Information Granularity Big Data And Computational Intelligence PDF Download

Information Granularity  Big Data  and Computational Intelligence PDF
Author: Witold Pedrycz
Publisher: Springer
ISBN: 331908254X
Size: 25.42 MB
Format: PDF, Mobi
Category : Computers
Languages : en
Pages : 444
View: 4853

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Information Granularity Big Data And Computational Intelligence Book Description:

The recent pursuits emerging in the realm of big data processing, interpretation, collection and organization have emerged in numerous sectors including business, industry and government organizations. Data sets such as customer transactions for a mega-retailer, weather monitoring, intelligence gathering, quickly outpace the capacities of traditional techniques and tools of data analysis. The 3V (volume, variability and velocity) challenges led to the emergence of new techniques and tools in data visualization, acquisition, and serialization. Soft Computing being regarded as a plethora of technologies of fuzzy sets (or Granular Computing), neurocomputing and evolutionary optimization brings forward a number of unique features that might be instrumental to the development of concepts and algorithms to deal with big data. This carefully edited volume provides the reader with an updated, in-depth material on the emerging principles, conceptual underpinnings, algorithms and practice of Computational Intelligence in the realization of concepts and implementation of big data architectures, analysis, and interpretation as well as data analytics. The book is aimed at a broad audience of researchers and practitioners including those active in various disciplines in which big data, their analysis and optimization are of genuine relevance. One focal point is the systematic exposure of the concepts, design methodology, and detailed algorithms. In general, the volume adheres to the top-down strategy starting with the concepts and motivation and then proceeding with the detailed design that materializes in specific algorithms and representative applications. The material is self-contained and provides the reader with all necessary prerequisites and augments some parts with a step-by-step explanation of more advanced concepts supported by a significant amount of illustrative numeric material and some application scenarios to motivate the reader and make some abstract concepts more tangible.

Big Data And Computational Intelligence In Networking PDF Download

Big Data and Computational Intelligence in Networking PDF
Author: Yulei Wu
Publisher: CRC Press
ISBN: 1498784879
Size: 54.32 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : en
Pages : 530
View: 1299

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Big Data And Computational Intelligence In Networking Book Description:

This book presents state-of-the-art solutions to the theoretical and practical challenges stemming from the leverage of big data and its computational intelligence in supporting smart network operation, management, and optimization. In particular, the technical focus covers the comprehensive understanding of network big data, efficient collection and management of network big data, distributed and scalable online analytics for network big data, and emerging applications of network big data for computational intelligence.

Data Science And Big Data An Environment Of Computational Intelligence PDF Download

Data Science and Big Data  An Environment of Computational Intelligence PDF
Author: Witold Pedrycz
Publisher: Springer
ISBN: 3319534742
Size: 78.67 MB
Format: PDF
Category : Computers
Languages : en
Pages : 303
View: 7411

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Data Science And Big Data An Environment Of Computational Intelligence Book Description:

This book presents a comprehensive and up-to-date treatise of a range of methodological and algorithmic issues. It also discusses implementations and case studies, identifies the best design practices, and assesses data analytics business models and practices in industry, health care, administration and business.Data science and big data go hand in hand and constitute a rapidly growing area of research and have attracted the attention of industry and business alike. The area itself has opened up promising new directions of fundamental and applied research and has led to interesting applications, especially those addressing the immediate need to deal with large repositories of data and building tangible, user-centric models of relationships in data. Data is the lifeblood of today’s knowledge-driven economy.Numerous data science models are oriented towards end users and along with the regular requirements for accuracy (which are present in any modeling), come the requirements for ability to process huge and varying data sets as well as robustness, interpretability, and simplicity (transparency). Computational intelligence with its underlying methodologies and tools helps address data analytics needs.The book is of interest to those researchers and practitioners involved in data science, Internet engineering, computational intelligence, management, operations research, and knowledge-based systems.

