Tabla de Contenidos: “…Sampling for modeling and validation -- 4.3.1. Test and training splits -- 4.3.2. …”

Tabla de Contenidos: “…4.2 Current State of Science -- 4.2.1 Theoretical Framework -- 4.2.2 Smoke Measurements -- 4.2.3 Smoke Plume Modeling -- 4.2.4 Interactive Processes -- 4.2.5 Smoke Decision Support Systems -- 4.3 Gaps in Understanding Plume Dynamics -- 4.3.1 Measurements -- 4.3.2 Plume Rise -- 4.3.3 Dispersion and Transport Modeling -- 4.3.4 Nighttime Smoke -- 4.3.5 Physics-Based Fire Models -- 4.3.6 Smoke Management for Prescribed Fires -- 4.4 Vision for Improving Plume Dynamics Science -- 4.4.1 New Research on Observational and Computational Capabilities -- 4.4.2 New Approaches and Tools -- 4.4.3 New Projects -- 4.4.4 Recent Policies and Integration with Smoke Impacts Research -- 4.5 Emerging Issues and Challenges -- 4.5.1 Coupled Modeling Systems -- 4.5.2 Improving Modeling Tools with Field Campaign Data -- 4.5.3 Real-Time Smoke Transport Modeling and Prediction -- 4.5.4 Smoke from Duff Burning Under Drought Conditions -- 4.5.5 Smoke Plume Dynamics and Climate Change -- 4.5.6 Smoke Dynamics in the Earth System -- 4.6 Conclusions -- 4.7 Key Findings -- References -- 5 Emissions -- 5.1 Introduction -- 5.2 Current State of the Science -- 5.2.1 Fuel Properties, Combustion Processes, and Emissions -- 5.2.2 Smoke Composition and Emission Factors -- 5.2.3 Emission Calculations -- 5.3 Existing Data, Tools, Models, and Other Technology -- 5.3.1 Emission Factors -- 5.3.2 Emission Inventories -- 5.3.3 Emission Models for Land Management -- 5.4 Gaps in Data, Understanding, and Tools/Technology -- 5.4.1 Emission Factors for Wildfires -- 5.4.2 Connecting Laboratory Studies with Field Observations -- 5.4.3 Variability of EFs with Combustion Conditions -- 5.4.4 Validation of Emission Inventories -- 5.4.5 Forecasting Wildfire Emissions -- 5.4.6 Measuring and Modeling PM2.5 -- 5.4.7 Emissions of Hazardous Air Pollutants -- 5.4.8 Emissions from Structure Fires -- 5.5 Conclusions…”

Tabla de Contenidos: “…2.6.1 Multilevel Analysis: Example Implementation -- 2.7 Structural Estimation -- 2.7.1 Model Selection -- 2.7.2 An Illustration -- 2.7.3 A Word on Standard Errors -- 2.7.4 Subject Heterogeneity: Finite Mixture Models -- 2.8 Concluding Remarks -- Acknowledgments -- References -- Chapter 3 Incorporating Behavioral Factors into Operations Theory -- 3.1 Types of Behavioral Models -- 3.1.1 Nonstandard Preferences -- 3.1.2 Nonstandard Decision‐making -- 3.1.3 Nonstandard Beliefs -- 3.2 Identifying Which Behavioral Factors to Include -- 3.2.1 Robustly Observed -- 3.2.2 One/A Few Factors Explain Many Phenomena -- 3.2.3 Boundaries and Observed Behavioral Factors -- 3.3 Nesting the Standard Model -- 3.3.1 Reference Dependence -- 3.3.2 Social Preferences and Comparison -- 3.3.3 Quantal Response Equilibrium -- 3.3.4 Cognitive Hierarchy in Games -- 3.3.5 Learning -- 3.3.6 Overconfidence -- 3.4 Developing Behavioral Operations Model -- 3.4.1 Parsimony Is Still Important -- 3.4.2 Adding One Versus Many Behavioral Factors -- 3.5 Modeling for Testable Predictions -- References -- Chapter 4 Behavioral Empirics and Field Experiments -- 4.1 Going to the Field to Study Behavioral Operations -- 4.1.1 External Validity and Identification of Effect Size -- 4.1.2 Overcome Observer Bias -- 4.1.3 Context -- 4.1.4 Time‐based Effects -- 4.1.5 Beyond Individual Decision‐making -- 4.2 Analyzing the Data: Common Empirical Methods -- 4.2.1 Reduced Form Analysis of Panel Data -- 4.2.2 Difference in Differences -- 4.2.3 Program or Policy Evaluations -- 4.2.4 Regression Discontinuity -- 4.2.5 Structural Estimation -- 4.3 Field Experiments (Creating the Data) -- 4.3.1 Experimental Design -- 4.3.2 Field Sites and Organizational Partners -- 4.3.3 Ethics and Human Subject Protocol -- 4.4 Conclusion: The Way Forward -- References -- Part II Classical Approaches to Analyzing Behavior…”

