Tabla de Contenidos: “….) -- REFLEXIÓN FINAL -- REFERENCIAS BIBLIOGRÁFICAS -- TECNOLOGÍAS DISRUPTIVAS EN LA ADMINISTRACIÓN PÚBLICA: INTELIGENCIA ARTIFICIAL Y (...)…”

Tabla de Contenidos: “…-- 4.4.2 Paradigm Shift in the Security of Healthcare Data Through Blockchain -- 4.5 Conclusion -- References -- 5 Application of Machine Learning and IoT for Smart Cities -- 5.1 Functionality of Image Analytics -- 5.2 Issues Related to Security and Privacy in IoT -- 5.3 Machine Learning Algorithms and Blockchain Methodologies -- 5.3.1 Intrusion Detection System -- 5.3.2 Deep Learning and Machine Learning Models -- 5.3.3 Artificial Neural Networks -- 5.3.4 Hybrid Approaches -- 5.3.5 Review and Taxonomy of Machine Learning -- 5.4 Machine Learning Open Source Tools for Big Data -- 5.5 Approaches and Challenges of Machine Learning Algorithms in Big Data -- 5.6 Conclusion -- References -- 6 Machine Learning Applications for IoT Healthcare -- 6.1 Introduction -- 6.2 Machine Learning -- 6.2.1 Types of Machine Learning Techniques -- 6.2.2 Applications of Machine Learning -- 6.3 IoT in Healthcare -- 6.3.1 IoT Architecture for Healthcare System -- 6.4 Machine Learning and IoT…”

Tabla de Contenidos: “…-- CCSP Exam Objectives -- CCSP Certification Exam Objective Map -- How to Contact the Publisher -- Assessment Test -- Answers to Assessment Test -- Chapter 1 Architectural Concepts -- Cloud Characteristics -- Business Requirements -- Understanding the Existing State -- Cost/Benefit Analysis -- Intended Impact -- Cloud Computing Service Categories -- Software as a Service -- Infrastructure as a Service -- Platform as a Service -- Cloud Deployment Models -- Private Cloud -- Public Cloud -- Hybrid Cloud -- Multi-Cloud -- Community Cloud -- Multitenancy -- Cloud Computing Roles and Responsibilities -- Cloud Computing Reference Architecture -- Virtualization -- Hypervisors -- Virtualization Security -- Cloud Shared Considerations -- Security and Privacy Considerations -- Operational Considerations -- Emerging Technologies -- Machine Learning and Artificial Intelligence -- Blockchain -- Internet of Things -- Containers -- Quantum Computing -- Edge and Fog Computing -- Confidential Computing -- DevOps and DevSecOps -- Summary -- Exam Essentials -- Review Questions -- Chapter 2 Data Classification -- Data Inventory and Discovery -- Data Ownership -- Data Flows -- Data Discovery Methods -- Information Rights Management -- Certificates and IRM -- IRM in the Cloud -- IRM Tool Traits -- Data Control -- Data Retention -- Data Audit and Audit Mechanisms -- Data Destruction/Disposal -- Summary -- Exam Essentials -- Review Questions -- Chapter 3 Cloud Data Security -- Cloud Data Lifecycle -- Create -- Store -- Use -- Share -- Archive -- Destroy -- Cloud Storage Architectures -- Storage Types…”

