The text covers recent advances in artificial intelligence, smart computing, and their applications in augmenting medical and health care systems. It will serve as an ideal reference text for graduate students and academic researchers in diverse engineering fields including electrical, electronics and communication, computer, and biomedical. The book- Presents architecture, characteristics, and applications of artificial intelligence and smart computing in health care systems Highlight privacy issues faced in health care and health informatics using artificial intelligence and smart computing technologies. Discusses nature-inspired computing algorithms for the brain-computer interface. Covers graph neural network application in the medical domain. Provides insights into the state-of-the-art Artificial Intelligence and Smart Computing enabling and emerging technologies. This book text discusses recent advances and applications of artificial intelligence and smart technologies in the field of healthcare. It highlights privacy issues faced in health care and health informatics using artificial intelligence and smart computing technologies. It covers nature-inspired computing algorithms such as genetic algorithms, particle swarm optimization algorithms, and common scrambling algorithms to study brain-computer interfaces. It will serve as an ideal reference text for graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and biomedical engineering.

This book is based on deep learning approaches used for the diagnosis of neurological disorders, including basics of deep learning algorithms using diagrams, data tables, and practical examples, for diagnosis of neurodegenerative and neurodevelopmental disorders. It includes application of feed-forward neural networks, deep generative models, convolutional neural networks, graph convolutional networks, and recurrent neural networks in the field of diagnosis of neurological disorders. Along with this, data pre-processing including scaling, correction, trimming, normalization is also included. Offers a detailed description of the deep learning approaches used for the diagnosis of neurological disorders Demonstrates concepts of deep learning algorithms using diagrams, data tables, and examples for the diagnosis of neurodegenerative disorders; neurodevelopmental, and psychiatric disorders. Helps build, train, and deploy different types of deep architectures for diagnosis Explores data pre-processing techniques involved in diagnosis Include real-time case studies and examples This book is aimed at graduate students and researchers in biomedical imaging and machine learning.

This book describes digital design techniques with exercises. The concepts and exercises discussed are useful to design digital logic from a set of given specifications. Looking at current trends of miniaturization, the contents provide practical information on the issues in digital design and various design optimization and performance improvement techniques at logic level. The book explains how to design using digital logic elements and how to improve design performance. The book also covers data and control path design strategies, architecture design strategies, multiple clock domain design and exercises , low-power design strategies and solutions at the architecture and logic-design level. The book covers 60 exercises with solutions and will be useful to engineers during the architecture and logic design phase. The contents of this book prove useful to hardware engineers, logic design engineers, students, professionals and hobbyists looking to learn and use the digital design techniques during various phases of design.

This volume deals with problems of modern effective algorithms for the numerical solution of the most frequently occurring elliptic partial differential equations. From the point of view of implementation, attention is paid to algorithms for both classical sequential and parallel computer systems. The first two chapters are devoted to fast algorithms for solving the Poisson and biharmonic equation. In the third chapter, parallel algorithms for model parallel computer systems of the SIMD and MIMD types are described. The implementation aspects of parallel algorithms for solving model elliptic boundary value problems are outlined for systems with matrix, pipeline and multiprocessor parallel computer architectures. A modern and popular multigrid computational principle which offers a good opportunity for a parallel realization is described in the next chapter. More parallel variants based in this idea are presented, whereby methods and assignments strategies for hypercube systems are treated in more detail. The last chapter presents VLSI designs for solving special tridiagonal linear systems of equations arising from finite-difference approximations of elliptic problems. For researchers interested in the development and application of fast algorithms for solving elliptic partial differential equations using advanced computer systems.

Highlights the contributions of different optimization techniques, decision analytics (predictive, prescriptive, and descriptive), multi-criteria decision making "Helps develop intelligent machines to provide solutions to real-world problems, which are not modelled or are too difficult to model mathematically in hospital management systems " Discusses machine learning-based analytics such as GAN networks, autoencoders, computational imaging, quantum computing will be rigorously applied to smart cloud computing Explores evolutionary algorithms that demonstrate their ability as robust approaches to cope with the fundamental steps of image processing, image analysis, and computer vision pipeline (e.g., restoration, segmentation, registration, classification, reconstruction, or tracking), Creates a bridge between Industrial Engineering concepts and Computational Intelligence for designing complex and convoluted hospital management problems

The theory of parsing is an important application area of the theory of formal languages and automata. The evolution of modem high-level programming languages created a need for a general and theoretically dean methodology for writing compilers for these languages. It was perceived that the compilation process had to be "syntax-directed," that is, the functioning of a programming language compiler had to be defined completely by the underlying formal syntax of the language. A program text to be compiled is "parsed" according to the syntax of the language, and the object code for the program is generated according to the semantics attached to the parsed syntactic entities. Context-free grammars were soon found to be the most convenient formalism for describing the syntax of programming languages, and accordingly methods for parsing context-free languages were devel oped. Practical considerations led to the definition of various kinds of restricted context-free grammars that are parsable by means of efficient deterministic linear-time algorithms."

