Albert Y. Zomaya

A major challenge for machine learning solutions is that their efficiency in real-world applications is constrained by the current lack of ability of the machine to explain its decisions and activities to human users. Biases based on race, gender, age or location have been a long-standing risk in training AI models. Furthermore, AI model performance can degrade because production data differs from training data. Explainable AI (XAI) is the practice of interpreting how and why a machine learning algorithm estimates its predictions. It can also help machine learning practitioners and data scientists understand and interpret a model's behaviour. XAI supports end-users to trust a model's auditability and the productive use of AI. It also mitigates AI compliance, legal, security and reputational risks. Among these applications, the security of IoT infrastructures is vitally essential for improving trust in broad-scale applications such as smart healthcare, smart manufacturing, smart agriculture and smart transportation. This comprehensive co-authored book offers a complete study of explainable artificial intelligence (XAI) for securing the Internet of things (IoT). The authors present innovative XAI solutions for securing IoT infrastructures against security attacks and privacy threats and cover advanced research topics including responsible security intelligence. Providing a systematic and thorough overview of the field, this book will be a valuable resource for ICT researchers, AI and data science engineers, security analysts, undergraduate and graduate students and professionals who wish to gain a fundamental understanding of intelligent security solutions.

A comprehensive overview of high-performance pattern recognition techniques and approaches to Computational Molecular Biology This book surveys the developments of techniques and approaches on pattern recognition related to Computational Molecular Biology. Providing a broad coverage of the field, the authors cover fundamental and technical information on these techniques and approaches, as well as discussing their related problems. The text consists of twenty nine chapters, organized into seven parts: Pattern Recognition in Sequences, Pattern Recognition in Secondary Structures, Pattern Recognition in Tertiary Structures, Pattern Recognition in Quaternary Structures, Pattern Recognition in Microarrays, Pattern Recognition in Phylogenetic Trees, and Pattern Recognition in Biological Networks. Surveys the development of techniques and approaches on pattern recognition in biomolecular dataDiscusses pattern recognition in primary, secondary, tertiary and quaternary structures, as well as microarrays, phylogenetic trees and biological networksIncludes case studies and examples to further illustrate the concepts discussed in the book Pattern Recognition in Computational Molecular Biology: Techniques and Approaches is a reference for practitioners and professional researches in Computer Science, Life Science, and Mathematics. This book also serves as a supplementary reading for graduate students and young researches interested in Computational Molecular Biology.

Reviews the latest advances in the all-important field of scalable computing In telecommunications and software engineering, scalability is the ability of a system, network, or process to either handle growing amounts of work in a graceful manner or be enlarged to accommodate that growth. It is a desirable property for many scientific, industrial, and business applications and an important feature for hardware. This immersive book summarizes the latest research achievements in the field of scalable computing and covers new topics that have emerged recently on computing and communications, such as unconventional computing, green and sustainable computing, cloud and volunteer computing, and more. Filled with contributions from world-renowned engineers, researchers, and IT professionals in diverse areas, Scalable Computing and Communications covers: Circuit and component designOperating systemsGreen computingNetwork-on-chip paradigmsComputational gridsHigh-performance computingSoftwareNetworking in scalable computing and mobile computingNext-generation networkingCloud computingPeer-to-peer systems Scalable Computing and Communications is well organized with basic concepts, software infrastructure and middleware, and applications and systems. Filled with numerous case studies, figures, and tables, it is a valuable book that offers great insight into future trends and emerging topics for professionals and students in the field.

Scheduling in Parallel Computing Systems: Fuzzy and Annealing Techniques advocates the viability of using fuzzy and annealing methods in solving scheduling problems for parallel computing systems. The book proposes new techniques for both static and dynamic scheduling, using emerging paradigms that are inspired by natural phenomena such as fuzzy logic, mean-field annealing, and simulated annealing. Systems that are designed using such techniques are often referred to in the literature as `intelligent' because of their capability to adapt to sudden changes in their environments. Moreover, most of these changes cannot be anticipated in advance or included in the original design of the system. Scheduling in Parallel Computing Systems: Fuzzy and Annealing Techniques provides results that prove such approaches can become viable alternatives to orthodox solutions to the scheduling problem, which are mostly based on heuristics. Although heuristics are robust and reliable when solving certain instances of the scheduling problem, they do not perform well when one needs to obtain solutions to general forms of the scheduling problem. On the other hand, techniques inspired by natural phenomena have been successfully applied for solving a wide range of combinatorial optimization problems (e.g. traveling salesman, graph partitioning). The success of these methods motivated their use in this book to solve scheduling problems that are known to be formidable combinatorial problems. Scheduling in Parallel Computing Systems: Fuzzy and Annealing Techniques is an excellent reference and may be used for advanced courses on the topic.

