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Index
Preface
Contents
Introduction
Networking Ways of Communication Between People on the Planet
Internet of Things (IoT)
Information Technology
Science
Technology
1 The Cluster Analysis in Big Data Mining
1.1 Introduction
1.2 Cluster Analysis, Problem Definition. Criteria of Quality and Metrics
1.3 Classification of Algorithms of Cluster Analysis
1.3.1 Hierarchical Algorithms. Agglomerative Algorithms
1.3.2 Divisional Algorithms
1.3.3 Not Hierarchical Algorithms
1.4 Fuzzy C-Means Method
1.4.1 Algorithm of Fuzzy C-Means
1.4.2 Definition of Initial Location of the Centers of Clusters
1.5 Gustavson-Kessel’s Fuzzy Cluster Analysis Algorithm
1.5.1 Description of Gustavson-Kessel Algorithm
1.6 Adaptive Robust Clustering Algorithms
1.6.1 Possibilistic Clustering Algorithm
1.6.2 Recurrent Fuzzy Clustering Algorithms
1.6.3 Robust Adaptive Algorithms of Probabilistic Fuzzy Clustering
1.7 Robust Recursive Algorithm of Possibilistic Fuzzy Clustering for Big Data
1.8 Application of Fuzzy Clustering Methods in the Problems of Automatic Classification
1.9 Conclusions
References
2 Deep Neural Networks and Hybrid GMDH-Neuro-fuzzy Networks in Big Data Analysis
2.1 Introduction
2.2 Autoassociators. Autoencoders
2.3 Boltzmann Machines (BM)
2.3.1 Energetic Models
2.3.2 Restricted Boltzmann Machine (RBM)
2.4 Training Method Contrastive Divergence (CD)
2.4.1 Training Algorithm Contrastive Divergence (CD-k)
2.4.2 Example
2.5 Stacked Autoassociators Networks
2.5.1 Stacked Autoencoder (SAE)
2.5.2 Stacked RBM
2.6 Deep Networks Learning
2.6.1 Deep Network Pretraining
2.6.2 Fine-Tuning
2.7 Deep Learning Regularization
2.7.1 Lp-Regularization of Linear Regression
2.7.2 Early Stopping
2.7.3 Dropout
2.7.4 Bagging (Ensemble Method)
2.8 Cascade Neo-fuzzy Neural Networks Structure Synthesis and Learning with Application of GMDH
2.8.1 The Neo-fuzzy Neuron
2.8.2 The Neo-fuzzy Neuron Learning Algorithm
2.8.3 The Neo-fuzzy Neural Network and Its Architecture Optimization Using the Group Method of Data Handling
2.8.4 The Experimental Investigations of Forecasting with Neo-fuzzy Neural Network
2.9 Evolving GMDH-Neuro-fuzzy Network with Small Number of Tuning Parameters
2.9.1 Evolving GMDH-Neuro-fuzzy System Architecture
2.9.2 Neuro-fuzzy Network with Small Number of Tuning Parameters as a Node of GMDH-System
2.9.3 Computational Experiments
2.10 A Deep GMDH System Based on the Extended Neo-fuzzy Neuron and Its Training
2.10.1 An Architecture of the Deep GMDH Neuro-fuzzy System
2.10.1.1 The Extended Neo-fuzzy Neuron
2.10.2 The Adjustment Procedures for All Parameters of the System
2.10.3 An Experimental Study
References
3 Pattern Recognition in Big Data Analysis
3.1 Introduction
3.2 FNN NEFClass. Architecture, Properties, the Algorithms of Learning of Base Rules and Membership Functions
3.3 Analysis NEFClass Properties. The Modified System NEFClassM
3.3.1 The Modified Model NEFCLASS
3.4 Experimental Studies. Comparative Analysis of FNN NEFClass and NEFClass-M in Classification Problems
3.5 Application of NEFClass in the Problem of Objects Recognition at Electro-Optical Images
3.5.1 Gradient Learning Algorithm for NEFClass
3.5.2 Genetic Method for Training System NEFClass
3.5.3 Experiments on Objects Recognition on Optical Images
3.6 Recognition of Images in Medical Diagnostics Using Fuzzy Neural Networks
3.6.1 Problem Statement
3.6.2 Training of NEFClass System
3.6.3 Experimental Investigations
