دانلود کتاب Advances in Machine Learning/Deep Learning-based Technologies: Selected Papers in Honour of Professor Nikolaos G. Bourbakis – Vol. 2 (Learning and Analytics in Intelligent Systems, 23)

فهرست مطالب :

Foreword
Further Reading
Preface
Contents
1 Introduction to Advances in Machine Learning/Deep Learning-Based Technologies
1.1 Editorial Note
1.2 Book Summary and Future Volumes
References
Part I Machine Learning/Deep Learning in Socializing and Entertainment
2 Semi-supervised Feature Selection Method for Fuzzy Clustering of Emotional States from Social Streams Messages
2.1 Introduction
2.2 The FS-EFCM Algorithm
2.2.1 EFCM Execution: Main Steps
2.2.2 Initial Parameter Setting
2.3 Experimental Results
2.3.1 Dataset
2.3.2 Feature Selection
2.3.3 FS-EFCM at Work
2.4 Conclusion
References
3 AI in (and for) Games
3.1 Introduction
3.2 Game Content and Databases
3.3 Intelligent Game Content Generation and Selection
3.3.1 Generating Content for a Language Education Game
3.4 Conclusions
References
Part II Machine Learning/Deep Learning in Education
4 Computer-Human Mutual Training in a Virtual Laboratory Environment
4.1 Introduction
4.1.1 Purpose and Development of the Virtual Lab
4.1.2 Different Playing Modes
4.1.3 Evaluation
4.2 Background and Related Work
4.3 Architecture of the Virtual Laboratory
4.3.1 Conceptual Design
4.3.2 State-Transition Diagrams
4.3.3 High Level Design
4.3.4 State Machine
4.3.5 Individual Scores
4.3.6 Quantization
4.3.7 Normalization
4.3.8 Composite Evaluation
4.3.9 Success Rate
4.3.10 Weighted Average
4.3.11 Artificial Neural Network
4.3.12 Penalty Points
4.3.13 Aggregate Score
4.4 Machine Learning Algorithms
4.4.1 Genetic Algorithm for the Weighted Average
4.4.2 Training the Artificial Neural Network with Back-Propagation
4.5 Implementation
4.5.1 Instruction Mode
4.5.2 Evaluation Mode
4.5.3 Computer Training Mode
4.5.4 Training Data Collection Sub-mode
4.5.5 Machine Learning Sub-mode
4.6 Training-Testing Process and Results
4.6.1 Training Data
4.6.2 Training and Testing on Various Data Set Groups
4.6.3 Genetic Algorithm Results
4.6.4 Artificial Neural Network Training Results
4.7 Conclusions
References
5 Exploiting Semi-supervised Learning in the Education Field: A Critical Survey
5.1 Introduction
5.2 Semi-supervised Learning
5.3 Literature Review
5.3.1 Performance Prediction
5.3.2 Dropout Prediction
5.3.3 Grade Level Prediction
5.3.4 Grade Point Value Prediction
5.3.5 Other Studies
5.3.6 Discussion
5.4 The Potential of SSL in the Education Field
5.5 Conclusions
References
Part III Machine Learning/Deep Learning in Security
6 Survey of Machine Learning Approaches in Radiation Data Analytics Pertained to Nuclear Security
6.1 Introduction
6.2 Machine Learning Methodologies in Nuclear Security
6.2.1 Nuclear Signature Identification
6.2.2 Background Radiation Estimation
6.2.3 Radiation Sensor Placement
6.2.4 Source Localization
6.2.5 Anomaly Detection
6.3 Conclusion
References
7 AI for Cybersecurity: ML-Based Techniques for Intrusion Detection Systems
7.1 Introduction
7.1.1 Why Does AI Pose Great Importance for Cybersecurity?
7.1.2 Contribution
7.2 ML-Based Models for Cybersecurity
7.2.1 K-Means
7.2.2 Autoencoder (AE)
7.2.3 Generative Adversarial Network (GAN)
7.2.4 Self Organizing Map
7.2.5 K-Nearest Neighbors (k-NN)
7.2.6 Bayesian Network
7.2.7 Decision Tree
7.2.8 Fuzzy Logic (Fuzzy Set Theory)
7.2.9 Multilayer Perceptron (MLP)
7.2.10 Support Vector Machine (SVM)
7.2.11 Ensemble Methods
7.2.12 Evolutionary Algorithms
7.2.13 Convolutional Neural Networks (CNN)
7.2.14 Recurrent Neural Network (RNN)
7.2.15 Long Short Term Memory (LSTM)
7.2.16 Restricted Boltzmann Machine (RBM)
7.2.17 Deep Belief Network (DBN)
7.2.18 Reinforcement Learning (RL)
7.3 Open Topics and Potential Directions
7.3.1 Novel Feature Representations
7.3.2 Unsupervised Learning Based Detection Systems
References
Part IV Machine Learning/Deep Learning in Time Series Forecasting
8 A Comparison of Contemporary Methods on Univariate Time Series Forecasting
8.1 Introduction
8.2 Related Work
8.3 Theoretical Background
8.3.1 ARIMA
8.3.2 Prophet
8.3.3 The Holt-Winters Seasonal Models
8.3.4 N-BEATS: Neural Basis Expansion Analysis
8.3.5 DeepAR
8.3.6 Trigonometric BATS
8.4 Experiments and Results
8.4.1 Datasets
8.4.2 Algorithms
8.4.3 Evaluation
8.4.4 Results
8.5 Conclusions
References
9 Application of Deep Learning in Recurrence Plots for Multivariate Nonlinear Time Series Forecasting
9.1 Introduction
9.2 Related Work
9.2.1 Background on Recurrence Plots
9.2.2 Time Series Imaging and Convolutional Neural Networks
9.3 Time Series Nonlinearity
9.4 Time Series Imaging
9.4.1 Dimensionality Reduction
9.4.2 Optimal Parameters
9.5 Convolutional Neural Networks
9.6 Model Pipeline and Architecture
9.6.1 Architecture
9.7 Experimental Setup
9.8 Results
9.9 Conclusion
References
Part V Machine Learning in Video Coding and Information Extraction
10 A Formal and Statistical AI Tool for Complex Human Activity Recognition
10.1 Introduction
10.2 The Hybrid Framework—Formal Languages
10.3 Formal Tool and Statistical Pipeline Architecture
10.4 DATA Pipeline
10.5 Tools for Implementation
10.6 Experimentation with Datasets to Identify the Ideal Model
10.6.1 KINISIS—Single Human Activity Recognition Modeling
10.6.2 DRASIS—Change of Human Activity Recognition Modeling
10.7 Conclusions
References
11 A CU Depth Prediction Model Based on Pre-trained Convolutional Neural Network for HEVC Intra Encoding Complexity Reduction
11.1 Introduction
11.2 H.265 High Efficiency Video Coding
11.2.1 Coding Tree Unit Partition
11.2.2 Rate Distortion Optimization
11.2.3 CU Partition and Image Texture Features
11.3 Proposed Methodology
11.3.1 The Hierarchical Classifier
11.3.2 The Methodology of Transfer Learning
11.3.3 Structure of Convolutional Neural Network
11.3.4 Dataset Construction
11.4 Experiments and Results
11.5 Conclusion
References