A visual, interdisciplinary approach to solving problems in numerical methods Computing for Numerical Methods Using Visual C++ fills the need for a complete, authoritative book on the visual solutions to problems in numerical methods using C++. In an age of boundless research, there is a need for a programming language that can successfully bridge the communication gap between a problem and its computing elements through the use of visual-ization for engineers and members of varying disciplines, such as biologists, medical doctors, mathematicians, economists, and politicians. This book takes an interdisciplinary approach to the subject and demonstrates how solving problems in numerical methods using C++ is dominant and practical for implementation due to its flexible language format, object-oriented methodology, and support for high numerical precisions. In an accessible, easy-to-follow style, the authors cover: Numerical modeling using C++ Fundamental mathematical tools MFC interfaces Curve visualization Systems of linear equations Nonlinear equations Interpolation and approximation Differentiation and integration Eigenvalues and Eigenvectors Ordinary differential equations Partial differential equations This reader-friendly book includes a companion Web site, giving readers free access to all of the codes discussed in the book as well as an equation parser called "MyParser" that can be used to develop various numerical applications on Windows. Computing for Numerical Methods Using Visual C++ serves as an excellent reference for students in upper undergraduate- and graduate-level courses in engineering, science, and mathematics. It is also an ideal resource for practitioners using Microsoft Visual C++.

A team of recognized experts leads the way to dependable computing systems With computers and networks pervading every aspect of daily life, there is an ever-growing demand for dependability. In this unique resource, researchers and organizations will find the tools needed to identify and engage state-of-the-art approaches used for the specification, design, and assessment of dependable computer systems. The first part of the book addresses models and paradigms of dependable computing, and the second part deals with enabling technologies and applications. Tough issues in creating dependable computing systems are also tackled, including: * Verification techniques * Model-based evaluation * Adjudication and data fusion * Robust communications primitives * Fault tolerance * Middleware * Grid security * Dependability in IBM mainframes * Embedded software * Real-time systems Each chapter of this contributed work has been authored by a recognized expert. This is an excellent textbook for graduate and advanced undergraduate students in electrical engineering, computer engineering, and computer science, as well as a must-have reference that will help engineers, programmers, and technologists develop systems that are secure and reliable.

Master the numerical simulation process required to design, test and support mobile and parallel computing systems. An accompanying ftp site contains all the Visual C++ based programs discussed in the text to help readers create their own programs. With its focus on problems and solutions, this is an excellent text for upper-level undergraduate and graduate students, and a must-have reference for researchers and professionals in the field of simulations. More information about Visual C++ based programs can be found at: ftp: //ftp.wiley.com/public/sci_tech_med/numerical_simulations/

This book introduces a practical approach to the modelling and computation of real-world systems. Multibody dynamics, planar and spatial modelling, and numerical methods are all pursued to obtain information about the behaviour of various dynamical systems. Each study presents the method of modelling and the ensuing differential equations governing the system behaviour. Integration of the equations yields results which are carefully discussed and which indicate how useful information may be obtained from the study. The studies include planar mechanisms, heavy equipment, automobile crash simulation and a spatial planetary system example. Research students, scientists and engineers will appreciate the practical approach taken in this book.

Scheduling in Parallel Computing Systems: Fuzzy and Annealing Techniques advocates the viability of using fuzzy and annealing methods in solving scheduling problems for parallel computing systems. The book proposes new techniques for both static and dynamic scheduling, using emerging paradigms that are inspired by natural phenomena such as fuzzy logic, mean-field annealing, and simulated annealing. Systems that are designed using such techniques are often referred to in the literature as `intelligent' because of their capability to adapt to sudden changes in their environments. Moreover, most of these changes cannot be anticipated in advance or included in the original design of the system. Scheduling in Parallel Computing Systems: Fuzzy and Annealing Techniques provides results that prove such approaches can become viable alternatives to orthodox solutions to the scheduling problem, which are mostly based on heuristics. Although heuristics are robust and reliable when solving certain instances of the scheduling problem, they do not perform well when one needs to obtain solutions to general forms of the scheduling problem. On the other hand, techniques inspired by natural phenomena have been successfully applied for solving a wide range of combinatorial optimization problems (e.g. traveling salesman, graph partitioning). The success of these methods motivated their use in this book to solve scheduling problems that are known to be formidable combinatorial problems. Scheduling in Parallel Computing Systems: Fuzzy and Annealing Techniques is an excellent reference and may be used for advanced courses on the topic.

Obstacle Avoidance in Multi-robot Systems: Experiments in Parallel Genetic Algorithms offers a novel framework for solving the path planning problem for robot manipulators. Simple and efficient solutions are proposed for the path planning problem based on genetic algorithms. One of the attractive features of genetic algorithms is their ability to solve formidable problems in a robust and straightforward manner. Moreover, genetic algorithms are inherently parallel in nature, which makes them ideal candidates for parallel computing implementations.By combining the robustness of genetic algorithms with the power of parallel computers, this book provides an effective and practical approach to solving path planning problems. The book gives details of implementations that allow a better understanding of the complexities involved in the development of parallel path planning algorithms. The material presented is interdisciplinary in nature — it combines topics from robotics, genetic algorithms, and parallel processing. The book can be used by practitioners and researchers in computer science and engineering.

This book aims to describe how parallel computer architectures can be used to enhance the performance of robots, and their great impact on future generations of robots. It provides an in-depth, consistent and rigorous treatment of the topic. A clear definition of tools with results is given which can be applied to parallel processing for robot kinematics and dynamics. Another advantageous feature is that the algorithms presented have been implemented using a parallel processing system, unlike many publications in the field which have presented results in only theoretical terms. This book also includes “benchmark” results that can be used for the development of future work, or can serve as a basis for comparison with other work. In addition, it surveys useful material to aid readers in pursuing further research.