Advances In Swarm And Computational Intelligence PDF Download

Advances in Swarm and Computational Intelligence PDF
Author: Ying Tan
Publisher: Springer
ISBN: 3319204696
Size: 59.90 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 496
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Advances In Swarm And Computational Intelligence Book Description:

This book and its companion volumes, LNCS volumes 9140, 9141 and 9142, constitute the proceedings of the 6th International Conference on Swarm Intelligence, ICSI 2015 held in conjunction with the Second BRICS Congress on Computational Intelligence, CCI 2015, held in Beijing, China in June 2015. The 161 revised full papers presented were carefully reviewed and selected from 294 submissions. The papers are organized in 28 cohesive sections covering all major topics of swarm intelligence and computational intelligence research and development, such as novel swarm-based optimization algorithms and applications; particle swarm opt8imization; ant colony optimization; artificial bee colony algorithms; evolutionary and genetic algorithms; differential evolution; brain storm optimization algorithm; biogeography based optimization; cuckoo search; hybrid methods; multi-objective optimization; multi-agent systems and swarm robotics; Neural networks and fuzzy methods; data mining approaches; information security; automation control; combinatorial optimization algorithms; scheduling and path planning; machine learning; blind sources separation; swarm interaction behavior; parameters and system optimization; neural networks; evolutionary and genetic algorithms; fuzzy systems; forecasting algorithms; classification; tracking analysis; simulation; image and texture analysis; dimension reduction; system optimization; segmentation and detection system; machine translation; virtual management and disaster analysis.

Granular Computing Based Machine Learning PDF Download

Granular Computing Based Machine Learning PDF
Author: Han Liu
Publisher: Springer
ISBN: 3319700588
Size: 45.36 MB
Format: PDF, Mobi
Category : Computers
Languages : en
Pages : 113
View: 1773

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Granular Computing Based Machine Learning Book Description:

This book explores the significant role of granular computing in advancing machine learning towards in-depth processing of big data. It begins by introducing the main characteristics of big data, i.e., the five Vs—Volume, Velocity, Variety, Veracity and Variability. The book explores granular computing as a response to the fact that learning tasks have become increasingly more complex due to the vast and rapid increase in the size of data, and that traditional machine learning has proven too shallow to adequately deal with big data. Some popular types of traditional machine learning are presented in terms of their key features and limitations in the context of big data. Further, the book discusses why granular-computing-based machine learning is called for, and demonstrates how granular computing concepts can be used in different ways to advance machine learning for big data processing. Several case studies involving big data are presented by using biomedical data and sentiment data, in order to show the advances in big data processing through the shift from traditional machine learning to granular-computing-based machine learning. Finally, the book stresses the theoretical significance, practical importance, methodological impact and philosophical aspects of granular-computing-based machine learning, and suggests several further directions for advancing machine learning to fit the needs of modern industries. This book is aimed at PhD students, postdoctoral researchers and academics who are actively involved in fundamental research on machine learning or applied research on data mining and knowledge discovery, sentiment analysis, pattern recognition, image processing, computer vision and big data analytics. It will also benefit a broader audience of researchers and practitioners who are actively engaged in the research and development of intelligent systems.

Modern Technologies For Big Data Classification And Clustering PDF Download

Modern Technologies for Big Data Classification and Clustering PDF
Author: Hari Seetha
Publisher:
ISBN: 9781522528050
Size: 31.90 MB
Format: PDF, ePub, Mobi
Category : Computers
Languages : en
Pages : 360
View: 2968

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Modern Technologies For Big Data Classification And Clustering Book Description:

Data has increased due to the growing use of web applications and communication devices. It is necessary to develop new techniques of managing data in order to ensure adequate usage. Modern Technologies for Big Data Classification and Clustering is an essential reference source for the latest scholarly research on handling large data sets with conventional data mining and provide information about the new technologies developed for the management of large data. Featuring coverage on a broad range of topics such as text and web data analytics, risk analysis, and opinion mining, this publication is ideally designed for professionals, researchers, and students seeking current research on various concepts of big data analytics. Topics Covered: The many academic areas covered in this publication include, but are not limited to: Data visualization Distributed Computing Systems Opinion Mining Privacy and security Risk analysis Social Network Analysis Text Data Analytics Web Data Analytics

Rough Sets Fuzzy Sets Data Mining And Granular Computing PDF Download

Rough Sets  Fuzzy Sets  Data Mining and Granular Computing PDF
Author: Hiroshi Sakai
Publisher: Springer Science & Business Media
ISBN: 3642106455
Size: 38.63 MB
Format: PDF, ePub, Mobi
Category : Computers
Languages : en
Pages : 528
View: 6198

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Rough Sets Fuzzy Sets Data Mining And Granular Computing Book Description:

