Demonstrates how unsupervised learning approaches can be used for dimensionality reduction Neatly explains algorithms with focus on the fundamentals and underlying mathematical concepts Describes the comparative study of the algorithms and discusses when and where each algorithm is best suitable for use Provides use cases, illustrative examples and visualizations of each algorithm Helps visualize and create compact representations of high dimensional and intricate data for various real-world applications and data analysis

Demonstrates how unsupervised learning approaches can be used for dimensionality reduction Neatly explains algorithms with focus on the fundamentals and underlying mathematical concepts Describes the comparative study of the algorithms and discusses when and where each algorithm is best suitable for use Provides use cases, illustrative examples and visualizations of each algorithm Helps visualize and create compact representations of high dimensional and intricate data for various real-world applications and data analysis

This edited book provides information on emerging fields of next-generation healthcare informatics with a special emphasis on emerging developments and applications of artificial intelligence, deep learning techniques, computational intelligence methods, Internet of medical things (IoMT), optimization techniques, decision making, nanomedicine, and cloud computing. The book provides a conceptual framework and roadmap for decision-makers for this transformation. The chapters involved in this book cover challenges and opportunities for diabetic retinopathy detection based on deep learning applications, deep learning accelerators in IoT and IoMT, health data analysis, deep reinforcement-based conversational AI agent in healthcare systems, examination of health data performance, multisource data in intelligent medicine, application of genetic algorithms in health care, mental disorder, digital healthcare system with big data analytics, encryption methods in healthcare data security, computation and cognitive bias in healthcare intelligence and pharmacogenomics, guided imagery therapy, cancer detection and prediction techniques, medical image processing for coronavirus, and imbalance learning in health care.

The term IoT, which was first proposed by Kevin Ashton, a British technologist, in 1999 has the potential to impact everything from new product opportunities to shop floor optimization to factory worker efficiency gains, that will power top-line and bottom-line gains. As IoT technology is being put to diversified use, the current technology needs to be improved to enhance privacy and built secure devices by adopting a security-focused approach, reducing the amount of data collected, increasing transparency and providing consumers with a choice to opt out. Therefore, the current volume has been compiled, in an effort to draw the various issues in IoT, challenges faced and existing solutions so far. Key Points: * Provides an overview of basic concepts and technologies of IoT with communication technologies ranging from 4G to 5G and its architecture. * Discusses recent security and privacy studies and social behavior of human beings over IoT. * Covers the issues related to sensors, business model, principles, paradigms, green IoT and solutions to handle relevant challenges. * Presents the readers with practical ideas of using IoT, how it deals with human dynamics, the ecosystem, the social objects and their relation. * Deals with the challenges involved in surpassing diversified architecture, protocol, communications, integrity and security.

This edited book provides information on emerging fields of next-generation healthcare informatics with a special emphasis on emerging developments and applications of artificial intelligence, deep learning techniques, computational intelligence methods, Internet of medical things (IoMT), optimization techniques, decision making, nanomedicine, and cloud computing. The book provides a conceptual framework and roadmap for decision-makers for this transformation. The chapters involved in this book cover challenges and opportunities for diabetic retinopathy detection based on deep learning applications, deep learning accelerators in IoT and IoMT, health data analysis, deep reinforcement-based conversational AI agent in healthcare systems, examination of health data performance, multisource data in intelligent medicine, application of genetic algorithms in health care, mental disorder, digital healthcare system with big data analytics, encryption methods in healthcare data security, computation and cognitive bias in healthcare intelligence and pharmacogenomics, guided imagery therapy, cancer detection and prediction techniques, medical image processing for coronavirus, and imbalance learning in health care.

