Note: 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 -- 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 -- 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) -- 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 -- 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 -- 5.2 Related Work -- 5.3 Evaluation Procedure -- 5.3.1 Datasets -- 5.3.2 Data Preprocessing -- 5.3.3 Pre-trained Word Embeddings -- 5.3.4 Deep Learning -- 5.4 Comparative Analysis and Discussion -- 5.5 Conclusion and Future Work -- References -- 6 A Good Defense Is a Strong DNN: Defending the IoT with Deep Neural Networks -- 6.1 Introduction -- 6.2 State of the Art in IoT Cyber Security -- 6.3 A Cause for Concern: IoT Cyber Security -- 6.3.1 Introduction to IoT Cyber Security -- 6.3.2 IoT Malware -- 6.4 Background of Machine Learning -- 6.4.1 Support Vector Machine (SVM) -- 6.4.2 Random Forest -- 6.4.3 Deep Neural Network (DNN) -- 6.5 Experiment -- 6.5.1 Training and Test Data -- 6.5.2 Baselines of the Machine Learning Models -- 6.6 Results and Discussion -- 6.6.1 Results -- 6.6.2 Discussion. , 6.7 Conclusion -- References -- Part IIIDeep Learning in the Medical Field -- 7 Survey on Deep Learning Techniques for Medical Imaging Application Area -- 7.1 Introduction -- 7.2 From Machine Learning to Deep Learning -- 7.3 Learning Algorithm -- 7.4 ANN -- 7.4.1 Activation Function in ANN -- 7.4.2 Training Process -- 7.5 DNN -- 7.5.1 Supervised Deep Learning -- 7.5.2 Unsupervised Learning -- 7.6 MRI Preprocessing -- 7.6.1 Inter-series Sorting -- 7.6.2 Registration -- 7.6.3 Normalization -- 7.6.4 Correction of the Bias Field -- 7.7 Deep Learning Applications in Medical Imagining -- 7.7.1 Classification -- 7.7.2 Detection -- 7.7.3 Segmentation -- 7.7.4 Registration -- 7.8 Conclusion -- References -- 8 Deep Learning Methods in Electroencephalography -- 8.1 Introduction -- 8.1.1 A Short Introduction to EEG -- 8.2 Literature Review -- 8.2.1 Public Datasets -- 8.2.2 Preprocessing Methods -- 8.2.3 Input Representation -- 8.2.4 Data Augmentation -- 8.2.5 Architectures -- 8.2.6 Features Visualization -- 8.2.7 Applications -- 8.3 Practical Example-Eriksen Flanker Task -- 8.3.1 Materials -- 8.4 Summary -- References -- Part IVDeep Learning in Systems Control -- 9 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 -- 9.1 Introduction -- 9.2 Centrifugal Chiller System Decomposition-Closed-Loop Simulations -- 9.3 MISO ARMAX and ANFIS Models of MIMO Centrifugal Chiller Plant -- 9.3.1 MISO ARMAX and ANFIS Evaporator Subsystem Models -- 9.3.2 MISO ARMAX and ANFIS Condenser Subsystem Models -- 9.4 Centrifugal Chiller PID Closed-Loop Control Strategies-Performance Analysis -- 9.5 Conclusions -- References -- 10 A Review of Deep Reinforcement Learning Algorithms and Comparative Results on Inverted Pendulum System -- 10.1 Introduction. , 10.2 Reinforcement Learning Background -- 10.2.1 Markov Decision Process -- 10.2.2 Deep-Q Learning -- 10.2.3 Double Deep-Q Learning -- 10.2.4 Double Dueling Deep-Q Learning -- 10.2.5 Reinforce -- 10.2.6 Asynchronous Deep Reinforcement Learning Methods -- 10.3 Inverted Pendulum Problem -- 10.4 Experimental Results -- 10.5 Conclusions -- References -- Part VDeep Learning in Feature Vector Processing -- 11 Stock Market Forecasting by Using Support Vector Machines -- 11.1 Introduction -- 11.2 Support Vector Machines -- 11.3 Determinants of Risk and Volatility in Stock Prices -- 11.4 Predictions of Stock Market Movements by Using SVM -- 11.4.1 Data Processing -- 11.4.2 The Proposed SVM Model -- 11.4.3 Feature Selection -- 11.5 Results and Conclusions -- References -- 12 An Experimental Exploration of Machine Deep Learning for Drone Conflict Prediction -- 12.1 Introduction -- 12.1.1 Airspace and Traffic Assumptions -- 12.1.2 Methodological Assumptions -- 12.2 A Brief Introduction to Artificial Neural Networks (ANNs) -- 12.3 Drone Test Scenarios and Traffic Samples -- 12.3.1 Experimental Design -- 12.3.2 ANN Design -- 12.3.3 Procedures -- 12.4 Results -- 12.4.1 Binary Classification Accuracy -- 12.4.2 Classification Sensitivity and Specificity -- 12.4.3 The Extreme Scenario -- 12.4.4 ROC Analysis -- 12.4.5 Summary of Results -- 12.5 Conclusions -- References -- 13 Deep Dense Neural Network for Early Prediction of Failure-Prone Students -- 13.1 Introduction -- 13.2 Literature Review -- 13.3 The Deep Dense Neural Network -- 13.4 Experimental Process and Results -- 13.5 Conclusions -- References -- Part VIEvaluation of Algorithm Performance -- 14 Non-parametric Performance Measurement with Artificial Neural Networks -- 14.1 Introduction -- 14.2 Data Envelopment Analysis -- 14.3 Artificial Neural Networks -- 14.4 Proposed Approach. , 14.4.1 Data Generation-Training and Testing Samples -- 14.4.2 ANN Architecture and Training Algorithm -- 14.5 Results -- 14.6 Conclusion -- References -- 15 A Comprehensive Survey on the Applications of Swarm Intelligence and Bio-Inspired Evolutionary Strategies -- 15.1 Introduction -- 15.2 Nature Inspired Intelligence -- 15.2.1 Swarm Intelligence -- 15.2.2 Algorithms Inspired by Organisms -- 15.3 Application Areas and Open Problems for NII -- 15.3.1 Applications of Swarm Intelligent Methods -- 15.3.2 Applications of Organisms-Inspired Algorithms -- 15.3.3 Comparison and Discussion -- 15.3.4 Are All These Algorithms Actually Needed? -- 15.4 Suggestions and Future Work -- References -- 16 Detecting Magnetic Field Levels Emitted by Tablet Computers via Clustering Algorithms -- 16.1 Introduction -- 16.2 Measurement of the Tablet Magnetic Field -- 16.2.1 Magnetic Field -- 16.2.2 Measuring Devices -- 16.2.3 TCO Standard -- 16.2.4 The Realized Experiment -- 16.2.5 A Typical Way of Working with the Tablet -- 16.3 Magnetic Field Clustering -- 16.3.1 K-Means Clustering -- 16.3.2 K-Medians Clustering -- 16.3.3 Self-Organizing Map Clustering -- 16.3.4 DBSCAN Clustering -- 16.3.5 Expectation-Maximization with Gaussian Mixture Models -- 16.3.6 Hierarchical Clustering -- 16.3.7 Fuzzy-C-Means Clustering -- 16.4 Evaluation of the Tablet User Exposure to ELF Magnetic Field -- 16.5 Results and Discussion -- 16.5.1 Measurement Results -- 16.5.2 Clustering Results -- 16.5.3 The foe Results Measurement -- 16.6 Conclusions -- References.