Tabla de Contenidos: “…-- 19.3 RF Amplifier Topology -- 19.4 Op Amp Parameters for RF Designers -- 19.4.1 Stage Gain -- 19.4.2 Phase Linearity -- 19.4.3 Frequency Response Peaking -- 19.4.4 −1dB Compression Point -- 19.4.5 Noise Figure -- 19.5 Wireless Systems -- 19.5.1 Broadband Amplifiers -- 19.5.2 IF Amplifiers -- 19.6 High-Speed Analog Input Drive Circuits -- 19.7 Conclusions -- 20 - Designing Low-Voltage Op Amp Circuits -- 20.1 Introduction -- 20.2 Critical Specifications -- 20.2.1 Output Voltage Swing -- 20.2.2 Dynamic Range -- 20.2.3 Input Common-Mode Range -- 20.2.4 Signal-to-Noise Ratio -- 20.3 Summary…”

Tabla de Contenidos: “…Tagging data to a specific region -- 10.3.2. Linear referencing: snapping points to the closest linestring -- 10.4. …”

Tabla de Contenidos: “…-- 2.3 Abschreibung -- 2.3.1 Wie berechnen Sie die lineare Abschreibung‌ für Ihr Auto? -- 2.3.2 Wie berechnen Sie die arithmetisch-degressive Abschreibung‌ für eine Maschine? …”

Tabla de Contenidos: “…Chapter 6 Risk‐Aware Cyber‐Physical Control for Resilient Smart Cities -- 6.1 Introduction -- 6.2 System Model -- 6.2.1 Communication Latency in Smart Grid Systems -- 6.2.2 Risk Model for Communication Links -- 6.2.3 History of Communication Links -- 6.3 Risk‐Aware Quality of Service Routing Using SDN -- 6.3.1 Constrained Shortest Path Routing Problem Formulation -- 6.3.2 SDN Architecture and Implementation -- 6.3.3 Risk‐Aware Routing Algorithm -- 6.4 Risk‐Aware Adaptive Control -- 6.4.1 Smart Grid Model -- 6.4.2 Parametric Feedback Linearization Control -- 6.4.3 Risk‐Aware Routing and Latency‐Adaptive Control Scheme -- 6.5 Simulation Environment and Numerical Analysis -- 6.5.1 Avoiding Vulnerable Communication Links While Meeting QoS Constraint -- 6.5.2 Algorithm Overhead Comparison -- 6.5.3 Impact of QoS Constraints -- 6.5.4 Impact on Distributed Control -- 6.6 Conclusions -- References -- Chapter 7 Wind Speed Prediction Using a Robust Possibilistic C‐Regression Model Method: A Case Study of Tunisia -- 7.1 Introduction -- 7.2 Data Collection and Method -- 7.2.1 Data Description -- 7.2.2 Robust Possibilistic C‐Regression Models -- 7.2.3 Wind Speed Data Analysis Procedure -- 7.3 Experiment and Discussion -- 7.4 Conclusion -- References -- Chapter 8 Intelligent Traffic: Formulating an Applied Research Methodology for Computer Vision and Vehicle Detection -- 8.1 Introduction -- 8.1.1 Introduction -- 8.1.2 Background -- 8.1.3 Problem Statement -- 8.1.3.1 Purpose of Research -- 8.1.3.2 Research Questions -- 8.1.3.3 Study Aim and Objectives -- 8.1.3.4 Significance and Structure of the Research -- 8.2 Literature Review -- 8.2.1 Introduction -- 8.2.2 Machine Learning, Deep Learning, and Computer Vision -- 8.2.2.1 Machine Learning -- 8.2.2.2 Deep Learning -- 8.2.2.3 Computer Vision -- 8.2.3 Object Recognition, Object Detection, and Object Tracking…”