Tabla de Contenidos: “…13.3.2 Mesh Generation and Study -- 13.3.3 Grid Independency -- 13.3.4 Validation -- 13.4 Results -- 13.4.1 Optimization of the Nozzle -- 13.4.2 Investigation of the Relation between Outlet Velocity and Entrainment Parameter (N) -- 13.4.3 Unsteady Case -- 13.5 Conclusion -- Declaration of interests -- References -- 14 Radiative Non-Newtonian Nanofluid Flow through Stretchable Disks: An Application to Solar Thermal Systems -- 14.1 Introduction -- 14.2 Problem Formulation -- 14.3 Numerical Solution -- 14.4 Results and Discussion -- 14.5 Conclusions -- References -- 15 Cooling of PV/ T System with Nanofluid and PCM -- 15.1 Introduction -- 15.1.1 Overview -- 15.1.2 Need for Cooling of Photovoltaics -- 15.2 Application of Nanofluid and PCM for Cooling of PV/T System -- 15.2.1 Nanofluids -- 15.2.2 Phase Change Materials -- 15.3 A Review of Studies Using Nanofluid and PCM for Cooling of PV/T System -- 15.4 Remarks and Future Scope -- 15.5 Conclusion -- Acknowledgment -- References -- 16 Revival of Functional Nanofluid Photothermal Materials for Solar Still Applications -- 16.1 Nanofluid Based Solar Stills -- 16.2 General Factors for Efficient Solar Still -- 16.2.1 Environmental Factors -- 16.2.2 Physical Factors -- 16.3 Development and Modifications -- 16.3.1 Conventional Single-effect Solar Still -- 16.3.2 Solar Reflectors -- 16.3.3 Wicked Type Solar Stills -- 16.4 Application of Nanofluids in Solar Still -- 16.4.1 Methodologies for the Fabrication of Nanofluids -- 16.4.2 Optical Properties of Nanofluids -- 16.4.3 Photothermal of Nanofluids -- 16.5 Carbon-based Nanofluid -- 16.6 Metallic/ Metal Oxide Nanofluids -- 16.7 Magnetic Nanofluids -- 16.8 Solar Thermal Collectors -- 16.9 Solar-driven Steam Generators -- 16.10 Remarks and Future Scope -- 16.11 Conclusion -- References -- 17 Nanotechnology in Solar Lighting…”