Although rigidity has been studied since the time of Lagrange (1788) and Maxwell (1864), it is only in the last twenty-five years that it has begun to find applications in the basic sciences. The modern era starts with Laman (1970), who made the subject rigorous in two dimensions, followed by the development of computer algorithms that can test over a million sites in seconds and find the rigid regions, and the associated pivots, leading to many applications. This workshop was organized to bring together leading researchers studying the underlying theory, and to explore the various areas of science where applications of these ideas are being implemented.

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.

Revealing the flaws in human decision making, this book explores how AI can be used to optimise decisions for improved business outcomes and efficiency, as well as looking ahead into the significant contributions Decision Intelligence (DI) can make to society and the ethical challenges it may raise. Offering an impressive framework of Decision Intelligence (DI), from the theories and concepts used to design autonomous intelligent agents to the technologies that power DI systems and the ways in which companies use decision-making building blocks to build DI solutions that enable businesses to democratise AI, this book provides a systematic approach to AI intelligence and human involvement. Replete with case studies on DI application, as well as wider discussions on the social implications of the technology, this book appeals to both students of AI and data solutions and businesses considering DI adoption.

This book establishes game-theoretical frameworks based on the mechanism design theory and proposes strategy-proof algorithms, to optimally allocate and price the related IoT services, so that the social welfare of IoT ecosystem or the service provider's revenue can be maximized and the IoT service provision can be sustainable. This book is written by experts based on the recent research results on the interaction between the service providers and users in the IoT system. Since the IoT networks are essentially supported by data, communication, and computing resources, the book focuses on three representative IoT services, including the data analytics services, the cloud/fog computing services for blockchain networks, and the wireless powered data crowdsourcing services. Researchers, scientists, and engineers in the field of resource allocation and service management for future IoT ecosystem can benefit from the book. As such, this book provides valuable insights and practical methods, especially the novel deep learning-based mechanism that can be considered in the emerging IoT technology.

Digital Intermediation offers a new framework for understanding content creation and distribution across automated media platforms - a new mediatisation process. The book draws on empirical and theoretical research to carefully identify and describe a number of unseen digital infrastructures that contribute to a predictive media production process through technologies, institutions and automation. Field data is drawn from several international sites, including Los Angeles, San Francisco, Portland, London, Amsterdam, Munich, Berlin, Hamburg, Sydney and Cartagena. By highlighting an increasingly automated content production and distribution process, the book responds to a number of regulatory debates on the societal impact of social media platforms. It highlights emerging areas of key importance that shape the production and distribution of social media content, including micro-platformization and digital first personalities. The book explains how technologies, institutions and automation are used within agencies to increase exposure for the talent they manage, while providing inside access to the processes and requirements of producers who create content for platform algorithms. Finally, it outlines user agency as a strategy for those who seek diversity in the information they access on automated social media content distribution platforms. The findings in this book provide key recommendations for policymakers working within digital media platforms, and will be invaluable reading for students and academics interested in automated media environments.

Graduate students and researchers in applied mathematics, optimization, engineering, computer science, and management science will find this book a useful reference which provides an introduction to applications and fundamental theories in nonlinear combinatorial optimization. Nonlinear combinatorial optimization is a new research area within combinatorial optimization and includes numerous applications to technological developments, such as wireless communication, cloud computing, data science, and social networks. Theoretical developments including discrete Newton methods, primal-dual methods with convex relaxation, submodular optimization, discrete DC program, along with several applications are discussed and explored in this book through articles by leading experts.

