Nature-inspired algorithms for big data frameworks:
"This book focuses on application of nature-inspired algorithms for handling issues and challenges posed by big data in diverse environments. It highlights the usability and performance measures of these techniques in dealing with data related problems of emerging areas. It also explore the rol...
Gespeichert in:
| Weitere Verfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) :
IGI Global,
2018.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | "This book focuses on application of nature-inspired algorithms for handling issues and challenges posed by big data in diverse environments. It highlights the usability and performance measures of these techniques in dealing with data related problems of emerging areas. It also explore the role of machine learning techniques for the optimization and learning involving data intensive applications"-- |
| Beschreibung: | 28 PDFs (xxii, 412 pages) Also available in print. |
| Format: | Mode of access: World Wide Web. |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781522558538 |
| Zugangseinschränkungen: | Restricted to subscribers or individual electronic text purchasers. |
Internformat
MARC
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| 245 | 0 | 0 | |a Nature-inspired algorithms for big data frameworks |c Hema Banati, Shikha Mehta, and Parmeet Kaur, editors. |
| 264 | 1 | |a Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) : |b IGI Global, |c 2018. | |
| 300 | |a 28 PDFs (xxii, 412 pages) | ||
| 336 | |a text |2 rdacontent | ||
| 337 | |a electronic |2 isbdmedia | ||
| 338 | |a online resource |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Section 1. Nature-inspired algorithms for high dimensions. Chapter 1. Deep learning for big data analytics ; Chapter 2. Genetic algorithm based pre-processing strategy for high dimensional micro-array gene classification: application of nature inspired intelligence ; Chapter 3. Subspace clustering of high dimensional data using differential evolution ; Chapter 4. Nature inspired feature selector for effective data classification in big data frameworks -- Section 2. Nature-inspired approaches for complex optimizations. Chapter 5. Motion planning of non-holonomic wheeled robots using modified bat algorithm ; Chapter 6. Application of nature-inspired algorithms for sensing error optimisation in dynamic environment ; Chapter 7. Wolf-swarm colony for signature gene selection using weighted objective method ; Chapter 8. Scheduling data intensive scientific workflows in cloud environment using nature inspired algorithms ; Chapter 9. PSO-based antenna pattern synthesis: a paradigm for secured data communications ; Chapter 10. Nature-inspired algorithms in wireless sensor networks ; Chapter 11. Aircraft aerodynamic parameter estimation using intelligent estimation algorithms -- Section 3. Nature-inspired solutions for web analytics. Chapter 12. Analysis of multiplex social networks using nature-inspired algorithms ; Chapter 13. Pedagogical software agents for personalized e-learning using soft computing techniques ; Chapter 14. Graph and neural network-based intelligent conversation system ; Chapter 15. Big data analytics using apache hive to analyze health data. | |
| 506 | |a Restricted to subscribers or individual electronic text purchasers. | ||
| 520 | 3 | |a "This book focuses on application of nature-inspired algorithms for handling issues and challenges posed by big data in diverse environments. It highlights the usability and performance measures of these techniques in dealing with data related problems of emerging areas. It also explore the role of machine learning techniques for the optimization and learning involving data intensive applications"-- |c Provided by publisher. | |
| 530 | |a Also available in print. | ||
| 538 | |a Mode of access: World Wide Web. | ||
| 588 | |a Description based on title screen (IGI Global, viewed 09/07/2018). | ||
| 650 | 0 | |a Big data. | |
| 650 | 0 | |a Computer algorithms. | |
| 650 | 0 | |a Evolutionary computation. | |
| 650 | 0 | |a Evolutionary programming (Computer science) | |
| 700 | 1 | |a Banati, Hema |d 1970- |e editor. | |
| 700 | 1 | |a Kaur, Parmeet |d 1976- |e editor. | |
| 700 | 1 | |a Mehta, Shikha, |e editor. | |
| 710 | 2 | |a IGI Global, |e publisher. | |
| 776 | 0 | |c (Original) |w (DLC)2017059210 | |
| 776 | 0 | 8 | |i Print version: |z 1522558527 |z 9781522558521 |w (DLC) 2017059210 |
| 856 | 4 | 0 | |l FWS01 |p ZDB-98-IGB |q FWS_PDA_IGB |u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-5852-1 |3 Volltext |
| 912 | |a ZDB-98-IGB | ||
| 049 | |a DE-863 | ||
Datensatz im Suchindex
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| contents | Section 1. Nature-inspired algorithms for high dimensions. Chapter 1. Deep learning for big data analytics ; Chapter 2. Genetic algorithm based pre-processing strategy for high dimensional micro-array gene classification: application of nature inspired intelligence ; Chapter 3. Subspace clustering of high dimensional data using differential evolution ; Chapter 4. Nature inspired feature selector for effective data classification in big data frameworks -- Section 2. Nature-inspired approaches for complex optimizations. Chapter 5. Motion planning of non-holonomic wheeled robots using modified bat algorithm ; Chapter 6. Application of nature-inspired algorithms for sensing error optimisation in dynamic environment ; Chapter 7. Wolf-swarm colony for signature gene selection using weighted objective method ; Chapter 8. Scheduling data intensive scientific workflows in cloud environment using nature inspired algorithms ; Chapter 9. PSO-based antenna pattern synthesis: a paradigm for secured data communications ; Chapter 10. Nature-inspired algorithms in wireless sensor networks ; Chapter 11. Aircraft aerodynamic parameter estimation using intelligent estimation algorithms -- Section 3. Nature-inspired solutions for web analytics. Chapter 12. Analysis of multiplex social networks using nature-inspired algorithms ; Chapter 13. Pedagogical software agents for personalized e-learning using soft computing techniques ; Chapter 14. Graph and neural network-based intelligent conversation system ; Chapter 15. Big data analytics using apache hive to analyze health data. |
