Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Contents -- Preface -- Acknowledgments -- Abbreviations -- Chapter 1 Introduction -- Chapter 2 Least-Squares Adjustments -- 2.1 Elementary Considerations -- 2.1.1 Statistical Nature of Surveying Measurements -- 2.1.2 Observational Errors -- 2.1.3 Accuracy and Precision -- 2.2 Stochastic and Mathematical Models -- 2.3 Mixed Model -- 2.3.1 Linearization -- 2.3.2 Minimization and Solution -- 2.3.3 Cofactor Matrices -- 2.3.4 A Posteriori Variance of Unit Weight -- 2.3.5 Iterations -- 2.4 Sequential Mixed Model -- 2.5 Model Specifications -- 2.5.1 Observation Equation Model -- 2.5.2 Condition Equation Model -- 2.5.3 Mixed Model with Observation Equations -- 2.5.4 Sequential Observation Equation Model -- 2.5.5 Observation Equation Model with Observed Parameters -- 2.5.6 Mixed Model with Conditions -- 2.5.7 Observation Equation Model with Conditions -- 2.6 Minimal and Inner Constraints -- 2.7 Statistics in Least-Squares Adjustment -- 2.7.1 Fundamental Test -- 2.7.2 Testing Sequential Least Squares -- 2.7.3 General Linear Hypothesis -- 2.7.4 Ellipses as Confidence Regions -- 2.7.5 Properties of Standard Ellipses -- 2.7.6 Other Measures of Precision -- 2.8 Reliability -- 2.8.1 Redundancy Numbers -- 2.8.2 Controlling Type-II Error for a Single Blunder -- 2.8.3 Internal Reliability -- 2.8.4 Absorption -- 2.8.5 External Reliability -- 2.8.6 Correlated Cases -- 2.9 Blunder Detection -- 2.9.1 Tau Test -- 2.9.2 Data Snooping -- 2.9.3 Changing Weights of Observations -- 2.10 Examples -- 2.11 Kalman Filtering -- Chapter 3 Recursive Least Squares -- 3.1 Static Parameter -- 3.2 Static Parameters and Arbitrary Time-Varying Variables -- 3.3 Dynamic Constraints -- 3.4 Static Parameters and Dynamic Constraints -- 3.5 Static Parameter, Parameters Subject to Dynamic Constraints, and Arbitrary Time-Varying Parameters…”

Tabla de Contenidos: “…Before we begin: the mathematical building blocks of neural networks -- 2.1. …”

Tabla de Contenidos: “…1.16 Tools -- Summary -- Exercises -- Chapter 2: Preliminaries:Mathematical -- 2.1 Predicates and Boolean Expressions -- 2.2 Control Flow Graph -- 2.2.1 Basic blocks -- 2.2.2 Flow graphs -- 2.2.3 Paths -- 2.2.4 Basis paths -- 2.2.5 Path conditions and domains -- 2.2.6 Domain and computation errors -- 2.2.7 Static code analysis tools and static testing -- 2.3 Execution History -- 2.4 Dominators and Post-Dominators -- 2.5 Program Dependence Graph -- 2.5.1 Data dependence -- 2.5.2 Control dependence -- 2.5.3 Call graph -- 2.6 Strings, Languages, and Regular Expressions -- 2.7 Tools -- Summary -- Exercises -- Part II: Test Generation -- Chapter 3: Domain Partitioning -- 3.1 Introduction -- 3.2 The Test Selection Problem -- 3.3 Equivalence Partitioning -- 3.3.1 Faults targeted -- 3.3.2 Relations -- 3.3.3 Equivalence classes for variables -- 3.3.4 Unidimensional partitioning versus multidimensional partitioning -- 3.3.5 A systematic procedure -- 3.3.6 Test selection -- 3.3.7 Impact of GUI design -- 3.4 Boundary Value Analysis -- 3.5 Category-Partition Method -- 3.5.1 Steps in the category-partition method -- Summary -- Exercises -- Chapter 4: Predicate Analysis -- 4.1 Introduction -- 4.2 Domain Testing -- 4.2.1 Domain errors -- 4.2.2 Border shifts -- 4.2.3 ON-OFF points -- 4.2.4 Undetected errors -- 4.2.5 Coincidental correctness -- 4.2.6 Paths to be tested -- 4.3 Cause-Effect Graphing -- 4.3.1 Notation used in cause-effect graphing -- 4.3.2 Creating cause-effect graphs -- 4.3.3 Decision table from cause-effect graph -- 4.3.4 Heuristics to avoid combinatorial explosion -- 4.3.5 Test generation from a decision table -- 4.4 Tests Using Predicate Syntax -- 4.4.1 A fault model -- 4.4.2 Missing or extra Boolean variable faults -- 4.4.3 Predicate constraints -- 4.4.4 Predicate testing criteria -- 4.4.5 BOR, BRO, and BRE adequate tests…”

