Machine Learning Paradigms : Advances in Deep Learning-based Technological Applications. 1st ed. 2020

種類:

電子ブック

責任表示:

edited by George A. Tsihrintzis, Lakhmi C. Jain

出版情報:

Cham : Springer International Publishing : Imprint: Springer, 2020

著者名:

• Tsihrintzis, George A.
• Jain, Lakhmi C.
• SpringerLink (Online service)

シリーズ名:

Learning and Analytics in Intelligent Systems ; 18

ISBN:

9783030497248 [3030497240]    

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 profes

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