“…Clasificaciones vegetacionales y predicción de cambios obtenidos de imagenes de satélite dela comuna de Ninhue se presentan en este articulos Tres imagenes del satélite SPOTÍXS de los años 1988, 1995 y 2000 fueron usadas para clasificar coberturas vegetacionalesi La cobertura vegetacional que mayores cambios posie tivos experimentó fue Ia plantación forestal, la cual aumento en un 166%, Por otro lado, Ia cobertura vegetacional que tuvo un cambio negativo fue Ia pradera con una disminución del 41%, La predicción de cambios de cobertura vegetacional se obtuvo mediante el uso de las Cadenas de Markovi En general, se aprecia que las coberturas de mayor biomasa (bosque nativo y plantación forestal) aumentarán su presencia, en cambio, aquellas coberturas de menor biomasa (praderas, espinos) tenderan a decrecer…”
Acceso restringido con credenciales UPSA.

“…Preservada y compatibilizada con el desarrollo de la zona por la gestión forestal realizada en el parque…”

Tabla de Contenidos: “…-- 3.1.2 Softmax and probability distributions -- 3.1.3 Interpreting the success of active learning -- 3.2 Algorithms for uncertainty sampling -- 3.2.1 Least confidence sampling -- 3.2.2 Margin of confidence sampling -- 3.2.3 Ratio sampling -- 3.2.4 Entropy (classification entropy) -- 3.2.5 A deep dive on entropy -- 3.3 Identifying when different types of models are confused -- 3.3.1 Uncertainty sampling with logistic regression and MaxEnt models -- 3.3.2 Uncertainty sampling with SVMs -- 3.3.3 Uncertainty sampling with Bayesian models -- 3.3.4 Uncertainty sampling with decision trees and random forests -- 3.4 Measuring uncertainty across multiple predictions -- 3.4.1 Uncertainty sampling with ensemble models -- 3.4.2 Query by Committee and dropouts -- 3.4.3 The difference between aleatoric and epistemic uncertainty -- 3.4.4 Multilabeled and continuous value classification -- 3.5 Selecting the right number of items for human review -- 3.5.1 Budget-constrained uncertainty sampling -- 3.5.2 Time-constrained uncertainty sampling -- 3.5.3 When do I stop if I'm not time- or budget-constrained? …”

Tabla de Contenidos: “…4.4.2 How to Implement -- Step 1: Data Preparation -- Step 2: Modeling Operator and Parameters -- Step 3: Evaluation -- Step 4: Execution and Interpretation -- 4.4.3 Conclusion -- 4.5 Artificial Neural Networks -- 4.5.1 How It Works -- Step 1: Determine the Topology and Activation Function -- Step 2: Initiation -- Step 3: Calculating Error -- Step 4: Weight Adjustment -- 4.5.2 How to Implement -- Step 1: Data Preparation -- Step 2: Modeling Operator and Parameters -- Step 3: Evaluation -- Step 4: Execution and Interpretation -- 4.5.3 Conclusion -- 4.6 Support Vector Machines -- Concept and Terminology -- 4.6.1 How It Works -- 4.6.2 How to Implement -- Implementation 1: Linearly Separable Dataset -- Step 1: Data Preparation -- Step 2: Modeling Operator and Parameters -- Step 3: Process Execution and Interpretation -- Example 2: Linearly Non-Separable Dataset -- Step 1: Data Preparation -- Step 2: Modeling Operator and Parameters -- Step 3: Execution and Interpretation -- Parameter Settings -- 4.6.3 Conclusion -- 4.7 Ensemble Learners -- Wisdom of the Crowd -- 4.7.1 How It Works -- Achieving the Conditions for Ensemble Modeling -- 4.7.2 How to Implement -- Ensemble by Voting -- Bootstrap Aggregating or Bagging -- Implementation -- Boosting -- AdaBoost -- Implementation -- Random Forest -- Implementation -- 4.7.3 Conclusion -- References -- 5 Regression Methods -- 5.1 Linear Regression -- 5.1.1 How it Works -- 5.1.2 How to Implement -- Step 1: Data Preparation -- Step 2: Model Building -- Step 3: Execution and Interpretation -- Step 4: Application to Unseen Test Data -- 5.1.3 Checkpoints -- 5.2 Logistic Regression -- 5.2.1 How It Works -- How Does Logistic Regression Find the Sigmoid Curve? …”