Tabla de Contenidos: “…12.3.3 LPG Detecting System -- 12.3.3.1 Materials Description -- 12.3.3.2 Circuit Diagram -- 12.3.3.3 Power Consumption -- 12.3.3.4 Components Required -- 12.3.4 Materials Description -- 12.3.4.1 NodeMCU 8266 -- 12.3.5 Online Switch -- 12.3.5.1 Components Required -- 12.3.5.2 Circuit Diagram -- 12.3.5.3 Materials Description -- 12.3.5.4 Projects in Smart House Systems -- 12.3.6 Introducing Image Processing -- 12.3.6.1 Image Processing -- 12.3.6.2 Machine Learning in Automation -- 12.3.6.3 Online Switch -- 12.3.6.4 Machine Learning Module -- 12.3.7 Plants Health Monitoring -- 12.3.7.1 Components Required -- 12.3.7.2 Working of the System -- 12.4 Future Scope -- 12.5 Conclusion -- References -- Chapter 13 Multi-Robot Navigation: A Biologically Inspired Framework -- 13.1 Introduction -- 13.1.1 Motivation -- 13.2 Optimization Algorithms -- 13.2.1 Mathematical Formulation -- 13.2.2 Gradient-Based Approaches -- 13.2.3 Gradient-Free Algorithm -- 13.2.4 Nature-Inspired Optimization Algorithms -- 13.2.5 Genetic Algorithms -- 13.2.6 Particle Swarm Optimization -- 13.2.7 Ant Colony Optimization -- 13.2.8 Grey Wolf Algorithm -- 13.2.9 Arithmetic Algorithm -- 13.2.10 Aquila Optimization Algorithm -- 13.2.11 Different Algorithms -- 13.3 Algorithms and Self-Organization -- 13.3.1 Algorithmic Attributes -- 13.3.2 Comparison With Classical Optimization Techniques -- 13.3.3 Self-Organized Systems -- 13.4 Future Research Directions -- 13.5 Conclusion -- References -- Chapter 14 Bidirectional LSTM for Heart Arrhythmia Detection -- 14.1 Introduction -- 14.2 About the Dataset -- 14.3 Flow of the Model -- 14.4 Results -- 14.5 Conclusion -- References -- Chapter 15 Study on Content-Based Image Retrieval -- 15.1 Introduction -- 15.2 Related Works -- 15.2.1 Conventional-Indexing Techniques -- 15.2.2 Dimensionality's Curse -- 15.2.2.1 Parallel Architecture -- 15.2.2.2 Hashing…”

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…”

“…The author, Ernst Bindel, integrates insights from theosophy and ancient texts to present a comprehensive view of numbers beyond their mathematical functions. The work aims to make complex ideas accessible to a broader audience by avoiding intricate mathematical operations and focusing on the symbolic and spiritual aspects of numbers. …”

“…The aim of this book is to provide the reader with a selection of methods in the field of mathematical modeling, simulation, and control of different dynamical systems. …”

“…LICS is an annual international forum on the broad range of topics that lie at the intersection of computer science and mathematical logic…”