Tabla de Contenidos: “…Innovation and Marketing -- Product Life Cycle (PLC) and Consumer Behaviour -- Summary -- End of Chapter Discussion Questions -- Questions -- References -- Chapter 12 Contemporary Consumer Research -- Learning Outcome -- Introduction -- The Need for Consumer Research -- Setting the Stage for Consumer Research -- Consumer Research Brief -- Consumer Research Proposal -- Perspectives and Paradigms On Consumer Research -- The Marketing Research Process in a Digital Age -- Definition of Problem and Research Objectives -- Formulation of Research Design -- Data Sources -- Secondary Data -- Primary Data -- Survey -- Experimentation -- Qualitative Vs Quantitative Consumer Research Data -- In-depth Interview -- Ethnography -- Focus Group Discussion -- Sampling Plan -- Research Instrument -- Data Collection -- Data Analysis -- Quantitative Analysis -- Qualitative Data Analysis -- Presentation of Findings -- Netnography in Consumer Research -- Big Data in Contemporary Marketing Research: An Overview -- Neuromarketing and the Contemporary Consumer Research -- Ethics and Consumer Research -- Summary -- End of Chapter Discussion Questions -- Questions -- References -- Chapter 13 Consumer Behaviour and Technology: A Look Into the Future -- Learning Outcome -- Introduction -- The Future of Technology and Consumer Behaviour -- Service Robots -- Wearable Technology -- Artificial Intelligence and Marketing -- STP and Consumer Demographics -- Consumer Persona -- Webographics -- Customer Trust and Loyalty -- Summary -- End of Chapter Discussion Questions -- Questions -- References -- Index…”

Tabla de Contenidos: “…Fairness in machine learning modeling -- Proxies for sensitive variables -- Sources of bias -- Biases introduced in data generation and collection -- Bias in model training and testing -- Bias in production -- Using explainability techniques -- Fairness assessment and improvement in Python -- Summary -- Questions -- References -- Part 3: Low-Bug Machine Learning Development and Deployment -- Chapter 8: Controlling Risks Using Test-Driven Development -- Technical requirements -- Test-driven development for machine learning modeling -- Unit testing -- Machine learning differential testing -- Tracking machine learning experiments -- Summary -- Questions -- References -- Chapter 9: Testing and Debugging for Production -- Technical requirements -- Infrastructure testing -- Infrastructure as Code tools -- Infrastructure testing tools -- Infrastructure testing using Pytest -- Integration testing of machine learning pipelines -- Integration testing using pytest -- Monitoring and validating live performance -- Model assertion -- Summary -- Questions -- References -- Chapter 10: Versioning and Reproducible Machine Learning Modeling -- Technical requirements -- Reproducibility in machine learning -- Data versioning -- Model versioning -- Summary -- Questions -- References -- Chapter 11: Avoiding and Detecting Data and Concept Drifts -- Technical requirements -- Avoiding drifts in your models -- Avoiding data drift -- Addressing concept drift -- Detecting drifts -- Practicing with alibi_detect for drift detection -- Practicing with evidently for drift detection -- Summary -- Questions -- References -- Part 4: Deep Learning Modeling -- Chapter 12: Going Beyond ML Debugging with Deep Learning -- Technical requirements -- Introduction to artificial neural networks -- Optimization algorithms -- Frameworks for neural network modeling…”

Tabla de Contenidos: “…-- Operating the platform -- Executive sponsor and operating committee -- Platform owner -- Business analyst/product owner -- Demand managers -- Platform architect -- Summary -- Chapter 10: Artificial Intelligence in ServiceNow -- Understanding AI and ML -- What is AI? …”

Tabla de Contenidos: “…Los cambios introducidos por la inteligencia artificial generativa en el espacio de la información -- 2.5. …”

