Author: Marina L. Gavrilova
Publisher: Springer Science & Business Media
ISBN: 354087562X
Category : Computers
Languages : en
Pages : 256
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Book Description
The LNCS journal Transactions on Computational Science reflects recent developments in the field of Computational Science, conceiving the field not as a mere ancillary science but rather as an innovative approach supporting many other scientific disciplines. The journal focuses on original high-quality research in the realm of computational science in parallel and distributed environments, encompassing the facilitating theoretical foundations and the applications of large-scale computations and massive data processing. It addresses researchers and practitioners in areas ranging from aerospace to biochemistry, from electronics to geosciences, from mathematics to software architecture, presenting verifiable computational methods, findings and solutions and enabling industrial users to apply techniques of leading-edge, large-scale, high performance computational methods. Transactions on Computational Science II is devoted to the subject of denotational mathematics for computational intelligence. Denotational mathematics, as a counterpart of conventional analytic mathematics, is a category of expressive mathematical structures that deals with high-level mathematical entities beyond numbers and sets, such as abstract objects, complex relations, behavioral information, concepts, knowledge, processes, granules, and systems. This volume includes 12 papers covering the following four important areas: foundations and applications of denotational mathematics; rough and fuzzy set theories; granular computing; and knowledge and information modeling.
Author: V. Sornlertlamvanich
Publisher: IOS Press
ISBN: 1614998345
Category : Computers
Languages : en
Pages : 456
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Book Description
Information modelling and knowledge bases have become ever more essential in recent years because of the need to handle and process the vast amounts of data which now form part of everyday life. The machine to machine communication of the Internet of Things (IoT), in particular, can generate unexpectedly large amounts of raw data. This book presents the proceedings of the 27th International Conference on Information Modelling and Knowledge Bases (EJC2017), held in Krabi, Thailand, in June 2017. The EJC conferences originally began in 1982 as a co-operative initiative between Japan and Finland, but have since become a world-wide research forum bringing together researchers and practitioners in information modelling and knowledge bases for the exchange of scientific results and achievements. Of the 42 papers submitted, 29 were selected for publication here, and these cover a wide range of information-modelling topics, including the theory of concepts, semantic computing, data mining, context-based information retrieval, ontological technology, image databases, temporal and spatial databases, document data management, software engineering, cross-cultural computing, environmental analysis, social networks, and WWW information. The book will be of interest to all those whose work involves dealing with large amounts of data.
Author: Yingmin Jia
Publisher: Springer Nature
ISBN: 9819786509
Category :
Languages : en
Pages : 687
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Book Description
Author: Rik Das
Publisher: CRC Press
ISBN: 1000280470
Category : Computers
Languages : en
Pages : 197
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Book Description
Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techniques is a comprehensive guide to research with invaluable image data. Social Science Research Network has revealed that 65% of people are visual learners. Research data provided by Hyerle (2000) has clearly shown 90% of information in the human brain is visual. Thus, it is no wonder that visual information processing in the brain is 60,000 times faster than text-based information (3M Corporation, 2001). Recently, we have witnessed a significant surge in conversing with images due to the popularity of social networking platforms. The other reason for embracing usage of image data is the mass availability of high-resolution cellphone cameras. Wide usage of image data in diversified application areas including medical science, media, sports, remote sensing, and so on, has spurred the need for further research in optimizing archival, maintenance, and retrieval of appropriate image content to leverage data-driven decision-making. This book demonstrates several techniques of image processing to represent image data in a desired format for information identification. It discusses the application of machine learning and deep learning for identifying and categorizing appropriate image data helpful in designing automated decision support systems. The book offers comprehensive coverage of the most essential topics, including: Image feature extraction with novel handcrafted techniques (traditional feature extraction) Image feature extraction with automated techniques (representation learning with CNNs) Significance of fusion-based approaches in enhancing classification accuracy MATLAB® codes for implementing the techniques Use of the Open Access data mining tool WEKA for multiple tasks The book is intended for budding researchers, technocrats, engineering students, and machine learning/deep learning enthusiasts who are willing to start their computer vision journey with content-based image recognition. The readers will get a clear picture of the essentials for transforming the image data into valuable means for insight generation. Readers will learn coding techniques necessary to propose novel mechanisms and disruptive approaches. The WEKA guide provided is beneficial for those uncomfortable coding for machine learning algorithms. The WEKA tool assists the learner in implementing machine learning algorithms with the click of a button. Thus, this book will be a stepping-stone for your machine learning journey. Please visit the author's website for any further guidance at https://www.rikdas.com/
Author: Das, Rik
Publisher: IGI Global
ISBN: 1522557768
Category : Computers
Languages : en
Pages : 303
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Book Description
Image data has portrayed immense potential as a foundation of information for numerous applications. Recent trends in multimedia computing have witnessed a rapid growth in digital image collections, resulting in a need for increased image data management. Feature Dimension Reduction for Content-Based Image Identification is a pivotal reference source that explores the contemporary trends and techniques of content-based image recognition. Including research covering topics such as feature extraction, fusion techniques, and image segmentation, this book explores different theories to facilitate timely identification of image data and managing, archiving, maintaining, and extracting information. This book is ideally designed for engineers, IT specialists, researchers, academicians, and graduate-level students seeking interdisciplinary research on image processing and analysis.
