“…You’ll learn how to build statistical and advanced plots using the powerful ggplot2 library. …”

“…To use statistical methods and SAS applications to forecast the future values of data taken over time, you need only follow this thoroughly updated classic on the subject. …”

“…SQL is essential for communicating with relational databases, the type of database where most of the world's data is stored. R is a statistical analysis tool: It uses SQL to interact with databases for the purpose of creating charts, plots, reports, visualizations, and even web applications that incorporate data into a final product. …”

“…Economic Policy -Budgetary Policy -Monetary Policy -Policy conclusions II I. Summary Statistical Annex…”

“…Economic Policy -The Government Budget -Monetary policy -Other measures -Policy problems IV. Conclusions Statistical Annex…”

“…Including: how to represent the anthropometry of the target market, good practices for reliable fit testing, and comprehensive statistical analyses for fit and sizing analysis…”

Tabla de Contenidos: “…5.2.2.2 Limit and trend checking -- 5.2.2.3 Partial least squares regression -- 5.2.2.4 Bayesian network -- 5.2.2.5 Artificial neural network -- 5.2.2.6 K-means clustering -- 5.2.2.7 Attribute oriented induction -- 5.2.2.8 Hidden Markov model -- 5.3 Remaining Useful Life Identification of Wearing Components -- 5.3.1 Theoretical Background -- 5.3.2 Techniques Catalogue -- 5.3.3 Physical Modelling -- 5.3.3.1 Industrial automation -- 5.3.3.2 Fleet's maintenance -- 5.3.3.3 Eolic systems -- 5.3.3.4 Medical systems -- 5.3.4 Artificial Neural Networks -- 5.3.4.1 Deep neural networks -- 5.3.5 Life Expectancy Models -- 5.3.5.1 Time series analysis with attribute oriented induction -- 5.3.5.2 Application to a pump -- 5.3.5.3 Application to industrial forklifts -- 5.3.5.4 Application to a gearbox -- 5.3.6 Expert Systems -- 5.4 Alerting and Prediction of Failures -- 5.4.1 Theoretical Background -- 5.4.2 Techniques Catalogue -- 5.4.2.1 Nearest neighbour cold-deck imputation -- 5.4.2.2 Support vector machine -- 5.4.2.3 Linear discriminant analysis -- 5.4.2.4 Pattern mining -- 5.4.2.5 Temporal pattern mining -- 5.4.2.6 Principal component analysis -- 5.4.2.7 Hidden Semi-Markov model with Bayes classification -- 5.4.2.8 Autoencoders -- 5.4.2.9 Convolutional neural network with Gramian angular fields -- 5.4.2.10 Recurrent neural network with long-short-term memory -- 5.4.2.11 Change detection algorithm -- 5.4.2.12 Fisher's exact test -- 5.4.2.13 Bonferroni correction -- 5.4.2.14 Hypothesis testing using univariate parametric statistics -- 5.4.2.15 Hypothesis testing using univariate non-parametricstatistics -- 5.4.2.16 Mean, thresholds, normality tests -- 5.5 Examples -- 5.5.1 Usage Patterns/k-means -- 5.5.1.1 Data analysis -- 5.5.1.2 Results -- 5.5.1.2.1 Plotting -- 5.5.1.2.2 Replicability of results -- 5.5.1.2.3 Summary of results…”

Tabla de Contenidos: “…2.1.1 Digitization of Everything -- 2.1.2 The Fourth Industrial Revolution -- 2.1.2.1 Characteristic 1: Velocity -- 2.1.2.2 Characteristic 2: Cross-Jurisdictional Economies of Scale Without Mass -- 2.1.2.3 Characteristic 3: Heavy Reliance on Intangible Assets, Especially Intellectual Property -- 2.1.2.4 Characteristic 4: The Importance of Data, User Participation, and Their Synergies With Intellectual Property -- 2.1.2.5 Characteristic 5: Fusion of Technologies -- 2.1.2.6 Characteristic 6: Consumption Externality -- 2.1.2.7 Characteristic 7: Indirect Network Effects -- 2.1.2.8 Characteristic 8: Lock-In Effects and Competition -- 2.2 Models for Digital Asset Valuation -- 2.2.1 Method 1: Intrinsic Value -- 2.2.1.1 1a: Intrinsic Cost of Production -- 2.2.1.2 1b: Direct Financial Conversion -- 2.2.2 Method 2: Extrinsic Value -- 2.2.2.1 2a: Market Value -- 2.2.2.2 2b: Usage Value -- 2.2.3 Method 3: Subjective Value -- 2.2.4 Method 4: Opportunity Value -- 2.3 Measuring the Digital Economy -- 2.3.1 Measuring Rate of Digitalization of Traditional Industries: The Enabler and Multiplier -- 2.3.2 Measuring Digital-Native Industries: The "Smarter," More Intelligent Disrupter -- Example: Platform Revolution -- 2.3.3 Measuring the Invisible Economy: The Opportunity Value -- 2.4 Digital Theory of Value -- 2.4.1 Law of Machine Time -- 2.4.1.1 Phenomenon 1a: The Underlying Exponential Function -- 2.4.1.2 Phenomenon 1b: The Future Cannot Be Projected From the Past using Current Statistical Methods -- 2.4.1.3 Principle 1a: Progress of Digital Economy Should Be Measured Against Machine Time -- 2.4.1.4 Principle 1b: Sensemaking Is a Universal Challenge and a Value Driver -- 2.4.1.5 Principle 1c: Risk Management Is an Island of Stability in the Sea of Change -- 2.4.2 Law of Recombination -- 2.4.2.1 Phenomenon 2a: Quality Data Is the New Oil…”