Tabla de Contenidos: “…4.3.2 Bayesian Multisource Classification Mechanism -- 4.3.3 A Refined Multisource Bayesian Model -- 4.3.4 Multisource Classification Using the MRF -- 4.3.5 Assumption of Inter-Source Independence -- 4.4 Evidential Reasoning -- 4.4.1 Concept Development -- 4.4.2 Belief Function and Belief Interval -- 4.4.3 Evidence Combination -- 4.4.4 Decision Rules for Evidential Reasoning -- 4.5 Dealing with Source Reliability -- 4.5.1 Using Classification Accuracy -- 4.5.2 Use of Class Separability -- 4.5.3 Data Information Class Correspondence Matrix -- 4.6 Concluding Remarks and Future Trends -- References -- Chapter 5 Support Vector Machines -- 5.1 Linear Classification -- 5.1.1 The Separable Case -- 5.1.2 The Nonseparable Case -- 5.2 Nonlinear Classification and Kernel Functions -- 5.2.1 Nonlinear SVMs -- 5.2.2 Kernel Functions -- 5.3 Parameter Determination -- 5.3.1 t-Fold Cross-Validations -- 5.3.2 Bound on Leave-One-Out Error -- 5.3.3 Grid Search -- 5.3.4 Gradient Descent Method -- 5.4 Multiclass Classification -- 5.4.1 One-Against-One, One-Against-Others, and DAG -- 5.4.2 Multiclass SVMs -- 5.4.2.1 Vapnik's Approach -- 5.4.2.2 Methodology of Crammer and Singer -- 5.5 Relevance Vector Machines -- 5.6 Twin Support Vector Machines -- 5.7 Deep Support Vector Machines -- 5.8 Concluding Remarks -- References -- Chapter 6 Decision Trees -- 6.1 ID3, C4.5, and SEE5.0 Decision Trees -- 6.1.1 ID3 -- 6.1.2 C4.5 -- 6.1.3 SEE5.0 (C5.0) -- 6.2 CHAID -- 6.3 CART -- 6.4 QUEST -- 6.4.1 Split Point Selection -- 6.4.2 Attribute Selection -- 6.5 Tree Induction from Artic fi ial Neural Networks -- 6.6 Pruning Decision Trees -- 6.6.1 Reduced Error Pruning -- 6.6.2 Pessimistic Error Pruning -- 6.6.3 Error-Based Pruning -- 6.6.4 Cost Complexity Pruning -- 6.6.5 Minimal Error Pruning -- 6.7 Ensemble Methods -- 6.7.1 Boosting -- 6.7.2 Random Forest -- 6.7.3 Rotation Forest…”

Tabla de Contenidos: “…8.3 Differential encoding -- 8.4 Speech compression -- 8.5 A-Law and μ-Law companding -- 8.6 Speech sampling -- 8.7 PCM-TDM systems -- 8.8 Exercises -- AUDIO SIGNALS -- 9.1 Introduction -- 9.2 Principles -- 9.3 Digital audio standards -- 9.4 Error control -- 9.5 Interleaving -- 9.6 CD audio system -- 9.7 Digital audio compression -- 9.8 The 44.1 kHz sampling rate -- 9.9 Exercise -- AUDIO COMPRESSION (MPEG-AUDIO AND DOLBY AC-3) -- 10.1 Introduction -- 10.2 Psycho-acoustic model -- 10.3 MPEG audio coding -- 10.4 Backward/forward adaptive bit allocation methods -- 10.5 Comparison between forward and backward adaptive methods -- 10.6 Dolby AC-1 and AC-2 -- 10.7 Dolby AC-3 coding -- 10.8 AC-3 parameters -- 10.9 Exercises -- ERROR CODING PRINCIPLES -- 11.1 Introduction -- 11.2 Modulo-2 arithmetic -- 11.3 Binary manipulation -- 11.4 Hamming distance -- 11.5 General probability theory -- 11.6 Error probability -- 11.7 Combinations of errors -- 11.8 Linear and cyclic codes -- 11.9 Block and convolutional coding -- 11.10 Systematic and unsystematic coding -- 11.11 Feedforward and feedback error correction -- 11.12 Error types -- 11.13 Coding gain -- 11.14 Exercises -- ERROR CODING (DETECTION) -- 12.1 Introduction -- 12.2 Parity -- 12.3 Block parity -- 12.4 Checksum -- 12.5 Cyclic redundancy checking (CRC) -- 12.6 Exercises -- ERROR CODING (CORRECTION) -- 13.1 Introduction -- 13.2 Longitudinal/vertical redundancy checks (LRC/VRC) -- 13.3 Hamming code -- 13.4 Representations of Hamming code -- 13.5 Single error correction/double error detection Hamming code -- 13.6 Reed-Solomon coding -- 13.7 Convolution codes -- 13.8 Tutorial -- DATA ENCRYPTION PRINCIPLES -- 14.1 Introduction -- 14.2 Government pressure -- 14.3 Cryptography -- 14.4 Legal issues -- 14.5 Basic encryption principles -- 14.6 Exercises -- DATA ENCRYPTION -- 15.1 Introduction…”

