Machine Learning Paradigms: Advances in Deep Learning-based Technological Applications

✍ Scribed by George A. Tsihrintzis, Lakhmi C. Jain

• Publisher: Springer International Publishing;Springer
• Year: 2020
• Tongue: English
• Leaves: 429
• Series: Learning and Analytics in Intelligent Systems 18
• Edition: 1st ed.
• Category: Library

✦ Synopsis

At the dawn of the 4^th 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.

✦ Table of Contents

Front Matter ....Pages i-xii
Machine Learning Paradigms: Introduction to Deep Learning-Based Technological Applications (George A. Tsihrintzis, Lakhmi C. Jain)....Pages 1-5
Front Matter ....Pages 7-7
Vision to Language: Methods, Metrics and Datasets (Naeha Sharif, Uzair Nadeem, Syed Afaq Ali Shah, Mohammed Bennamoun, Wei Liu)....Pages 9-62
Deep Learning Techniques for Geospatial Data Analysis (Arvind W. Kiwelekar, Geetanjali S. Mahamunkar, Laxman D. Netak, Valmik B Nikam)....Pages 63-81
Deep Learning Approaches in Food Recognition (Chairi Kiourt, George Pavlidis, Stella Markantonatou)....Pages 83-108
Front Matter ....Pages 109-109
Deep Learning for Twitter Sentiment Analysis: The Effect of Pre-trained Word Embedding (Akrivi Krouska, Christos Troussas, Maria Virvou)....Pages 111-124
A Good Defense Is a Strong DNN: Defending the IoT with Deep Neural Networks (Luke Holbrook, Miltiadis Alamaniotis)....Pages 125-145
Front Matter ....Pages 147-147
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
Deep Learning Methods in Electroencephalography (Krzysztof Kotowski, Katarzyna Stapor, Jeremi Ochab)....Pages 191-212
Front Matter ....Pages 213-213
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
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
Front Matter ....Pages 257-257
Stock Market Forecasting by Using Support Vector Machines (K. Liagkouras, K. Metaxiotis)....Pages 259-271
An Experimental Exploration of Machine Deep Learning for Drone Conflict Prediction (Brian Hilburn)....Pages 273-290
Deep Dense Neural Network for Early Prediction of Failure-Prone Students (Georgios Kostopoulos, Maria Tsiakmaki, Sotiris Kotsiantis, Omiros Ragos)....Pages 291-306
Front Matter ....Pages 307-307
Non-parametric Performance Measurement with Artificial Neural Networks (Gregory Koronakos, Dionisios N. Sotiropoulos)....Pages 309-335
A Comprehensive Survey on the Applications of Swarm Intelligence and Bio-Inspired Evolutionary Strategies (Alexandros Tzanetos, Georgios Dounias)....Pages 337-378
Detecting Magnetic Field Levels Emitted by Tablet Computers via Clustering Algorithms (Alessia Amelio, Ivo Rumenov Draganov)....Pages 379-430

✦ Subjects

Computer Science; Computational Intelligence