Tabla de Contenidos: “…6.3.4 Constant Voltage Constant Current Constant Voltage Charging -- 6.3.5 Pulse Charging -- 6.3.6 Charging Termination -- 6.3.7 Comparison of Charging Algorithms for Lithium‐Ion Batteries -- 6.4 Optimal Charging Current Profiles for Lithium‐Ion Batteries -- 6.4.1 Energy Loss Modeling -- 6.4.2 Minimization of Energy Loss -- 6.5 Lithium Titanate Oxide Battery with Extreme Fast Charging Capability -- 6.6 Summary -- References -- Chapter 7 Battery Balancing -- 7.1 Background -- 7.2 Battery Sorting -- 7.2.1 Battery Sorting Based on Capacity and Internal Resistance -- 7.2.2 Battery Sorting Based on a Self‐organizing Map -- 7.3 Battery Passive Balancing -- 7.3.1 Fixed Shunt Resistor -- 7.3.2 Switched Shunt Resistor -- 7.3.3 Shunt Transistor -- 7.4 Battery Active Balancing -- 7.4.1 Balancing Criterion -- 7.4.2 Balancing Control -- 7.4.3 Balancing Circuits -- 7.4.3.1 Cell to Cell -- 7.4.3.2 Cell to Pack -- 7.4.3.3 Pack to Cell -- 7.4.3.4 Cell to Energy Storage Tank to Cell -- 7.4.3.5 Cell to Pack to Cell -- 7.5 Battery Active Balancing Systems -- 7.5.1 Active Balancing System Based on the SOC as a Balancing Criterion -- 7.5.1.1 Battery Balancing Criterion -- 7.5.1.2 Battery Balancing Circuit -- 7.5.1.3 Battery Balancing Control -- 7.5.1.4 Experimental Results -- 7.5.2 Active Balancing System Based on FL Controller -- 7.5.2.1 Balancing Principle -- 7.5.2.2 Design of FL Controller -- 7.5.2.3 Adaptability of FL Controller -- 7.5.2.4 Battery Balancing Criterion -- 7.5.2.5 Experimental Results -- 7.6 Summary -- References -- Chapter 8 Battery Management Systems in Electric Vehicles -- 8.1 Background -- 8.2 Battery Management Systems -- 8.2.1 Battery Parameter Acquisition Module -- 8.2.2 Battery System Balancing Module -- 8.2.3 Battery Information Management Module -- 8.2.4 Thermal Management Module -- 8.3 Typical Structure of BMSs -- 8.3.1 Centralized BMS…”

Tabla de Contenidos:

