Nature Inspired Computing for Data Science
Published on 24. January 2020
Book
Hardback
XII, 295 pages
978-3-030-33819-0 (ISBN)
Description
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.
More details
Series
Edition
2020 ed.
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
35 s/w Abbildungen, 98 farbige Abbildungen
XII, 295 p. 133 illus., 98 illus. in color.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 23 mm
Weight
635 gr
ISBN-13
978-3-030-33819-0 (9783030338190)
DOI
10.1007/978-3-030-33820-6
Schweitzer Classification
Other editions
Additional editions
Minakhi Rout | Jitendra Kumar Rout | Himansu Das
Nature Inspired Computing for Data Science
Book
01/2021
Springer
€106.99
Shipment within 7-9 days
Minakhi Rout | Jitendra Kumar Rout | Himansu Das
Nature Inspired Computing for Data Science
E-Book
11/2019
Springer
€96.29
Available for download
Content
An Efficient Classification of Tuberous Sclerosis Disease Using Nature Inspired PSO and ACO based Optimized Neural Network.- Mid-term Home Health Care Planning Problem with Flexible Departing Way for Caregivers.- Performance Analysis of NASNet on Unconstrained Ear Recognition.- Optimization of performance parameter for Vehicular Ad-hoc NETwork (VANET) using Swarm Intelligence.- Development of Fast and Reliable Nature-Inspired Computing for Supervised Learning in High-Dimensional Data.- Application of Genetic Algorithms for Unit Commitment and Economic Dispatch Problems in microgrids.- Application of Genetic Algorithms for Designing Micro-Hydro Power Plants in Rural Isolated Areas - a case study in San Miguelito, Honduras.- Performance Evaluation of Different Machine Learning Methods and Deep-Learning Based Convolutional Neural Network for Health Decision Making.- Clustering Bank Customer Complaints on Social Media for Analytical CRM via Multi-Objective Particle Swarm Optimization.- Benchmarking Gene Selection Techniques for Prediction of Distinct Carcinoma from Gene Expression Data: A Computational Study.- An Evolutionary Algorithm based Hybrid Parallel Framework for Asia Foreign Exchange Rate prediction.