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08019cam a2201045 i 4500 |
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908030598 |
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20240223121953.0 |
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|a Computational intelligence for big data analysis :
|b frontier advances and applications /
|c D.P. Acharjya, Satchidananda Dehuri, Sugata Sanyal, editors.
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|a London :
|b Springer,
|c 2015.
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|c r015.
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|a 1 online resource :
|b color illustrations.
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|a text
|b txt
|2 rdacontent.
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|a computer
|b c
|2 rdamedia.
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|a online resource
|b cr
|2 rdacarrier.
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|a Adaptation, learning and optimization ;
|v vol. 19.
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| 504 |
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|a Includes bibliographical references and index.
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|a Vendor-supplied metadata.
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|a The work presented in this book is a combination of theoretical advancements of big data analysis, cloud computing, and their potential applications in scientific computing. The theoretical advancements are supported with illustrative examples and its applications in handling real life problems. The applications are mostly undertaken from real life situations. The book discusses major issues pertaining to big data analysis using computational intelligence techniques and some issues of cloud computing. An elaborate bibliography is provided at the end of each chapter. The material in this book includes concepts, figures, graphs, and tables to guide researchers in the area of big data analysis and cloud computing.
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|a Preface -- Acknowledgment -- Contents -- Part I: Theoretical Foundation of Big Data Analysis -- “Atrain Distributed System� (ADS): An Infinitely Scalable Architecture for Processing Big Data of Any 4Vs -- 1 Introduction -- 2 “r-Train� (train) and “r-Atrain� (atrain): The Data Structures for Big Data -- 2.1 Larray -- 2.2 Homogeneous Data Structure “r-Train� (train) for Homogeneous Big Data -- 2.3 r-Atrain (Atrain): A Powerful Heterogeneous Data Structure for Big Data -- 3 Solid Matrix and Solid Latrix (for Big Data and Temporal Big Data)
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|a 3.1 Solid Matrix and Solid Latrix3.2 3-D Solid Matrix (3-SM) and Some Characterizations -- 4 Algebra of Solid Matrices (Whose Elements Are Numbers) -- 5 Homogeneous Data Structure �MT� for Solid Matrix/Latrix -- 5.1 Implementation of a 3-SM (3-SL) -- 6 Hematrix and Helatrix: Storage Model for Heterogeneous Big Data -- 7 Atrain Distributed System (ADS) for Big Data -- 7.1 Atrain Distributed System (ADS) -- 7.2 �Multi-horse Cart Topology� and �Cycle Topology� for ADS -- 8 The Heterogeneous Data Structure �r-Atrain� in an Atrain Distributed System (ADS)
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|a 8.1 Coach of a r-Atrain in an ADS8.2 Circular Train and Circular Atrain -- 8.3 Fundamental Operations on �r-Atrain� in an ADS for Big Data -- 9 Heterogeneous Data Structures �MA� for Solid Helatrix of Big Data -- 10 Conclusion -- References -- Big Data Time Series Forecasting Model: A Fuzzy-Neuro Hybridize Approach -- 1 Introduction -- 2 Foundations of Fuzzy Set -- 3 Fuzzy-Neuro Hybridization and Big Data Time Series -- 3.1 Artificial Neural Network: An Overview -- 3.2 Fuzzy-Neuro Hybridized Approach: A New Paradigm for the Big Data Time Series Forecasting.
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|a 4 Description of Data Set5 Proposed Approach and Algorithm -- 5.1 EIBD Approach -- 5.2 Algorithm for the Big Data Time Series Forecasting Model -- 6 Fuzzy-Neuro Forecasting Model for Big Data: Detail Explanation -- 7 Performance Analysis Parameters -- 8 Empirical Analysis -- 8.1 Forecasting with the M-factors -- 8.2 Forecasting with Two-factors -- 8.3 Forecasting with Three-factors -- 8.4 Statistical Significance -- 9 Conclusion and Discussion -- References -- Learning Using Hybrid Intelligence Techniques -- 1 Introduction.
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|a 2 Gene Selection Using Intelligent Hybrid PSO and Quick-Reduct Algorithm2.1 Particle Swarm Optimization -- 2.2 Proposed Algorithm -- 2.3 Implementation and Results -- 3 Rough Set Aided Hybrid Gene Selection for Cancer Classification -- 3.1 Rough Set -- 3.2 Gene Selection Based on Rough Set Method -- 3.3 Supervised Correlation Based Reduct Algorithm (CFS-RST) -- 3.4 Implementation and Results -- 4 Hybrid Data Mining Technique (CFS + PLS) for Improving Classification Accuracy of Microarray Data -- 4.1 SIMPLS and Dimension Reduction in the Classification Framework.
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|a Computational intelligence.
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| 650 |
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|a Electronic data processing
|x Data entry.
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|a Big data.
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| 650 |
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|a Intelligence informatique.
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| 650 |
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|a Saisie des donn{u1925}s (Informatique)
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|a Donn{u1925}s volumineuses.
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|a TECHNOLOGY & ENGINEERING
|x Engineering (General)
|2 bisacsh.
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|a TECHNOLOGY & ENGINEERING
|x Reference.
|2 bisacsh.
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|a Big data.
|2 fast.
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| 650 |
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|a Computational intelligence.
|2 fast.
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| 650 |
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|a Electronic data processing
|x Data entry.
|2 fast.
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| 700 |
1 |
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|a Acharjya, D. P.,
|d 1969-
|1 https://id.oclc.org/worldcat/entity/E39PCjBPr6FGt9QgV8RFKQdt4y,
|e editor.
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| 700 |
1 |
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|a Dehuri, Satchidananda,
|e editor.
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| 700 |
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|a Sanyal, Sugata,
|e editor.
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| 710 |
2 |
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|a SpringerLink (Online service)
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|z 9783319165974.
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| 830 |
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|a Adaptation, learning and optimization ;
|v vol. 19.
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