Tabla de Contenidos: “…Chapter 10 Research Design Machine Maintenance Management Software Module for Garment Industry -- 10.1 Introduction -- 10.2 Building a Maintenance Process for Garment Industry Machine -- 10.2.1 Maintenance Process for Machinery -- 10.2.2 Information in the Maintenance Management Machine Records -- 10.3 Designing a "Machine Maintenance Management" Software Module -- 10.3.1 Database Design -- 10.3.2 Designing a "Machine Maintenance Management" Software Module -- 10.4 Conclusion -- References -- Part 3: Adoption of ICT for Digitalization, Artificial Intelligence, and Machine Learning -- Chapter 11 Performance Comparison of Prediction of a Hydraulic Jump Depth in a Channel Using Various Machine Learning Models -- Nomenclature -- 11.1 Introduction -- 11.2 Related Works -- 11.3 Materials and Methods -- 11.3.1 Equation of the Hydraulic Jump -- 11.3.2 Data Used in the Study -- 11.4 Machine Learning Models -- 11.4.1 Features of Machine Learning Models -- 11.4.2 Support Vector Machine (SVM) -- 11.4.3 Decision Tree (DT) -- 11.4.4 Random Forest (RF) -- 11.4.5 Artificial Neural Network (ANN) -- 11.5 Results and Discussion -- 11.6 Conclusions -- References -- Chapter 12 Creating a Video from Facial Image Using Conditional Generative Adversarial Network -- 12.1 Introduction -- 12.2 Related Works -- 12.3 Methodology -- 12.3.1 The Proposed Model -- 12.3.2 Conditional Generative Adversarial Network (cGAN) -- 12.3.3 Hidden Affine Transformation -- 12.4 Experiments -- 12.4.1 Dataset -- 12.4.2 Dlib -- 12.4.3 Evaluation -- 12.4.4 Result -- 12.5 Conclusion -- References -- Chapter 13 Deep Learning Framework for Detecting, Classifying, and Recognizing Invoice Metadata -- 13.1 Introduction -- 13.2 Related Works -- 13.3 Invoice Data Analysis -- 13.4 Proposed Method -- 13.5 Experiments -- 13.6 Conclusion and Perspectives -- References…”

Tabla de Contenidos: “…Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Elevating Surveillance Integrity-Mathematical Insights into Background Subtraction in Image Processing -- 1.1 Introduction -- 1.2 Background Subtraction -- 1.3 Mathematics Behind Background Subtraction -- 1.4 Gaussian Mixture Model -- 1.4.1 Gaussian Mixture Model (GMM) Algorithm for Background Subtraction -- 1.4.2 Gaussian Mixture Model (GMM) Algorithm - A Simple Example -- 1.5 Principal Component Analysis -- 1.6 Applications -- 1.6.1 Military Surveillance -- 1.6.2 Visual Observation of Animals in Forests -- 1.6.3 Marine Surveillance -- 1.6.4 Defense Surveillance Systems -- 1.7 Conclusion -- References -- Chapter 2 Machine Learning and Artificial Intelligence in the Detection of Moving Objects Using Image Processing -- 2.1 Introduction -- 2.2 Moving Object Detection -- 2.3 Envisaging the Object Detection -- 2.3.1 Filtering Algorithm -- 2.3.2 Identification of Object Detection in Bad Weather Circumstance -- 2.3.3 Color Clustering -- 2.3.4 Dangerous Animal Detection -- 2.3.5 UAV Video End-of-Line Detection and Tracking in Live Traffic -- 2.3.5.1 Contextual Detection -- 2.3.5.2 Calculation of Location of a Car -- 2.3.6 Estimation of Crowd -- 2.3.7 Parking Lot Management -- 2.3.8 Public Automatic Anomaly Detection Systems -- 2.3.9 Modification of Robust Principal Component Analysis -- 2.3.10 Logistics Automation -- 2.3.11 Detection of Criminal Behavior in Humans -- 2.3.12 UAV Collision Avoidance and Control System -- 2.3.13 An Overview of Potato Growth Stages -- 2.4 Conclusion -- References -- Chapter 3 Machine Learning and Imaging-Based Vehicle Classification for Traffic Monitoring Systems -- 3.1 Introduction -- 3.2 Methods -- 3.2.1 Data Preparation -- 3.2.2 Model Training -- 3.2.3 Hardware and Software Configuration -- 3.3 Result -- 3.4 Conclusion…”