Tabla de Contenidos: “…7.1.1 Purpose of Battery Modeling -- 7.1.2 Battery Modeling Requirement of BMS -- 7.2 Common Battery Models and Their Deficiencies -- 7.2.1 Non-circuit Models -- 7.2.2 Equivalent Circuit Models -- 7.3 External Characteristics of the Li-Ion Power Battery and Their Analysis -- 7.3.1 Electromotive Force Characteristic of the Li-Ion Battery -- 7.3.2 Over-potential Characteristics of the Li-Ion Battery -- 7.4 A Power Battery Model Based on a Three-Order RC Network -- 7.4.1 Establishment of a New Power Battery Model -- 7.4.2 Estimation of Model Parameters -- 7.5 Model Parameterization and Its Online Identification -- 7.5.1 Offline Extension Method of Model Parameters -- 7.5.2 Online Identification Method of Model Parameters -- 7.6 Battery Cell Simulation Model -- 7.6.1 Realization of Battery Cell Simulation Model Based on Matlab/Simulink -- 7.6.2 Model Validation -- References -- Part III Functions of BMS -- 8 Battery Monitoring -- 8.1 Discussion on Real Time and Synchronization -- 8.1.1 Factors Causing Delay -- 8.1.2 Synchronization -- 8.1.3 Negative Impact of Non-real-time and Non-synchronous Problems -- 8.1.4 Proposal on Solution -- 8.2 Battery Voltage Monitoring -- 8.2.1 Voltage Monitoring Based on a Photocoupler Relay Switch Array (PhotoMOS) -- 8.2.2 Voltage Monitoring Based on a Differential Operational Amplifier -- 8.2.3 Voltage Monitoring Based on a Special Integrated Chip -- 8.2.4 Comparison of Various Voltage Monitoring Schemes -- 8.2.5 Significance of Accurate Voltage Monitoring for Effective Capacity Utilization of the Battery Pack -- 8.3 Battery Current Monitoring -- 8.3.1 Accuracy -- 8.3.2 Current Monitoring Based on Series Resistance -- 8.3.3 Current Monitoring Based on a Hall Sensor -- 8.3.4 A Compromised Method -- 8.4 Temperature Monitoring -- 8.4.1 Importance of Temperature Monitoring -- 8.4.2 Common Implementation Schemes…”