Tabla de Contenidos: “…Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgment -- 1 Introduction to Cognitive Computing -- 1.1 Introduction: Definition of Cognition, Cognitive Computing -- 1.2 Defining and Understanding Cognitive Computing -- 1.3 Cognitive Computing Evolution and Importance -- 1.4 Difference Between Cognitive Computing and Artificial Intelligence -- 1.5 The Elements of a Cognitive System -- 1.5.1 Infrastructure and Deployment Modalities -- 1.5.2 Data Access, Metadata, and Management Services -- 1.5.3 The Corpus, Taxonomies, and Data Catalogs -- 1.5.4 Data Analytics Services -- 1.5.5 Constant Machine Learning -- 1.5.6 Components of a Cognitive System -- 1.5.7 Building the Corpus -- 1.5.8 Corpus Administration Governing and Protection Factors -- 1.6 Ingesting Data Into Cognitive System -- 1.6.1 Leveraging Interior and Exterior Data Sources -- 1.6.2 Data Access and Feature Extraction -- 1.7 Analytics Services -- 1.8 Machine Learning -- 1.9 Machine Learning Process -- 1.9.1 Data Collection -- 1.9.2 Data Preparation -- 1.9.3 Choosing a Model -- 1.9.4 Training the Model -- 1.9.5 Evaluate the Model -- 1.9.6 Parameter Tuning -- 1.9.7 Make Predictions -- 1.10 Machine Learning Techniques -- 1.10.1 Supervised Learning -- 1.10.2 Unsupervised Learning -- 1.10.3 Reinforcement Learning -- 1.10.4 The Significant Challenges in Machine Learning -- 1.11 Hypothesis Space -- 1.11.1 Hypothesis Generation -- 1.11.2 Hypotheses Score -- 1.12 Developing a Cognitive Computing Application -- 1.13 Building a Health Care Application -- 1.13.1 Healthcare Ecosystem Constituents -- 1.13.2 Beginning With a Cognitive Healthcare Application -- 1.13.3 Characterize the Questions Asked by the Clients -- 1.13.4 Creating a Corpus and Ingesting the Content -- 1.13.5 Training the System…”

Tabla de Contenidos: “…-- 18.5 Conclusion -- References -- 19 Wild Animals in the City: Considering and Connecting with Animals in Zoos and Aquariums -- 19.1 Introduction -- 19.2 Animal Welfare -- 19.3 Human-Animal Interactions -- 19.4 Wildness in Zoos -- 19.5 Compassionate Education Programs -- 19.6 Real Connections with Artificial Means -- 19.7 Conclusion -- References -- 20 Comment: Encountering Urban Animals: Towards the Zoöpolis -- 20.1 The Urban, the Animal -- 20.2 Urban Animal Encounters and the Politics of Spatial Access -- 20.2.1 The Home -- 20.2.2 The Zoo -- 20.2.3 The Streets/Parks/Margins -- 20.3 Towards the Zoöpolis -- 20.3.1 'Articulating With' Animals -- 20.3.2 Making Visible Relationalities -- 20.3.3 Re-Storying the City to Imagine Otherwise -- 20.4 Conclusion -- References -- Part IV Wild Animals -- 21 Should We Provide the Bear Necessities? …”

Tabla de Contenidos: “…4.2 - Maintenance analysis procedures -- 4.2.1 - The MSG Series Procedures -- 4.2.2 - Reliability-Centered Maintenance -- 4.2.3 - MSG-3 Logic -- 4.2.4 - Structures -- 4.2.5 - Fatigue Damage -- 4.2.6 - Environmental Deterioration -- 4.2.7 - Accidental Damage -- 4.2.8 - Systems and Power Plants -- 4.2.9 - Setting Task Frequencies/Intervals -- 4.3 - Statistical reliability assessment -- 4.4 - Logistic support analysis -- 4.4.1 - LSA Tasks -- 4.4.2 - Failure Mode Effect Analysis -- 4.5 - Fault tree analysis -- 4.5.1 - Qualitative Analysis of a Fault Tree -- 4.5.2 - Quantitative Analysis of a Fault Tree -- 4.6 - Level of repair analysis -- 4.7 - Logistic support analysis record -- 4.8 - LSA models -- 4.9 - Elements of ILS -- 4.10 - Support equipment -- 4.11 - Facilities -- 4.12 - Data -- Chapter 5 - Intelligent Structural Rating System Based on Back-Propagation Network -- 5.1 - Introduction -- 5.2 - Artificial neural network -- 5.2.1 - Basic Theory -- 5.2.2 - Back-Propagation Network -- 5.3 - Design BPN for AD -- 5.3.1 - BPN Configuration -- 5.3.2 - Case Study -- 5.4 - Discussion -- 5.4.1 - Selection of Number of Nodes in Hidden Layers and Parameter Ratio -- 5.4.2 - Selection of Training Algorithms -- 5.5 - Conclusions -- Chapter 6 - Fault Tree Analysis for Composite Structural Damage -- 6.1 - Introduction -- 6.2 - Basic principles of fault tree analysis -- 6.2.1 - Elements of FTA -- 6.2.2 - Boolean Algebra Theorems -- 6.3 - FTA for composite damage -- 6.4 - Qualitative analysis -- 6.4.1 - Minimal Cut Sets -- 6.4.2 - Structure Importance Analysis -- 6.4.3 - Probability Importance Analysis -- 6.4.4 - Relative Probability Importance Analysis -- 6.5 - Quantitative analysis -- 6.6 - Discussion -- 6.7 - Potential solutions -- 6.7.1 - Material Design -- 6.7.2 - Fabrication Process -- 6.7.3 - Personnel Training -- 6.7.4 - Surface Protection…”

