“…It forms the basis of a complex web of dependency that supports entire ecosystems, providing food for thousands of creatures, from elephants, giraffes, and fruit bats, to forest hornbills, monkeys, insects, and fish…”

“…Known as the most feared animals in the equatorial forests of Ecuador, they hack and dice any unfortunate prey that gets in their path and bring the remains back to their nest. …”

“…This report examines how biodiversity co-benefits in REDD (Reducing Emissions from Deforestation and Forest Degradation) can be enhanced, both at the design and implementation level. …”

“…How can extractivism form part of new types of forest management combining conservation and development?…”

“…In the modern era of industrial revolution, urbanization, and deforestation of forest land, carbon (C) sequestration through well-known activities called "land use, land-use change and forestry (LULUCF)" could establish a win-win situation from a climate change and sustainable development perspective. …”

“…Understanding forest fire regimes involves characterizing spatial distribution, recurrence, intensity, seasonality, size, and severity. …”

Tabla de Contenidos: “…-- 1.1 Künstliche Intelligenz, Machine Learning und Deep Learning -- 1.1.1 Künstliche Intelligenz -- 1.1.2 Machine Learning -- 1.1.3 Die Repräsentation anhand der Daten erlernen -- 1.1.4 Das »Deep« in Deep Learning -- 1.1.5 Deep Learning in drei Diagrammen erklärt -- 1.1.6 Was Deep Learning heute schon leisten kann -- 1.1.7 Schenken Sie dem kurzfristigen Hype keinen Glauben -- 1.1.8 Das Versprechen der KI -- 1.2 Bevor es Deep Learning gab: eine kurze Geschichte des Machine Learnings -- 1.2.1 Probabilistische Modellierung -- 1.2.2 Die ersten neuronalen Netze -- 1.2.3 Kernel-Methoden -- 1.2.4 Entscheidungsbäume, Random Forests und Gradient Boosting Machines -- 1.2.5 Zurück zu neuronalen Netzen -- 1.2.6 Das Besondere am Deep Learning -- 1.2.7 Der Stand des modernen Machine Learnings -- 1.3 Warum Deep Learning? …”

Tabla de Contenidos: “…3.2.2 Indirect Method Sensors -- 3.2.3 Dynamometer -- 3.2.4 Accelerometer -- 3.2.5 Acoustic Emission Sensor -- 3.2.6 Current Sensors -- 3.3 Other Sensors -- 3.3.1 Temperature Sensors -- 3.3.2 Optical Sensors -- 3.4 Interaction of Sensors During Machining Operation -- 3.4.1 Milling Machining -- 3.4.2 Turning Machining -- 3.4.3 Drilling Machining Operation -- 3.5 Sensor Fusion Technique -- 3.6 Interaction of Internet of Things -- 3.6.1 Identification -- 3.6.2 Sensing -- 3.6.3 Communication -- 3.6.4 Computation -- 3.6.5 Services -- 3.6.6 Semantics -- 3.7 IoT Technologies in Manufacturing Process -- 3.7.1 IoT Challenges -- 3.7.2 IoT-Based Energy Monitoring System -- 3.8 Industrial Application -- 3.8.1 Integrated Structure -- 3.8.2 Monitoring the System Related to Service Based on Internet of Things -- 3.9 Decision Making Methods -- 3.9.1 Artificial Neural Network -- 3.9.2 Fuzzy Inference System -- 3.9.3 Support Vector Mechanism -- 3.9.4 Decision Trees and Random Forest -- 3.9.5 Convolutional Neural Network -- 3.10 Conclusion -- References -- Chapter 4 Application of Internet of Things (IoT) in the Automotive Industry -- 4.1 Introduction -- 4.2 Need For IoT in Automobile Field -- 4.3 Fault Diagnosis in Automobile -- 4.4 Automobile Security and Surveillance System in IoT-Based -- 4.5 A Vehicle Communications -- 4.6 The Smart Vehicle -- 4.7 Connected Vehicles -- 4.7.1 Vehicle-to-Vehicle (V2V) Communications -- 4.7.2 Vehicle-to-Infrastructure (V2I) Communications -- 4.7.3 Vehicle-to-Pedestrian (V2P) Communications -- 4.7.4 Vehicle to Network (V2N) Communication -- 4.7.5 Vehicle to Cloud (V2C) Communication -- 4.7.6 Vehicle to Device (V2D) Communication -- 4.7.7 Vehicle to Grid (V2G) Communications -- 4.8 Conclusion -- References -- Chapter 5 IoT for Food and Beverage Manufacturing -- 5.1 Introduction -- 5.2 The Influence of IoT in a Food Industry…”

