Tabla de Contenidos: “…Analyser l'ensemble du réseau routier principal en Afrique -- Carte 2.6. La configuration du réseau routier africain avec la longueur des routes symbolisant la durée du trajet et les couleurs reflétant le potentiel du marché atteignable, avec et sans délai aux frontières -- Graphique 2.2. …”

Tabla de Contenidos: “…Politiques environnementale et sociale -- Rôle des politiques environnementale et sociale pour attirer les IDE -- La protection de l'environnement constitue maintenant une politique nationale clé en Chine -- Un cadre réglementaire bien conçu a été mis en place dans le domaine de l'environnement -- Encadré 3.2. Principes directeurs de l'OCDE à l'intention des entreprises multinationales -- Évaluation de l'impact environnemental des projets d'investissement proposés -- Pénalités pour rejets excessifs d'effluents -- « Concours de laxisme » et « concours de beauté »…”

Tabla de Contenidos: “…Travailleurs qui ont obtenu un permis de travail saisonnier par principal pays de destination, 2007-14 -- Tableau 1.3. …”

Tabla de Contenidos: “…4.4.2 EEG Data Producer -- 4.5 Information Obtained by EEG Signals -- 4.5.1 System Structure -- 4.5.2 Numerical Examination -- 4.5.3 EEG Circumference -- 4.6 Discussion -- 4.6.1 Comparison Between IQ Levels With Different Methods -- 4.7 Conclusion -- References -- Chapter 5 Machine Learning Methods in Radio Frequency and Microwave Domain -- 5.1 Introduction -- 5.2 Background on Machine Learning -- 5.2.1 Clustering -- 5.2.2 Principal Component Analysis -- 5.2.3 Naïve Bayes Algorithms -- 5.2.4 Support Vector Machines -- 5.2.5 Artificial Neural Networks -- 5.3 ML in RF Circuit Modeling and Synthesis -- 5.4 Conclusion -- References -- Chapter 6 A Survey: Emotion Detection Using Facial Reorganization Using Convolutional Neural Network (CNN) and Viola-Jones Algorithm -- 6.1 Introduction -- 6.1.1 Purpose -- 6.1.2 Process Flow -- 6.2 Review of Literature -- 6.3 Report on Present Investigation -- 6.3.1 Analysis of the Model -- 6.3.1.1 Emotion Recognition -- 6.4 Algorithms -- 6.4.1 CNN -- 6.4.2 Advantages -- 6.4.3 Disadvantages -- 6.5 Viola-Jones Algorithm -- 6.5.1 Training -- 6.5.2 Detection -- 6.6 Diagram -- 6.6.1 Working Diagram for Systems -- 6.6.2 The Application's Use Case Diagram -- 6.7 Results and Discussion -- 6.8 Limitations and Future Scope -- 6.9 Summary and Conclusion -- References -- Chapter 7 Power Quality Events Classification Using Digital Signal Processing and Machine Learning Techniques -- 7.1 Introduction -- 7.2 Methodology for the Identification of PQ Events -- 7.3 Power Quality Problems Arising in the Modern Power System -- 7.3.1 Sag -- 7.3.2 Swell -- 7.3.3 Overvoltage -- 7.3.4 Undervoltage -- 7.3.5 Impulsive Transient -- 7.3.6 Oscillatory Transient -- 7.3.7 Harmonics -- 7.4 Digital Signal Processing-Based Feature Extraction of PQ Events -- 7.4.1 Wavelet Transform-Based Feature Extraction -- 7.4.2 Multiresolution Analysis…”

Tabla de Contenidos: “…Hiérarchie des recommandations -- 9.2. Principes -- 9.3. Mesures visant à inciter les banques à se livrer concurrence -- 9.3.1. …”