| ctrlnum | (CaBNVSL)slc20195385 (OCoLC)1051151139 |
| dewey-full | 006.3/823 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3/823 |
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| discipline | Informatik |
| format | Electronic eBook |
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| id | ZDB-98-IGB-00193079 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-26T14:51:53Z |
| institution | BVB |
| isbn | 9781522558538 |
| language | English |
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| spelling | Nature-inspired algorithms for big data frameworks Hema Banati, Shikha Mehta, and Parmeet Kaur, editors. Hershey, Pennsylvania (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA) : IGI Global, 2018. 28 PDFs (xxii, 412 pages) text rdacontent electronic isbdmedia online resource rdacarrier Includes bibliographical references and index. Section 1. Nature-inspired algorithms for high dimensions. Chapter 1. Deep learning for big data analytics ; Chapter 2. Genetic algorithm based pre-processing strategy for high dimensional micro-array gene classification: application of nature inspired intelligence ; Chapter 3. Subspace clustering of high dimensional data using differential evolution ; Chapter 4. Nature inspired feature selector for effective data classification in big data frameworks -- Section 2. Nature-inspired approaches for complex optimizations. Chapter 5. Motion planning of non-holonomic wheeled robots using modified bat algorithm ; Chapter 6. Application of nature-inspired algorithms for sensing error optimisation in dynamic environment ; Chapter 7. Wolf-swarm colony for signature gene selection using weighted objective method ; Chapter 8. Scheduling data intensive scientific workflows in cloud environment using nature inspired algorithms ; Chapter 9. PSO-based antenna pattern synthesis: a paradigm for secured data communications ; Chapter 10. Nature-inspired algorithms in wireless sensor networks ; Chapter 11. Aircraft aerodynamic parameter estimation using intelligent estimation algorithms -- Section 3. Nature-inspired solutions for web analytics. Chapter 12. Analysis of multiplex social networks using nature-inspired algorithms ; Chapter 13. Pedagogical software agents for personalized e-learning using soft computing techniques ; Chapter 14. Graph and neural network-based intelligent conversation system ; Chapter 15. Big data analytics using apache hive to analyze health data. Restricted to subscribers or individual electronic text purchasers. "This book focuses on application of nature-inspired algorithms for handling issues and challenges posed by big data in diverse environments. It highlights the usability and performance measures of these techniques in dealing with data related problems of emerging areas. It also explore the role of machine learning techniques for the optimization and learning involving data intensive applications"-- Provided by publisher. Also available in print. Mode of access: World Wide Web. Description based on title screen (IGI Global, viewed 09/07/2018). Big data. Computer algorithms. Evolutionary computation. Evolutionary programming (Computer science) Banati, Hema 1970- editor. Kaur, Parmeet 1976- editor. Mehta, Shikha, editor. IGI Global, publisher. (Original) (DLC)2017059210 Print version: 1522558527 9781522558521 (DLC) 2017059210 FWS01 ZDB-98-IGB FWS_PDA_IGB http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-5852-1 Volltext |
| spellingShingle | Nature-inspired algorithms for big data frameworks Section 1. Nature-inspired algorithms for high dimensions. Chapter 1. Deep learning for big data analytics ; Chapter 2. Genetic algorithm based pre-processing strategy for high dimensional micro-array gene classification: application of nature inspired intelligence ; Chapter 3. Subspace clustering of high dimensional data using differential evolution ; Chapter 4. Nature inspired feature selector for effective data classification in big data frameworks -- Section 2. Nature-inspired approaches for complex optimizations. Chapter 5. Motion planning of non-holonomic wheeled robots using modified bat algorithm ; Chapter 6. Application of nature-inspired algorithms for sensing error optimisation in dynamic environment ; Chapter 7. Wolf-swarm colony for signature gene selection using weighted objective method ; Chapter 8. Scheduling data intensive scientific workflows in cloud environment using nature inspired algorithms ; Chapter 9. PSO-based antenna pattern synthesis: a paradigm for secured data communications ; Chapter 10. Nature-inspired algorithms in wireless sensor networks ; Chapter 11. Aircraft aerodynamic parameter estimation using intelligent estimation algorithms -- Section 3. Nature-inspired solutions for web analytics. Chapter 12. Analysis of multiplex social networks using nature-inspired algorithms ; Chapter 13. Pedagogical software agents for personalized e-learning using soft computing techniques ; Chapter 14. Graph and neural network-based intelligent conversation system ; Chapter 15. Big data analytics using apache hive to analyze health data. Big data. Computer algorithms. Evolutionary computation. Evolutionary programming (Computer science) |
| title | Nature-inspired algorithms for big data frameworks |
| title_auth | Nature-inspired algorithms for big data frameworks |
| title_exact_search | Nature-inspired algorithms for big data frameworks |
| title_full | Nature-inspired algorithms for big data frameworks Hema Banati, Shikha Mehta, and Parmeet Kaur, editors. |
| title_fullStr | Nature-inspired algorithms for big data frameworks Hema Banati, Shikha Mehta, and Parmeet Kaur, editors. |
| title_full_unstemmed | Nature-inspired algorithms for big data frameworks Hema Banati, Shikha Mehta, and Parmeet Kaur, editors. |
| title_short | Nature-inspired algorithms for big data frameworks |
| title_sort | nature inspired algorithms for big data frameworks |
| topic | Big data. Computer algorithms. Evolutionary computation. Evolutionary programming (Computer science) |
| topic_facet | Big data. Computer algorithms. Evolutionary computation. Evolutionary programming (Computer science) |
| url | http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-5852-1 |
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