RANDOM FORESTS BASED MULTIPLE CLASSIFIER SYSTEM FOR POWER-LINE SCENE CLASSIFICATION

Random Forests Based Multiple Classifier System for Power-line Scene Classification

The increasing use of electrical energy has yielded more necessities of electric utilities including transmission lines and electric pylons which require a real-time risk monitoring to prevent massive economical damages. Recently, Airborne Laser Scanning (ALS) has become one of primary data acquisition tool for corridor mapping due to its ability of direct 3D measurements. In particular, for po...

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3d Classification of Power-line Scene from Airborne Laser Scanning Data Using Random Forests

Since the introduction of Airborne Laser Scanning (ALS) know as an alternative aerial-based data acquisition tool, the requirement of the 3D model reconstruction in both urban and power-line scenes has dramatically increased. Especially, electric utilities including power-line and tower are crucial infrastructures that require considerable resources to be monitored and managed effectively. For ...

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Random Forest Classifier Based ECG Arrhythmia Classification

Heart Rate Variability (HRV) analysis is a non-invasive tool for assessing the autonomic nervous system and for arrhythmia detection and classification. This paper presents a Random Forest classifier based diagnostic system for detecting cardiac arrhythmias using ECG data. The authors use features extracted from ECG signals using HRV analysis and DWT for classification. The experimental results...

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Random Forest Classifier Based ECG Arrhythmia Classification

Heart Rate Variability (HRV) analysis is a non-invasive tool for assessing the autonomic nervous system and for arrhythmia detection and classification. This paper presents a Random Forest classifier based diagnostic system for detecting cardiac arrhythmias using ECG data. The authors use features extracted from ECG signals using HRV analysis and DWT for classification. The experimental results...

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Random Forest Classifier Based ECG Arrhythmia Classification

Heart Rate Variability (HRV) analysis is a non-invasive tool for assessing the autonomic nervous system and for arrhythmia detection and classification. This paper presents a Random Forest classifier based diagnostic system for detecting cardiac arrhythmias using ECG data. The authors use features extracted from ECG signals using HRV analysis and DWT for classification. The experimental results...

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RANDOM FORESTS BASED MULTIPLE CLASSIFIER SYSTEM FOR POWER-LINE SCENE CLASSIFICATION

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