Tabla de Contenidos: “…2.6.1 Use of Texture and Context -- 2.6.2 Using Ancillary Multisource Data -- 2.7 Epilogue -- References -- Chapter 3 Dimensionality Reduction: Feature Extraction and Selection -- 3.1 Feature Extraction -- 3.1.1 Principal Component Analysis -- 3.1.2 Minimum/Maximum Autocorrelation Factors -- 3.1.3 Maximum Noise Fraction (MNF) Transformation -- 3.1.4 Independent Component Analysis -- 3.1.5 Projection Pursuit -- 3.2 Feature Selection -- 3.2.1 Greedy Search Methods -- 3.2.2 Simulated Annealing -- 3.2.3 Separability Indices -- 3.2.4 Filter-Based Methods -- 3.2.4.1 Correlation-Based Feature Selection -- 3.2.4.2 Information Gain -- 3.2.4.3 Gini Impurity Index -- 3.2.4.4 Minimum Redundancy-Maximum Relevance -- 3.2.4.5 Chi-Square Test -- 3.2.4.6 Relief-F -- 3.2.4.7 Symmetric Uncertainty -- 3.2.4.8 Fisher's Test -- 3.2.4.9 OneR -- 3.2.5 Wrappers -- 3.2.5.1 Genetic Algorithm -- 3.2.5.2 Particle Swarm Optimization -- 3.2.5.3 Feature Selection with SVMs -- 3.2.6 Embedded Methods -- 3.2.6.1 K-Nearest Neighbor-Based Feature Selection -- 3.2.6.2 Feature Selection with Ensemble Learners -- 3.2.6.3 Hilbert-Schmidt Independence Criterion with Lasso -- 3.3 Concluding Remarks -- References -- Chapter 4 Multisource Image Fusion and Classification -- 4.1 Image Fusion -- 4.1.1 Image Fusion Methods -- 4.1.1.1 PCA-Based Image Fusion -- 4.1.1.2 IHS-Based Image Fusion -- 4.1.1.3 Brovey Transform -- 4.1.1.4 Gram-Schmidt Transform -- 4.1.1.5 Wavelet Transform -- 4.1.1.6 Deep Learning for Image Fusion -- 4.1.2 Assessment of Fused Image Quality -- 4.1.3 Performance Evaluation of Fusion Methods -- 4.2 Multisource Classification Using the Stacked-Vector Method -- 4.3 The Extension of Bayesian Classification Theory -- 4.3.1 An Overview -- 4.3.1.1 Feature Extraction -- 4.3.1.2 Probability or Evidence Generation -- 4.3.1.3 Multisource Consensus…”

Tabla de Contenidos: “…. ° Todos en el aula guardarán silencio, el que continuarán observando al salir del aula hasta la puerta principal del Seminario, saliendo todos de dos en dos cada unocon su respectivo compañero. …”

Tabla de Contenidos: “…8.10.3 Stability of MPC -- Exercises -- Part IV Learning -- Chapter 9 Unsupervised Learning -- 9.1 Chebyshev Bounds -- 9.2 Entropy -- 9.2.1 Categorical Distribution -- 9.2.2 Ising Distribution -- 9.2.3 Normal Distribution -- 9.3 Prediction -- 9.3.1 Conditional Expectation Predictor -- 9.3.2 Affine Predictor -- 9.3.3 Linear Regression -- 9.4 The Viterbi Algorithm -- 9.5 Kalman Filter on Innovation Form -- 9.6 Viterbi Decoder -- 9.7 Graphical Models -- 9.7.1 Ising Distribution -- 9.7.2 Normal Distribution -- 9.7.3 Markov Random Field -- 9.8 Maximum Likelihood Estimation -- 9.8.1 Categorical Distribution -- 9.8.2 Ising Distribution -- 9.8.3 Normal Distribution -- 9.8.4 Generalizations -- 9.9 Relative Entropy and Cross Entropy -- 9.9.1 Gibbs' Inequality -- 9.9.2 Cross Entropy -- 9.10 The Expectation Maximization Algorithm -- 9.11 Mixture Models -- 9.12 Gibbs Sampling -- 9.13 Boltzmann Machine -- 9.14 Principal Component Analysis -- 9.14.1 Solution -- 9.14.2 Relation to Rank‐Constrained Optimization -- 9.15 Mutual Information -- 9.15.1 Channel Model -- 9.15.2 Orthogonal Case -- 9.15.3 Nonorthogonal Case -- 9.15.4 Relationship to PCA -- 9.16 Cluster Analysis -- Exercises -- Chapter 10 Supervised Learning -- 10.1 Linear Regression -- 10.1.1 Least‐Squares Estimation -- 10.1.2 Maximum Likelihood Estimation -- 10.1.3 Maximum a Posteriori Estimation -- 10.2 Regression in Hilbert Spaces -- 10.2.1 Infinite‐Dimensional LS Problem -- 10.2.2 The Kernel Trick -- 10.3 Gaussian Processes -- 10.3.1 Gaussian MAP Estimate -- 10.3.2 The Kernel Trick -- 10.4 Classification -- 10.4.1 Linear Regression -- 10.4.2 Logistic Regression -- 10.5 Support Vector Machines -- 10.5.1 Hebbian Learning -- 10.5.2 Quadratic Programming Formulation -- 10.5.3 Soft Margin Classification -- 10.5.4 The Dual Problem -- 10.5.5 Recovering the Primal Solution -- 10.5.6 The Kernel Trick…”

