Palm vein recognition is a new promising field in biometrics. The palm vein pattern provides highly discriminating features that are difficult to forge because it resides underneath the palmar skin. However, the issues of extracting the palm vein features and the high dimension of the feature space are still open. Therefore, in this paper, we propose an improved scheme of palm vein recognition method based on the Linear Discrimination Analysis (LDA) to extract the discriminative features with low dimension. LDA is later followed by the matching procedure using cosine distance nearest neighbor classifier. The performance of the proposed scheme produced 99.50% for identification rate, 100% for verification rate and 0.0% of Equal Error Rate (EER). The experiments prove that the proposed method has a better performance compared with Principal Component Analysis and Gabor filter methods.
Keeping customers satisfied is truly essential for saying that business is successful especially in the telecom. Many companies experience different techniques that can predict churn rates and help in designing effective plans for customer retention since the cost of acquiring a new customer is much higher than the cost of retaining the existing one. In this paper, three machine learning algorithms have been used to predict churn namely, Naïve Bayes, SVM and decision trees using two benchmark datasets IBM Watson dataset, which consist of 7033 observations, 21 attributes and cell2cell dataset that contains 71,047 observations and 57 attributes. The models' performance has been measured by the area under the curve (AUC) and they scored 0.82, 0.87, 0.77 respectively for IBM dataset and 0.98, 0.99, 0.98 respectively for cell2cell dataset. The proposed models also obtained better accuracy than the previous studies using the same datasets.
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