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Heart disease is the leading cause of death in the world according to the World Health Organization (WHO). Researchers are more interested in using machine learning techniques to help medical staff diagnose or detect heart disease early. In this paper, we propose an efficient medical decision support system based on twin support vector machines (Twin-SVM) for heart disease diagnosing with binary target...
Water quality can be difficult to measure. Each water body can contain dramatically different levels of pollution. Water quality issues influence human and environmental health, so the more we monitor our water the better we will be able to recognize and prevent contamination problems. This paper presents the performance evaluation of Artificial Neural Network (ANN) and Support Vector Machines (SVM)...
We propose in this paper, the use of the Contourlet Transform for evaluating the quality of the degraded historical document. To facilitate the binarization, we first improve the quality of the document image by applying the Contourlet Transform, in order to select significant coefficients. After reconstruction, a local thresholding method is used for extracting the foreground text. The proposed method...
This paper deals with the contribution of Curvelet transform to generate more accurate word image descriptors for Arabic keyword spotting in ancient documents. Due to its properties, Curvelets can tolerate more scale distortions and more directional features in images. The process of Curvelet descriptor generation is applied to each word image in the dataset. Therefore, dynamic time warping algorithm...
We propose an efficient framework for combining pixel and object-based approaches for Remote Sensing Image Classification using Support Vector Machines (SVMs) and Dempster-Shafer Theory of Evidence (DSTE). The pixel-based technique employs the multispectral information for assigning a pixel to a class according to the spectral similarities between the classes, and the object-based technique operates...
In this paper we propose a system for word spotting in Arabic historical document using Ridgelet transform and Dynamic Time Warping (DTW). First, a preprocessing and segmentation processes are applied to all document pages to create a word image dataset. Keeping each word into its original size, Ridgelet descriptor is generated without applying the normalization criteria for Radon transform, where...
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