Tabla de Contenidos: “…13.4 Error Analysis -- 13.5 Conclusion -- References -- Chapter 14 Video Watermarking Technique Based on Motion Frames by Using Encryption Method -- 14.1 Introduction -- 14.2 Methodology Used -- 14.2.1 Discrete Wavelet Transform -- 14.2.2 Singular-Value Decomposition -- 14.3 Literature Review -- 14.4 Watermark Encryption -- 14.5 Proposed Watermarking Scheme -- 14.5.1 Watermark Embedding -- 14.5.2 Watermark Extraction -- 14.6 Experimental Results -- 14.7 Conclusion -- References -- Chapter 15 Feature Extraction and Selection for Classification of Brain Tumors -- 15.1 Introduction -- 15.2 Related Work -- 15.3 Methodology -- 15.3.1 Contrast Enhancement -- 15.3.2 K-Means Clustering -- 15.3.3 Canny Edge Detection -- 15.3.4 Feature Extraction -- 15.3.5 Feature Selection -- 15.3.5.1 Genetic Algorithm for Feature Selection -- 15.3.5.2 Particle Swarm Optimization for Feature Selection -- 15.4 Results -- 15.5 Future Scope -- 15.6 Conclusion -- References -- Chapter 16 Student's Self-Esteem on the Self-Learning Module in Mathematics 6 -- 16.1 Introduction -- 16.1.1 Research Questions -- 16.1.2 Scope and Limitation -- 16.1.3 Significance of the Study -- 16.2 Methodology -- 16.2.1 Research Design -- 16.2.2 Respondents of the Study -- 16.2.3 Sampling Procedure -- 16.2.4 Locale of the Study -- 16.2.5 Data Collection -- 16.2.6 Instrument of the Study -- 16.2.7 Validation of Instrument -- 16.3 Results and Discussion -- 16.4 Conclusion -- 16.5 Recommendation -- References -- Chapter 17 Effects on Porous Nanofluid due to Internal Heat Generation and Homogeneous Chemical Reaction -- Nomenclature -- 17.1 Introduction -- 17.2 Mathematical Formulations -- 17.3 Method of Local Nonsimilarity -- 17.4 Results and Discussions -- 17.5 Concluding Remarks -- References -- Chapter 18 Numerical Solution of Partial Differential Equations: Finite Difference Method -- 18.1 Introduction…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Dedication Page -- Contents -- Foreword -- Acknowledgments -- General Introduction -- Chapter 1 Concepts, Libraries, and Essential Tools in Machine Learning and Deep Learning -- 1.1 Learning Styles for Machine Learning -- 1.1.1 Supervised Learning -- 1.1.1.1 Overfitting and Underfitting -- 1.1.1.2 K-Folds Cross-Validation -- 1.1.1.3 Train/Test Split -- 1.1.1.4 Confusion Matrix -- 1.1.1.5 Loss Functions -- 1.1.2 Unsupervised Learning -- 1.1.3 Semi-Supervised Learning -- 1.1.4 Reinforcement Learning -- 1.2 Essential Python Tools for Machine Learning -- 1.2.1 Data Manipulation with Python -- 1.2.2 Python Machine Learning Libraries -- 1.2.2.1 Scikit-learn -- 1.2.2.2 TensorFlow -- 1.2.2.3 Keras -- 1.2.2.4 PyTorch -- 1.2.3 Jupyter Notebook and JupyterLab -- 1.3 HephAIstos for Running Machine Learning on CPUs, GPUs, and QPUs -- 1.3.1 Installation -- 1.3.2 HephAIstos Function -- 1.4 Where to Find the Datasets and Code Examples -- Further Reading -- Chapter 2 Feature Engineering Techniques in Machine Learning -- 2.1 Feature Rescaling: Structured Continuous Numeric Data -- 2.1.1 Data Transformation -- 2.1.1.1 StandardScaler -- 2.1.1.2 MinMaxScaler -- 2.1.1.3 MaxAbsScaler -- 2.1.1.4 RobustScaler -- 2.1.1.5 Normalizer: Unit Vector Normalization -- 2.1.1.6 Other Options -- 2.1.1.7 Transformation to Improve Normal Distribution -- 2.1.1.8 Quantile Transformation -- 2.1.2 Example: Rescaling Applied to an SVM Model -- 2.2 Strategies to Work with Categorical (Discrete) Data -- 2.2.1 Ordinal Encoding -- 2.2.2 One-Hot Encoding -- 2.2.3 Label Encoding -- 2.2.4 Helmert Encoding -- 2.2.5 Binary Encoding -- 2.2.6 Frequency Encoding -- 2.2.7 Mean Encoding -- 2.2.8 Sum Encoding -- 2.2.9 Weight of Evidence Encoding -- 2.2.10 Probability Ratio Encoding -- 2.2.11 Hashing Encoding -- 2.2.12 Backward Difference Encoding…”

Tabla de Contenidos: “…4.4 Grover search algorithm -- 4.5 Quantum walks -- 4.5.1 Quantum-walk search algorithm -- 4.6 Basic quantum communication protocols -- 4.6.1 Quantum teleportation -- 4.6.2 Superdense coding -- 4.7 Bibliographic remarks and further readings -- II Sequential quantum programs -- 5 Quantum while-programs -- 5.1 Syntax -- 5.2 Operational semantics -- 5.3 Denotational semantics -- 5.3.1 Basic properties -- 5.3.2 Quantum domains -- 5.3.3 Semantic functions of loops -- 5.3.4 Change and access of quantum variables -- 5.3.5 Termination and divergence -- 5.3.6 Semantic functions as quantum operations -- 5.4 Illustrative example: Grover search -- 5.5 Classical recursion in quantum programming -- 5.5.1 Syntax -- 5.5.2 Operational semantics -- 5.5.3 Denotational semantics -- 5.5.4 Fixed point characterisation -- 5.6 Adding classical variables -- 5.7 Bibliographic remarks and further readings -- 6 Quantum Hoare logic -- 6.1 Quantum predicates -- 6.1.1 Quantum weakest preconditions -- 6.1.2 Commutativity of quantum predicates -- 6.2 Correctness formulas of quantum programs -- 6.3 Weakest preconditions of quantum programs -- 6.4 Proof system for partial correctness -- 6.5 Proof system for total correctness -- 6.6 An illustrative example: verification of Grover search -- 6.7 Auxiliary inference rules -- 6.8 Bibliographic remarks and further readings -- III Verification and analysis -- 7 Verification of quantum programs -- 7.1 Architecture of a quantum program verifier -- 7.1.1 Generating verification conditions -- 7.1.2 Proving verification conditions -- 7.1.3 Validity of the verifier -- 7.2 Localisation of correctness reasoning -- 7.3 Birkhoff-von Neumann quantum logic -- 7.3.1 Orthomodular lattice of closed subspaces -- 7.3.2 Propositional quantum logic -- 7.3.3 First-order quantum logic -- 7.3.4 Effect algebra and unsharp quantum logic…”

