LEADER 04011nam 22005295 450 001 996418275903316 005 20240603032250.0 010 $a3-030-49724-0 024 7 $a10.1007/978-3-030-49724-8 035 $a(CKB)4100000011354810 035 $a(DE-He213)978-3-030-49724-8 035 $a(MiAaPQ)EBC6273589 035 $a(PPN)269149163 035 $a(EXLCZ)994100000011354810 100 $a20200723d2020 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning Paradigms $eAdvances in Deep Learning-based Technological Applications /$fedited by George A. Tsihrintzis, Lakhmi C. Jain 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XII, 430 p. 178 illus., 154 illus. in color.) 225 1 $aLearning and Analytics in Intelligent Systems,$x2662-3447 ;$v18 311 $a3-030-49723-2 327 $aChapter 1: Introduction to Deep Learning-based Technological Applications -- Chapter 2: Vision to Language: Methods, Metrics and Datasets -- Chapter 3: Deep Learning Techniques for Geospatial Data Analysis -- Chapter 4: Deep Learning Approaches in Food Recognition -- Chapter 5: Deep Learning for Twitter Sentiment Analysis: the Effect of pre-trained Word Embedding -- Chapter 6: A Good Defense is a Strong DNN: Defending the IoT with Deep Neural Networks -- Chapter 7: Survey on Deep Learning Techniques for Medical Imaging Application Area -- Chapter 8: Deep Learning Methods in Electroencephalography. 330 $aAt the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep Learning in Systems Control, (5) Deep Learning in Feature Vector Processing, and (6) Evaluation of Algorithm Performance. This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest. 410 0$aLearning and Analytics in Intelligent Systems,$x2662-3447 ;$v18 606 $aMachine learning 606 $aComputational intelligence 606 $aMachine Learning$3https://scigraph.springernature.com/ontologies/product-market-codes/I21010 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 615 0$aMachine learning. 615 0$aComputational intelligence. 615 14$aMachine Learning. 615 24$aComputational Intelligence. 676 $a006.31 702 $aTsihrintzis$b George A$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aJain$b Lakhmi C$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996418275903316 996 $aMachine Learning Paradigms$91995437 997 $aUNISA