An XGBoost Machine Learning Technique for RF Energy Harvesting Prediction in IP-enabled IoT Devices

Keyphrases

• Machine Learning Techniques 100%
• IoT Devices 100%
• RF Energy Harvesting 100%
• Support Vector Regressor 100%
• XGBoost Machine Learning Model 100%
• Normalized Root-mean-square Error (NRMSE) 75%
• XGBoost Model 50%
• Mean Absolute Error 25%
• Machine Learning 25%
• Wireless 25%
• High Demand 25%
• Machine Learning Models 25%
• Extreme Gradient Boosting 25%
• XGBoost 25%
• Performance Metrics 25%
• Ambient Environment 25%
• Model Support 25%
• Energy Management 25%
• Energy Availability 25%
• Connected Devices 25%
• Electrical Energy 25%
• RF Power 25%
• Energy Available 25%
• Interconnected Devices 25%
• Optimum Energy 25%
• Foreknowledge 25%
• Wirelessly 25%
• DC Power 25%
• Energy Restriction 25%
• Regressor Models 25%

Computer Science

• Extreme Gradient Boosting 100%
• Machine Learning Technique 100%
• Internet-Of-Things 100%
• Support Vector 80%
• Root Mean Squared Error 60%
• Machine Learning Model 40%
• Mean Absolute Error 20%
• Performance Metric 20%
• Ambient Environment 20%
• Connected Device 20%
• Machine Learning 20%
• Learning System 20%
• Resources Management 20%

Material Science

• Xgboost 100%
• Energy Harvesting 100%