Tabla de Contenidos: “…11.2.3 Day-to-Day Example -- 11.2.3.1 Optical Character Recognition (OCR) -- 11.2.3.2 Face Recognition -- 11.2.3.3 Recognition of Speech -- 11.2.3.4 Medical Findings -- 11.2.3.5 Extraction of Acquaintance -- 11.2.3.6 Compression -- 11.2.3.7 Additional Examples -- 11.2.4 Discriminant -- 11.2.5 Algorithms -- 11.3 Clustering -- 11.3.1 Data Examples Using Natural Clusters -- 11.4 Clustering (k-means) -- 11.4.1 Outline -- 11.4.2 Example -- 11.4.2.1 Problem -- 11.4.2.2 Solution -- 11.4.3 Some Methods for Initialization -- 11.4.4 Disadvantages -- 11.4.5 Use Case: Image Compression and Segmentation -- 11.4.5.1 Segmentation of Images -- 11.4.5.2 Compression of Data -- 11.5 Reduction of Dimensionality -- 11.5.1 Introduction -- 11.5.1.1 Feature Selection -- 11.5.1.2 Feature Extraction -- 11.5.1.3 Error Measures -- 11.5.2 Benefits of Reducing Dimensionality -- 11.5.3 Subset Selection -- 11.5.3.1 Selecting Forward -- 11.5.3.2 Remarks -- 11.5.3.3 Selection in Reverse -- 11.6 The Ensemble Method -- 11.6.1 Random Forest -- 11.6.2 Algorithm -- 11.6.3 Benefits and Drawbacks -- 11.6.3.1 Benefits -- 11.6.3.2 Drawbacks -- 11.6.4 Deep Learning and Neural Networks -- 11.6.4.1 Definition -- 11.6.4.2 Remarks -- 11.6.5 Applications -- 11.6.6 Artificial Neural Network -- 11.6.6.1 Biological Motivation -- 11.7 Transfer of Learning -- 11.8 Learning Through Reinforcement -- 11.9 Processing of Natural Languages -- 11.10 Word Embeddings -- 11.11 Conclusion -- References -- Chapter 12 Recognition Attendance System Ensuring COVID-19 Security -- 12.1 Introduction -- 12.2 Literature Survey -- 12.3 Software Requirements -- 12.3.1 Operating System - Windows 7 and Above -- 12.3.2 IDE-Visual Studio Code -- 12.3.3 Programming Languages: Python, HTML, CSS, JS, and PHP -- 12.4 Hardware Requirements -- 12.4.1 Three Processors and Above -- 12.4.2 RAM - 2GB (Minimum Capacity)…”

Tabla de Contenidos: “…3.2.2 Indirect Method Sensors -- 3.2.3 Dynamometer -- 3.2.4 Accelerometer -- 3.2.5 Acoustic Emission Sensor -- 3.2.6 Current Sensors -- 3.3 Other Sensors -- 3.3.1 Temperature Sensors -- 3.3.2 Optical Sensors -- 3.4 Interaction of Sensors During Machining Operation -- 3.4.1 Milling Machining -- 3.4.2 Turning Machining -- 3.4.3 Drilling Machining Operation -- 3.5 Sensor Fusion Technique -- 3.6 Interaction of Internet of Things -- 3.6.1 Identification -- 3.6.2 Sensing -- 3.6.3 Communication -- 3.6.4 Computation -- 3.6.5 Services -- 3.6.6 Semantics -- 3.7 IoT Technologies in Manufacturing Process -- 3.7.1 IoT Challenges -- 3.7.2 IoT-Based Energy Monitoring System -- 3.8 Industrial Application -- 3.8.1 Integrated Structure -- 3.8.2 Monitoring the System Related to Service Based on Internet of Things -- 3.9 Decision Making Methods -- 3.9.1 Artificial Neural Network -- 3.9.2 Fuzzy Inference System -- 3.9.3 Support Vector Mechanism -- 3.9.4 Decision Trees and Random Forest -- 3.9.5 Convolutional Neural Network -- 3.10 Conclusion -- References -- Chapter 4 Application of Internet of Things (IoT) in the Automotive Industry -- 4.1 Introduction -- 4.2 Need For IoT in Automobile Field -- 4.3 Fault Diagnosis in Automobile -- 4.4 Automobile Security and Surveillance System in IoT-Based -- 4.5 A Vehicle Communications -- 4.6 The Smart Vehicle -- 4.7 Connected Vehicles -- 4.7.1 Vehicle-to-Vehicle (V2V) Communications -- 4.7.2 Vehicle-to-Infrastructure (V2I) Communications -- 4.7.3 Vehicle-to-Pedestrian (V2P) Communications -- 4.7.4 Vehicle to Network (V2N) Communication -- 4.7.5 Vehicle to Cloud (V2C) Communication -- 4.7.6 Vehicle to Device (V2D) Communication -- 4.7.7 Vehicle to Grid (V2G) Communications -- 4.8 Conclusion -- References -- Chapter 5 IoT for Food and Beverage Manufacturing -- 5.1 Introduction -- 5.2 The Influence of IoT in a Food Industry…”

Tabla de Contenidos: “…Decision trees, random forests, and gradient boosting machines -- 1.2.5. …”

Tabla de Contenidos: “…8.2 Disease diagnosis -- 8.3 Pattern recognition tools for the disease diagnosis -- 8.3.1 Artificial neural networks -- 8.3.2 K-nearest neighbor -- 8.3.3 Support vector machines -- 8.3.4 Random forests -- 8.3.5 Bagging -- 8.3.6 AdaBoost -- 8.3.7 XGBoost -- 8.3.8 Deep learning -- 8.3.9 Convolutional neural network -- 8.3.10 Transfer learning -- 8.4 Case study of COVID-19 detection -- 8.4.1 Experimental data -- 8.4.2 Performance evaluation measures -- 8.4.3 Feature extraction using transfer learning -- 8.4.4 Experimental results -- 8.5 Discussion -- 8.6 Conclusions -- References -- 9 Brain-computer interface in Internet of Things environment -- 9.1 Introduction -- 9.1.1 Components of BCI -- 9.1.2 Types of BCI -- 9.1.3 How does BCI work? …”