Tabla de Contenidos: “…7.1 Introduction -- 7.2 Related Work -- 7.3 Materials and Methods -- 7.3.1 Methodology for the Current Work -- 7.3.1.1 Data Collection for Wheat Crop -- 7.3.1.2 Data Pre-Processing -- 7.3.1.3 Implementation of the Proposed Hybrid Model -- 7.3.2 Techniques Used for Feature Selection -- 7.3.2.1 ReliefF Algorithm -- 7.3.2.2 Genetic Algorithm -- 7.3.3 Implementation of Machine Learning Techniques for Wheat Yield Prediction -- 7.3.3.1 K-Nearest Neighbor -- 7.3.3.2 Artificial Neural Network -- 7.3.3.3 Logistic Regression -- 7.3.3.4 Naïve Bayes -- 7.3.3.5 Support Vector Machine -- 7.3.3.6 Linear Discriminant Analysis -- 7.4 Experimental Result and Analysis -- 7.5 Conclusion -- Acknowledgment -- References -- Chapter 8 A Status Quo of Machine Learning Algorithms in Smart Agricultural Systems Employing IoT-Based WSN: Trends, Challenges and Futuristic Competences -- 8.1 Introduction -- 8.2 Types of Wireless Sensor for Smart Agriculture -- 8.3 Application of Machine Learning Algorithms for Smart Decision Making in Smart Agriculture -- 8.4 ML and WSN-Based Techniques for Smart Agriculture -- 8.5 Future Scope in Smart Agriculture -- 8.6 Conclusion -- References -- Part III: Smart City and Villages -- Chapter 9 Impact of Data Pre-Processing in Information Retrieval for Data Analytics -- 9.1 Introduction -- 9.1.1 Tasks Involved in Data Pre-Processing -- 9.2 Related Work -- 9.3 Experimental Setup and Methodology -- 9.3.1 Methodology -- 9.3.2 Application of Various Data Pre-Processing Tasks on Datasets -- 9.3.3 Applied Techniques -- 9.3.3.1 Decision Tree -- 9.3.3.2 Naive Bayes -- 9.3.3.3 Artificial Neural Network -- 9.3.4 Proposed Work -- 9.3.4.1 PIMA Diabetes Dataset (PID) -- 9.3.5 Cleveland Heart Disease Dataset -- 9.3.6 Framingham Heart Study -- 9.3.7 Diabetic Dataset -- 9.4 Experimental Result and Discussion -- 9.5 Conclusion and Future Work -- References…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Quality Assurance in Data Science: Need, Challenges and Focus -- 1.1 Introduction -- 1.1.1 Quality Assurance and Testing -- 1.1.2 Data Science and Quality Assurance -- 1.1.3 Background -- 1.2 Testing and Quality Assurance -- 1.2.1 Key Terminologies Associated With Testing -- 1.3 Product Quality and Test Efforts -- 1.3.1 Testing Metrics -- 1.3.2 How to Improve the Business Value to Products Using Test Automation -- 1.3.3 Data Analysis and Management in Test Automation -- 1.3.4 Data Models in Data Science -- 1.4 Data Masking in Data Model and Associated Risks -- 1.5 Prediction in Data Science -- Case Study -- 1.6 Role of Metrics in Evaluation -- 1.7 Quantity of Data in Quality Assurance -- 1.8 Identifying the Right Data Sources -- 1.8.1 Need to Gather Up-to-Date Data -- 1.8.2 Synthesising Existing Advanced Technologies for Continuous Business Improvements -- 1.9 Conclusion -- References -- Chapter 2 Design and Implementation of Social Media Mining - Knowledge Discovery Methods for Effective Digital Marketing Strategies -- 2.1 Introduction -- 2.1.1 Objectives of the Study -- 2.2 Literature Review -- 2.3 Novel Framework for Social Media Data Mining and Knowledge Discovery -- 2.4 Classification for Comparison Analysis -- 2.5 Clustering Methodology to Provide Digital Marketing Strategies -- 2.5.1 Status (Text Form) -- 2.5.2 Images (Photos) -- 2.5.3 Video Post -- 2.5.4 Link Post -- 2.6 Experimental Results -- 2.7 Conclusion -- References -- Chapter 3 A Study on Big Data Engineering Using Cloud Data Warehouse -- 3.1 Introduction -- 3.2 Comparison Study of Different Cloud Data Warehouses -- 3.2.1 Amazon Redshift -- 3.2.2 High-Level Architecture of Amazon Redshift -- 3.2.3 Features of Amazon Redshift Cloud Data Warehouse -- 3.2.4 Pricing of Amazon Redshift Cloud Data Warehouse…”

Tabla de Contenidos: “…Case study -- 5.1 Various machine learning libraries used -- 5.1.1 Handling missing values -- 6. Experimental results and discussions -- 7. Conclusion -- References -- Further reading…”