Tabla de Contenidos: “…CART -- 18.5.4 Detailed Look at the Statistical Analysis -- 18.5.5 Early Results on Defect Data Sets -- 18.6 Summary -- Part IV: Sharing Models -- Chapter 19: Sharing Models: Challenges and Methods -- Chapter 20: Ensembles of Learning Machines -- 20.1 When and Why Ensembles Work -- 20.1.1 Intuition -- 20.1.2 Theoretical Foundation -- 20.2 Bootstrap Aggregating (Bagging) -- 20.2.1 How Bagging Works -- 20.2.2 When and Why Bagging Works -- 20.2.3 Potential Advantages of Bagging for SEE -- 20.3 Regression Trees (RTs) for Bagging -- 20.4 Evaluation Framework -- 20.4.1 Choice of Data Sets and Preprocessing Techniques -- 20.4.1.1 PROMISE data -- 20.4.1.2 ISBSG data -- 20.4.2 Choice of Learning Machines -- 20.4.3 Choice of Evaluation Methods -- 20.4.4 Choice of Parameters -- 20.5 Evaluation of Bagging+RTs in SEE -- 20.5.1 Friedman Ranking -- 20.5.2 Approaches Most Often Ranked First or Second in Terms of MAE, MMRE and PRED(25) -- 20.5.3 Magnitude of Performance Against the Best -- 20.5.4 Discussion -- 20.6 Further Understanding of Bagging+RTs in SEE -- 20.7 Summary -- Chapter 21: How to Adapt Models in a Dynamic World -- 21.1 Cross-Company Data and Questions Tackled…”

Tabla de Contenidos: “…4.5.3 Satellite Communication Based on Quantum Computing -- 4.5.4 Machine Learning &amp -- Artificial Intelligence -- 4.6 Optical Quantum Computing -- 4.7 Experimental Realisation of Quantum Computer -- 4.7.1 Hetero-Polymers -- 4.7.2 Ion Traps -- 4.7.3 Quantum Electrodynamics Cavity -- 4.7.4 Quantum Dots -- 4.8 Challenges of Quantum Computing -- 4.9 Conclusion and Future Scope -- References -- Chapter 5 Feature Engineering for Flow-Based IDS -- 5.1 Introduction -- 5.1.1 Intrusion Detection System -- 5.1.2 IDS Classification -- 5.2 IP Flows -- 5.2.1 The Architecture of Flow-Based IDS -- 5.2.2 Wireless IDS Designed Using Flow-Based Approach -- 5.2.3 Comparison of Flow- and Packet-Based IDS -- 5.3 Feature Engineering -- 5.3.1 Curse of Dimensionality -- 5.3.2 Feature Selection -- 5.3.3 Feature Categorization -- 5.4 Classification of Feature Selection Technique -- 5.4.1 The Wrapper, Filter, and Embedded Feature Selection -- 5.4.2 Correlation, Consistency, and PCA-Based Feature Selection -- 5.4.3 Similarity, Information Theoretical, Sparse Learning, and Statistical-Based Feature Selection -- 5.4.4 Univariate and Multivariate Feature Selection -- 5.5 Tools and Library for Feature Selection -- 5.6 Literature Review on Feature Selection in Flow-Based IDS -- 5.7 Challenges and Future Scope -- 5.8 Conclusions -- Acknowledgement -- References -- Chapter 6 Environmental Aware Thermal (EAT) Routing Protocol for Wireless Sensor Networks -- 6.1 Introduction -- 6.1.1 Single Path Routing Protocol -- 6.1.2 Multipath Routing Protocol -- 6.1.3 Environmental Influence on WSN -- 6.2 Motivation Behind the Work -- 6.3 Novelty of This Work -- 6.4 Related Works -- 6.5 Proposed Environmental Aware Thermal (EAT) Routing Protocol -- 6.5.1 Sensor Node Environmental Modeling and Analysis -- 6.5.2 Single Node Environmental Influence Modeling -- 6.5.3 Multiple Node Modeling…”

