Abstract—A support vector machine (SVM) learns the decision surface from two different classes of the input points, in many applications there are misclassifications in some of the input points. In this paper a bi-objective quadratic programming model is utilized and different feature quality measures are optimized simultaneously using the weighting method for solving our bi-objective quadratic programming problem. The experimental results, give evidence of the effectiveness of the weighting parameters on reducing the misclassification between two classes of the input points. The main contributions of this paper include constructing a system of a bi-objective support vector machine (BO-SVM) plus deep convolutional neural networks (CNNs)for detection the pneumonia disease using X-ray images.
Yahia, H. A. (2024). PNEUMONIA DISEASE DETECTION BY BI-OBJECTIVE SUPPORT VECTOR MACHINE (BO-SVM) ON DEEP FEATURES. Alexandria Journal of Managerial Research and Information Systems, 3(3), 105-110. doi: 10.21608/ajmris.2024.376696
MLA
Hager Ali Yahia. "PNEUMONIA DISEASE DETECTION BY BI-OBJECTIVE SUPPORT VECTOR MACHINE (BO-SVM) ON DEEP FEATURES", Alexandria Journal of Managerial Research and Information Systems, 3, 3, 2024, 105-110. doi: 10.21608/ajmris.2024.376696
HARVARD
Yahia, H. A. (2024). 'PNEUMONIA DISEASE DETECTION BY BI-OBJECTIVE SUPPORT VECTOR MACHINE (BO-SVM) ON DEEP FEATURES', Alexandria Journal of Managerial Research and Information Systems, 3(3), pp. 105-110. doi: 10.21608/ajmris.2024.376696
VANCOUVER
Yahia, H. A. PNEUMONIA DISEASE DETECTION BY BI-OBJECTIVE SUPPORT VECTOR MACHINE (BO-SVM) ON DEEP FEATURES. Alexandria Journal of Managerial Research and Information Systems, 2024; 3(3): 105-110. doi: 10.21608/ajmris.2024.376696
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