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Computational intelligence for multimedia big data on the cloud with engineering applications /
Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications covers timely topics, including the neural network (NN), particle swarm optimization (PSO), evolutionary algorithm (GA), fuzzy sets (FS) and rough sets (RS), etc. Furthermore, the book highlights recent res...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London :
Academic Press,
[2018]
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| Colección: | Intelligent data centric systems.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Front Cover; Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications; Copyright; Contents; Contributors; Foreword; Preface; Organization of the Book; Audience; 1 A Cloud-Based Big Data System to Support Visually Impaired People; 1.1 Introduction; 1.2 Related Work; 1.3 Background; 1.3.1 Internet of Things (IoT); 1.3.2 Cloud Computing; 1.3.3 Face Detection and Recognition; 1.3.4 Optical Character Recognition (OCR); 1.4 Problem Statement; 1.5 System Architecture; 1.5.1 Top-Level Architecture; 1.6 Big Data Analytics; 1.6.1 Text Recognition
- 1.6.2 Face Recognition1.7 Prototype; 1.8 Evaluation; 1.9 Conclusion; References; 2 Computational Intelligence in Smart Grid Environment; 2.1 Introduction; 2.1.1 Power Load Forecasting; 2.1.2 Electricity Price Forecasting; 2.1.3 Smart Grid Optimization; 2.2 Related Work and Open Issues; 2.2.1 Power Load Forecasting; 2.2.1.1 Stream Forecasting; 2.2.1.2 Adaptivity; 2.2.2 Prediction of Electricity Spot Prices in Smart Grid; 2.2.3 Optimization and Metaheuristics in Big Data and Microgrids; 2.3 Overview of Methods Used in Smart Grid Problems; 2.3.1 Forecasting Methods
- 2.3.1.1 Statistical Techniques2.3.1.2 Arti cial Intelligence Techniques; 2.3.1.3 Hybrid Techniques (Ensemble Learning); 2.3.2 Optimization Methods; 2.3.2.1 Particle Swarm Optimization; 2.3.2.2 Arti cial Bee Colony; 2.3.2.3 Genetic Algorithm; 2.3.2.4 Hyper-Heuristics; 2.4 Proposed Methods; 2.4.1 Electricity Price Forecasting; 2.4.2 Power Load Forecasting; 2.4.2.1 Adaptive Ensemble Learning for Power Load Forecasting; 2.4.2.2 Online Support Vector Regression; 2.4.2.3 Data; 2.4.2.4 Results; 2.5 Future Work; 2.6 Conclusions; Acknowledgment; References
- 3 Patient Facial Emotion Recognition and Sentiment Analysis Using Secure Cloud With Hardware Acceleration3.1 Introduction; 3.2 System Overview; 3.3 Background; 3.3.1 Facial Emotion Recognition; 3.3.2 Big Data Analytics on the Cloud; 3.3.3 Deep Learning Using Convolutional Neural Networks (CNNs); 3.4 System Architecture; 3.4.1 Face Detection in Images; 3.4.2 Facial Emotion Recognition Using CNNs; 3.4.3 The CNN Model Training; 3.5 System Implementation; 3.5.1 A Secure, Multi-tenant Cloud Storage System; 3.6 Experiments; 3.6.1 Dataset; 3.6.2 GPU Benchmarking and Comparison
- 3.6.3 Facial Emotion Recognition Accuracy3.6.4 Model Performance and Power With Hardware Acceleration; 3.7 DeepPain: Mapping Patient Emotions to Pain Intensity Levels; 3.8 Conclusions and Future Work; Acknowledgments; References; 4 Novel Computational Intelligence Techniques for Automatic Pain Detection and Pain Intensity Level Estimation From Facial Expressions Using Distributed Computing for Big Data; 4.1 Introduction; 4.2 Background and History of Computational Techniques; 4.2.1 Feature Extraction Techniques; 4.2.2 Dimension Reduction Techniques