Growth of Online Purchase in Saudi Arabia Retail Industry
Mohamad Kebah1, Valliappan Raju2, Zahir Osman3,
1Mohamad Kebah, Doctor of Philosophy of Management (PhD) student at Limkokwing University of Technology and Creative, Malaysia.
2Dr. Valliappan Raju, Senior Lecturer at the Centre of Postgraduate Studies at Limkokwing University, Malaysia.
3Dr Zahir Osman, Senior Lecturer at the Faculty of Business and Management, Open University of Malaysia (OUM).
Manuscript received on 6 August 2019. | Revised Manuscript received on 12 August 2019. | Manuscript published on 30 September 2019. | PP: 869-872 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4054098319/19©BEIESP | DOI: 10.35940/ijrte.C4054.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This study explores an ensemble technique for building a composite of pre-trained VGG16, VGG19, and Resnet56 classifiers using probability voting-based technique. The resulted composite classifiers were tested to solve image classification problems using a subset of Cifar10 dataset. The classifier performance was measured using accuracy metric. Some experimentation results show that the ensemble methods of pre-trained VGG19-Resnet56 and VGG16-VGG19-Resnet models outperform the accuracy of its individual model and other composite models made of these three models.
Keywords: Ensemble Classifiers, VGG16, VGG19, Resnet56, Probability Voting Technique, CIFAR-10.
Scope of the Article: Online Learning Systems