Tabla de Contenidos: “…. -- 6.1 Introduction -- 6.2 Related works -- 6.3 Proposed model -- 6.3.1 Tokenization -- 6.3.2 Stop words removal -- 6.3.3 Normalization -- 6.3.4 Feature extraction -- 6.3.4.1 CountVectorizer -- 6.3.4.2 Term frequency and inverse document frequency -- 6.3.4.3 Word2vec -- 6.3.4.4 Improved adaptive synthetic sampling (ADASYN) -- 6.4 Results and discussion -- 6.5 Conclusion -- References -- 7 Emotion detection from text data using machine learning for human behavior analysis -- 7.1 Introduction -- 7.1.1 Human behavior analysis -- 7.1.1.1 Theories of emotion -- 7.1.1.1.1 Social cognitive theory -- 7.1.1.2 Theories of personality -- 7.1.1.2.1 Social identity theory -- 7.1.1.2.2 Self-determination theory -- 7.1.2 Models of emotion -- 7.1.3 Affective computing for emotion detection -- 7.1.4 Natural language processing for emotion detection -- 7.1.4.1 Applications -- 7.1.4.2 Challenges -- 7.2 Available tools and resources -- 7.2.1 Tools for emotion detection -- 7.2.2 Datasets -- 7.2.3 Feature extraction tools -- 7.3 Methods and materials -- 7.3.1 Rule-based approaches -- 7.3.1.1 Lexicon-based approach -- 7.3.1.2 Keyword-based approach -- 7.3.1.3 Regular expressions -- 7.3.2 Statistical learning -- 7.3.2.1 Machine learning-based approaches -- 7.3.2.2 Deep learning methods -- 7.3.2.3 Contextual adaptation -- 7.3.2.4 Personalized bots -- 7.3.3 Explainable AI for emotion detection -- 7.4 Outlook -- 7.4.1 Ethical considerations -- 7.4.2 Limitations of NLP in emotion detection -- 7.5 Conclusion -- References -- 8 Optimization of effectual sentiment analysis in film reviews using machine learning techniques -- 8.1 Introduction -- 8.2 Literature Survey -- 8.3 Proposed System -- 8.3.1 Hybrid algorithm Design -- 8.3.2 Solution Representation…”

Tabla de Contenidos: “…Knowledge Based Log Analysis (KBLA) -- 11.1 Overview of KBLA -- 11.2 Invoking KBLA -- 11.2.1 KBLA task selection 1 - IMS Log Utilities -- 11.2.2 KBLA task selection 2 - IMS Log Formatting -- 11.2.3 KBLA task selection 4 - IMS Knowledge Based Analysis -- 11.2.4 IMS KBLA - Log Data Set Analysis -- 11.2.5 KBLA MSC Link Performance Formatting -- 11.2.6 KBLA Statistic Log Record Analysis -- 11.2.7 KBLA trace entry filtering -- 11.2.8 KBLA IRLM Lock Trace Analysis -- 11.2.9 KBLA DBCTL Transaction Analysis -- Chapter 12. …”

Tabla de Contenidos: “…RMF Monitor III enhancements -- C.1 Activity by storage class -- C.2 Data set activity -- C.3 LRU statistics -- C.4 Display SMS -- C.5 VARY SMS -- C.6 SETSMS -- C.7 IDCAMS SHCDS -- Glossary -- Abbreviations and acronyms -- Related publications -- IBM Redbooks -- Other publications -- Online resources -- How to get IBM Redbooks -- Help from IBM -- Index -- Back cover…”

