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Nature-inspired algorithms for big d...

Banati, Hema, (1970-)

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Nature-inspired algorithms for big data frameworks

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Nature-inspired algorithms for big data frameworks/ Hema Banati, Shikha Mehta, and Parmeet Kaur, editors.
其他作者:	Banati, Hema,
出版者:	Hershey, Pennsylvania :IGI Global, : 2018.,
面頁冊數:	1 online resource (xxii, 412 p.)
內容註:	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.
標題:	Evolutionary programming (Computer science) -
電子資源:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-5852-1
ISBN:	9781522558538 (e-book)

Nature-inspired algorithms for big data frameworks
Nature-inspired algorithms for big data frameworks[electronic resource] /Hema Banati, Shikha Mehta, and Parmeet Kaur, editors. - Hershey, Pennsylvania :IGI Global,2018. - 1 online resource (xxii, 412 p.)

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.

ISBN: 9781522558538 (e-book)Subjects--Topical Terms:

568531
Evolutionary programming (Computer science)

LC Class. No.: QA76.618 / .N376 2018e

Dewey Class. No.: 006.3/823

Nature-inspired algorithms for big data frameworks
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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"--Provided by publisher.
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