This book features a collection of high-quality, peer-reviewed research papers presented at the 7th International Conference on Innovations in Computer Science & Engineering (ICICSE 2019), held at Guru Nanak Institutions, Hyderabad, India, on 16-17 August 2019. Written by researchers from academia and industry, the book discusses a wide variety of industrial, engineering, and scientific applications of the emerging techniques in the field of computer science.

This book emerged out of a project initiated and funded by the Defense Advanced Research Projects Agency (DARPA) that sought to build on efforts to transform agent-based models into platforms for predicting and evaluating policy responses to real world challenges around the world. It began with the observation that social science theories of human behavior are often used to estimate the consequences of alternative policy responses to important issues and challenges. However, alternative theories that remain subject to contradictory claims are ill suited to inform policy. The vision behind the DARPA project was to mine the social sciences literature for alternative theories of human behavior, and then formalize, instantiate, and integrate them within the context of an agent-based modeling system. The research team developed an experimental platform to evaluate the conditions under which alternative theories and groups of theories applied. The end result was a proof of concept developed from the ground up of social knowledge that could be used as an informative guide for policy analysis. This book describes in detail the process of designing and implementing a pilot system that helped DARPA assess the feasibility of a computational social science project on a large scale.

This book introduces Industrial AI in multiple dimensions. Industrial AI is a systematic discipline which focuses on developing, validating and deploying various machine learning algorithms for industrial applications with sustainable performance. Combined with the state-of-the-art sensing, communication and big data analytics platforms, a systematic Industrial AI methodology will allow integration of physical systems with computational models. The concept of Industrial AI is in infancy stage and may encompass the collective use of technologies such as Internet of Things, Cyber-Physical Systems and Big Data Analytics under the Industry 4.0 initiative where embedded computing devices, smart objects and the physical environment interact with each other to reach intended goals. A broad range of Industries including automotive, aerospace, healthcare, semiconductors, energy, transportation, mining, construction, and industrial automation could harness the power of Industrial AI to gain insights into the invisible relationship of the operation conditions and further use that insight to optimize their uptime, productivity and efficiency of their operations. In terms of predictive maintenance, Industrial AI can detect incipient changes in the system and predict the remains useful life and further to optimize maintenance tasks to avoid disruption to operations.

This book presents a collection of research findings and proposals on computer science and computer engineering, introducing readers to essential concepts, theories, and applications. It also shares perspectives on how cutting-edge and established methodologies and techniques can be used to obtain new and interesting results. Each chapter focuses on a specific aspect of computer science or computer engineering, such as: software engineering, complex systems, computational intelligence, embedded systems, and systems engineering. As such, the book will bring students and professionals alike up to date on key advances in these areas.

Computational Intelligence in Biomedical Imaging is a comprehensive overview of the state-of-the-art computational intelligence research and technologies in biomedical images with emphasis on biomedical decision making. Biomedical imaging offers useful information on patients' medical conditions and clues to causes of their symptoms and diseases. Biomedical images, however, provide a large number of images which physicians must interpret. Therefore, computer aids are demanded and become indispensable in physicians' decision making. This book discusses major technical advancements and research findings in the field of computational intelligence in biomedical imaging, for example, computational intelligence in computer-aided diagnosis for breast cancer, prostate cancer, and brain disease, in lung function analysis, and in radiation therapy. The book examines technologies and studies that have reached the practical level, and those technologies that are becoming available in clinical practices in hospitals rapidly such as computational intelligence in computer-aided diagnosis, biological image analysis, and computer-aided surgery and therapy.

This book examines key issues in ensuring the operational reliability of energy facilities. In this regard, it analyzes mathematical models of diagnostic signals that arise during the operation of power equipment; reviews the main findings of research into their characteristics; presents diagnostics methods for selected types of electric power and heat engineering equipment; and covers a range of diagnostic and monitoring systems and devices for power equipment. Given its scope, the book offers a valuable resource for researchers, engineers and specialists, as well as instructors and graduate students at institutions of higher learning.