Tabla de Contenidos: “…What's new in Version 5 -- 2.1 New nodes in the toolkit -- 2.1.1 Mapping node -- 2.1.2 XMLTransformation node -- 2.1.3 Transport nodes -- 2.1.4 User-defined nodes and parsers -- 2.2 What's different from WebSphere MQ Integrator V2.1 -- 2.3 What's new in ESQL -- 2.3.1 XML namespace support -- 2.3.2 SQL schemas and modules -- 2.3.3 ESQL modules and module-level variables -- 2.3.4 Enhanced bitstream handling -- 2.3.5 Enhanced (new) string functions -- 2.3.6 Enhanced (new) numeric functions -- 2.4 What's different in ESQL -- 2.4.1 Debugger -- 2.5 What's new with the MRM -- 2.6 What's different with the MRM -- 2.7 Debugging and problem solving facilities -- 2.7.1 The workbench tasks view -- 2.8 Version control and configuration management -- 2.9 User-defined nodes -- 2.10 Administration -- 2.11 Support for accounting and statistics data -- Chapter 3. Installation and verification -- 3.1 Setting up a standalone development system -- 3.2 Installing base required software -- 3.3 Installing MDAC Version 2.7 SP1 -- 3.4 Installing IBM Agent Controller -- 3.5 Installing WebSphere Business Integration Message Broker -- 3.5.1 Installation -- 3.5.2 Registering the purchased licenses -- 3.6 Create the default configuration -- 3.6.1 Using the Getting Started Wizard -- 3.6.2 Using a sample application -- Chapter 4. …”

Tabla de Contenidos: “…8.4.3.1 Example of embedded SoC for multimodal localization -- 8.4.3.2 Smart phones -- 8.4.3.3 Smart glasses -- 8.4.3.4 Autonomous mobile robots -- 8.4.3.5 Unmanned aerial vehicles -- 8.4.3.6 Autonomous driving vehicles -- 8.4.4 Discussion -- 8.5 Application Domains -- 8.5.1 Scene Mapping -- 8.5.1.1 Aircraft inspection -- 8.5.1.2 SenseFly eBee classic -- 8.5.2 Pedestrian Localization -- 8.5.2.1 Indoor localization in large-scale buildings -- 8.5.2.2 Precise localization of mobile devices in unknown environments -- 8.5.3 Automotive Navigation -- 8.5.3.1 Autonomous driving -- 8.5.3.2 Smart factory -- 8.5.4 Mixed Reality -- 8.5.4.1 Virtual cane system for visually impaired individuals -- 8.5.4.2 Engineering, construction and maintenance -- 8.6 Conclusion -- References -- 9 Self-Supervised Learning from Web Data for Multimodal Retrieval -- 9.1 Introduction -- 9.1.1 Annotating Data: A Bottleneck for Training Deep Neural Networks -- 9.1.2 Alternatives to Annotated Data -- 9.1.3 Exploiting Multimodal Web Data -- 9.2 Related Work -- 9.2.1 Contributions -- 9.3 Multimodal Text-Image Embedding -- 9.4 Text Embeddings -- 9.5 Benchmarks -- 9.5.1 InstaCities1M -- 9.5.2 WebVision -- 9.5.3 MIRFlickr -- 9.6 Retrieval on InstaCities1M and WebVision Datasets -- 9.6.1 Experiment Setup -- 9.6.2 Results and Conclusions -- 9.6.3 Error Analysis -- 9.6.3.1 Visual features confusion -- 9.6.3.2 Errors from the dataset statistics -- 9.6.3.3 Words with different meanings or uses -- 9.7 Retrieval in the MIRFlickr Dataset -- 9.7.1 Experiment Setup -- 9.7.2 Results and Conclusions -- 9.8 Comparing the Image and Text Embeddings -- 9.8.1 Experiment Setup -- 9.8.2 Results and Conclusions -- 9.9 Visualizing CNN Activation Maps -- 9.10 Visualizing the Learned Semantic Space with t-SNE -- 9.10.1 Dimensionality Reduction with t-SNE -- 9.10.2 Visualizing Both Image and Text Embeddings…”

