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
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"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
