LEADER 04438nam 22006495 450 001 9910484337403321 005 20200701094840.0 010 $a3-030-33820-7 024 7 $a10.1007/978-3-030-33820-6 035 $a(CKB)4100000009940073 035 $a(MiAaPQ)EBC6113624 035 $a(DE-He213)978-3-030-33820-6 035 $a(PPN)243769563 035 $a(EXLCZ)994100000009940073 100 $a20191126d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNature Inspired Computing for Data Science /$fedited by Minakhi Rout, Jitendra Kumar Rout, Himansu Das 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (xii, 295 pages) $cillustrations 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v871 311 $a3-030-33819-3 320 $aIncludes bibliographical references. 327 $aAn 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. 330 $aThis book discusses the current research and concepts in data science and how these can be addressed using different nature-inspired optimization techniques. Focusing on various data science problems, including classification, clustering, forecasting, and deep learning, it explores how researchers are using nature-inspired optimization techniques to find solutions to these problems in domains such as disease analysis and health care, object recognition, vehicular ad-hoc networking, high-dimensional data analysis, gene expression analysis, microgrids, and deep learning. As such it provides insights and inspiration for researchers to wanting to employ nature-inspired optimization techniques in their own endeavors. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v871 606 $aEngineering?Data processing 606 $aComputational intelligence 606 $aBig data 606 $aArtificial intelligence 606 $aData Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T11040 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aEngineering?Data processing. 615 0$aComputational intelligence. 615 0$aBig data. 615 0$aArtificial intelligence. 615 14$aData Engineering. 615 24$aComputational Intelligence. 615 24$aBig Data. 615 24$aArtificial Intelligence. 676 $a006.38 702 $aRout$b Minakhi$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRout$b Jitendra Kumar$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aDas$b Himansu$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484337403321 996 $aNature Inspired Computing for Data Science$92851840 997 $aUNINA