Tabla de Contenidos: “…Cover -- Brief Contents -- Contents -- Foreword -- Preface -- Acknowledgements -- About the Author -- Chapter 1: Measurements and Instruments -- 1.1 Introduction -- 1.2 Terminology -- 1.2.1 Advantages of Instrumentation Systems -- 1.2.2 Block Schematics of Measuring Systems -- 1.2.3 Other Systems -- 1.2.4 Objectives of Measurement -- 1.2.5 Comparison between Analog and Digital Instruments -- 1.2.6 Factors for the Selection of Analog and Digital Equipments -- 1.3 Performance Characteristics -- 1.3.1 Definitions -- 1.4 Significant Figures -- 1.5 Dynamic Characteristics -- 1.6 Types of Errors -- 1.6.1 Gross Errors -- 1.6.2 Systematic Errors -- 1.6.3 Random Errors -- 1.7 Statistical Analysis -- 1.7.1 Probability of Errors and Gaussian Curve -- 1.8 Measurement Standards -- 1.9 Suspension Galvanometer -- 1.10 D'Arsonval Movement -- 1.10.1 Taut-Band Suspension -- 1.10.2 Temperature Compensation -- 1.10.3 Shunt Resistor -- 1.10.4 Ayrton Shunt -- 1.11 Direct Current Meters -- 1.12 D'Arsonval Meter Movement Used in DC Voltmeters -- 1.12.1 Ammeter Loading Effect -- 1.13 DC Voltmeters -- 1.13.1 Multirange Voltmeter -- 1.14 Ohmmeter -- 1.14.1 Series-Type Ohmmeter -- 1.14.2 Shunt-Type Ohmmeter -- 1.14.3 D'Arsonval Meter Movement Used in Ohmmeter -- 1.14.4 Multiple Range Ohmmeters -- 1.14.5 Electrolyte Capacitor Leakage Tests -- 1.14.6 For Non-Electrolyte Capacitors -- 1.15 Multimeter -- 1.16 Alternating Current-Indicating Instruments -- 1.16.1 Electrodynamometer -- 1.17 Rectifier-Type Instruments -- 1.18 Meter Protection -- 1.19 Extension of Range -- 1.20 Frequency Compensation -- 1.21 Electronic Voltmeter (for DC) -- 1.22 Electronic Voltmeter (for AC) -- 1.22.1 Average Reading Voltmeter -- 1.22.2 Peak Reading Voltmeter -- 1.22.3 Peak-To-Peak Detector -- 1.23 DC Meter with Amplifier -- 1.24 Chopper-Stabilised Amplifier -- 1.25 AC Voltmeter using Rectifiers…”

Tabla de Contenidos: “…14.1.2 Distributed resources jobs -- 14.1.3 Database maintenance jobs -- 14.2 Database maintenance -- 14.2.1 Database statistic and check -- 14.2.2 Database Maintenance Plan -- 14.3 Microsoft SQL Server and Windows 2000 tuning -- 14.3.1 Windows 2000 Advanced Server tuning -- 14.3.2 Microsoft SQL Server 2000 tuning -- Chapter 15. …”

Tabla de Contenidos: “…A Smart WeeDrone for Sustainable Agriculture -- 8.1 Introduction -- 8.1.1 Competition Between Crops and Weeds -- 8.2 Related Work -- 8.3 WeeDrone -- 8.4 Features of WeeDrone -- 8.4.1 Shortest Path Algorithm -- 8.4.2 OpenDroneMap -- 8.4.3 Machine Learning Algorithm -- 8.4.4 Removal of Diseased/Dead Plant -- 8.4.5 Sowing and Spreading Manure -- 8.4.6 Data Analysis -- 8.5 Design and Working Principle -- 8.6 Outputs and Simulation -- 8.6.1 Autonomous Flight -- 8.6.1.1 Drone Flight -- 8.6.1.2 Automatic Flight Subsystem -- 8.6.2 Image Processing -- 8.6.3 Robotic Arm Design -- 8.6.3.1 Statistical Analysis of WeeDrone Model -- 8.7 Conclusion -- References -- 9. …”

Tabla de Contenidos: “…. -- 13.1 Introduction -- 13.2 Literature survey -- 13.3 Methodology -- 13.3.1 Distinguishing midlife crisis symptoms -- 13.3.2 Designing of the prediction model -- 13.3.3 Application of LDA and statistical comparison -- 13.3.3.1 Formulation of Dirichlet distribution -- 13.3.3.2 Categorization in Bayesian model -- 13.3.3.3 Concept of topic modeling -- 13.4 Result and discussion -- 13.5 Conclusion and future scope -- References -- 14 Autonomous robotic system for ultraviolet disinfection -- 14.1 Introduction -- 14.2 Background -- 14.2.1 Ultraviolet light for disinfection -- 14.2.2 Exposure time for deactivation of the bacteria -- 14.2.3 Flow chart of UV bot control logic -- 14.2.4 Calculations related to the time for disinfection -- 14.3 Implementation -- 14.4 Model topology -- 14.4.1 UV-C light robotic vehicle -- 14.5 Conclusion -- References -- 15 Emerging health start-ups for economic feasibility: opportunities during COVID-19 -- 15.1 Introduction -- 15.2 Health-tech verticals for start-ups…”

“…This 2020 edition of OECD Research and Development Expenditure in Industry provides statistical data on R&D expenditure broken down by industrial and service sectors. …”