Nature inspired computing for data science
著者
書誌事項
Nature inspired computing for data science
（Studies in computational intelligence, v. 871）
Springer, c2020
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注記
Includes bibliographical references
内容説明・目次
内容説明
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.
目次
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.
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