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Heart disease classification is one of the most important topics in clinical decision support systems (CDSS). However, the performance of classification is greatly affected by feature selection. Canonical correlation analysis (CCA) is a popular method to extract effective features from two relevant data sets. In this paper, we employ discriminant minimum class locality preserving canonical correlation...
With the development of machine learning techniques, artificial intelligence applications in medicine are becoming hot topic in health information systems. In this research, we construct a new basic heart failure disease database which contains 1715 patients and 400 features. Then, we propose a new machine learning method called Polynomial Smooth Support Vector Machine(PSSVM) to help doctors diagnose...
Deep Convolutional Neural Networks (DCNNs) have been demonstrated as effective models for understanding image content. The computation behind DCNNs highly relies on the capability of hardware resources due to the deep structure. DCNNs have been implemented on different large-scale computing platforms. However, there is a trend that DCNNs have been embedded into light-weight local systems, which requires...
This paper proposes a novel Shift with Importance Sampling (SIS) scheme to improve the efficiency in DPM-based object detection but maintain its high accuracy. For fast and efficient object detection, the cascade-boosting structure is the commonly-used approach in the literature. However, its detection performance is quite lower due to non-robust features and a fully-scanning on image especially when...
Deep Convolutional Neural Networks (DCNN), a branch of Deep Neural Networks which use the deep graph with multiple processing layers, enables the convolutional model to finely abstract the high-level features behind an image. Large-scale applications using DCNN mainly operate in high-performance server clusters, GPUs or FPGA clusters; it is restricted to extend the applications onto mobile/wearable...
This paper proposes a novel Shift with Importance Sampling (SIS) scheme to improve the efficiency in pedestrian detection but maintain its high accuracy. For fast and efficient object detection, the cascade-Adaboost structure is the commonly-used approach in the literature. However, its detection performance is quite lower due to non-robust features and a fully-scanning on image especially when deformable...
The transient caused by capacitor switching is an important power quality (PQ) problem. Switching the capacitor may cause a disturbance to a voltage-sensitive load or a resonance in the system. This paper presents a new method to identify the positions of the transients caused by capacitors through the PQ monitoring system. The discrete wavelet transform is used to extract the features of transients...
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