Welcome to the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2009), held at the Indian Institute of Technology (IIT), Delhi, India, during December 15-18, 2009. RSFDGrC is a series of conferences spanning over the last 15 years. It investigates the me- ing points among the four major areas outlined in its title. This year, it was co-organized with the Third International Conference on Pattern Recognition and Machine Intelligence (PReMI 2009), which provided additional means for multi-facetedinteractionofboth scientists andpractitioners.Itwasalsothe core component of this year's Rough Set Year in India project. However, it remained a fully international event aimed at building bridges between countries. The ?rst sectin contains the invited papers and a short report on the abo- mentioned project. Let us note that all the RSFDGrC 2009 plenary speakers, Ivo Düntsch, Zbigniew Suraj, Zhongzhi Shi, Sergei Kuznetsov, Qiang Shen, and Yukio Ohsawa, contributed with the full-length articles in the proceedings. The remaining six sections contain 56 regular papers that were selected out of 130 submissions, each peer-reviewed by three PC members. We thank the authors for their high-quality papers submitted to this volume and regret that many deserving papers could not be accepted because of our urge to maintain strict standards. It is worth mentioning that there was quite a good number of papers on the foundations of rough sets and fuzzy sets, many of them authored byIndianresearchers.ThefuzzysettheoryhasbeenpopularinIndiaforalonger time. Now, we can see the rising interest in the rough set theory.

Granular Computing And Decision Making PDF Download

Granular Computing and Decision Making PDF
Author: Witold Pedrycz
Publisher: Springer
ISBN: 3319168290
Size: 72.70 MB
Format: PDF, Kindle
Category : Computers
Languages : en
Pages : 368
View: 518

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Granular Computing And Decision Making Book Description:

This volume is devoted to interactive and iterative processes of decision-making– I2 Fuzzy Decision Making, in brief. Decision-making is inherently interactive. Fuzzy sets help realize human-machine communication in an efficient way by facilitating a two-way interaction in a friendly and transparent manner. Human-centric interaction is of paramount relevance as a leading guiding design principle of decision support systems. The volume provides the reader with an updated and in-depth material on the conceptually appealing and practically sound methodology and practice of I2 Fuzzy Decision Making. The book engages a wealth of methods of fuzzy sets and Granular Computing, brings new concepts, architectures and practice of fuzzy decision-making providing the reader with various application studies. The book is aimed at a broad audience of researchers and practitioners in numerous disciplines in which decision-making processes play a pivotal role and serve as a vehicle to produce solutions to existing problems. Those involved in operations research, management, various branches of engineering, social sciences, logistics, and economics will benefit from the exposure to the subject matter. The book may serve as a useful and timely reference material for graduate students and senior undergraduate students in courses on decision-making, Computational Intelligence, operations research, pattern recognition, risk management, and knowledge-based systems.

Handbook Of Computational Intelligence In Manufacturing And Production Management PDF Download

Handbook of Computational Intelligence in Manufacturing and Production Management PDF
Author: Laha, Dipak
Publisher: IGI Global
ISBN: 1599045842
Size: 18.39 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : en
Pages : 516
View: 557

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Handbook Of Computational Intelligence In Manufacturing And Production Management Book Description:

During the last two decades, computer and information technologies have forced great changes in the ways businesses manage operations in meeting the desired quality of products and services, customer demands, competition, and other challenges. The Handbook of Computational Intelligence in Manufacturing and Production Management focuses on new developments in computational intelligence in areas such as forecasting, scheduling, production planning, inventory control, and aggregate planning, among others. This comprehensive collection of research provides cutting-edge knowledge on information technology developments for both researchers and professionals in fields such as operations and production management, Web engineering, artificial intelligence, and information resources management.

Dynamic Laser Speckle And Applications PDF Download

Dynamic Laser Speckle and Applications PDF
Author: Hector J. Rabal
Publisher: CRC Press
ISBN: 1420060163
Size: 77.35 MB
Format: PDF, Docs
Category : Science
Languages : en
Pages : 282
View: 1533

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Dynamic Laser Speckle And Applications Book Description:

Speckle study constitutes a multidisciplinary area with inherent complexities. In order to conquer challenges such as the variability of samples and sensitive measurements, researchers must develop a theoretical and statistical understanding of both biological and non-biological metrology using dynamic speckle laser. Dynamic Laser Speckle and Applications discusses the main methodologies used to analyze biospeckle phenomena with a strong focus on experimentation. After establishing a theoretical background in both speckle and biospeckle, the book presents the main methodologies for statistical and image analysis. It then deals with the concept of frequency decomposition before moving on to a discussion of fuzzy methods to treat dynamic speckle data. The book dedicates two sections to applications, including agricultural approaches. Additional features include photo images of experiments and software to aid in easy start-up of dynamic speckle usage. A systematic approach to new dynamic speckle laser phenomena, this book provides the physical theory and statistical background needed to analyze images formed by laser illumination in biological and non-biological samples.