Tabla de Contenidos: “…3.2 Deep learning approaches for digital signal processing -- 3.3 Optical IM/DD systems based on deep learning -- 3.3.1 ANN receiver -- 3.3.1.1 PAM transmission -- 3.3.1.2 Sliding window FFNN processing -- 3.3.2 Auto-encoders -- 3.3.2.1 Auto-encoder design based on a feed-forward neural network -- 3.3.2.2 Auto-encoder design based on a recurrent neural network -- 3.3.3 Performance -- 3.3.4 Distance-agnostic transceiver -- 3.4 Implementation on a transmission link -- 3.4.1 Conventional PAM transmission with ANN-based receiver -- 3.4.2 Auto-encoder implementation -- 3.5 Outlook -- References -- 4 Machine learning techniques for passive optical networks -- 4.1 Background -- 4.2 The validation of NN effectiveness -- 4.3 NN for nonlinear equalization -- 4.4 End to end deep learning for optimal equalization -- 4.5 FPGA implementation of NN equalizer -- 4.6 Conclusions and perspectives -- References -- 5 End-to-end learning for fiber-optic communication systems -- 5.1 Introduction -- 5.2 End-to-end learning -- 5.3 End-to-end learning for fiber-optic communication systems -- 5.3.1 Direct detection -- 5.3.2 Coherent systems -- 5.3.2.1 Nonlinear phase noise channel -- 5.3.2.2 Perturbation models (NLIN and GN) -- 5.3.2.3 Split-step Fourier method (SSFM) -- 5.4 Gradient-free end-to-end learning -- 5.5 Conclusion -- Acknowledgments -- References -- 6 Deep learning techniques for optical monitoring -- 6.1 Introduction -- 6.2 Building blocks of deep learning-based optical monitors -- 6.2.1 Digital coherent reception as a data-acquisition method -- 6.2.2 Deep learning and representation learning -- 6.2.3 Combination of digital coherent reception and deep learning -- 6.3 Deep learning-based optical monitors -- 6.3.1 Training mode of DL-based optical monitors -- 6.3.2 Advanced topics for the training mode of DL-based optical monitors…”

Tabla de Contenidos: “…Theoretical framework -- 3.1 Time tariff -- 3.1.1 General law of the electricity industry for hourly tariffs -- 3.2 User's valid tariff and provisional tariff regulation -- 4. …”

Tabla de Contenidos: “…Local Basis Functions -- 4.1.4 Experimental Model Validation -- 4.1.4.1 Quantifying Modeling Performance -- 4.1.5 Mutually Orthogonal Basis Functions -- 4.1.6 Multi-Antenna Environments and Mutual Coupling -- 4.2 Oscillator Phase Noise -- 4.2.1 Phase-Noise Power Spectrum and Leeson's Equation -- 4.2.2 Phase-Noise Modeling: Free-Running Oscillator -- 4.2.3 Phase-Noise Modeling: Phase-Locked Loop -- 4.3 Data Converters -- 4.3.1 Modeling of Quantization Noise -- 4.4 Statistical Modeling -- 4.4.1 The Bussgang Theorem and the System Model -- 4.5 Stochastic Modeling of Power Ampli ers -- 4.6 Oscillator Phase Noise -- 4.7 Stochastic Modeling of Data Converters -- 4.8 Model Concatenation and Simulations -- 4.8.1 Signal-to-Interference and Noise Ratio -- 4.8.2 Simulations -- 4.8.3 Simulation Results -- References -- 5 Multicarrier Waveforms -- 5.1 Multicarrier Waveforms -- 5.1.1 The Principle of Orthogonality -- 5.1.2 OFDM-Based Waveforms…”