Tabla de Contenidos: “…Security monitoring and control -- 4.4. Artificial intelligence applications -- 4.4.1. Forecast of state variables based on the dynamic state estimation method -- 4.4.2. …”

Tabla de Contenidos: “…5.2.3 Kinematics of Rotating Frames -- Rotational Kinematics Expressed by Quaternions -- 5.2.4 Projective Geometry -- Multiview Geometry -- Singular Cases -- 3D Reconstruction -- 5.3 Pose Measurement Methods -- 5.3.1 Dead Reckoning -- Odometry -- Inertial Navigation -- 5.3.2 Heading Measurement -- 5.3.3 Active Markers and Global Position Measurement -- Global Navigation Satellite System -- 5.3.4 Navigation Using Environmental Features -- Straight-Line Features -- Split-and-Merge Algorithm -- Evolving Straight-Line Clustering -- Hough Transform -- Color Features -- Artificial Pattern Markers -- Natural Local Image Features -- 5.3.5 Matching of Environment Models: Maps -- 5.4 Sensors -- 5.4.1 Sensor Characteristics -- 5.4.2 Sensor Classifications -- References -- Chapter 6: Nondeterministic Events in Mobile Systems -- 6.1 Introduction -- 6.2 Basics of Probability -- 6.2.1 Discrete Random Variable -- 6.2.2 Continuous Random Variable -- 6.2.3 Bayes' Rule -- 6.3 State Estimation -- 6.3.1 Disturbances and Noise -- 6.3.2 Estimate Convergence and Bias -- 6.3.3 Observability -- 6.4 Bayesian Filter -- 6.4.1 Markov Chains -- 6.4.2 State Estimation From Observations -- 6.4.3 State Estimation From Observations and Actions -- 6.4.4 Localization Example -- 6.4.5 Environment Sensing -- 6.4.6 Motion in the Environment -- 6.4.7 Localization in the Environment -- 6.5 Kalman Filter -- 6.5.1 Kalman Filter in Matrix Form -- 6.5.2 Extended Kalman Filter -- 6.5.3 Kalman Filter Derivatives -- 6.6 Particle Filter -- References -- Chapter 7: Autonomous Guided Vehicles -- 7.1 Introduction -- 7.2 Autonomous Transportation Vehicles -- 7.2.1 About -- 7.2.2 Setup -- 7.2.3 Sensors -- 7.2.4 Localization and Mapping -- 7.2.5 Control -- 7.2.6 Path Planning -- 7.2.7 Decision Making -- 7.3 Wheeled Mobile Robots in Agriculture -- 7.3.1 Introduction (About) -- 7.3.2 Service Unit Setup…”