3.7 Medical Images of Breast Tumors Diagnostics with Application of Hybrid CNN–FNN Networks
3.7.1 State-of-Art Problem Analysis
3.7.2 Data Set Description
3.7.3 Convolutional Neural Networks Brief Description
3.7.4 CNN Model for Image Classification
3.7.5 Experimental Investigations and Results Analysis
References
4 Intellectual Analysis of Systemic World Conflicts and Global Forecast for the 21st Century
4.1 Introduction
4.2 Identifying the Regularity of the Emergence of Systemic World Conflicts, Based on the Analysis of Big Historical Data
4.2.1 Fibonacci Pattern of the Emergence of Systemic World Conflicts
4.2.2 Conflict of the 21st Century and Analysis of Its Nature
4.2.3 Modeling the Total Impact of the Aggregate of 12 Global Threats on Different Countries and Groups of Countries
4.2.4 Conclusions
4.3 Interrelation Between Periodic Processes in the Global Economy and Systemic World Conflicts
4.3.1 Periodicity of Global Systemic Conflicts and Economic Processes
4.3.2 Analysis of the Relationship Between Systemic World Conflicts and the Global Economy
4.3.3 Conclusions
4.4 Metric Aspects of Periodic Processes in Economy and Society
4.4.1 Initial Definitions
4.4.2 Structural Analysis of Global System Conflicts
4.4.3 Confirmation of the F-Pattern by Other Independent Studies
4.4.4 F-Principle as the Basis of a Metric Study of Global Civilization Processes
4.4.5 Conclusions
4.5 Big Solar Spiral of Stirring up Global Systemic Conflicts
4.5.1 Synchronous Variation of Solar Activity and Formation of C-Waves of Global Systemic Conflicts
4.5.2 Visualization of the Process of “Stirring Up” of the Family of \left\{ {{\varvec C}_{{\varvec K}} } \right\}_{{{\varvec K} \in {\varvec I}\left( {1;7} \right)}} -Waves of Global Systemic Conflicts
4.5.3 Local “Stirring Up” by {\varvec H}_{{\varvec W}}^{{\left( {\varvec K} \right)}} -Ensemble of Schwabe–Wolf Solar Cycles of Evolution Phases of {\varvec C}_{{\varvec k}} -Wave of Global Systemic Conflicts
4.5.4 Scenarios “XXI–2k” and “XXI–3k” of Global Civilizational Processes During the Seventh Systemic Global Conflict
4.5.5 Conclusions
4.6 Influence of Global Threats on the Sustainable Development of Countries and Regions of the World
4.6.1 The Methodology of Sustainable Development Evaluation in Terms of Quality and Security of the Human Life
4.6.2 Some Basic Definitions and Concepts
4.6.3 Synthesis of Topologies of BBNs
4.6.4 Modelling the Influence of Global Threats on the Sustainable Development of Countries and Regions of the World with the Use of BBNs
4.6.5 Interpretation of the Generalized Results of Modeling
4.6.6 Visualization of Data on Indicators of Sustainable Development for Countries and Regions of the World
4.6.7 Conclusions
4.7 The General Concept of the Periodic Systemic World Conflicts
4.7.1 Some Concepts and Definitions
4.7.2 Geometric Images of C_{{\rm K}}^{{}} -Waves and Ensemble of \left( {SWC} \right)_{\alpha } -Waves of Systemic World Conflicts
4.7.3 Significant Features of SWC-Concept
4.7.4 Correlation of Processes of Evolutionary Development of Civilization \varPi_{C}^{Ed} and Development of C-Waves of Systemic World Conflicts \pi_{swc}^{es} \left( {{{\cal L}}_{c} \left( {m,n} \right)} \right)
4.7.5 The Problem of Identification (Recognition) of C-Waves of Systemic World Conflicts for Big Historical Data
4.7.6 Big C -Waves of Systemic World Conflicts
4.8 Conclusions
References
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