Author: Georg Hager
Publisher: CRC Press
ISBN: 1439811938
Category : Computers
Languages : en
Pages : 350
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Book Description
Written by high performance computing (HPC) experts, Introduction to High Performance Computing for Scientists and Engineers provides a solid introduction to current mainstream computer architecture, dominant parallel programming models, and useful optimization strategies for scientific HPC. From working in a scientific computing center, the author
Author: George Angelos Papadopoulos
Publisher: Springer Science & Business Media
ISBN: 038784810X
Category : Computers
Languages : en
Pages : 964
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Book Description
This volume constitutes the published proceedings of the 17th International Conference on Information Systems Development. They present the latest and greatest concepts, approaches, and techniques of systems development - a notoriously transitional field.
Author: Carl A. Gunter
Publisher: MIT Press
ISBN: 9780262570954
Category : Computers
Languages : en
Pages : 450
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Book Description
Semantics of Programming Languages exposes the basic motivations and philosophy underlying the applications of semantic techniques in computer science. It introduces the mathematical theory of programming languages with an emphasis on higher-order functions and type systems. Designed as a text for upper-level and graduate-level students, the mathematically sophisticated approach will also prove useful to professionals who want an easily referenced description of fundamental results and calculi. Basic connections between computational behavior, denotational semantics, and the equational logic of functional programs are thoroughly and rigorously developed. Topics covered include models of types, operational semantics, category theory, domain theory, fixed point (denotational). semantics, full abstraction and other semantic correspondence criteria, types and evaluation, type checking and inference, parametric polymorphism, and subtyping. All topics are treated clearly and in depth, with complete proofs for the major results and numerous exercises.
Author: Max Bramer
Publisher: Springer Science & Business Media
ISBN: 1447147391
Category : Computers
Languages : en
Pages : 444
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Book Description
The papers in this volume are the refereed papers presented at AI-2012, the Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, held in Cambridge in December 2012 in both the technical and the application streams. They present new and innovative developments and applications, divided into technical stream sections on Data Mining, Data Mining and Machine Learning, Planning and Optimisation, and Knowledge Management and Prediction, followed by application stream sections on Language and Classification, Recommendation, Practical Applications and Systems, and Data Mining and Machine Learning. The volume also includes the text of short papers presented as posters at the conference. This is the twenty-ninth volume in the Research and Development in Intelligent Systems series, which also incorporates the twentieth volume in the Applications and Innovations in Intelligent Systems series. These series are essential reading for those who wish to keep up to date with developments in this important field.
Author: Sheela Ramanna
Publisher: Springer Science & Business Media
ISBN: 3642286992
Category : Technology & Engineering
Languages : en
Pages : 507
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Book Description
This book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The multidisciplinary nature of machine learning makes it a very fascinating and popular area for research. The book is aiming at students, practitioners and researchers and captures the diversity and richness of the field of machine learning and intelligent systems. Several chapters are devoted to computational learning models such as granular computing, rough sets and fuzzy sets An account of applications of well-known learning methods in biometrics, computational stylistics, multi-agent systems, spam classification including an extremely well-written survey on Bayesian networks shed light on the strengths and weaknesses of the methods. Practical studies yielding insight into challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non-stationary data streams are a key part of this book.
