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Machine Learning Paradigms

Advances in Deep Learning-based Technological Applications

  • Book
  • © 2020

Overview

Editors:
  1. George A. Tsihrintzis

    1. Department of Informatics, University of Piraeus, Piraeus, Greece

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    You can also search for this editor in PubMed   Google Scholar

  2. Lakhmi C. Jain

    1. University of Technology Sydney, NSW, Australia

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    You can also search for this editor in PubMed   Google Scholar

  • Presents recent advances in Deep Learning Theory and Applications
  • Includes theoretical advances as well as application areas
  • Written by experts in the field

Part of the book series: Learning and Analytics in Intelligent Systems (LAIS, volume 18)

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Table of contents (16 chapters)

  1. Front Matter

    Pages i-xii
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  2. Machine Learning Paradigms: Introduction to Deep Learning-Based Technological Applications

    • George A. Tsihrintzis, Lakhmi C. Jain
    Pages 1-5

  3. Deep Learning in Sensing

    1. Front Matter

      Pages 7-7
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    2. Vision to Language: Methods, Metrics and Datasets

      • Naeha Sharif, Uzair Nadeem, Syed Afaq Ali Shah, Mohammed Bennamoun, Wei Liu
      Pages 9-62

    3. Deep Learning Techniques for Geospatial Data Analysis

      • Arvind W. Kiwelekar, Geetanjali S. Mahamunkar, Laxman D. Netak, Valmik B Nikam
      Pages 63-81

    4. Deep Learning Approaches in Food Recognition

      • Chairi Kiourt, George Pavlidis, Stella Markantonatou
      Pages 83-108

  4. Deep Learning in Social Media and IOT

    1. Front Matter

      Pages 109-109
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    2. Deep Learning for Twitter Sentiment Analysis: The Effect of Pre-trained Word Embedding

      • Akrivi Krouska, Christos Troussas, Maria Virvou
      Pages 111-124

    3. A Good Defense Is a Strong DNN: Defending the IoT with Deep Neural Networks

      • Luke Holbrook, Miltiadis Alamaniotis
      Pages 125-145

  5. Deep Learning in the Medical Field

    1. Front Matter

      Pages 147-147
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    2. Survey on Deep Learning Techniques for Medical Imaging Application Area

      • Shymaa Abou Arkoub, Amir Hajjam El Hassani, Fabrice Lauri, Mohammad Hajjar, Bassam Daya, Sophie Hecquet et al.
      Pages 149-189

    3. Deep Learning Methods in Electroencephalography

      • Krzysztof Kotowski, Katarzyna Stapor, Jeremi Ochab
      Pages 191-212

  6. Deep Learning in Systems Control

    1. Front Matter

      Pages 213-213
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    2. The Implementation and the Design of a Hybriddigital PI Control Strategy Based on MISO Adaptive Neural Network Fuzzy Inference System Models–A MIMO Centrifugal Chiller Case Study

      • Roxana-Elena Tudoroiu, Mohammed Zaheeruddin, Sorin Mihai Radu, Dumitru Dan Burdescu, Nicolae Tudoroiu
      Pages 215-235

    3. A Review of Deep Reinforcement Learning Algorithms and Comparative Results on Inverted Pendulum System

      • Recep Özalp, Nuri Köksal Varol, Burak Taşci, Ayşegül Uçar
      Pages 237-256

  7. Deep Learning in Feature Vector Processing

    1. Front Matter

      Pages 257-257
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    2. Stock Market Forecasting by Using Support Vector Machines

      • K. Liagkouras, K. Metaxiotis
      Pages 259-271

    3. An Experimental Exploration of Machine Deep Learning for Drone Conflict Prediction

      • Brian Hilburn
      Pages 273-290

    4. Deep Dense Neural Network for Early Prediction of Failure-Prone Students

      • Georgios Kostopoulos, Maria Tsiakmaki, Sotiris Kotsiantis, Omiros Ragos
      Pages 291-306

  8. Evaluation of Algorithm Performance

    1. Front Matter

      Pages 307-307
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Keywords

About this book

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.


Reviews

“A very important and truly outstanding Contribution … . I recommend it as a ‘must read’ reference for researchers, practitioners, and higher research degrees students who want to experience truly exciting deep dive into a full-fledged deep learning. … the most important advantage of the book is the fact that it leaves its reader with a heightened ability to think – in different ways – about developing, evaluating, and implementing deep learning-based techniques and technologies in real life environments.” (Edward Szczerbicki, Intelligent Decision Technologies, Vol. 15, 2021)

Editors and Affiliations

  • Department of Informatics, University of Piraeus, Piraeus, Greece

    George A. Tsihrintzis

  • University of Technology Sydney, NSW, Australia

    Lakhmi C. Jain

Bibliographic Information

  • Book Title: Machine Learning Paradigms

  • Book Subtitle: Advances in Deep Learning-based Technological Applications

  • Editors: George A. Tsihrintzis, Lakhmi C. Jain

  • Series Title: Learning and Analytics in Intelligent Systems

  • DOI: https://doi.org/10.1007/978-3-030-49724-8

  • Publisher: Springer Cham

  • eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

  • Hardcover ISBN: 978-3-030-49723-1Published: 24 July 2020

  • Softcover ISBN: 978-3-030-49726-2Published: 25 July 2021

  • eBook ISBN: 978-3-030-49724-8Published: 23 July 2020

  • Series ISSN: 2662-3447

  • Series E-ISSN: 2662-3455

  • Edition Number: 1

  • Number of Pages: XII, 430

  • Number of Illustrations: 24 b/w illustrations, 154 illustrations in colour

  • Topics: Machine Learning, Computational Intelligence

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