Tabla de Contenidos: “…Basic and additional spanloads 8.2.2 ... Linearized lifting-surface theory 8.2.3 ... Lifting-line theory 8.2.4 ... 3D lift in ground effect 8.2.5 ... …”

Tabla de Contenidos: “…4.5.2 AlphaGo Zero -- 4.5.3 AlphaZero -- 4.6 Manipulation von Objekten -- 4.7 Populäre Umgebungen für das Deep-Reinforcement-Learning -- 4.7.1 OpenAI Gym -- 4.7.2 DeepMind Lab -- 4.7.3 UnityML-Agents -- 4.8 Drei Arten von KI -- 4.8.1 Artificial Narrow Intelligence -- 4.8.2 Artificial General Intelligence -- 4.8.3 Artificial Super Intelligence -- 4.8.4 Zusammenfassung -- Teil II -- Die nötige Theorie -- 5 Der (Code-)Karren vor dem (Theorie-)Pferd -- 5.1 Voraussetzungen -- 5.2 Installation -- 5.3 Ein flaches Netzwerk in Keras -- 5.3.1 Der MNIST-Datensatz handgeschriebener Ziffern -- 5.3.2 Ein schematisches Diagramm des Netzwerks -- 5.3.3 Die Daten laden -- 5.3.4 Die Daten umformatieren -- 5.3.5 Die Architektur eines neuronalen Netzes entwerfen -- 5.3.6 Trainieren eines Deep-Learning-Modells -- 5.4 Zusammenfassung -- 6 Künstliche Neuronen, die Hotdogs erkennen -- 6.1 Das Einmaleins der biologischen Neuroanatomie -- 6.2 Das Perzeptron -- 6.2.1 Der Hotdog/Nicht-Hotdog-Detektor -- 6.2.2 Die wichtigste Gleichung in diesem Buch -- 6.3 Moderne Neuronen und Aktivierungsfunktionen -- 6.3.1 Das Sigmoid-Neuron -- 6.3.2 Das Tanh-Neuron -- 6.3.3 ReLU: Rectified Linear Units -- 6.4 Ein Neuron auswählen -- 6.5 Zusammenfassung -- Schlüsselkonzepte -- 7 Künstliche neuronale Netze -- 7.1 Die Eingabeschicht -- 7.2 Vollständig verbundene Schichten -- 7.3 Ein vollständig verbundenes Netzwerk zum Erkennen von Hotdogs -- 7.3.1 Forwardpropagation durch die erste verborgene Schicht -- 7.3.2 Forwardpropagation durch nachfolgende Schichten -- 7.4 Die Softmax-Schicht eines Netzwerks zum Klassifizieren von Fastfood -- 7.5 Zurück zu unserem flachen Netzwerk -- 7.6 Zusammenfassung -- Schlüsselkonzepte -- 8 Deep Networks trainieren -- 8.1 Kostenfunktionen -- 8.1.1 Quadratische Kosten -- 8.1.2 Gesättigte Neuronen -- 8.1.3 Kreuzentropie-Kosten…”

