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Nature inspired computing for data s...

Rout, Minakhi.

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Nature inspired computing for data science

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Nature inspired computing for data science/ edited by Minakhi Rout, Jitendra Kumar Rout, Himansu Das.
其他作者:	Rout, Minakhi.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	xii, 295 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Natural computation. -
電子資源:	https://doi.org/10.1007/978-3-030-33820-6
ISBN:	9783030338206

Nature inspired computing for data science
Nature inspired computing for data science[electronic resource] /edited by Minakhi Rout, Jitendra Kumar Rout, Himansu Das. - Cham :Springer International Publishing :2020. - xii, 295 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.8711860-949X ;. - Studies in computational intelligence ;v.871..

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.

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.

ISBN: 9783030338206

Standard No.: 10.1007/978-3-030-33820-6doiSubjects--Topical Terms:

1002233
Natural computation.

LC Class. No.: QA76.9.N37 / N388 2020

Dewey Class. No.: 620.00285

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505 0 $a 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.
520 $a 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.
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