Tremendous achievements in the area of semiconductor electronics turn - croelectronics into nanoelectronics. Actually, we observe a real technical boom connected with achievements in nanoelectronics. It results in devel- mentofverycomplexintegratedcircuits, particularlythe?eldprogrammable logic devices (FPLD). Up-to-day FPLD chips are so huge, that it is enough only one chip to implement a really complex digital system including a da- path and a control unit. Because of the extreme complexity of modern - crochips, it is very important to develop e?ective design methods oriented on particular properties of logic elements. The development of digital s- tems with use of FPLD microchips is not possible without use of di?erent hardware description languages(HDL), such as VHDL and Verilog. Di?erent computer-aided design tools (CAD) are wide used to develop digital system hardware. As majorityof researchespoint out, the design processis nowvery similar to the process of program development. It allows a researcher to pay more attention to some speci?c problems, where there are no standard f- mal methods of their solution. But application of all these achievements does not guaranteeper sedevelopmentof some competitiveelectronic product, - pecially in the acceptable time-to-market. This problem solution is possible only if a researcher possesses fundamental knowledge of a design process and knows exactly the mode of operation of industrial CAD tools in use. As it is known, any digital system can be represented as a composition of a da- path and a control uni

This book covers computational statistics-based approaches for Artificial Intelligence. The aim of this book is to provide comprehensive coverage of the fundamentals through the applications of the different kinds of mathematical modelling and statistical techniques and describing their applications in different Artificial Intelligence systems. The primary users of this book will include researchers, academicians, postgraduate students, and specialists in the areas of data science, mathematical modelling, and Artificial Intelligence. It will also serve as a valuable resource for many others in the fields of electrical, computer, and optical engineering. The key features of this book are: Presents development of several real-world problem applications and experimental research in the field of computational statistics and mathematical modelling for Artificial Intelligence Examines the evolution of fundamental research into industrialized research and the transformation of applied investigation into real-time applications Examines the applications involving analytical and statistical solutions, and provides foundational and advanced concepts for beginners and industry professionals Provides a dynamic perspective to the concept of computational statistics for analysis of data and applications in intelligent systems with an objective of ensuring sustainability issues for ease of different stakeholders in various fields Integrates recent methodologies and challenges by employing mathematical modeling and statistical techniques for Artificial Intelligence

This book exposes how inequalities based on class and social background arise from employment practices in the digital age. It considers instances where social media is used in recruitment to infiltrate private lives and hide job advertisements based on locality; where algorithms assess socio-economic data to filter candidates; where human interviewers are replaced by artificial intelligence with design that disadvantages users of classed language; and where already vulnerable groups become victims of digitalisation and remote work. The author examines whether these practices create risks of discrimination based on certain protected attributes, including ‘social origin’ in international labour law and laws in Australia and South Africa, ‘social condition’ and ‘family status’ in laws within Canada, and others. The book proposes essential law reform and improvements to workplace policy.

The technique of randomization has been employed to solve numerous prob lems of computing both sequentially and in parallel. Examples of randomized algorithms that are asymptotically better than their deterministic counterparts in solving various fundamental problems abound. Randomized algorithms have the advantages of simplicity and better performance both in theory and often in practice. This book is a collection of articles written by renowned experts in the area of randomized parallel computing. A brief introduction to randomized algorithms In the aflalysis of algorithms, at least three different measures of performance can be used: the best case, the worst case, and the average case. Often, the average case run time of an algorithm is much smaller than the worst case. 2 For instance, the worst case run time of Hoare's quicksort is O(n ), whereas its average case run time is only O( n log n). The average case analysis is conducted with an assumption on the input space. The assumption made to arrive at the O( n log n) average run time for quicksort is that each input permutation is equally likely. Clearly, any average case analysis is only as good as how valid the assumption made on the input space is. Randomized algorithms achieve superior performances without making any assumptions on the inputs by making coin flips within the algorithm. Any analysis done of randomized algorithms will be valid for all p0: .sible inputs."

This book aims to systematically review and design different intelligent control algorithms for the small-signal stability assessment of HPS. With the growing consciousness of global warming and the fast depletion of natural power generation resources, the existing power system is on the verge of transitions to a "hybrid power system (HPS)" integrated with distributed energy resources. The recent results and requirements for the developments of intelligent control algorithms have motivated the authors to introduce this book for extensively analyzing the performance of HPS against unknown/uncertain disturbances. This book introduces fractional-order resilient control methodologies for arresting small-signal instability of HPS. The prospective investigation has been performed on the MATLAB platform. This book is helpful for undergraduate, postgraduate students, and research scholars working in power system stability, control applications, and soft computing in particular.

Reversible grammar allows computational models to be built that are equally well suited for the analysis and generation of natural language utterances. This task can be viewed from very different perspectives by theoretical and computational linguists, and computer scientists. The papers in this volume present a broad range of approaches to reversible, bi-directional, and non-directional grammar systems that have emerged in recent years. This is also the first collection entirely devoted to the problems of reversibility in natural language processing. Most papers collected in this volume are derived from presentations at a workshop held at the University of California at Berkeley in the summer of 1991 organised under the auspices of the Association for Computational Linguistics. This book will be a valuable reference to researchers in linguistics and computer science with interests in computational linguistics, natural language processing, and machine translation, as well as in practical aspects of computability.