Tabla de Contenidos: “…6.7.3 Data Transformation -- 6.8 Output Data -- 6.9 Design &amp -- Implementation -- 6.9.1 Integration Design -- 6.9.2 High-Level Process Flow -- 6.9.3 Solution Flow -- 6.10 Dashboard Development -- 6.10.1 Landing Page -- 6.10.2 Approach and Design -- 6.10.3 Helpline Information -- 6.10.3.1 Approach and Design -- 6.10.4 Symptom Detection -- 6.10.4.1 Approach and Design -- 6.10.5 Testing Lab Information -- 6.10.5.1 Approach and Design -- 6.10.6 Hospital Information -- 6.10.6.1 Approach and Design -- 6.10.7 Oxygen Suppliers Information -- 6.10.7.1 Approach and Design -- 6.10.8 COVID Cases Information -- 6.10.8.1 Approach and Design -- 6.10.9 Vaccination Information -- 6.10.9.1 Approach and Design -- 6.10.10 Patients' Information -- 6.10.10.1 Approach and Design -- 6.11 Advantages and its Impact -- 6.12 Conclusion and Future Scope -- References -- Chapter 7 A Complete Study on Machine Learning Algorithms for Medical Data Analysis -- 7.1 Introduction -- 7.1.1 Importance of Machine Learning Algorithms in Medical Data Analysis -- 7.2 Pre-Processing Medical Data for Machine Learning -- 7.3 Supervised Learning Algorithms for Medical Data Analysis -- 7.3.1 Linear Regression Algorithm -- 7.3.2 Logistic Regression Algorithm -- 7.3.3 Decision Trees Algorithm -- 7.3.3.1 Advantages of Decision Tree Algorithm -- 7.3.3.2 Limitations of Decision Tree Algorithm -- 7.3.4 Random Forest Algorithm -- 7.3.4.1 Advantages of Random Forest Algorithm -- 7.3.4.2 Limitations of Random Forest Algorithm -- 7.3.4.3 Applications of Random Forest Algorithm in Medical Data Analysis -- 7.3.5 Support Vector Machine Algorithm -- 7.3.5.1 Advantages of SVM Algorithm -- 7.3.5.2 Limitations of SVM Algorithm -- 7.3.5.3 Applications of SVM Algorithm in Medical Data Analysis -- 7.3.6 Naive Bayes Algorithm -- 7.3.7 KNN (K-Nearest Neighbor Algorithm) -- 7.3.7.1 Applications of K-NN Algorithm…”

Tabla de Contenidos: “…8.7.7 Filtering using queries -- 8.7.8 Discarding data with projection -- 8.7.9 Sorting large data sets -- 8.8 Achieving better data throughput -- 8.8.1 Optimize your code -- 8.8.2 Optimize your algorithm -- 8.8.3 Processing data in parallel -- Summary -- Chapter 9: Practical data analysis -- 9.1 Expanding your toolkit -- 9.2 Analyzing the weather data -- 9.3 Getting the code and data -- 9.4 Basic data summarization -- 9.4.1 Sum -- 9.4.2 Average -- 9.4.3 Standard deviation -- 9.5 Group and summarize -- 9.6 The frequency distribution of temperatures -- 9.7 Time series -- 9.7.1 Yearly average temperature -- 9.7.2 Rolling average -- 9.7.3 Rolling standard deviation -- 9.7.4 Linear regression -- 9.7.5 Comparing time series -- 9.7.6 Stacking time series operations -- 9.8 Understanding relationships -- 9.8.1 Detecting correlation with a scatter plot -- 9.8.2 Types of correlation -- 9.8.3 Determining the strength of the correlation -- 9.8.4 Computing the correlation coefficient -- Summary -- Chapter 10: Browser-based visualization -- 10.1 Expanding your toolkit -- 10.2 Getting the code and data -- 10.3 Choosing a chart type -- 10.4 Line chart for New York City temperature -- 10.4.1 The most basic C3 line chart -- 10.4.2 Adding real data -- 10.4.3 Parsing the static CSV file -- 10.4.4 Adding years as the X axis -- 10.4.5 Creating a custom Node.js web server -- 10.4.6 Adding another series to the chart -- 10.4.7 Adding a second Y axis to the chart -- 10.4.8 Rendering a time series chart -- 10.5 Other chart types with C3 -- 10.5.1 Bar chart -- 10.5.2 Horizontal bar chart -- 10.5.3 Pie chart -- 10.5.4 Stacked bar chart -- 10.5.5 Scatter plot chart -- 10.6 Improving the look of our charts -- 10.7 Moving forward with your own projects -- Summary -- Chapter 11: Server-side visualization -- 11.1 Expanding your toolkit -- 11.2 Getting the code and data…”