Tabla de Contenidos: “…5.2 Modeling Structure -- 5.2.1 Single-Junction Solar Cell Model -- 5.2.2 Modeling of Multijunction Solar PV Cell -- 5.3 MPPT Design Techniques -- 5.3.1 Design of MPPT Scheme Based on P&amp -- O Technique -- 5.3.2 Design of MPPT Scheme Based on FLA -- 5.4 Results and Discussions -- 5.4.1 Single-Junction Solar Cell -- 5.4.2 Multijunction Solar PV Cell -- 5.4.3 Implementation of MPPT Scheme Based on P&amp -- O Technique -- 5.4.4 Implementation of MPPT Scheme Based on FLA -- 5.5 Conclusion -- References -- Chapter 6 Particle Swarm Optimization: An Overview, Advancements and Hybridization -- 6.1 Introduction -- 6.2 The Particle Swarm Optimization: An Overview -- 6.3 PSO Algorithms and Pseudo-Code -- 6.3.1 PSO Algorithm -- 6.3.2 Pseudo-Code for PSO -- 6.3.3 PSO Limitations -- 6.4 Advancements in PSO and Its Perspectives -- 6.4.1 Inertia Weight -- 6.4.2 Constriction Factors -- 6.4.3 Topologies -- 6.4.4 Analysis of Convergence -- 6.5 Hybridization of PSO -- 6.5.1 PSO Hybridization with Artificial Bee Colony (ABC) -- 6.5.2 PSO Hybridization with Ant Colony Optimization (ACO) -- 6.5.3 PSO Hybridization with Genetic Algorithms (GA) -- 6.6 Area of Applications of PSO -- 6.7 Conclusions -- References -- Chapter 7 Application of Genetic Algorithm in Sensor Networks and Smart Grid -- 7.1 Introduction -- 7.2 Communication Sector -- 7.2.1 Sensor Networks -- 7.3 Electrical Sector -- 7.3.1 Smart Microgrid -- 7.4 A Brief Outline of GAs -- 7.5 Sensor Network's Energy Optimization -- 7.6 Sensor Network's Coverage and Uniformity Optimization Using GA -- 7.7 Use GA for Optimization of Reliability and Availability for Smart Microgrid -- 7.8 GA Versus Traditional Methods -- 7.9 Summaries and Conclusions -- References -- Chapter 8 AI-Based Predictive Modeling of Delamination Factor for Carbon Fiber-Reinforced Polymer (CFRP) Drilling Process -- 8.1 Introduction…”

Tabla de Contenidos: “…-- 1.1 Introduction -- 1.2 Interpretation of terms related to sensors -- 1.2.1 About the term "sensor" -- 1.2.2 Definitions of key terms related to devices with elements of artificial intelligence -- 1.3 Key trends in the development of sensors (sensor devices) and microelectromechanical systems -- 1.3.1 The method of analogy -- 1.3.2 Complication of organisms and sensors as a tendency of evolution -- 1.3.3 Features and forms of intelligence -- 1.4 Suggestions for improving terminology in the field of sensors and microelectromechanical systems -- 1.5 Conclusion -- Acknowledgment -- References -- 2 - Interfacing sensors to microcontrollers: a direct approach -- 2.1 Introduction -- 2.2 Sensors -- 2.2.1 Resistive sensors -- 2.2.1.1 Single resistive sensor -- 2.2.1.2 Differential resistive sensor -- 2.2.1.3 Bridge-type resistive sensor -- 2.2.2 Capacitive sensors -- 2.2.2.1 Single capacitive sensor -- 2.2.2.2 Lossy capacitive sensor -- 2.2.2.3 Differential capacitive sensor -- 2.2.2.4 Bridge-type capacitive sensor -- 2.3 Microcontrollers -- 2.3.1 General description -- 2.3.2 Time-interval measurement -- 2.4 Interface circuits -- 2.4.1 Operating principle -- 2.4.2 Circuits for resistive sensors -- 2.4.2.1 Single resistive sensor -- 2.4.2.2 Differential resistive sensor -- 2.4.2.3 Bridge-type resistive sensor -- 2.4.3 Circuits for capacitive sensors -- 2.4.3.1 Single capacitive sensor -- 2.4.3.2 Lossy capacitive sensor -- 2.4.3.3 Differential capacitive sensor -- 2.4.3.4 Bridge-type capacitive sensor -- 2.5 Applications -- 2.5.1 Temperature measurement -- 2.5.2 Position measurement -- 2.5.3 Magnetic field measurement…”

Tabla de Contenidos: “…Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Foreword -- Preface -- Acknowledgments -- Chapter 1: Introduction -- 1.1 WIND ENERGY BACKGROUND -- 1.2 ORGANIZATION OF THIS BOOK -- 1.2.1 Who Should Use This Book -- 1.2.2 Note for Instructors -- 1.2.3 Datasets Used in the Book -- Part I: Wind Field Analysis -- Chapter 2: A Single Time Series Model -- 2.1 TIME SCALE IN SHORT- TERM FORECASTING -- 2.2 SIMPLE FORECASTING MODELS -- 2.2.1 Forecasting Based on Persistence Model -- 2.2.2 Weibull Distribution -- 2.2.3 Estimation of Parameters in Weibull Distribution -- 2.2.4 Goodness of Fit -- 2.2.5 Forecasting Based on Weibull Distribution -- 2.3 DATA TRANSFORMATION AND STANDARDIZATION -- 2.4 AUTOREGRESSIVE MOVING AVERAGE MODELS -- 2.4.1 Parameter Estimation -- 2.4.2 Decide Model Order -- 2.4.3 Model Diagnostics -- 2.4.4 Forecasting Based on ARMA Model -- 2.5 OTHER METHODS -- 2.5.1 Kalman Filter -- 2.5.2 Support Vector Machine -- 2.5.3 Artificial Neural Network -- 2.6 PERFORMANCE METRICS -- 2.7 COMPARING WIND FORECASTING METHODS -- Chapter 3: Spatio temporal Models -- 3.1 COVARIANCE FUNCTIONS AND KRIGING -- 3.1.1 Properties of Covariance Functions -- 3.1.2 Power Exponential Covariance Function -- 3.1.3 Kriging -- 3.2 SPATIO-TEMPORAL AUTOREGRESSIVE MODELS -- 3.2.1 Gaussian Spatio-temporal Autoregressive Model -- 3.2.2 Informative Neighborhood -- 3.2.3 Forecasting and Comparison -- 3.3 SPATIO-TEMPORAL ASYMMETRY AND SEPARABILITY -- 3.3.1 Definition and Quantification -- 3.3.2 Asymmetry of Local Wind Field -- 3.3.3 Asymmetry Quantification -- 3.3.4 Asymmetry and Wake Effect -- 3.4 ASYMMETRIC SPATIO-TEMPORAL MODELS -- 3.4.1 Asymmetric Non-separable Spatio-temporal Model -- 3.4.2 Separable Spatio-temporal Models -- 3.4.3 Forecasting Using Spatio-temporal Model -- 3.4.4 Hybrid of Asymmetric Model and SVM -- 3.5 CASE STUDY…”

Tabla de Contenidos: “…Intro -- Nanotechnology Applications for Solar Energy Systems -- Contents -- About the Editor -- List of Contributors -- Preface -- 1 Solar Energy Applications -- 1.1 Introduction and Recent Advances -- 1.2 Solar Energy Applications -- 1.2.1 Electricity Production Using Photovoltaics at Large Scale -- 1.2.2 Small-Scale Electricity Production for Houses and Commercial Buildings -- 1.2.3 Off-Grid Applications Using Photovoltaics -- 1.2.4 Concentrating Solar Thermal Electricity -- 1.2.5 Solar Thermochemical Processes -- 1.2.6 Solar Water Heating -- 1.2.7 Heating of Solar Architecture -- 1.2.8 Air Conditioning Through Water Evaporation -- 1.2.9 Artificial Photosynthesis -- 1.2.10 Decomposing Waste and Biofuels Production -- 1.3 Classification of Solar Energy Devices -- 1.3.1 Concentrating Solar Power -- 1.3.2 Building Integrated Solar Systems -- 1.3.3 Solar-Thermal Collectors -- 1.3.4 Solar Thermochemistry -- 1.3.5 Solar Thermal Energy Storage -- 1.3.6 Solar-Driven Water Distillation -- 1.4 Benefits and Opportunities -- 1.5 Challenges -- 1.6 Future Aspects -- 1.7 Conclusion -- References -- 2 Application of Nanofluid for Solar Stills -- 2.1 Introduction -- 2.2 Desalination Technology -- 2.2.1 What is a Solar Still? …”

