LEADER 03121nam 2200529 450 001 9910554227403321 005 20231110215923.0 010 $a1-5231-5446-2 010 $a3-11-070251-7 024 7 $a10.1515/9783110702514 035 $a(CKB)5590000000532538 035 $a(DE-B1597)549641 035 $a(DE-B1597)9783110702514 035 $a(MiAaPQ)EBC6701278 035 $a(Au-PeEL)EBL6701278 035 $a(OCoLC)1262308295 035 $a(EXLCZ)995590000000532538 100 $a20220502d2021 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aMachine learning for sustainable development /$fedited by Kamal Kant Hiran [and three others] 210 1$aBerlin ;$aBoston :$cDe Gruyter,$d[2021] 210 4$dİ2021 215 $a1 online resource (XIII, 201 p.) 225 0 $aDe Gruyter Frontiers in Computational Intelligence ;$v9 300 $aIncludes index. 311 $a3-11-070248-7 327 $tFrontmatter -- $tPreface -- $tContents -- $tAbout editors -- $tList of contributors -- $tChapter 1. A framework for applying artificial intelligence (AI) with Internet of nanothings (IoNT) -- $tChapter 2 Opportunities and challenges in transforming higher education through machine learning -- $tChapter 3 Efficient renewable energy integration: a pertinent problem and advanced time series data analytics solution -- $tChapter 4 A comprehensive review on the application of machine learning techniques for analyzing the smart meter data -- $tChapter 5 Application of machine learning algorithms for facial expression analysis -- $tChapter 6 Prediction of quality analysis for crop based on machine learning model -- $tChapter 7 Data model recommendations for real-time machine learning applications: a suggestive approach -- $tChapter 8 Machine learning for sustainable agriculture -- $tChapter 9 Application of machine learning in SLAM algorithms -- $tChapter 10 Machine learning for weather forecasting -- $tChapter 11 Applications of conventional machine learning and deep learning for automation of diagnosis: case study -- $tIndex 330 $aThe book will focus on the applications of machine learning for sustainable development. Machine learning (ML) is an emerging technique whose diffusion and adoption in various sectors (such as energy, agriculture, internet of things, infrastructure) will be of enormous benefit. The state of the art of machine learning models is most useful for forecasting and prediction of various sectors for sustainable development. 410 3$aDe Gruyter Frontiers in Computational Intelligence 606 $aSustainable development 606 $aMachine learning 615 0$aSustainable development. 615 0$aMachine learning. 676 $a338.927 686 $aST 300$2rvk 702 $aHiran$b Kamal Kant$f1982- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910554227403321 996 $aMachine Learning for Sustainable Development$92819018 997 $aUNINA