms.