Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data discusses the insight of data processing applications in various domains through soft computing techniques and enormous advancements in the field. The book focuses on the cross-disciplinary mechanisms and ground-breaking research ideas on novel techniques and data processing approaches in handling structured and unstructured healthcare data. It also gives insight into various information-processing models and many memories associated with it while processing the information for forecasting future trends and decision making. This book is an excellent resource for researchers and professionals who work in the Healthcare Industry, Data Science, and Machine learning. - Focuses on data-centric operations in the Healthcare industry - Provides the latest trends in healthcare data analytics and practical implementation outcomes of the proposed models - Addresses real-time challenges and case studies in the Healthcare industry

Deep learning can provide more accurate results compared to machine learning. It uses layered algorithmic architecture to analyze data. It produces more accurate results since learning from previous results enhances its ability. The multi-layered nature of deep learning systems has the potential to classify subtle abnormalities in medical images, clustering patients with similar characteristics into risk-based cohorts, or highlighting relationships between symptoms and outcomes within vast quantities of unstructured data. Exploring this potential, Deep Learning for Smart Healthcare: Trends, Challenges and Applications is a reference work for researchers and academicians who are seeking new ways to apply deep learning algorithms in healthcare, including medical imaging and healthcare data analytics. It covers how deep learning can analyze a patient’s medical history efficiently to aid in recommending drugs and dosages. It discusses how deep learning can be applied to CT scans, MRI scans and ECGs to diagnose diseases. Other deep learning applications explored are extending the scope of patient record management, pain assessment, new drug design and managing the clinical trial process. Bringing together a wide range of research domains, this book can help to develop breakthrough applications for improving healthcare management and patient outcomes.

Explainable artificial intelligence is proficient in operating and analyzing the unconstrainted environment in fields like robotic medicine, robotic treatment, and robotic surgery, which rely on computational vision for analyzing complex situations. Explainable artificial intelligence is a well-structured customizable technology that makes it possible to generate promising unbiased outcomes. The model’s adaptability facilitates the management of heterogeneous healthcare data and the visualization of biological structures through virtual reality. Explainable artificial intelligence has newfound applications in the healthcare industry, such as clinical trial matching, continuous healthcare monitoring, probabilistic evolutions, and evidence-based mechanisms. Principles and Methods of Explainable Artificial Intelligence in Healthcare discusses explainable artificial intelligence and its applications in healthcare, providing a broad overview of state-of-the-art approaches for accurate analysis and diagnosis. The book also encompasses computational vision processing techniques that handle complex data like physiological information, electronic healthcare records, and medical imaging data that assist in earlier prediction. Covering topics such as neural networks and disease detection, this reference work is ideal for industry professionals, practitioners, academicians, researchers, scholars, instructors, and students.

This book highlights how optimized big data applications can be used for patient monitoring and clinical diagnosis. In fact, IoT-based applications are data-driven and mostly employ modern optimization techniques. The book also explores challenges, opportunities, and future research directions, discussing the stages of data collection and pre-processing, as well as the associated challenges and issues in data handling and setup.

This book describes the application of signal and image processing technologies, artificial intelligence, and machine learning techniques to support Covid-19 diagnosis and treatment. The book focuses on two main applications: critical diagnosis requiring high precision and speed, and treatment of symptoms, including those affecting the cardiovascular and neurological systems. The areas discussed in this book range from signal processing, time series analysis, and image segmentation to detection and classification. Technical approaches include deep learning, transfer learning, transformers, AutoML, and other machine learning techniques that can be considered not only for Covid-19 issues but also for different medical applications, with slight adjustments to the problem under study. The Covid-19 pandemic has impacted the entire world and changed how societies and individuals interact. Due to the high infection and mortality rates, and the multiple consequences of the virus infection in the human body, the challenges were vast and enormous. These necessitated the integration of different disciplines to address the problems. As a global response, researchers across academia and industry made several developments to provide computational solutions to support epidemiologic, managerial, and health/medical decisions. To that end, this book provides state-of-the-art information on the most advanced solutions.

The convergence of Internet of Things (IoT) technology and blockchain offers transformative potential for the development of smart cities, enhancing industry operations and healthcare systems. IoT devices generate vast amounts of data to optimize urban infrastructure and improve service delivery, while blockchain provides a secure, transparent framework for managing data. Across industries, this collaboration leads to smarter manufacturing processes and efficient logistics. In healthcare, it enhances patient care through secure data sharing and streamlined administrative processes. A concerted effort to address these technical, regulatory, and ethical challenges is crucial for effective and responsible integration of IoT and blockchain in smart cities for improved urban living and healthcare services. Applying Internet of Things and Blockchain in Smart Cities: Industry and Healthcare Perspectives explores the application of IoT and blockchain technology for smart city integration in healthcare industries and business processes. It offers solutions for this effective convergence, through aspects like cloud and digital technology, or security and privacy practices. This book covers topics such as machine learning, energy management, and wearable devices, and is a useful resource for business owners, computer engineers, agriculturalists, security professionals, healthcare workers, academicians, researchers, and scientists.