Machine learning paradigms : advances in deep learning-based technological applications - Τσιχριντζής, Γεώργιος Α. (Springer, 2020)
Contents: Intro – Foreword – Preface – Contents – 1 Machine Learning Paradigms: Introduction to Deep Learning-Based Technological Applications – 1.1 Editorial Note – References – Part IDeep Learning in Sensing – 2 Vision to Language: Methods, Metrics and Datasets – 2.1 Introduction – 2.2 Challenges in Image Captioning – 2.2.1 Understanding and Predicting `Importance' in Images – 2.2.2 Visual Correctness of Words – 2.2.3 Automatic Evaluation Metrics – 2.2.4 Image Specificity – 2.2.5 Natural-Sounding Descriptions – 2.3 Image Captioning Models and Their Taxonomy Contents: 2.3.1 Example Lookup-Based Models – 2.3.2 Generation-based Models – 2.4 Assessment of Image Captioning Models – 2.4.1 Human Evaluation – 2.4.2 Automatic Evaluation Metrics – 2.4.3 Distraction Task(s) Based Methods – 2.5 Datasets for Image Captioning – 2.5.1 Generic Captioning Datasets – 2.5.2 Stylised Captioning Datasets – 2.5.3 Domain Specific Captioning Datasets – 2.6 Applications of Visual Captioning – 2.6.1 Medical Image Captioning – 2.6.2 Life-Logging – 2.6.3 Commentary for Sports' Videos – 2.6.4 Captioning for Newspapers – 2.6.5 Captioning for Assistive Technology Contents: 2.6.6 Other Applications – 2.7 Extensions of Image Captioning to Other Vision-to-Language Tasks – 2.7.1 Visual Question Answering – 2.7.2 Visual Storytelling – 2.7.3 Video Captioning – 2.7.4 Visual Dialogue – 2.7.5 Visual Grounding – 2.8 Conclusion and Future Works – References – 3 Deep Learning Techniques for Geospatial Data Analysis – 3.1 Introduction – 3.2 Deep Learning: A Brief Overview – 3.2.1 Deep Learning Architectures – 3.2.2 Deep Neural Networks – 3.2.3 Convolutional Neural Network (CNN) – 3.2.4 Recurrent Neural Networks (RNN) – 3.2.5 Auto-Encoders (AE) Contents: 3.3 Geospatial Analysis: A Data Science Perspective – 3.3.1 Enabling Technologies for Geospatial Data Collection – 3.3.2 Geospatial Data Models – 3.3.3 Geospatial Data Management – 3.4 Deep Learning for Remotely Sensed Data Analytics – 3.4.1 Data Pre-processing – 3.4.2 Feature Engineering – 3.4.3 Geospatial Object Detection – 3.4.4 Classification Tasks in Geospatial Analysis – 3.5 Deep Learning for GPS Data Analytics – 3.6 Deep Learning for RFID Data Analytics – 3.7 Conclusion – References – 4 Deep Learning Approaches in Food Recognition – 4.1 Introduction – 4.2 Background Contents: 4.2.1 Popular Deep Learning Frameworks – 4.3 Deep Learning Methods for Food Recognition – 4.3.1 Food Image Datasets – 4.3.2 Approach #1: New Architecture Development – 4.3.3 Approach #2: Transfer Learning and Fine-Tuning – 4.3.4 Approach #3: Deep Learning Platforms – 4.4 Comparative Study – 4.4.1 New Architecture Against Pre-trained Models – 4.4.2 Deep Learning Platforms Against Each Other – 4.5 Conclusions – References – Part IIDeep Learning in Social Media and IOT – 5 Deep Learning for Twitter Sentiment Analysis: The Effect of Pre-trained Word Embedding – 5.1 Introduction.