Raied Salman

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—Calculating Euclidean distance matrix is a data intensive operation and becomes computationally prohibitive for large datasets. Recent development of Graphics Processing Units (GPUs) has produced superb performance on scientific computing problems using massive parallel processing cores. However, due to the limited size of device memory, many GPU based(More)
k-means has recently been recognized as one of the best algorithms for clustering unsupervised data. Since k-means depends mainly on distance calculation between all data points and the centers, the time cost will be high when the size of the dataset is large (for example more than 500millions of points). We propose a two stage algorithm to reduce the time(More)
GPUSVM (Graphic Processing Unit Support Vector Machine) is a Computing Unified Device Architecture (CUDA) based Support Vector Machine (SVM) package. It is designed to offer an end-user a fully functional and user friendly SVM tool which utilizes the power of GPUs. The core package includes an efficient cross validation tool, a fast training tool and a(More)
The Support Vector Machine (SVM) is an efficient tool in machine learning with high accuracy performance. However, in order to achieve the highest accuracy performance, n-fold cross validation is commonly used to identify the best hyperparameters for SVM. This becomes a weak point of SVM due to the extremely long training time for various hyperparameters of(More)
has been always observed that the effectiveness of MIS as a support tool for management decisions degenerate after time of implementation, despite the substantial investments being made. This is true for organizations at the initial stages of MIS implementations, manual or computerized. A survey of a sample of middle to top managers in business and(More)
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