Tabla de Contenidos: “…References -- 4 An accurate deep learning-based computer-aided diagnosis system for early diagnosis of prostate cancer -- 4.1 Introduction -- 4.2 Methods -- 4.2.1 Feature Extraction -- 4.2.2 CNN-based classification -- 4.3 Experimental results -- 4.4 Conclusion -- References -- 5 Adaptive graph convolutional neural network and its biomedical applications -- 5.1 Introduction -- 5.2 Related work -- 5.2.1 Evolution of graph convolutional neural networks -- 5.2.1.1 Spatial graph convolutional neural networks -- 5.2.1.2 Spectral graph convolutional neural networks -- 5.2.2 Neural network on molecular graph -- 5.2.3 Attention on graph -- 5.2.4 Neural network for survival analysis -- 5.3 Method -- 5.3.1 Spectral graph convolution-LL layer -- 5.3.1.1 Learning residual graph Laplacian -- 5.3.1.2 Re-parameterization on feature transform -- 5.3.2 Adaptive graph convolution network architecture -- 5.3.3 Graph attention network on adaptive graph -- 5.3.4 DeepGraphSurv framework -- 5.4 Experiment -- 5.4.1 Drug-property prediction -- 5.4.1.1 Baseline model -- 5.4.1.2 Dataset -- 5.4.1.3 Experimental result -- 5.4.2 DeepGraphSurv and survival prediction -- 5.4.2.1 Dataset -- 5.4.2.2 Baseline model -- 5.4.2.3 Experimental result -- 5.5 Conclusion -- References -- Further reading -- 6 Deep slice interpolation via marginal super-resolution, fusion, and refinement -- 6.1 Introduction -- 6.2 Related work -- 6.2.1 Traditional slice interpolation methods -- 6.2.2 Learning-based super-resolution methods -- 6.3 Problem formulation and baseline convolutional neural networks approaches -- 6.4 The proposed algorithm -- 6.4.1 Marginal super-resolution -- 6.4.2 Two-view fusion and refinement -- 6.4.3 Comparison with baseline convolutional neural networks approaches -- 6.5 Experiments -- 6.5.1 Implementation details -- 6.5.2 Dataset -- 6.5.3 Evaluation metrics…”

Tabla de Contenidos: “…6.3.2.1 Data augmentation based on domain knowledge of optical communication -- 6.3.2.1.1 Data augmentation on polarization state -- 6.3.2.1.2 Data augmentation on the frequency offset -- 6.3.2.2 Transfer learning for adaptation of DNNs -- 6.3.2.3 Federated learning for collaborative DNN training over multiple operators -- 6.3.3 Inference mode of DL-based optical monitors -- 6.3.4 Advanced topics for inference modes of DL-based optical monitors -- 6.3.4.1 Cloud-based vs. edge-based implementations -- 6.3.4.1.1 Cloud-based implementation of inference mode -- 6.3.4.1.2 Edge-based implementation of inference mode -- 6.3.4.2 Estimating the model uncertainty in inference mode -- 6.4 Tips for designing DNNs for DL-based optical monitoring -- 6.4.1 Shallow vs. deep network -- 6.4.2 DNN architecture for optical monitoring -- 6.4.2.1 Fully-connected DNNs -- 6.4.2.2 Convolutional neural networks -- 6.4.2.3 DNN architecture for the optical monitoring -- 6.5 Experimental verifications -- 6.5.1 Experimental setup for data collection -- 6.5.2 Neural network architecture for OSNR estimation task -- 6.5.2.1 DNN used in this experiment -- DNN #1 (FC-DNN): -- DNN #2 (CNN-1): -- 6.5.2.2 Results and discussion -- 6.5.3 Detailed experimental evaluation of CNN-based OSNR estimators -- 6.5.3.1 DNN used in this section -- DNN #3 (CNN-2): -- 6.5.3.2 Results and discussion -- 6.5.4 Versatile monitoring using DNN -- 6.5.4.1 DNN architecture used in this experiment -- DNN #4 (CNN-3): -- 6.5.4.2 Results and discussion -- 6.5.5 Data augmentation based on domain knowledge of optical transceivers -- 6.5.5.1 DNN used in this section -- DNN #5 (CNN-4): -- 6.5.5.2 Results and discussion -- 6.5.6 Estimating uncertainty by dropout at inference -- 6.5.6.1 DNN used in this experiment -- DNN #6 (CNN-5): -- 6.5.6.2 Results and discussion…”

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…”

“…El cortometraje de animación, ese otro cine olvidado, es hoy un género en alza para la audiencia de cualquier edad gracias a las plataformas de recomendación en línea que median el acceso gratuito a su visionado y su clasificación cualitativa. Su libertad experimental y hondura crítica son muy provechosas en el aprendizaje de competencias clave para la vida con cinefórums en el aula. …”

