| Dewey Class |
006.31 |
| Title |
Machine Learning Paradigms ([EBook]) : Advances in Deep Learning-based Technological Applications / / edited by George A. Tsihrintzis, Lakhmi C. Jain. |
| Added Personal Name |
Tsihrintzis, George A. |
| Jain, Lakhmi C. |
| Other name(s) |
SpringerLink (Online service) |
| Edition statement |
1st ed. 2020. |
| Publication |
Cham : : Springer International Publishing : : Imprint: Springer, , 2020. |
| Physical Details |
XII, 430 p. 178 illus., 154 illus. in color. : online resource. |
| Series |
Learning and Analytics in Intelligent Systems 2662-3447 ; ; 18 |
| ISBN |
9783030497248 |
| Summary Note |
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.: |
| Contents note |
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. |
| Mode of acces to digital resource |
Digital book. Cham Springer Nature 2020. - Mode of access: World Wide Web. System requirements: Internet Explorer 6.0 (or higher) or Firefox 2.0 (or higher). Available as searchable text in PDF format |
| System details note |
- Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users). |
| Internet Site |
https://doi.org/10.1007/978-3-030-49724-8 |
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