Tabla de Contenidos: “…7.3.8 Deep Learning Algorithm -- 7.3.9 Deep Learning Application -- 7.4 Unsupervised Learning Algorithms for Medical Data Analysis -- 7.4.1 Clustering Algorithm -- 7.4.2 Principal Component Analysis Algorithm -- 7.4.3 Independent Component Analysis Algorithm -- 7.4.4 Association Rule Mining Algorithm -- 7.5 Applications of Machine-Learning Algorithms in Medical Data Analysis -- 7.6 Limitations and Challenges of Machine Learning Algorithms in Medical Data Analysis -- 7.7 Future Research Directions and Machine Learning Developments in the Realm of Medical Data Analysis -- 7.8 Conclusion -- References -- Part II: Applications and Analytics -- Chapter 8 Fog Computing in Healthcare: Application Taxonomy, Challenges and Opportunities -- 8.1 Introduction -- 8.2 Research Methodology -- 8.3 Application Taxonomy in FC-Based Healthcare -- 8.3.1 Diagnosis -- 8.3.2 Monitoring -- 8.3.3 Notification -- 8.3.4 Zest of Applications of FC in Healthcare -- 8.4 Challenges in FC-Based Healthcare -- 8.4.1 QoS Optimization -- 8.4.2 Patient Authentication and Access Control -- 8.4.3 Data Processing -- 8.4.4 Data Privacy Preservation -- 8.4.5 Energy Efficiency -- 8.5 Research Opportunities -- 8.5.1 Research Opportunity in Computing -- 8.5.2 Research Opportunity in Security -- 8.5.3 Research Opportunity in Services -- 8.5.4 Research Opportunity in Implementation -- 8.6 Conclusion -- References -- Chapter 9 IoT-Driven Predictive Maintenance Approach in Industry 4.0: A Fiber Bragg Grating (FBG) Sensor Application -- 9.1 Introduction -- 9.2 Review of Related Research Articles -- 9.2.1 Studies on FBG Sensors and Their Role in Industry 4.0 -- 9.2.1.1 Magnetostrictive Material -- 9.2.1.2 Magneto-Optical (MO) Materials -- 9.2.1.3 Magnetic Fluid (MF) Materials -- 9.2.1.4 Magnetically Sensitive Materials and Their Application -- 9.2.1.5 Optical Fiber Current Sensors…”

Tabla de Contenidos: “…Using DCE objects with IBM Tivoli Access Manager -- 5.1 Introduction -- 5.2 Data representation -- 5.3 Configuration scenarios -- 5.3.1 Scenario 1 -- 5.3.2 Scenario 2 -- 5.3.3 Scenario 3 -- 5.4 Managing objects in a shared environment -- 5.4.1 Creating a user with IBM Tivoli Access Manager -- 5.4.2 Creating a group with IBM Tivoli Access Manager -- 5.4.3 Adding a member to a group using IBM Tivoli Access Manager -- 5.4.4 Deleting a user using IBM Tivoli Access Manager -- 5.4.5 Deleting a group using IBM Tivoli Access Manager -- 5.4.6 Removing a member from an IBM Tivoli Access Manager group -- 5.4.7 Creating a principal with DCE -- 5.4.8 Creating a DCE group -- 5.4.9 Adding a member to a group using DCE -- 5.4.10 Deleting a user using DCE…”

