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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.
Artificial intelligence (AI) has emerged as a transformative force across various domains, revolutionizing the way we perceive and address challenges in healthcare. The convergence of AI and healthcare holds immense promise, offering unprecedented opportunities to enhance medical diagnosis, treatment, and patient care. In today’s world, the intersection of AI and healthcare stands as one of the most promising frontiers for innovation and progress. Artificial Intelligence Transformations for Healthcare Applications: Medical Diagnosis, Treatment, and Patient Care embodies this convergence, offering a comprehensive exploration of how AI is revolutionizing various aspects of healthcare delivery. At its core, this book addresses the urgent need for more effective and efficient healthcare solutions in an increasingly complex and data-rich environment. Covering topics such as chronic disease, image classification, and precision medicine, this book is an essential resource for healthcare professionals, medical researchers, AI and machine learning specialists, healthcare administrators and executives, medical educators and students, biomedical engineers, healthcare IT professionals, policy makers and regulators, academicians, and more.
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
Even though many data analytics tools have been developed in the past years, their usage in the field of cyber twin warrants new approaches that consider various aspects including unified data representation, zero-day attack detection, data sharing across threat detection systems, real-time analysis, sampling, dimensionality reduction, resource-constrained data processing, and time series analysis for anomaly detection. Further study is required to fully understand the opportunities, benefits, and difficulties of data analytics and the internet of things in today’s modern world. New Approaches to Data Analytics and Internet of Things Through Digital Twin considers how data analytics and the internet of things can be used successfully within the field of digital twin as well as the potential future directions of these technologies. Covering key topics such as edge networks, deep learning, intelligent data analytics, and knowledge discovery, this reference work is ideal for computer scientists, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students.
In an era marked by rapid technological advancements and the increasing integration of artificial intelligence (AI) into various sectors, the intersection of AI technologies with service marketing stands as a pivotal frontier. It is essential to explore the intricate nexus between AI technologies and service marketing strategies. Integrating AI-Driven Technologies Into Service Marketing elucidates the transformative impact of AI on key facets of service marketing, ranging from customer engagement and relationship management to market segmentation and product customization. It underscores the imperative for stakeholders in emerging economies to harness the power of AI technologies in crafting innovative and adaptive service marketing strategies. The book navigates the complexities of AI adoption while offering pragmatic recommendations for fostering responsible and inclusive AI-driven service marketing ecosystems. Covering topics such as customer engagement, influencer marketing, and sentiment analysis, this book is an excellent resource for scholars, researchers, educators, business professionals, managers, academicians, postgraduate students, and more.
This book tackles the recent research trends on the role of AI in advancing automotive manufacturing, augmented reality, sustainable development in smart cities, telemedicine, and robotics. It sheds light on the recent AI innovations in classical machine learning, deep learning, Internet of Things (IoT), Blockchain, knowledge representation, knowledge management, big data, and natural language processing (NLP). The edited book covers empirical and reviews studies that primarily concentrate on the aforementioned issues, which would assist scholars in pursuing future research in the domain and identifying the possible future developments of AI applications.
Healthcare and pharmaceuticals are rapidly advancing with technological innovations, and the lack of understanding of AI algorithms poses a significant challenge in these fields. The need for more transparency in AI decision-making processes raises concerns about accountability, ethical implications, and regulatory compliance. As stakeholders in these critical sectors seek clarity and understanding, Analyzing Explainable AI in Healthcare and the Pharmaceutical Industry provides a reliable resource to discover new solutions. This book serves as a comprehensive guide, unraveling the complexities of explainable artificial intelligence (XAI) and its pivotal role in transforming healthcare and pharmaceutical practices. Demystifying AI algorithms and revealing their decision-making mechanisms equips readers with the foundational knowledge needed to confidently navigate AI integration in these domains. From healthcare professionals to policymakers, its insights cater to a diverse audience, fostering cross-disciplinary collaboration and facilitating informed decision-making.
Social media applications have emerged in the last 20 years to meet the different needs of individuals, and private sector and public organizations have not been indifferent to these technologies. Social media tools help public institutions and organizations communicate directly with citizens as well as enable two-way communication and enable citizens to participate in all stages from agenda setting to evaluation of policy processes. Central and local governments, which use innovative methods to involve citizens in this process, attach significance to the development of e-participation tools. Ensuring the participation of citizens in policy processes not only determines the wishes and priorities of citizens but also uses scarce resources effectively and efficiently. Global Perspectives on Social Media Usage Within Governments reveals the best practices of various countries regarding the use of social media by central and local governments according to public administration models. The book presents various case studies on the impact of public administration models on social media use in order to contribute to public administration and social media use. Covering topics such as climate action, knowledge behaviors, and citizen participation, this premier reference source is an essential resource for government officials, public administrators, public policy scholars, social media experts, public affairs scholars, students and educators of higher education, librarians, researchers, and academicians.