Tabla de Contenidos: “…7.3 High Peclet number advection and diffusion -- 7.3.1 Residual-based stabilization -- 7.4 Transient advection and diffusion illustration -- 7.5 Low-Mach solver strategies -- 7.6 Low-Mach fluids operators -- 7.6.1 Kinetic energy conservation -- 7.7 Validation -- Acknowledgments -- References -- 8 Boundary conditions for turbulence simulation -- 8.1 Introduction -- 8.2 A motivating example -- 8.2.1 Continuous case -- 8.2.2 Discretization -- 8.2.3 Lessons learned -- 8.3 General framework -- 8.3.1 Physical BCs -- 8.3.2 Artificial BCs -- 8.3.3 Symmetry BCs -- 8.4 BCs for compressible Navier-Stokes -- 8.4.1 The characteristic BC framework -- 8.4.2 Refinements and extensions -- 8.4.2.1 Viscous flow -- 8.4.2.2 Rigid-wall no-slip BC -- 8.4.2.3 Inlet/outlet impedance: taming drift -- 8.4.2.4 Sponge layers for multidimensional nonreflectivity -- 8.4.2.5 Other extensions -- 8.4.3 Summation-by-parts approach -- 8.4.4 Case study: high-speed turbulent jet -- 8.5 BCs for incompressible Navier-Stokes -- 8.5.1 Rigid no-slip and inlet BCs -- 8.5.2 Far-field BCs -- 8.5.3 Outlet BCs -- 8.5.4 Dealing with pressure -- 8.5.5 Case study: flow over a sphere -- 8.6 Turbulence modeling in boundary conditions -- 8.6.1 Triggering natural instabilities -- 8.6.2 Synthetic turbulence injection -- 8.7 BCs in other discretization schemes -- 8.7.1 Other schemes with body-fitted meshes -- 8.7.2 Non-conforming BCs: immersed boundary and interface methods -- References -- 9 Numerical methods in large-eddy simulation -- 9.1 Scope of the chapter -- 9.2 Large-eddy simulation: from practice to theory -- 9.2.1 LES: statement of the problem -- 9.2.2 Removal of small scales in LES: mathematical models for the LES filter -- 9.2.2.1 The filtered Navier-Stokes equations model -- 9.2.2.2 A more realistic model: the twice-filtered Navier-Stokes equations…”

Tabla de Contenidos: “…6.2 Related Work -- 6.3 Datasets -- 6.4 Experimental Setup -- 6.5 Results and Discussion -- 6.5.1 Evaluation Metrics -- 6.6 Conclusion -- 6.6.1 Significance of the Study -- References -- Chapter 7 Exploring the Potential of Dingo Optimizer: A Promising New Metaheuristic Approach -- 7.1 Introduction -- 7.2 Architecture of Dingo Optimizer -- 7.3 Initialization Process -- 7.3.1 Population Size -- 7.3.2 Dingo Population Initialization -- 7.3.3 Fitness Assessment -- 7.3.4 Best Dingo -- 7.3.5 Recordkeeping -- 7.4 Iteration Phase -- 7.6 Other Optimization Techniques -- 7.7 Conclusion -- References -- Chapter 8 Bioinspired Genetic Algorithm in Medical Applications -- 8.1 Introduction -- 8.2 The Genetic Algorithm -- 8.3 Radiology -- 8.4 Oncology -- 8.5 Endocrinology -- 8.6 Obstetrics and Gynecology -- 8.7 Pediatrics -- 8.8 Surgery -- 8.9 Infectious Diseases -- 8.10 Radiotherapy -- 8.11 Rehabilitation Medicine -- 8.12 Neurology -- 8.13 Health Care Management -- 8.14 Conclusion -- References -- Chapter 9 Artificial Immune System Algorithms for Optimizing Nanoparticle Design in Targeted Drug Delivery -- 9.1 Introduction -- 9.2 Artificial Immune Cells -- 9.3 The Artificial Immune System Architecture -- References -- Chapter 10 Diabetic Retinopathy Detection by Retinal Blood Vessel Segmentation and Classification Using Ensemble Model -- 10.1 Introduction -- 10.2 Literature Review -- 10.3 Proposed System -- 10.4 Conclusion and Future Scope -- References -- Chapter 11 Diabetes Prognosis Model Using Various Machine Learning Techniques -- 11.1 Introduction -- 11.1.1 Disease Identification -- 11.1.2 Data, Information, and Knowledge -- 11.1.3 Knowledge Discovery in Databases -- 11.1.4 Predictive Analytics -- 11.1.5 Supervised Learning and Machine Learning -- 11.1.6 Predictive Models -- 11.1.7 Data Validation and Cleaning -- 11.1.8 Discretization…”

