Machine learning paradigms : advances in deep learning-based technological applications
- George A. Tsihrintzis, Lakhmi C. Jain, editors.
- text file
- Cham : Springer, 2020.
- Copyright notice
- Physical description
- 1 online resource (xii, 430 pages)
- Learning and analytics in intelligent systems ; v. 18.
- Machine Learning Paradigms: Introduction to Deep Learning-Based Technological Applications
- Part I Deep Learning in Sensing
- Vision to Language: Methods, Metrics and Datasets
- Deep Learning Techniques for Geospatial Data Analysis
- Deep Learning Approaches in Food Recognition
- Part II Deep Learning in Social Media and IOT
- Deep Learning for Twitter Sentiment Analysis: The Effect of Pre-trained Word Embedding
- A Good Defense Is a Strong DNN: Defending the IoT with Deep Neural Networks
- Part III Deep Learning in the Medical Field
- Survey on Deep Learning Techniques for Medical Imaging Application Area
- Deep Learning Methods in Electroencephalography
- Part IV Deep Learning in Systems Control
- 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
- A Review of Deep Reinforcement Learning Algorithms and Comparative Results on Inverted Pendulum System
- Part V Deep Learning in Feature Vector Processing
- Stock Market Forecasting by Using Support Vector Machines
- An Experimental Exploration of Machine Deep Learning for Drone Conflict Prediction
- Deep Dense Neural Network for Early Prediction of Failure-Prone Students
- Part VI Evaluation of Algorithm Performance
- Non-parametric Performance Measurement with Artificial Neural Networks
- A Comprehensive Survey on the Applications of Swarm Intelligence and Bio-Inspired Evolutionary Strategies
- Detecting Magnetic Field Levels Emitted by Tablet Computers via Clustering Algorithms.
- Publisher's summary
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.
(source: Nielsen Book Data)
- Publication date
- Learning and analytics in intelligent systems, 2662-3447 ; v. 18
- 9783030497248 (electronic bk.)
- 3030497240 (electronic bk.)
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