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Collective Intelligence for Smart Cities
Collective Intelligence for Smart Cities begins with an overview of the fundamental issues and concepts of smart cities. Surveying the current state-of-the-art research in the field, the book delves deeply into key smart city developments such as health and well-being, transportation, safety, energy, environment and sustainability. In addition, the book focuses on the role of IoT cloud computing and big data, specifically in smart city development. Users will find a unique, overarching perspective that ties together these concepts based on collective intelligence, a concept for quantifying mass activity familiar to many social science and life science researchers.Sections explore how group d...
Chaos-based Cryptography
Chaos-based cryptography, attracting many researchers in the past decade, is a research field across two fields, i.e., chaos (nonlinear dynamic system) and cryptography (computer and data security). It Chaos' properties, such as randomness and ergodicity, have been proved to be suitable for designing the means for data protection. The book gives a thorough description of chaos-based cryptography, which consists of chaos basic theory, chaos properties suitable for cryptography, chaos-based cryptographic techniques, and various secure applications based on chaos. Additionally, it covers both the latest research results and some open issues or hot topics. The book creates a collection of high-quality chapters contributed by leading experts in the related fields. It embraces a wide variety of aspects of the related subject areas and provide a scientifically and scholarly sound treatment of state-of-the-art techniques to students, researchers, academics, personnel of law enforcement and IT practitioners who are interested or involved in the study, research, use, design and development of techniques related to chaos-based cryptography.
Computer Science and Convergence
Computer Science and Convergence is proceedings of the 3rd FTRA International Conference on Computer Science and its Applications (CSA-11) and The 2011 FTRA World Convergence Conference (FTRA WCC 2011). The topics of CSA and WCC cover the current hot topics satisfying the world-wide ever-changing needs. CSA-11 will be the most comprehensive conference focused on the various aspects of advances in computer science and its applications and will provide an opportunity for academic and industry professionals to discuss the latest issues and progress in the area of CSA. In addition, the conference will publish high quality papers which are closely related to the various theories and practical app...
New Challenges for Intelligent Information and Database Systems
The book consists of 35 extended chapters which have been based on selected submissions to the poster session organized during the 3rd Asian Conference on Intelligent Information and Database Systems (20-22 April 2011 in Daegu, Korea). The book is organized into four parts, which are information retrieval and management, data mining and computational intelligence, service composition and user-centered approach, and intelligent management and e-business, respectively. All chapters in the book discuss theoretical and practical issues related to integration of artificial intelligence and database technologies in order to develop various intelligent information systems in many different domains....
Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2011
The purpose of the 12th Conference Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2011) held on July 6-8, 2011 in Sydney, Australia was to bring together scientists, engineers, computer users, and students to share their experiences and exchange new ideas and research results about all aspects (theory, applications and tools) of computer and information sciences, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them. The conference organizers selected 14 outstanding papers from SNPD 2011, all of which you will find in this volume of Springer’s Studies in Computational Intelligence.
Combinatorial Machine Learning
Decision trees and decision rule systems are widely used in different applications as algorithms for problem solving, as predictors, and as a way for knowledge representation. Reducts play key role in the problem of attribute (feature) selection. The aims of this book are (i) the consideration of the sets of decision trees, rules and reducts; (ii) study of relationships among these objects; (iii) design of algorithms for construction of trees, rules and reducts; and (iv) obtaining bounds on their complexity. Applications for supervised machine learning, discrete optimization, analysis of acyclic programs, fault diagnosis, and pattern recognition are considered also. This is a mixture of research monograph and lecture notes. It contains many unpublished results. However, proofs are carefully selected to be understandable for students. The results considered in this book can be useful for researchers in machine learning, data mining and knowledge discovery, especially for those who are working in rough set theory, test theory and logical analysis of data. The book can be used in the creation of courses for graduate students.
Computer and Information Science 2011
The series "Studies in Computational Intelligence" (SCI) publishes new developments and advances in the various areas of computational intelligence – quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life science, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, ...
Computational Optimization and Applications in Engineering and Industry
Contemporary design in engineering and industry relies heavily on computer simulation and efficient algorithms to reduce the cost and to maximize the performance and sustainability as well as profits and energy efficiency. Solving an optimization problem correctly and efficiently requires not only the right choice of optimization algorithms and simulation methods, but also the proper implementation and insight into the problem of interest. This book consists of ten self-contained, detailed case studies of real-world optimization problems, selected from a wide range of applications and contributed from worldwide experts who are working in these exciting areas. Optimization topics and applicat...
Intelligent Data Analysis for e-Learning
Intelligent Data Analysis for e-Learning: Enhancing Security and Trustworthiness in Online Learning Systems addresses information security within e-Learning based on trustworthiness assessment and prediction. Over the past decade, many learning management systems have appeared in the education market. Security in these systems is essential for protecting against unfair and dishonest conduct-most notably cheating-however, e-Learning services are often designed and implemented without considering security requirements. This book provides functional approaches of trustworthiness analysis, modeling, assessment, and prediction for stronger security and support in online learning, highlighting the...
Complex-Valued Neural Networks with Multi-Valued Neurons
Complex-Valued Neural Networks have higher functionality, learn faster and generalize better than their real-valued counterparts. This book is devoted to the Multi-Valued Neuron (MVN) and MVN-based neural networks. It contains a comprehensive observation of MVN theory, its learning, and applications. MVN is a complex-valued neuron whose inputs and output are located on the unit circle. Its activation function is a function only of argument (phase) of the weighted sum. MVN derivative-free learning is based on the error-correction rule. A single MVN can learn those input/output mappings that are non-linearly separable in the real domain. Such classical non-linearly separable problems as XOR an...