Tabla de Contenidos: “…4.4.2 Experimental Design for A/B Tests -- 4.4.3 Setting Up A/B Tests: A Step-by-Step Guide -- 4.4.4 Statistical Significance in A/B Tests -- 4.4.5 Advanced A/B Testing Techniques -- 4.4.6 Potential Pitfalls in A/B Testing -- 4.4.7 Interpreting A/B Test Results -- 4.5 Hypothesis Testing in Marketing -- 4.5.1 Introduction to Hypothesis Testing -- 4.5.2 Common Hypothesis Tests in Marketing -- 4.5.3 Significance Levels and P-Values -- 4.6 Customer Segmentation and Processing -- 4.6.1 K-Means Clustering -- 4.6.2 Hierarchical Clustering in Customer Segmentation -- 4.6.3 Recency, Frequency, Monetary Analysis in Marketing -- 4.7 Practical Examples: Inferential Analytics for Customer Segmentation and Hypothesis Testing for Marketing Campaign Performance -- 4.7.1 Inferential Analytics for Customer Segmentation -- 4.7.2 Hypothesis Testing for Marketing Campaign Performance -- 4.8 Conclusion -- 4.9 References -- Chapter 5 Predictive Analytics and Machine Learning -- 5.1 Introduction -- 5.1.1 Overview of Predictive Analytics -- 5.1.2 Machine Learning in Marketing -- 5.1.3 Common Challenges in Predictive Analytics and Machine Learning in Marketing -- 5.1.4 Misconceptions in Predictive Analytics and Machine Learning in Marketing -- 5.2 Predictive Analytics Techniques -- 5.2.1 Linear and Logistic Regression -- 5.2.2 Time Series Forecasting -- 5.3 Machine Learning Techniques -- 5.3.1 Supervised Learning for Marketing -- 5.3.2 Unsupervised Learning for Marketing -- 5.3.3 Reinforcement Learning for Marketing -- 5.4 Model Evaluation and Selection -- 5.4.1 Model Accuracy, Precision, and Recall -- 5.4.2 ROC Curves and AUC -- 5.4.3 Cross-Validation Techniques -- 5.4.4 Model Complexity and Overfitting -- 5.5 Churn Prediction, Customer Lifetime Value, and Propensity Modeling -- 5.5.1 Understanding Churn and Its Importance -- 5.5.2 CLV Computation and Applications…”

“…Leaders who act as emotional guides by sharing their vision and creating and validating emotional meaning will inspire people to do their best work, even in turbulent and challenging times…”

“…Moreover, the development of common validation procedures for PCMs is an important issue that should be addressed in order to achieve commercial deployment and implementation of these kinds of materials in latent storage systems. …”

“…The book focuses on the major mental health issues faced by women in social care and presents empirically validated ways of assessing and treating clients…”

“…Within this work these models are both validated on model flames and applied to technical systems…”

“…The developed method is validated on a theoretical and a real scheduling case…”

“…Such local-cultural concepts appear to be valid even when contrasted with other, concurring models of reality. …”