Tabla de Contenidos: “…8.2 Methodology -- 8.3 AI-Based Predictive Modeling -- 8.3.1 Linear Regression -- 8.3.2 Random Forests -- 8.3.3 XGBoost -- 8.3.4 SVM -- 8.4 Performance Indices -- 8.4.1 Root Mean Squared Error (RMSE) -- 8.4.2 Mean Squared Error (MSE) -- 8.4.3 R2 (R-Squared) -- 8.5 Results and Discussion -- 8.5.1 Key Performance Metrics (KPIs) During the Model Training Phase -- 8.5.2 Key Performance Index Metrics (KPIs) During the Model Testing Phase -- 8.5.3 K Cross Fold Validation -- 8.6 Conclusions -- References -- Chapter 9 Performance Comparison of Differential Evolutionary Algorithm-Based Contour Detection to Monocular Depth Estimation for Elevation Classification in 2D Drone-Based Imagery -- 9.1 Introduction -- 9.2 Literature Survey -- 9.3 Research Methodology -- 9.3.1 Dataset and Metrics -- 9.4 Result and Discussion -- 9.5 Conclusion -- References -- Chapter 10 Bioinspired MOPSO-Based Power Allocation for Energy Efficiency and Spectral Efficiency Trade-Off in Downlink NOMA -- 10.1 Introduction -- 10.2 System Model -- 10.3 User Clustering -- 10.4 Optimal Power Allocation for EE-SE Tradeoff -- 10.4.1 Multiobjective Optimization Problem -- 10.4.2 Multiobjective PSO -- 10.4.3 MOPSO Algorithm for EE-SE Trade-Off in Downlink NOMA -- 10.5 Numerical Results -- 10.6 Conclusion -- References -- Chapter 11 Performances of Machine Learning Models and Featurization Techniques on Amazon Fine Food Reviews -- 11.1 Introduction -- 11.1.1 Related Work -- 11.2 Materials and Methods -- 11.2.1 Data Cleaning and Pre-Processing -- 11.2.2 Feature Extraction -- 11.2.3 Classifiers -- 11.3 Results and Experiments -- 11.4 Conclusion -- References -- Chapter 12 Optimization of Cutting Parameters for Turning by Using Genetic Algorithm -- 12.1 Introduction -- 12.2 Genetic Algorithm GA: An Evolutionary Computational Technique -- 12.3 Design of Multiobjective Optimization Problem…”

Tabla de Contenidos: “…Using bagging to improve prediction -- 10.1.3. Using random forests to further improve prediction -- 10.1.4. Gradient-boosted trees -- 10.1.5. …”

Tabla de Contenidos: “…9.2.2 Evaluation Process -- 9.3 Question and Answer Model -- 9.3.1 Most Widely-used Question Types -- 9.4 A Short Introduction to AI and Machine Learning -- 9.5 Selection of Machine Learning Algorithms to address our Problem -- 9.5.1 Reinforced Learning (RL) -- 9.6 Evaluation Process -- 9.6.1 Question Delivery -- 9.6.2 Question Attributes -- 9.7 Evaluator States and Actions -- 9.8 Implementation -- 9.8.1 Listing 1 -- 9.8.2 Listing 2 -- 9.8.3 Implementation Details -- 9.8.4 Testing the Evaluator -- 9.8.5 TestCase Output -- 9.9 Conclusion -- References -- 10 Investigating Artificial Intelligence Usage for Revolution in E-Learning during COVID-19 -- 10.1 Introduction -- 10.2 Review of Existing Literature -- 10.3 Objective of the Study -- 10.4 Research Methodology -- 10.5 Data Analysis and Discussion -- 10.6 Implications and Conclusion -- 10.7 Limitation and Future Scope -- Acknowledgement -- References -- 11 Employee Churn Management Using AI -- 11.1 Introduction -- 11.2 Proposed Methodology -- 11.2.1 Dataset Review -- 11.3 Model Building -- 11.3.1 Train Test Split -- 11.3.2 Model Building -- 11.3.3 Random Forest Classifier -- 11.3.4 XGBoost -- 11.4 Comparison -- 11.4.1 AUC-ROC Curve -- 11.5 Conclusion -- References -- 12 Machine Learning: Beginning of a New Era in the Dominance of Statistical Methods of Forecasting -- 12.1 Introduction -- 12.2 Analyzing Prominent Studies -- 12.3 Tabulation of prominent studies forecasting Time Series Data using Machine Learnings Techniques -- 12.4 Conclusion -- References -- 13 Recurrent Neural Network-Based Long Short-Term Memory Deep Neural Network Model for Forex Prediction -- 13.1 Introduction -- 13.2 Related Work -- 13.3 Working Principle of LSTM -- 13.4 Results and Simulations Study -- 13.4.1 Data Preparation -- 13.4.2 Performance Measure -- 13.5 Results and Discussion -- 13.6 Conclusion -- References…”