Tabla de Contenidos: “…2.1.8 Radiation and Hertz Dipole -- 2.1.9 Fundamental Antenna Parameters -- 2.1.9.1 Radiation Power Density -- 2.1.9.2 Radiation Intensity -- 2.1.9.3 Directivity -- 2.1.9.4 Input Impedance, Radiation and Loss Resistance -- 2.1.9.5 Gain and Radiation Ef ciency -- 2.1.10 Dipole Antennas -- 2.1.11 Pocklington Integro-Differential Equation for a Straight Thin Wire -- 2.2 Introduction to Numerical Methods in Electromagnetics -- 2.2.1 Weighted Residual Approach -- 2.2.1.1 Fundamental Lemma of Variational Calculus -- 2.2.2 The Finite Element Method (FEM) -- 2.2.2.1 Basic Concepts of FEM -- 2.2.2.2 One-Dimensional FEM -- 2.2.2.3 Incorporation of Boundary Conditions -- 2.2.2.4 Computational Example: 1D Problem -- 2.2.2.5 Two-Dimensional FEM -- 2.2.2.6 The Weak Formulation for Generalized Helmholtz Equation -- 2.2.2.7 Computation of Fluxes on the Domain Boundary -- 2.2.2.8 Computation of Sources on a Finite Element -- 2.2.2.9 Three-Dimensional Elements -- 2.2.3 The Boundary Element Method (BEM) -- 2.2.3.1 Integral Equation Formulation -- 2.2.3.2 Boundary Element Discretization -- 2.2.3.3 Constant Boundary Elements -- 2.2.3.4 Linear and Quadratic Elements -- 2.2.4 Numerical Solution of Integral Equations Over Unknown Sources -- References -- 3 Incident Electromagnetic Field Dosimetry -- 3.1 Assessment of External Electric and Magnetic Fields at Low Frequencies -- 3.1.1 Fields Generated by Power Lines -- 3.1.1.1 The Electric Field -- 3.1.1.2 The Magnetic Field -- 3.1.2 Fields Generated by Substation Transformers -- 3.1.2.1 The Electric Field -- 3.1.2.2 The Magnetic Field -- 3.1.3 Assessment of Circular Current Density Induced in the Body -- 3.1.4 On the Basic Principles of Measurement of LF Fields -- 3.1.4.1 Measurement of LF Electric Fields -- 3.1.4.2 Measurement of LF Magnetic Fields -- 3.1.4.3 Comparison of Calculated and Experimental Results…”

Tabla de Contenidos: “…9.6 Problem Constraints -- 9.6.1 Genetic Algorithm -- 9.6.2 Balanced Source Distribution with DL Cost -- 9.7 Virtual Network Embedding -- 9.7.1 Specification of Virtual Network Embedding -- 9.7.2 System Model -- 9.7.3 Open Flow Enabled Network -- 9.7.4 Network Model -- 9.7.5 Interference Model -- 9.8 Algorithm on Interference Modeling and Channel Selection Process -- 9.8.1 Interference Aware Routing Algorithm -- 9.8.2 Channel Assignment Algorithm -- 9.8.3 The MCM Algorithm -- 9.9 Performance Evaluation -- 9.9.1 Network Model -- 9.9.2 Load Design Algorithm -- 9.9.3 Simulation Settings -- 9.9.4 Performance Metrics -- 9.10 Performance Results -- 9.10.1 Handling with WL Intervention -- 9.10.2 Evaluating the Multicast Gain -- 9.10.3 Clique Utilization Balancing -- 9.10.4 Analysis of Switch Resource Consumption -- 9.10.5 Embedding Method Selection: Integer Linear Programming Vs Genetic Algorithm -- 9.11 Conclusion -- References -- Chapter 10 Advanced Wireless Mobile Network on Financial Literacy -- 10.1 Introduction -- 10.2 Statement of the Problem -- 10.3 Objectives of the Study -- 10.4 Hypothesis -- 10.5 Sampling Design -- 10.6 Literature Review -- 10.7 Methodology -- 10.8 Measurement of Financial Literacy -- 10.9 Elements of Financial Literacy -- 10.10 Financial Literacy Among Scheduled Community -- 10.11 Age Wise Status of Financial Literacy -- 10.12 Financial Literacy Among Scheduled Communities of Different Age Group - ANOVA -- 10.12.1 Null Hypothesis -- 10.13 Financial Literacy and its Relationship with Gender -- 10.13.1 Null Hypothesis -- 10.14 Financial Literacy and its Relationship with Marital Status -- 10.14.1 Null Hypothesis -- 10.15 Financial Literacy and its Relationship with Religion -- 10.15.1 Null Hypothesis -- 10.16 Financial Literacy Among Scheduled Communities of Different Educational Qualification - ANOVA -- 10.16.1 Null Hypothesis…”