Tabla de Contenidos: “…6.2.2 Wrapper Method -- 6.2.2.1 Procedure -- 6.2.2.2 Advantages and Disadvantages -- 6.2.2.3 Forward Selection Algorithm -- 6.2.2.4 Backward Selection Algorithm -- 6.2.3 Embedded Method -- 6.2.3.1 Least Absolute Shrinkage and Selection Operator -- 6.2.3.2 Advantages -- 6.2.3.3 Disadvantages -- 6.3 Feature Extraction -- 6.3.1 Principal Component Analysis -- 6.3.1.1 Procedure -- 6.3.1.2 Implementation -- 6.3.1.3 Advantages -- 6.3.1.4 Disadvantages -- 6.3.2 Linear Discriminant Analysis -- 6.3.2.1 Concept -- 6.3.2.2 Implementation -- 6.3.2.3 Advantages -- 6.3.2.4 Disadvantages -- 6.4 Feature Learning -- 6.4.1 Supervised Learning -- 6.4.2 Unsupervised Learning -- 6.4.2.1 Procedure -- 6.4.2.2 Advantages -- 6.4.2.3 Disadvantages -- 6.4.3 Deep Learning -- 6.4.3.1 Neural Network Architecture -- 6.4.3.2 Training Process -- 6.4.3.3 Advantages -- 6.4.3.4 Disadvantages -- 6.4.4 Machine Learning and Deep Learning -- 6.5 Future Research and Development -- 6.6 Future Scope -- 6.7 Conclusion -- References -- Chapter 7 Fusion of Phase and Local Features for CBIR -- 7.1 Introduction -- 7.2 Overview of the Proposed System -- 7.3 Proposed Hybrid-Shape Descriptors -- 7.3.1 Global Feature Extraction Using ZMs -- 7.3.1.1 Recurrence Relation for Radial Polynomials Rpq(r) -- 7.3.1.2 Recurrence Relation for Trigonometric Functions -- 7.3.2 Local Feature Extraction Using Hough Transform -- 7.3.3 Features Dimension -- 7.3.4 Effectiveness of the Proposed Descriptors -- 7.4 Similarity Measurement -- 7.5 Experimental Study and Performance Evaluation -- 7.5.1 Precision and Recall (P - R) -- 7.5.2 Database Construction -- 7.5.3 Experimental Study -- 7.5.3.1 Evaluation of Image Retrieval Performance on Subject Databases -- 7.5.3.2 Evaluation of Image Retrieval Performance on Geometric and Photometric Transformed Databases -- 7.5.3.3 Evaluation of Scalability and Time Complexity…”

Tabla de Contenidos: “…3.3.1.7 Different statistical method -- 3.3.1.7.1 Central tendency -- Mean -- Why do not use the mean -- Median -- Mode -- Variance and standard deviation -- Z-score -- Quartiles -- Percentile -- 3.4 Measurement and scaling concepts -- 3.4.1 Comparative scales -- 3.4.1.1 Paired comparison scale -- 3.4.1.2 Rank order scale -- 3.4.1.3 Constant sum scale -- 3.4.1.4 Q-sort scale -- 3.4.2 Non-comparative scales -- 3.4.2.1 Continuous rating scale -- 3.4.2.2 Itemized rating scale -- 3.4.2.2.1 Likert scale -- 3.4.2.2.2 Stapel scale -- 3.4.2.2.3 Semantic differential scale -- 3.5 Various types of scale -- 3.5.1 Nominal -- 3.5.2 Ordinal -- 3.5.3 Interval -- 3.5.4 Ratio -- 3.6 Primary data analysis with Python -- 3.7 Conclusion -- 3.8 Case study -- 3.8.1 Case study: taxonomy of data in a healthcare organization -- 3.8.2 Case study: taxonomy of data in the automobile industry -- 3.8.3 Case study on the data theory -- 3.9 Exercise -- 3.9.1 Objective type question -- 3.9.2 Descriptive type question -- Further reading -- 4 Multivariate data analytics and cognitive analytics -- Abbreviations -- 4.1 Introduction -- 4.2 Factor analytics -- 4.3 Principal component analytics -- 4.4 Cluster analytics -- 4.4.1 K-means -- 4.4.1.1 Algorithms -- 4.4.1.2 K-means clustering -- 4.1.2.1 Steps of the K-means clustering algorithm -- 4.1.2.2 Practice problems based on K-means clustering algorithm -- 4.4.2 Cluster analysis of driverless car dataset -- 4.4.2.1 Problem -- 4.5 Linear regression analysis -- 4.5.1 Mathematical expression for regression analysis -- 4.5.2 Solved example of linear regression analysis of driverless car -- 4.5.2.1 Problem -- 4.5.2.2 Solution -- 4.6 Logistic regression analysis -- 4.7 Application of analytics across value chain -- 4.8 Multivariate data analytics with Python -- 4.9 Conclusion -- 4.10 Case study…”