Tabla de Contenidos: “…Chapter 13 Energy-Efficient Fog-Assisted System for Monitoring Diabetic Patients with Cardiovascular Disease -- 13.1 Introduction -- 13.2 Literature Review -- 13.3 Architectural Design of the Proposed Framework -- 13.4 Fog Services -- 13.4.1 Information Processing -- 13.4.2 Algorithm for Extracting Heart Rate and QT Interval -- 13.4.3 Activity Status Categorization and Fall Detection Algorithm -- 13.4.4 Interoperability -- 13.4.5 Security -- 13.4.6 Implementation of the Framework and Testbed Scenario -- 13.4.7 Sensor Layer Implementation -- 13.5 Smart Gateway and Fog Services Implementation -- 13.6 Cloud Servers -- 13.7 Experimental Results -- 13.8 Future Directions -- 13.9 Conclusion -- References -- Chapter 14 Medical Appliances Energy Consumption Prediction Using Various Machine Learning Algorithms -- 14.1 Introduction -- 14.2 Literature Review -- 14.3 Methodology -- 14.3.1 Dataset -- 14.3.2 Data Analysis and Pre-Processing -- 14.3.3 Descriptive Statistics -- 14.3.4 Correlation Matrix -- 14.3.5 Feature Selection -- 14.3.6 Data Scaling -- 14.4 Machine Learning Algorithms Used -- 14.4.1 Multiple Linear Regressor -- 14.4.2 Kernel Ridge Regression -- 14.4.3 Stochastic Gradient Descent (SGD) -- 14.4.4 Support Vector Machine (Support Vector Regression) -- 14.4.5 K-Nearest Neighbor Regressor (KNN) -- 14.4.6 Random Forest Regressor -- 14.4.7 Extremely Randomized Trees Regressor (Extra Trees Regressor) -- 14.4.8 Gradient Boosting Machine/Regressor (GBM) -- 14.4.9 Light GBM (LGBM) -- 14.4.10 Multilayer Perceptron Regressor (MLP) -- 14.4.11 Implementation -- 14.5 Results and Analysis -- 14.6 Model Analysis -- 14.7 Conclusion and Future Work -- References -- Part 3: Future of Blockchain and Deep Learning -- Chapter 15 Deep Learning-Based Smart e-Healthcare for Critical Babies in Hospitals -- 15.1 Introduction -- 15.2 Literature Survey -- 15.2.1 Methodology…”

Tabla de Contenidos: “…-- 13.3 Corporations' Use Of Machine Learning To Strengthen Their Cyber Security Systems -- 13.4 Cyber Attack/Cyber Security Threats And Attacks -- 13.4.1 Malware -- 13.4.2 Data Breach -- 13.4.3 Structured Query Language Injection (SQL-I) -- 13.4.4 Cross-Site Scripting (XSS) -- 13.4.5 Denial-Of-Service (DOS) Attack -- 13.4.6 Insider Threats -- 13.4.7 Birthday Attack -- 13.4.8 Network Intrusions -- 13.4.9 Impersonation Attacks -- 13.4.10 DDoS Attacks Detection On Online Systems -- 13.5 Different Machine Learning Techniques In Cyber Security -- 13.5.1 Support Vector Machine (SVM) -- 13.5.2 K-Nearest Neighbor (KNN) -- 13.5.3 Naïve Bayes -- 13.5.4 Decision Tree -- 13.5.5 Random Forest (RF) -- 13.5.6 Multilayer Perceptron (MLP) -- 13.6 Application Of Machine Learning -- 13.6.1 ML in Aviation Industry -- 13.6.2 Cyber ML Under Cyber Security Monitoring -- 13.6.3 Battery Energy Storage System (BESS) Cyber Attack Mitigation -- 13.6.4 Energy-Based Cyber Attack Detection in Large-Scale Smart Grids -- 13.6.5 IDS for Internet of Vehicles (IoV) -- 13.7 Deep Learning Techniques In Cyber Security -- 13.7.1 Deep Auto-Encoder -- 13.7.2 Convolutional Neural Networks (CNN) -- 13.7.3 Recurrent Neural Networks (RNNs) -- 13.7.4 Deep Neural Networks (DNNs) -- 13.7.5 Generative Adversarial Networks (GANs) -- 13.7.6 Restricted Boltzmann Machine (RBM) -- 13.7.7 Deep Belief Network (DBN)…”

Tabla de Contenidos: “…Conclusion -- References -- 3 - Prediction of breast cancer diagnosis using random forest classifier -- 1. Introduction -- 2. Data set used -- 3. …”

“…The Bengal (Indian) tiger Panthera tigris tigris, distributed throughout the humid forests and grasslands of Bangladesh, Bhutan, China, India and Nepal. …”

“…However, despite these anticipated benefits, international organizations such as the FAO, OECD and UN have published reports expressing concerns that biofuel promotion may lead to deforestation, water pollution and water shortages. The impacts of biofuel use are extensive, cross-sectoral and complex, and as such, comprehensive analyses are required in order to assess the extent to which biofuels can contribute to sustainable societies. …”

“…Après 4,5 milliards d’années d’évolution, notre planète s’épuise du fait de l’exploitation intensive des énergies fossiles et de l’eau, de la surconsommation d’engrais agricoles et de la déforestation. Elle pourrait vivre la plus importante des extinctions massives. …”