Optimization and Assessment of Residual Chlorine using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) Modeling
Manahel Mohammad Al-Araimi1, Varghese Manappallil Joy2, Lakkim Setty Nageswara Rao3, Shaik Feroz4
1Manahel Mohammad Al-Araimi , MIE Dept., College of Engineering, National University of Science and Technology, Muscat ,Oman.
2Varghese.M.J, CCCI, College of Engineering, National University of Science and Technology, Muscat ,Oman.
3Lakkim Setty Nageswara Rao, MIE Dept., College of Engineering, National University of Science and Technology, Muscat ,Oman.
4Shaik Feroz, College of Engineering, National University of Science and Technology, Muscat ,Oman.
Manuscript received on 15 August 2019. | Revised Manuscript received on 25 August 2019. | Manuscript published on 30 September 2019. | PP: 258-263 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4122098319/2019©BEIESP | DOI: 10.35940/ijrte.C4122.098319
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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: Discrete Optimization