The term IoT, which was first proposed by Kevin Ashton, a British technologist, in 1999 has the potential to impact everything from new product opportunities to shop floor optimization to factory worker efficiency gains, that will power top-line and bottom-line gains. As IoT technology is being put to diversified use, the current technology needs to be improved to enhance privacy and built secure devices by adopting a security-focused approach, reducing the amount of data collected, increasing transparency and providing consumers with a choice to opt out. Therefore, the current volume has been compiled, in an effort to draw the various issues in IoT, challenges faced and existing solutions so far. Key Points: * Provides an overview of basic concepts and technologies of IoT with communication technologies ranging from 4G to 5G and its architecture. * Discusses recent security and privacy studies and social behavior of human beings over IoT. * Covers the issues related to sensors, business model, principles, paradigms, green IoT and solutions to handle relevant challenges. * Presents the readers with practical ideas of using IoT, how it deals with human dynamics, the ecosystem, the social objects and their relation. * Deals with the challenges involved in surpassing diversified architecture, protocol, communications, integrity and security.

This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep learning is a new area of machine learning research which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non-immediately related fields, for example between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g. classification) and/or unsupervised (e.g. pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition.

The digital age is ripe with emerging advances and applications in technological innovations. Mimicking the structure of complex systems in nature can provide new ideas on how to organize mechanical and personal systems. The Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms is an essential scholarly resource on current algorithms that have been inspired by the natural world. Featuring coverage on diverse topics such as cellular automata, simulated annealing, genetic programming, and differential evolution, this reference publication is ideal for scientists, biological engineers, academics, students, and researchers that are interested in discovering what models from nature influence the current technology-centric world.

Data has increased due to the growing use of web applications and communication devices. It is necessary to develop new techniques of managing data in order to ensure adequate usage. Modern Technologies for Big Data Classification and Clustering is an essential reference source for the latest scholarly research on handling large data sets with conventional data mining and provide information about the new technologies developed for the management of large data. Featuring coverage on a broad range of topics such as text and web data analytics, risk analysis, and opinion mining, this publication is ideally designed for professionals, researchers, and students seeking current research on various concepts of big data analytics. Topics Covered: The many academic areas covered in this publication include, but are not limited to: Data visualization Distributed Computing Systems Opinion Mining Privacy and security Risk analysis Social Network Analysis Text Data Analytics Web Data Analytics

As the amount of accumulated data across a variety of fields becomes harder to maintain, it is essential for a new generation of computational theories and tools to assist humans in extracting knowledge from this rapidly growing digital data. Global Trends in Intelligent Computing Research and Development brings together recent advances and in depth knowledge in the fields of knowledge representation and computational intelligence. Highlighting the theoretical advances and their applications to real life problems, this book is an essential tool for researchers, lecturers, professors, students, and developers who have seek insight into knowledge representation and real life applications.

Interest in e-government, both in industry and in academia, has grown rapidly over the past decade, and continues to grow. ""Global E-Government: Theory, Applications and Benchmarking"" is written by experts from academia and industry, examining the practices of e-government in developing and developed countries, presenting recent theoretical research in e-government, and providing a platform to benchmark the best practices in implementing e-government programs. ""Global E-Government: Theory, Applications and Benchmarking"" provides helpful examples from practitioners and managers involving real-life applications, while academics and researchers in the fields of information systems and e-government contribute theoretical insights.

Technological advancements have extracted a vast amount of useful knowledge and information for applications and services. These developments have evoked intelligent solutions that have been utilsed in efforts to secure this data and avoid potential complex problems. Advances in Secure Computing, Internet Services, and Applications presents current research on the applications of computational intelligence in order to focus on the challenge humans face when securing knowledge and data. This book is a vital reference source for researchers, lecturers, professors, students, and developers, who have interest in secure computing and recent advanced in real life applications.

Technology has become profoundly integrated into modern society; however, this increases the risk of vulnerabilities, such as hacking and other system errors, along with other online threats. Security, Privacy, and Anonymization in Social Networks: Emerging Research and Opportunities is a pivotal reference source for the most up-to-date research on edge clustering models and weighted social networks. Presenting widespread coverage across a range of applicable perspectives and topics, such as neighborhood attacks, fast k-degree anonymization (FKDA), and vertex-clustering algorithms, this book is ideally designed for academics, researchers, post-graduates, and practitioners seeking current research on undirected networks and greedy algorithms for social network anonymization.