Techniques and principles of minimax theory play a key role in many areas of research, including game theory, optimization, and computational complexity. In general, a minimax problem can be formulated as min max f(x, y) (1) ",EX !lEY where f(x, y) is a function defined on the product of X and Y spaces. There are two basic issues regarding minimax problems: The first issue concerns the establishment of sufficient and necessary conditions for equality minmaxf(x,y) = maxminf(x,y). (2) "'EX !lEY !lEY "'EX The classical minimax theorem of von Neumann is a result of this type. Duality theory in linear and convex quadratic programming interprets minimax theory in a different way. The second issue concerns the establishment of sufficient and necessary conditions for values of the variables x and y that achieve the global minimax function value f(x*, y*) = minmaxf(x, y). (3) "'EX !lEY There are two developments in minimax theory that we would like to mention.

Features In-depth coverage of the role of AI in smart healthcare. Research guideline for AI and data science researchers/practitioners interested in the healthcare sector. Comprehensive coverage on security and privacy issues for AI in smart healthcare.

This book gathers selected high-quality research papers presented at the International Conference on Paradigms of Communication, Computing and Data Sciences (PCCDS 2021), held at the National Institute of Technology, Kurukshetra, India, during May 07-09, 2021. It discusses high-quality and cutting-edge research in the areas of advanced computing, communications, and data science techniques. The book is a collection of latest research articles in computation algorithm, communication, and data sciences, intertwined with each other for efficiency.

Focuses on the Internet of Healthcare Things and innovative solutions developed for use in the application of healthcare services Discusses artificial intelligence applications, experiments, core concepts, and cutting-edge themes Demonstrates new approaches to analysing medical data and identifying ailments using AI to improve overall quality of life Introduces fundamental concepts for designing the Internet of Healthcare Things solutions Includes pertinent case studies and applications

This interdisciplinary book covers a wide range of subjects, from pure mathematics (knots, braids, homotopy theory, number theory) to more applied mathematics (cryptography, algebraic specification of algorithms, dynamical systems) and concrete applications (modeling of polymers and ionic liquids, video, music and medical imaging). The main mathematical focus throughout the book is on algebraic modeling with particular emphasis on braid groups. The research methods include algebraic modeling using topological structures, such as knots, 3-manifolds, classical homotopy groups, and braid groups. The applications address the simulation of polymer chains and ionic liquids, as well as the modeling of natural phenomena via topological surgery. The treatment of computational structures, including finite fields and cryptography, focuses on the development of novel techniques. These techniques can be applied to the design of algebraic specifications for systems modeling and verification. This book is the outcome of a workshop in connection with the research project Thales on Algebraic Modeling of Topological and Computational Structures and Applications, held at the National Technical University of Athens, Greece in July 2015. The reader will benefit from the innovative approaches to tackling difficult questions in topology, applications and interrelated research areas, which largely employ algebraic tools.

A more accessible approach than most competitor texts, which move into advanced, research-level topics too quickly for today's students. Part I is comprehensive in providing all necessary mathematical underpinning, particularly for those who need more opportunity to develop their mathematical competence. More confident students may move directly to Part II and dip back into Part I as a reference. Ideal for use as an introductory text for courses in quantum computing. Fully worked examples illustrate the application of mathematical techniques. Exercises throughout develop concepts and enhance understanding. End-of-chapter exercises offer more practice in developing a secure foundation.

To lead a data science team, you need to expertly articulate technology roadmaps, support a data-driven culture, and plan a data strategy that drives a competitive business plan. In this practical guide, you'll learn leadership techniques the authors have developed building multiple high-performance data teams. In How to Lead in Data Science you'll master techniques for leading data science at every seniority level, from heading up a single project to overseeing a whole company's data strategy. You'll find advice on plotting your long-term career advancement, as well as quick wins you can put into practice right away. Throughout, carefully crafted assessments and interview scenarios encourage introspection, reveal personal blind spots, and show development areas to help advance your career. Leading a data science team takes more than the typical set of business management skills. You need specific know-how to articulate technology roadmaps, support a data-driven culture, and plan a data strategy that drives a competitive business plan. Whether you're looking to manage your team better or work towards a seat at your company's top leadership table, this book will show you how.