This book presents adaptive logics as an intuitive and powerful framework for modeling defeasible reasoning. It examines various contexts in which defeasible reasoning is useful and offers a compact introduction into adaptive logics. The author first familiarizes readers with defeasible reasoning, the adaptive logics framework, combinations of adaptive logics, and a range of useful meta-theoretic properties. He then offers a systematic study of adaptive logics based on various applications. The book presents formal models for defeasible reasoning stemming from different contexts, such as default reasoning, argumentation, and normative reasoning. It highlights various meta-theoretic advantages of adaptive logics over other logics or logical frameworks that model defeasible reasoning. In this way the book substantiates the status of adaptive logics as a generic formal framework for defeasible reasoning.

The ever-growing popularity of Google over the recent decade has required a specific method of man-machine communication: human query should be short, whereas the machine answer may take a form of a wide range of documents. This type of communication has triggered a rapid development in the domain of Information Extraction, aimed at providing the asker with a more precise information.

The recent success of intelligent personal assistants supporting users in searching or even extracting information and answers from large collections of electronic documents signals the onset of a new era in man-machine communication - we shall soon explain to our small devices what we need to know and expect valuable answers quickly and automatically delivered.

The progress of man-machine communication is accompanied by growth in the significance of applied Computational Linguistics - we need machines to understand much more from the language we speak naturally than it is the case of up-to-date search systems. Moreover, we need machine support in crossing language barriers that is necessary more and more often when facing the global character of the Web.

This books reports on the latest developments in the field. It contains 15 chapters written by researchers who aim at making linguistic theories work - for the better understanding between the man and the machine.

This book makes available a self-contained collection of modern research addressing the general constrained optimization problems using evolutionary algorithms. Broadly the topics covered include constraint handling for single and multi-objective optimizations; penalty function based methodology; multi-objective based methodology; new constraint handling mechanism; hybrid methodology; scaling issues in constrained optimization; design of scalable test problems; parameter adaptation in constrained optimization; handling of integer, discrete and mix variables in addition to continuous variables; application of constraint handling techniques to real-world problems; and constrained optimization in dynamic environment. There is also a separate chapter on hybrid optimization, which is gaining lots of popularity nowadays due to its capability of bridging the gap between evolutionary and classical optimization. The material in the book is useful to researchers, novice, and experts alike. The book will also be useful for classroom teaching and future research.

This book offers a self-study program on how mathematics, computer science and science can be profitably and seamlessly intertwined. This book focuses on two variable ODE models, both linear and nonlinear, and highlights theoretical and computational tools using MATLAB to explain their solutions. It also shows how to solve cable models using separation of variables and the Fourier Series.

How Creativity Happens In The Brain is about the brain mechanisms of creativity, how a grapefruit-sized heap of meat crackling with electricity manages to be so outrageously creative. It has a sharp focus: to stick exclusively to sound, mechanistic explanations and convey what we can, and cannot, say about how brains give rise to creative ideas.

This book presents the proceedings of the Joint Conference of the Asian Council on Ergonomics and Design and Southeast Asian Network of Ergonomics Societies (ACED SEANES), held on December 2-4, 2020. By highlighting the latest theories and models, as well as cutting-edge technologies and applications, and by combining findings from a range of disciplines including engineering, design, robotics, healthcare, management, computer science, human biology and behavioral science, it provides researchers and practitioners alike with a comprehensive, timely guide on human factors and ergonomics. It also offers an excellent source of innovative ideas to stimulate future discussions and developments aimed at applying knowledge and techniques to optimize system performance, while at the same time promoting the health, safety and wellbeing of individuals. The proceedings include papers from researchers and practitioners, scientists and physicians, institutional leaders, managers and policy makers that contribute to constructing the Human Factors and Ergonomics approach across a variety of methodologies, domains and productive sectors.

