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Article
Comparison of a Classifier Performance Testing Methods: Support Vector Machine Classifier on Mammogram Images Classification

Author: Sura Jasim Mohammed
Journal: Journal of Kufa for Mathematics and Computer مجلة الكوفة للرياضيات والحاسوب ISSN: 11712076 Year: 2019 Volume: 6 Issue: 1 Pages: 8-12
Publisher: University of Kufa جامعة الكوفة

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Abstract

This paper compares between testing performance methods of classifier algorithm on a standard database of mammogram images. Mammographic interchange society dataset (MIAS) is used in this work. For classifying these images tumors a multiclass support vector machine (SVM) classifier is used. Evaluating this classifier accuracy for classifying the mammogram tumors into the malignant, benign or normal case is done using two evaluating classifier methods that are a hold-out method and one of the cross-validation methods. Then selecting the better test method depending on the obtained classifier accuracy and the running time consumed with each method. The classifier accuracy, training time and the classification time are considered for comparison purpose


Article
A Proposed Measurement for Video Quality of Experience

Authors: Rana Fareed Ghani --- Amal Sufiuh Ajrash
Journal: Al-Nahrain Journal of Science مجلة النهرين للعلوم ISSN: (print)26635453,(online)26635461 Year: 2019 Volume: 22 Issue: 3 Pages: 75-81
Publisher: Al-Nahrain University جامعة النهرين

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Abstract

Technological development in the last years leads to increase the access speed in the networks that allow a huge number of users watching videos online. The Quality of Experience (QoE) Knowledge of services that provide from the network is a very critical matter to have a strong design of multimedia streaming networks. This paper provides a video streaming QoE prediction metric that does not require any information on the reference video. The proposed system extract numbers of features from videos that used to train the neural network and finally prediction the QoE value. Verify models prediction using 10-fold cross-validation that in a regular way split dataset (training set and test set) with multiple percentages. The proposed system verifies the best result.

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