“…What You Will Learn Build end-to-end predictive analytics systems in R Build an experimental design to gather your own data and conduct analysis Build a recommender system from scratch using different approaches Use and leverage RShiny to build reactive programming applications Build systems for varied domains including market research, network analysis, social media analysis, and more Explore various R Packages such as RShiny, ggplot, recommenderlab, dplyr, and find out how to use them effectively Communicate modeling results using Shiny Dashboards Perform multi-variate time-series analysis prediction, supplemented with sensitivity analysis and risk modeling In Detail R offers a large variety of packages and libraries for fast and accurate data analysis and visualization. …”

Tabla de Contenidos: “…Effects of Working Height on Postural Strain -- Effects of Display Position on Postural Strain -- 7 Visual Tasks, Functions, and Symptoms -- VISUAL ISSUES IN VDT STUDIES -- Field Surveys -- Visual Complaints -- Measurements of Visual Status -- Experimental Field and Laboratory Studies of Visual Functions in VDT Work -- The Need for Job and Task Analysis -- Are There Unique Features of VDT Tasks? …”

“…Furthermore, we show that IGA can regularize the inverse problem of FE digital image correlation in experimental mechanics…”

Tabla de Contenidos: “…Dishman -- Linking goals and aging : experimental and life-span approaches / Robin L. West, Natalie C. …”
Enlace del recurso

Tabla de Contenidos: “…Argumentos desde el 'yo' unitario, indivisible y experimentable como tal; D. El 'cuerpo vivido' presupone precisamente la distinción real del alma respecto del cuerpo; 3. …”

Tabla de Contenidos: “…La dimensió observacional-experimental; 5.3. La dimensió validesa interna-validesa externa; 6. …”

Tabla de Contenidos: “…Construyendo un marco conceptual experimental adecuado a la complejidad y naturaleza del objeto; 2. …”

Tabla de Contenidos: “…La marcación diferencial del objeto directo en el español de Argentina: un estudio experimental -- Capítulo 8. Copiá α, copiá. Condiciones sobre la duplicación verbal en el español del Río de la Plata -- Sección III: Aspectos sintácticos y semánticos -- Capítulo 1. …”
Enlace del recurso

Tabla de Contenidos: “…3.2.1.2 Preparation and Characterization of N4-(3-(triethoxysilyl)propyl)pyrimidine-2,4,6-triamine(DAPyPTS)3.2.2 Preparation and Characterization of the MCM-41; 3.2.3 Preparation and Characterization of the FunctionalizedMaterials; 3.2.3.1 Preparation and Characterization of the MaterialsFunctionalized with CyPTS; 3.2.3.2 Preparation and Characterization of the MaterialsFunctionalized with DAPyPTS; 3.3 Experimental; 3.3.1 Reagents and Materials; 3.3.2 Synthesis of MCM-41 Mesoporous Silica; 3.3.3 Synthesis of the Organic Ligands…”

Tabla de Contenidos: “…PRESAGIOS DE UNA NUEVA CIENCIALOS EMPIRISTAS DEL SIGLO XVIII; LA ESTÉTICA PRERROMÁNTICA EN LOS PAÍSES LATINOS; BAUMGARTEN Y LOS PRECURSORES DE KANT; KANT; SCHILLER; SCHELLING; HEGEL; SCHOPENHAUER; LOS EPÍGONOS DEL IDEALISMO; LOS ÚLTIMOS ROMÁNTICOS; LA ESTÉTICA ECLÉCTICA DEL SIGLO XIX; TERCERA ÉPOCA: CRISIS DE CRECIMIENTO; LA ESTÉTICA EXPERIMENTAL; LA ESTÉTICA HEDONISTA Y SUS VARIANTES; LA ESTÉTICA DE LA ENDOPATÍA; LA TEORÍA EXPRESIONISTA DE BENEDETTO CROCE; ESTÉTICA PSICOANALÍTICA; LA ESTÉTICA FORMALISTA Y LA CIENCIA DEL ARTE; ESTÉTICA SOCIOLÓGICA; ESTÉTICA MARXISTA…”

Tabla de Contenidos: “…PRINCIPIOS BÁSICOS DE RMN; 9.3. DETECCIÓN EXPERIMENTAL DEL FENÓMENO DE RMN; 9.4. INTERACCIÓN NÚCLEO-ENTORNO ESTRUCTURAL; 9.5. …”