The book consists of 35 extended chapters which have been selected and invited from the submissions to the 4th International Conference on Computational Collective Intelligence Technologies and Applications (ICCCI 2012) held on November 28-30, 2012 in Ho Chi Minh City, Vietnam. The book is organized into six parts, which are semantic web and ontologies, social networks and e-learning, agent and multiagent systems, data mining methods and applications, soft computing, and optimization and control, respectively. All chapters in the book discuss theoretical and practical issues connected with computational collective intelligence and related technologies. The editors hope that the book can be useful for graduate and Ph.D. students in Computer Science, in particular participants in courses on Soft Computing, Multiagent Systems, and Data Mining. This book can be also useful for researchers working on the concept of computational collective intelligence in artificial populations. It is the hope of the editors that readers of this volume can find many inspiring ideas and use them to create new cases of intelligent collectives. Many such challenges are suggested by particular approaches and models presented in individual chapters of this book. The editors hope that readers of this volume can find many inspiring ideas and influential practical examples and use them in their future work.

Membrane Computing was introduced as a computational paradigm in Natural Computing. The models introduced, called Membrane (or P) Systems, provide a coherent platform to describe and study living cells as computational systems. Membrane Systems have been investigated for their computational aspects and employed to model problems in other fields, like: Computer Science, Linguistics, Biology, Economy, Computer Graphics, Robotics, etc. Their inherent parallelism, heterogeneity and intrinsic versatilityallow them to model a broad range of processes and phenomena, being also an efficient means to solve and analyze problems in a novel way.

Membrane Computing has been used to model biological systems, becoming with time a thorough modeling paradigm comparable, in its modeling and predicting capabilities, to more established models in this area. This book is the result of the need to collect, in an organic way, different facets of this paradigm.

The chapters of this book, together with the web pages accompanying them, present different applications of Membrane Systems to Biology. Deterministic, non-deterministic and stochastic systems paired with different algorithms and methodologies show the full potential of this framework.

The book is addressed to researchers interested in applications of discrete biological models and the interplay between Membrane Systems and other approaches to analyze complex systems."

This book highlights the ability of neural networks (NNs) to be excellent pattern matchers and their importance in information retrieval (IR), which is based on index term matching. The book defines a new NN-based method for learning image similarity and describes how to use fuzzy Gaussian neural networks to predict personality.It introduces the fuzzy Clifford Gaussian network, and two concurrent neural models: (1) concurrent fuzzy nonlinear perceptron modules, and (2) concurrent fuzzy Gaussian neural network modules.Furthermore, it explains the design of a new model of fuzzy nonlinear perceptron based on alpha level sets and describes a recurrent fuzzy neural network model with a learning algorithm based on the improved particle swarm optimization method.

This book presents the state-of-the-art in various aspects of analysis and mining of online social networks. Within the broader context of online social networks, it focuses on important and upcoming topics of social network analysis and mining such as the latest in sentiment trends research and a variety of techniques for community detection and analysis. The book collects chapters that are expanded versions of the best papers presented at the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM'2015), which was held in Paris, France in August 2015. All papers have been peer reviewed and checked carefully for overlap with the literature. The book will appeal to students and researchers in social network analysis/mining and machine learning.

With the intriguing development of technologies in several industries, along with the advent of ubiquitous computational resources, there are now ample opportunities to develop innovative computational technologies in order to solve a wide range of issues concerning uncertainty, imprecision, and vagueness in various real-life problems. The challenge of blending modern computational techniques with traditional computing methods has inspired researchers and academics alike to focus on developing innovative computational techniques. In the near future, computational techniques may provide vital solutions by effectively using evolving technologies such as computer vision, natural language processing, deep learning, machine learning, scientific computing, and computational vision. A vast number of intelligent computational algorithms are emerging, along with increasing computational power, which has significantly expanded the potential for developing intelligent applications. These proceedings of the International Conference on Inventive Computation Technologies [ICICT 2019] cover innovative computing applications in the areas of data mining, big data processing, information management, and security.

Business and medical professionals rely on large data sets to identify trends or other knowledge that can be gleaned from the collection of it. New technologies concentrate on data's management, but do not facilitate users' extraction of meaningful outcomes. Pattern and Data Analysis in Healthcare Settings investigates the approaches to shift computing from analysis on-demand to knowledge on-demand. By providing innovative tactics to apply data and pattern analysis, these practices are optimized into pragmatic sources of knowledge for healthcare professionals. This publication is an exhaustive source for policy makers, developers, business professionals, healthcare providers, and graduate students concerned with data retrieval and analysis.