Tabla de Contenidos: “…6.3.3.2 Gradient Boosting Classifier -- 6.3.3.3 K-Nearest Neighbors -- 6.3.3.4 Logistic Regression -- 6.3.3.5 Artificial Neural Networks -- 6.3.3.6 Support Vector Machines (SVM) -- 6.3.3.7 Naïve Bayes Classifier -- 6.4 Results -- 6.5 Conclusions -- References -- Chapter 7 Detection of Malicious Emails and URLs Using Text Mining -- 7.1 Introduction -- 7.2 Related Works -- 7.3 Dataset Description -- 7.4 Proposed Architecture -- 7.5 Methodology -- 7.5.1 Methodology for the URL Dataset -- 7.5.2 Methodology for the Email Dataset -- 7.5.2.1 Overcoming the Overfitting Problem -- 7.5.2.2 Tokenization -- 7.5.2.3 Applying Machine Learning Algorithms -- 7.5.3 Detecting Presence of Malicious URLs in Otherwise Non-Malicious Emails -- 7.5.3.1 Preparation of Dataset -- 7.5.3.2 Creation of Features -- 7.5.3.3 Applying Machine Learning Algorithms -- 7.6 Results -- 7.6.1 URL Dataset -- 7.6.2 Email Dataset -- 7.6.3 Final Dataset -- 7.7 Conclusion -- References -- Chapter 8 Quantum Data Traffic Analysis for Intrusion Detection System -- 8.1 Introduction -- 8.2 Literature Overview -- 8.3 Methodology -- 8.3.1 Autoviz -- 8.3.2 Dataset -- 8.3.3 Proposed Models -- 8.3.3.1 Decision Tree -- 8.3.3.2 Random Forest Classifier Algorithm -- 8.3.3.3 AdaBoost Classifier -- 8.3.3.4 Ridge Classifier -- 8.3.3.5 Logistic Regression -- 8.3.3.6 SVM-Linear Kernel -- 8.3.3.7 Naive Bayes -- 8.3.3.8 Quadratic Discriminant Analysis -- 8.4 Results -- 8.5 Conclusion -- References -- Chapter 9 Quantum Computing in Netnomy: A Networking Paradigm in e-Pharmaceutical Setting -- 9.1 Introduction -- 9.2 Discussion -- 9.2.1 Exploring Market Functioning via Quantum Network Economy -- 9.2.1.1 Internal Networking Marketing -- 9.2.1.2 Layered Marketing -- 9.2.1.3 Role of Marketing in Pharma Network Organizations -- 9.2.1.4 Role of Marketing in Vertical Networking Organizations…”

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: “…Multiple condition coverage -- 6.2.7. Linear code sequence and jump (LCSAJ) coverage -- 6.2.8. …”

“…"At last, after a decade of mounting interest in log-linear and related models for the analysis of discrete multivariate data, particularly in the form of multidimensional tables, we now have a comprehensive text and general reference on the subject. …”

“…The test chemical should be directly dissolved in the aqueous media saturated in air at a concentration which should not exceed half its solubility. For linear and non-linear regressions on the test chemical data in definitive or upper tier tests, the minimum number of samples collected should be 5 and 7 respectively. …”

“…Learn the fundamental tools and methodologies used in data science Discover best practices regarding the ETL (Extract, Transform, and Load) process and data validation Use the open science framework: practice version control, replication, and data pipelining Grasp the effectiveness of CDFs (Common Data Formats) in visualizing distributions Choose the correct analytic model for your data Comprehend statistical inference, effect size, confidence intervals, and hypothesis testing Discern the relationship between variables: understand scatter plots and scatter plot alternatives Understand correlation, linear least squares, linear regression, and logistic regression Master the Zen of testing your data and your conclusions…”

“…Based on the general theory of linear models, it provides an in-depth overview of the following: analysis of variance (ANOVA) for models with fixed, random, and mixed effects; regression analysis is also first presented for linear models with fixed, random, and mixed effects before being expanded to nonlinear models; statistical multi-decision problems like statistical selection procedures (Bechhofer and Gupta) and sequential tests; and design of experiments from a mathematical-statistical point of view. …”

“…Created for software engineers and budding data scientists, the course requires basic familiarity with Python programming; as well as statistics concepts such as linear and logistic regression, machine learning concepts like classification, and linear algebra. …”