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＜電子ブック＞
Machine Learning Paradigms : Advances in Deep Learning-based Technological Applications / edited by George A. Tsihrintzis, Lakhmi C. Jain
(Learning and Analytics in Intelligent Systems. ISSN:26623455 ; 18)

版	1st ed. 2020.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2020
本文言語	英語
大きさ	XII, 430 p. 178 illus., 154 illus. in color : online resource
著者標目	Tsihrintzis, George A editor Jain, Lakhmi C editor SpringerLink (Online service)
件　名	LCSH:Machine learning LCSH:Computational intelligence FREE:Machine Learning FREE:Computational Intelligence
一般注記	Chapter 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 At 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 HTTP:URL=https://doi.org/10.1007/978-3-030-49724-8

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