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: “…Intro -- Foreword -- Acknowledgments -- Disclaimer -- Contents -- 1 Assessing the State of Smoke Science -- 1.1 Recent Trends -- 1.2 Environmental and Social Context -- 1.3 Overview of This Assessment -- References -- 2 Fuels and Consumption -- 2.1 Introduction -- 2.1.1 Understanding How Fuels Contribute to Smoke -- 2.2 Wildland Fuels -- 2.2.1 Fuel Characteristics -- 2.2.2 Traditional Methods to Estimate Wildland Fuel Loadings -- 2.2.3 Emerging Technologies and Methods -- 2.3 Fuel Consumption -- 2.3.1 Indirect Estimates of Fuel Consumption -- 2.3.2 Direct Measures of Fuel Consumption -- 2.4 Gaps in Wildland Fuels Characterization -- 2.4.1 Scaling from Fine-Scale to Coarse-Scale Fuel Characterization -- 2.4.2 Challenges in Forest Floor Characterization -- 2.4.3 Modeling Spatial and Temporal Dynamics of Wildland Fuels -- 2.5 Vision for Improving Fuel Science in Support of Smoke Science -- 2.6 Science Delivery to Managers -- 2.7 Research Needs -- 2.8 Conclusions -- References -- 3 Fire Behavior and Heat Release as Source Conditions for Smoke Modeling -- 3.1 Introduction -- 3.2 Current State of Science -- 3.2.1 Representing Fire in Smoke Models -- 3.2.2 Remote Sensing -- 3.2.3 Effects of Management Actions -- 3.3 Gaps in Understanding the Link Between Fire Behavior and Plume Dynamics -- 3.3.1 Heat Release -- 3.3.2 Fire Spread -- 3.3.3 Plume Cores -- 3.4 Vision for Improving Smoke Science -- 3.5 Emerging Issues and Challenges -- 3.5.1 Magnitude of Fire and Smoke Impacts -- 3.5.2 Managing Fuels to Minimize Air Quality Impacts -- 3.5.3 Need for Dispersion Climatologies -- 3.5.4 When and Where is Coupled Fire-Atmosphere Modeling Needed? …”

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. …”

Tabla de Contenidos: “…13.5.4.1 Artificial Neural Networks (ANNs) -- 13.5.4.2 Decision Trees -- 13.5.4.3 Support Vector Machines (SVMs) -- 13.5.4.4 Random Forest -- 13.5.4.5 Gaussian Process -- 13.6 Case Studies of ML-Based BMS Applications in Industry -- 13.6.1 Machine Learning Approach to Predict SOH of Li-Ion Batteries -- 13.6.2 Anomaly Detection in Battery Management System Using Machine Learning -- 13.6.3 Optimization of Battery Life Cycle Using Machine Learning -- 13.6.4 Prediction of Remaining Useful Life Using Machine Learning -- 13.6.5 Fault Diagnosis of Battery Management System Using Machine Learning -- 13.6.6 Battery Parameter Estimation Using Machine Learning -- 13.6.7 Optimization of Battery Charging Using Machine Learning -- 13.6.8 ML Approach to Estimate State of Charge -- 13.6.9 Battery Capacity Estimation Using ML Approach -- 13.6.10 Anomaly Detection in Batteries Using Machine Learning -- 13.6.11 ML-Based BMS for Li-Ion Batteries -- 13.6.12 Battery Management System Based on Deep Learning for Electric Vehicles -- 13.6.13 A Review of ML Approaches for BMS -- 13.6.14 Battery Management Systems Using Machine Learning Techniques -- 13.6.15 Machine Learning for Lithium-Ion Battery Management: Challenges and Opportunities -- 13.6.16 An ML-Based BMS for Hybrid EVs -- 13.6.17 Battery Management System for EVs Using ML Techniques -- 13.6.18 A Hybrid BMS Using Machine Learning Techniques -- 13.7 Challenges -- 13.8 Conclusion -- References -- Chapter 14 ML Applications in Healthcare -- 14.1 Introduction -- 14.1.1 Supervised Learning -- 14.1.2 Unsupervised Learning -- 14.1.3 Semi-Supervised Learning -- 14.1.4 Reinforcement Learning -- 14.2 Applications of Machine Learning in Health Sciences -- 14.2.1 Diagnosis and Prediction of Disease -- 14.2.1.1 Predicting Thyroid Disease -- 14.2.1.2 Predicting Cardiovascular Disease -- 14.2.1.3 Predicting Cancer…”

Tabla de Contenidos: “…4.3.7 K-Means Method -- 4.3.8 Watershed Method -- 4.3.9 Comparison of Different Segmentation Techniques Based on the Advantages and Disadvantages -- 4.3.10 Comparison of Different Segmentation Techniques Based on Accuracy -- 4.3.11 Comparison of Region Based and Threshold Based Segmentation Techniques Based on Different Parameters -- 4.4 Machine Learning -- 4.4.1 Supervised Learning -- 4.4.2 Unsupervised Learning -- 4.4.3 Reinforcement Learning -- 4.4.4 K-Nearest Neighbour (KNN) -- 4.4.5 Support Vector Machine (SVM) -- 4.4.6 Random Forest -- 4.5 Deep Learning (DL) -- 4.5.1 Convolutional Neural Networks (CNN) -- 4.5.1.1 Convolution Layer -- 4.5.1.2 Pooling Layer -- 4.5.1.3 Architecture of CNN -- 4.5.1.4 Comparison of Different Variations of CNN Techniques -- 4.5.2 Long Short-Term Memory (LSTM) -- 4.5.3 Artificial Neural Network (ANN) -- 4.5.4 Accuracy of Different Models Discussed Above -- 4.5.5 Accuracy of Other Different Techniques Being Used -- 4.6 Performance Metrics -- 4.6.1 Accuracy -- 4.6.2 Precision -- 4.6.3 Recall -- 4.6.4 Specificity -- 4.6.5 F1-Measure -- 4.7 Method Wise Trend of Using Techniques for Detection of Brain Tumor -- 4.8 Conclusion -- References -- Chapter 5 Advancements in Tumor Detection and Classification -- 5.1 Introduction -- 5.2 Imaging Techniques Used in Tumor Detection and Classification -- 5.2.1 X-Ray -- 5.2.2 CT Scan -- 5.2.3 MRI -- 5.2.4 Ultrasound -- 5.3 Molecular Biology Techniques -- 5.3.1 PCR -- 5.3.2 FISH -- 5.3.3 Next-Generation Sequencing -- 5.3.4 Western Blotting -- 5.4 Machine Learning and Artificial Intelligence -- 5.5 Tumor Classification -- 5.5.1 TNM Staging System -- 5.5.2 Histological Grading -- 5.5.3 Molecular Subtyping -- 5.6 Challenges and Future Directions -- References -- Chapter 6 Classification of Brain Tumor Using Machine Learning Techniques: A Comparative Study -- 6.1 Introduction…”

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