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Smart Healthcare for Sustainable Urban Development
With the development of information technology, the concept of smart healthcare has been evolving gradually. A new generation of information technologies, such as the internet of things, cloud computing, big data, and artificial intelligence, have transformed the old medical system and improved the efficiency, convenience, and personalization of healthcare. These changes are necessary to keep up with the requirements of individual people and the improvements in the efficiency of medical care, which largely enhances the experience of medical and health services. Smart Healthcare for Sustainable Urban Development discusses current challenges of digital healthcare adoption as well as how the internet of things and big data technologies can help promote digital healthcare adoption and improve healthcare efficiency. The book also considers how information technologies can support the adoption of smart health for overall improved healthcare delivery and access. Covering topics such as artificial intelligence and smart hospitals, this reference work is ideal for researchers, scholars, practitioners, academicians, industry professionals, instructors, and students
Contemporary Computing
This book constitutes the first part of the refereed proceedings of the Third International Conference, IC3 2010, held in Noida, India, in August 2010. The 23 revised full papers presented were carefully reviewed and selected from numerous submissions.
Data Science and Analytics
This two-volume set (CCIS 1229 and CCIS 1230) constitutes the refereed proceedings of the 5th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2019, held in Gurugram, India, in November 2019. The 74 revised full papers presented were carefully reviewed and selected from total 353 submissions. The papers are organized in topical sections on data centric programming; next generation computing; social and web analytics; security in data science analytics; big data analytics.
Software Engineering
This book presents selected proceedings of the annual convention of the Computer Society of India. Divided into 10 topical volumes, the proceedings present papers on state-of-the-art research, surveys, and succinct reviews. They cover diverse topics ranging from communications networks to big data analytics, and from system architecture to cyber security. This book focuses on Software Engineering, and informs readers about the state of the art in software engineering by gathering high-quality papers that represent the outcomes of consolidated research and innovations in Software Engineering and related areas. In addition to helping practitioners and researchers understand the chief issues involved in designing, developing, evolving and validating complex software systems, it provides comprehensive information on developing professional careers in Software Engineering. It also provides insights into various research issues such as software reliability, verification and validation, security and extensibility, as well as the latest concepts like component-based development, software process models, process-driven systems and human-computer collaborative systems.
Data Visualization and Knowledge Engineering
This book presents the fundamentals and advances in the field of data visualization and knowledge engineering, supported by case studies and practical examples. Data visualization and engineering has been instrumental in the development of many data-driven products and processes. As such the book promotes basic research on data visualization and knowledge engineering toward data engineering and knowledge. Visual data exploration focuses on perception of information and manipulation of data to enable even non-expert users to extract knowledge. A number of visualization techniques are used in a variety of systems that provide users with innovative ways to interact with data and reveal patterns...
Applying AI-Based IoT Systems to Simulation-Based Information Retrieval
Communication based on the internet of things (IoT) generates huge amounts of data from sensors over time, which opens a wide range of applications and areas for researchers. The application of analytics, machine learning, and deep learning techniques over such a large volume of data is a very challenging task. Therefore, it is essential to find patterns, retrieve novel insights, and predict future behavior using this large amount of sensory data. Artificial intelligence (AI) has an important role in facilitating analytics and learning in the IoT devices. Applying AI-Based IoT Systems to Simulation-Based Information Retrieval provides relevant frameworks and the latest empirical research fin...
Diverse Perspectives and State-of-the-Art Approaches to the Utilization of Data-Driven Clinical Decision Support Systems
The medical domain is home to many critical challenges that stand to be overcome with the use of data-driven clinical decision support systems (CDSS), and there is a growing set of examples of automated diagnosis, prognosis, drug design, and testing. However, the current state of AI in medicine has been summarized as “high on promise and relatively low on data and proof.” If such problems can be addressed, a data-driven approach will be very important to the future of CDSSs as it simplifies the knowledge acquisition and maintenance process, a process that is time-consuming and requires considerable human effort. Diverse Perspectives and State-of-the-Art Approaches to the Utilization of D...
Leveraging AI Technologies for Preventing and Detecting Sudden Cardiac Arrest and Death
Machine learning approaches have great potential in increasing the accuracy of cardiovascular risk prediction and avoiding unnecessary treatment. The application of machine learning techniques may improve heart failure outcomes and management, including cost savings by improving existing diagnostic and treatment support systems. Additionally, artificial intelligence technologies can assist physicians in making better clinical decisions, enabling early detection of subclinical organ dysfunction, and improving the quality and efficiency of healthcare delivery. Further study on these innovative technologies is required in order to appropriately utilize the technology in healthcare. Leveraging A...
Diagnostic Applications of Health Intelligence and Surveillance Systems
Health surveillance and intelligence play an important role in modern health systems as more data must be collected and analyzed. It is crucial that this data is interpreted and analyzed effectively and efficiently in order to assist with diagnoses and predictions. Diagnostic Applications of Health Intelligence and Surveillance Systems is an essential reference book that examines recent studies that are driving artificial intelligence in the health sector and helping practitioners to predict and diagnose diseases. Chapters within the book focus on health intelligence and how health surveillance data can be made useful and meaningful. Covering topics that include computational intelligence, data analytics, mobile health, and neural networks, this book is crucial for healthcare practitioners, IT specialists, academicians, researchers, and students.
Mathematical Models Using Artificial Intelligence for Surveillance Systems
This book gives comprehensive insights into the application of AI, machine learning, and deep learning in developing efficient and optimal surveillance systems for both indoor and outdoor environments, addressing the evolving security challenges in public and private spaces. Mathematical Models Using Artificial Intelligence for Surveillance Systems aims to collect and publish basic principles, algorithms, protocols, developing trends, and security challenges and their solutions for various indoor and outdoor surveillance applications using artificial intelligence (AI). The book addresses how AI technologies such as machine learning (ML), deep learning (DL), sensors, and other wireless device...