“…This book specifically focuses on very recent advances in maintenance and management of infrastructures robotics, automated inspection, remote sensing, and application of new and emerging computing like artificial intelligence, evolutionary computing, fuzzy logic, genetic algorithms, knowledge discovery and engineering, machine learning, neural network computing, optimization and search, parallel processing, vision and image processing…”

Tabla de Contenidos: “…3.2.4 Binary Logistic Regression with Keras on TensorFlow -- 3.3 Support Vector Machine -- 3.3.1 Linearly Separable Data -- 3.3.2 Not Fully Linearly Separable Data -- 3.3.3 Nonlinear SVMs -- 3.3.4 SVMs for Regression -- 3.3.5 Application of SVMs -- 3.3.5.1 SVM Using scikit-learn for Classification -- 3.3.5.2 SVM Using scikit-learn for Regression -- 3.4 Artificial Neural Networks -- 3.4.1 Multilayer Perceptron -- 3.4.2 Estimation of the Parameters -- 3.4.2.1 Loss Functions -- 3.4.2.2 Backpropagation: Binary Classification -- 3.4.2.3 Backpropagation: Multi-class Classification -- 3.4.3 Convolutional Neural Networks -- 3.4.4 Recurrent Neural Network -- 3.4.5 Application of MLP Neural Networks -- 3.4.6 Application of RNNs: LST Memory -- 3.4.7 Building a CNN -- 3.5 Many More Algorithms to Explore -- 3.6 Unsupervised Machine Learning Algorithms -- 3.6.1 Clustering -- 3.6.1.1 K-means -- 3.6.1.2 Mini-batch K-means -- 3.6.1.3 Mean Shift -- 3.6.1.4 Affinity Propagation -- 3.6.1.5 Density-based Spatial Clustering of Applications with Noise -- 3.7 Machine Learning Algorithms with HephAIstos -- References -- Further Reading -- Chapter 4 Natural Language Processing -- 4.1 Classifying Messages as Spam or Ham -- 4.2 Sentiment Analysis -- 4.3 Bidirectional Encoder Representations from Transformers -- 4.4 BERT's Functionality -- 4.5 Installing and Training BERT for Binary Text Classification Using TensorFlow -- 4.6 Utilizing BERT for Text Summarization -- 4.7 Utilizing BERT for Question Answering -- Further Reading -- Chapter 5 Machine Learning Algorithms in Quantum Computing -- 5.1 Quantum Machine Learning -- 5.2 Quantum Kernel Machine Learning -- 5.3 Quantum Kernel Training -- 5.4 Pegasos QSVC: Binary Classification -- 5.5 Quantum Neural Networks -- 5.5.1 Binary Classification with EstimatorQNN -- 5.5.2 Classification with a SamplerQNN…”