This book is focused on the Internet of Things (IoT) services and smart environments that can be of assistance to the elderly and individuals living with dementia or some sensory impairment. The book outlines the requirements of the systems that aim to furnish some digital sensory or cognitive assistance to the individuals and their caregivers. Internet of Things and Smart Environments: Assistive Technologies for Disability, Dementia, and Aging covers the important evolutions of the IoT, the sensors, actuators, wireless communication and pervasive computing systems, and other enabling technologies that power up this megatrend infrastructure. The use of the IoT-based systems in improving the conventional assistive technologies and provisions of ambient assisted living are also covered. The book takes an impartial, and yet holistic, view to providing research insights and inspirations for more development works in the areas related to assistive IoT. It will show the potentials of using normally available interactive devices, like smartphones or smart TVs, which can be supplemented with low-cost gadgets or apps to provide assistive capabilities. It aims to accentuate the need for taking a comprehensive and combinatory view of the comprising topics and approaches that are based on the visions and ideas from all stakeholders. The book will examine these points and considerations to conclude with recommendations for future development works and research directions. This book can be of value to a diverse array of audience. The researchers and developers in healthcare and medicine, aged care and disability services, as well as those working in the IoT-related fields, may find many parts of this book useful and stimulating. It can be of great value to postgraduate and research students working in these areas. It can also be adapted for use in upper-level classroom courses relevant to communication and smart technologies, IoT applications, and assistive technologies. Many parts of the book can be of interest to the elderly and individuals living with a disability, as well as their families and caregivers. From an industry perspective, it can be of interest to software, hardware, and particularly app developers working on the IoT applications, smart homes and environments, and assistive technologies for the elderly and people living with disability or dementia.

This book provides a systematic and comprehensive description of Non-Axiomatic Logic, which is the result of the author's research for about three decades.Non-Axiomatic Logic is designed to provide a uniform logical foundation for Artificial Intelligence, as well as an abstract description of the "laws of thought" followed by the human mind. Different from "mathematical" logic, where the focus is the regularity required when demonstrating mathematical conclusions, Non-Axiomatic Logic is an attempt to return to the original aim of logic, that is, to formulate the regularity in actual human thinking. To achieve this goal, the logic is designed under the assumption that the system has insufficient knowledge and resources with respect to the problems to be solved, so that the "logical conclusions" are only valid with respect to the available knowledge and resources. Reasoning processes according to this logic covers cognitive functions like learning, planning, decision making, problem solving, etc.This book is written for researchers and students in Artificial Intelligence and Cognitive Science, and can be used as a textbook for courses at graduate level, or upper-level undergraduate, on Non-Axiomatic Logic.

This book addresses the challenging topic of modeling adaptive networks, which often manifest inherently complex behavior. Networks by themselves can usually be modeled using a neat, declarative, and conceptually transparent Network-Oriented Modeling approach. In contrast, adaptive networks are networks that change their structure; for example, connections in Mental Networks usually change due to learning, while connections in Social Networks change due to various social dynamics. For adaptive networks, separate procedural specifications are often added for the adaptation process. Accordingly, modelers have to deal with a less transparent, hybrid specification, part of which is often more at a programming level than at a modeling level. This book presents an overall Network-Oriented Modeling approach that makes designing adaptive network models much easier, because the adaptation process, too, is modeled in a neat, declarative, and conceptually transparent Network-Oriented Modeling manner, like the network itself. Thanks to this approach, no procedural, algorithmic, or programming skills are needed to design complex adaptive network models. A dedicated software environment is available to run these adaptive network models from their high-level specifications. Moreover, because adaptive networks are described in a network format as well, the approach can simply be applied iteratively, so that higher-order adaptive networks in which network adaptation itself is adaptive (second-order adaptation), too can be modeled just as easily. For example, this can be applied to model metaplasticity in cognitive neuroscience, or second-order adaptation in biological and social contexts. The book illustrates the usefulness of this approach via numerous examples of complex (higher-order) adaptive network models for a wide variety of biological, mental, and social processes. The book is suitable for multidisciplinary Master's and Ph.D. students without assuming much prior knowledge, although also some elementary mathematical analysis is involved. Given the detailed information provided, it can be used as an introduction to Network-Oriented Modeling for adaptive networks. The material is ideally suited for teaching undergraduate and graduate students with multidisciplinary backgrounds or interests. Lecturers will find additional material such as slides, assignments, and software.

