Nature Inspired Algorithms For Big Data Frameworks
Download Nature Inspired Algorithms For Big Data Frameworks PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Nature Inspired Algorithms For Big Data Frameworks book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Nature-Inspired Algorithms for Big Data Frameworks
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 Computing for Data Science
This book discusses the current research and concepts in data science and how these can be addressed using different nature-inspired optimization techniques. Focusing on various data science problems, including classification, clustering, forecasting, and deep learning, it explores how researchers are using nature-inspired optimization techniques to find solutions to these problems in domains such as disease analysis and health care, object recognition, vehicular ad-hoc networking, high-dimensional data analysis, gene expression analysis, microgrids, and deep learning. As such it provides insights and inspiration for researchers to wanting to employ nature-inspired optimization techniques in their own endeavors.
Nature-inspired Algorithms for Big Data Frameworks
Author: Hema Banati
language: en
Publisher: Engineering Science Reference
Release Date: 2019
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.