Tabla de Contenidos: “…12.2.1 Bernoulli approximation method -- 12.2.2 Trial solution method -- 12.3 Results -- 12.3.1 The generalized space-time fractional biological population model -- 12.3.2 The density-independent fractional diffusion-reaction equation -- 12.3.3 The space-time fractional generalized Duffing model -- 12.4 Conclusion -- Conflict of interest -- References -- 13 Unsupervised ANN model for solving fractional differential equations -- 13.1 Introduction -- 13.2 Preliminaries -- 13.2.1 Architecture of artificial neural networks -- 13.2.2 Riemann-Liouville fractional derivative [19] -- 13.2.3 Caputo fractional derivative [19] -- 13.2.4 Grünwald-Letnikov fractional derivative [15] -- 13.3 Modeling of neural networks for FDEs -- 13.4 Simulation results and discussion -- 13.5 Conclusion -- Acknowledgment -- References -- 14 Solitary wave solution for time-fractional SMCH equation in fuzzy environment -- 14.1 Introduction -- 14.2 Basic concept of fuzzy set theory and fractional theory -- 14.2.1 Definition 1 [43,44] -- 14.2.2 Definition 2 [43,44] -- 14.2.3 Definition 3 [43,44] -- 14.2.4 Definition 4 [43,44] -- 14.2.5 Definition 5 [43,44] -- 14.2.6 Definition 6 [43,44] -- 14.3 Method description -- 14.3.1 Local fractional Taylor theorem [28,33] -- 14.3.2 Definition 7 [28,33] -- 14.3.3 Definition 8 [28,33] -- 14.3.4 Some basic results related to FRDTM -- 14.4 Implementation of FRDTM on fuzzified SMCH equation -- 14.5 Results and discussion -- 14.6 Conclusion -- References -- 15 Piecewise concept in fractional models -- 15.1 Introduction -- 15.2 Piecewise derivative and integral with global, classical, and fractional types -- 15.3 Piecewise derivative with fractional derivatives -- 15.3.1 Piecewise derivative having classical and fractional derivatives -- 15.3.2 Piecewise derivative with global and fractional derivatives…”

Tabla de Contenidos: “…Cover -- Copyright -- Roadmap to the Syllabus -- Brief Contents -- Contents -- Preface -- Why and How I Wrote this Book -- C Programming Language -- About the Book -- Structure of the Book -- Salient Features and Strengths of the Book -- Typographical Conventions -- Web Resources -- Acknowledgements -- Part - I: Computer Fundamentals -- 1: Basics of Computer -- 1.1 Introduction -- 1.2 Digital and Analog Computers -- 1.3 Characteristics of Computer -- 1.4 History of Computer -- 1.5 Generations of Computer -- 1.5.1 First Generation (1940 to 1956): Using Vacuum Tubes -- 1.5.2 Second Generation (1956 to 1963): Using Transistors -- 1.5.3 Third Generation (1964 to 1971): Using Integrated Circuits -- 1.5.4 Fourth Generation (1971 to present): Using Microprocessors -- 1.5.5 Fifth Generation (Present and Next): Using Artificial Intelligence -- 1.6 Classification of Computer -- 1.6.1 Microcomputers -- 1.6.2 Minicomputers -- 1.6.3 Mainframe Computers -- 1.6.4 Supercomputers -- 1.7 The Computer System -- 1.7.1 The Input-Process-Output Concept -- 1.7.2 Components of Computer Hardware -- 1.8 Central Processing Unit -- 1.8.1 Arithmetic Logic Unit -- 1.8.2 Registers -- 1.8.3 Control Unit -- 1.9 Memory Unit -- 1.9.1 Cache Memory -- 1.9.2 Primary Memory -- 1.9.3 Secondary Memory -- 1.10 Instruction Format -- 1.11 Instruction Set -- 1.12 Instruction Cycle -- 1.13 Microprocessor -- 1.14 Interconnecting the Units of a Computer -- 1.14.1 System Bus -- 1.14.2 Expansion Bus -- 1.14.3 External Ports -- 1.15 Performance of a Computer -- 1.16 Inside a Computer Cabinet -- 1.16.1 Motherboard -- 1.16.2 Ports and Interfaces -- 1.16.3 Expansion Slots -- 1.16.4 Ribbon Cables -- 1.16.5 Memory Chips -- 1.16.6 Storage Devices -- 1.16.7 Processor -- 1.17 Application of Computers -- 1.18 Summary -- Exercise Questions -- Additional Questions…”