Nature-Inspired Algorithms for Big Data Frameworks (Advances in Computational Intelligence and Robotics). Questo articolo non è disponibile.
Lingua: inglese
Editore: Engineering Science Reference, 2018
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Codice articolo ERICA80015225585276
- Titolo
- Nature-Inspired Algorithms for Big Data Frameworks (Advances in Computational Intelligence and Robotics)
- Editore
- Engineering Science Reference
- Anno di pubblicazione
- 2018
- Condizione
- New
- Tipo di libro
- book
- Rilegatura
- hardcover
- Lingua
- inglese
- ISBN 10
- 1522558527
- ISBN 13
- 9781522558521
As technology continues to become more sophisticated, mimicking natural processes and phenomena becomes more of a reality. Continued research in the field of natural computing enables an understanding of the world around us, in addition to opportunities for manmade computing to mirror the natural processes and systems that have existed for centuries.
Nature-Inspired Algorithms for Big Data Frameworks is a collection of innovative research on the methods and applications of extracting meaningful information from data using algorithms that are capable of handling the constraints of processing time, memory usage, and the dynamic and unstructured nature of data. Highlighting a range of topics including genetic algorithms, data classification, and wireless sensor networks, this book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the application of nature and biologically inspired algorithms for handling challenges posed by big data in diverse environments.
Nature-Inspired Algorithms for Big Data Frameworks is a collection of innovative research on the methods and applications of extracting meaningful information from data using algorithms that are capable of handling the constraints of processing time, memory usage, and the dynamic and unstructured nature of data. Highlighting a range of topics including genetic algorithms, data classification, and wireless sensor networks, this book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the application of nature and biologically inspired algorithms for handling challenges posed by big data in diverse environments.
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