Mismatch or best match? This book demonstrates that best matching of individual entities to each other is essential to ensure smooth conduct and successful competitiveness in any distributed system, natural and artificial. Interactions must be optimized through best matching in planning and scheduling, enterprise network design, transportation and construction planning, recruitment, problem solving, selective assembly, team formation, sensor network design, and more. Fundamentals of best matching in distributed and collaborative systems are explained by providing: Methodical analysis of various multidimensional best matching processes Comprehensive taxonomy, comparing different best matching problems and processes Systematic identification of systems' hierarchy, nature of interactions, and distribution of decision-making and control functions Practical formulation of solutions based on a library of best matching algorithms and protocols, ready for direct applications and apps development. Designed for both academics and practitioners, oriented to systems engineers and applied operations researchers, diverse types of best matching processes are explained in production, manufacturing, business and service, based on a new reference model developed at Purdue University PRISM Center: "The PRISM Taxonomy of Best Matching". The book concludes with major challenges and guidelines for future basic and applied research in the area of best matching.

This book contains selected papers from the 9th International Conference on Information Science and Applications (ICISA 2018) and provides a snapshot of the latest issues encountered in technical convergence and convergences of security technology. It explores how information science is core to most current research, industrial and commercial activities and consists of contributions covering topics including Ubiquitous Computing, Networks and Information Systems, Multimedia and Visualization, Middleware and Operating Systems, Security and Privacy, Data Mining and Artificial Intelligence, Software Engineering, and Web Technology. The proceedings introduce the most recent information technology and ideas, applications and problems related to technology convergence, illustrated through case studies, and reviews converging existing security techniques. Through this volume, readers will gain an understanding of the current state-of-the-art information strategies and technologies of convergence security. The intended readership includes researchers in academia, industry and other research institutes focusing on information science and technology.

This volume comprises a selection of works presented at the Numerical and Evolutionary Optimization (NEO 2016) workshop held in September 2016 in Tlalnepantla, Mexico. The development of powerful search and optimization techniques is of great importance in today's world and requires researchers and practitioners to tackle a growing number of challenging real-world problems. In particular, there are two well-established and widely known fields that are commonly applied in this area: (i) traditional numerical optimization techniques and (ii) comparatively recent bio-inspired heuristics. Both paradigms have their unique strengths and weaknesses, allowing them to solve some challenging problems while still failing in others. The goal of the NEO workshop series is to bring together experts from these and related fields to discuss, compare and merge their complementary perspectives in order to develop fast and reliable hybrid methods that maximize the strengths and minimize the weaknesses of the underlying paradigms. In doing so, NEO promotes the development of new techniques that are applicable to a broader class of problems. Moreover, NEO fosters the understanding and adequate treatment of real-world problems particularly in emerging fields that affect all of us, such as healthcare, smart cities, big data, among many others. The extended papers presented in the book contribute to achieving this goal.

The author defines "Geometric Algebra Computing" as the geometrically intuitive development of algorithms using geometric algebra with a focus on their efficient implementation, and the goal of this book is to lay the foundations for the widespread use of geometric algebra as a powerful, intuitive mathematical language for engineering applications in academia and industry. The related technology is driven by the invention of conformal geometric algebra as a 5D extension of the 4D projective geometric algebra and by the recent progress in parallel processing, and with the specific conformal geometric algebra there is a growing community in recent years applying geometric algebra to applications in computer vision, computer graphics, and robotics. This book is organized into three parts: in Part I the author focuses on the mathematical foundations; in Part II he explains the interactive handling of geometric algebra; and in Part III he deals with computing technology for high-performance implementations based on geometric algebra as a domain-specific language in standard programming languages such as C++ and OpenCL. The book is written in a tutorial style and readers should gain experience with the associated freely available software packages and applications. The book is suitable for students, engineers, and researchers in computer science, computational engineering, and mathematics.