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Data Augmentation and Random Multi-Model Deep Learning for Data Classification

Fatma Harby1, Adel Thaljaoui1, Durre Nayab2, Suliman Aladhadh3,*, Salim EL Khediri3,4, Rehan Ullah Khan3

1 Computer Science Department, Future Academy-Higher Future Institute for Specialized Technological Studies, Egypt
2 Department of Computer Systems Engineering, Faculty of Electrical and Computer Engineering, University of Engineering and Technology, Peshawar, 25120, Pakistan
3 Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
4 Department of Computer Sciences, Faculty of Sciences of Gafsa, University of Gafsa, Gafsa, Tunisia

* Corresponding Author: Suliman Aladhadh. Email: email

Computers, Materials & Continua 2023, 74(3), 5191-5207.


In the machine learning (ML) paradigm, data augmentation serves as a regularization approach for creating ML models. The increase in the diversification of training samples increases the generalization capabilities, which enhances the prediction performance of classifiers when tested on unseen examples. Deep learning (DL) models have a lot of parameters, and they frequently overfit. Effectively, to avoid overfitting, data plays a major role to augment the latest improvements in DL. Nevertheless, reliable data collection is a major limiting factor. Frequently, this problem is undertaken by combining augmentation of data, transfer learning, dropout, and methods of normalization in batches. In this paper, we introduce the application of data augmentation in the field of image classification using Random Multi-model Deep Learning (RMDL) which uses the association approaches of multiDL to yield random models for classification. We present a methodology for using Generative Adversarial Networks (GANs) to generate images for data augmenting. Through experiments, we discover that samples generated by GANs when fed into RMDL improve both accuracy and model efficiency. Experimenting across both MNIST and CIAFAR-10 datasets show that, error rate with proposed approach has been decreased with different random models.


Cite This Article

APA Style
Harby, F., Thaljaoui, A., Nayab, D., Aladhadh, S., Khediri, S.E. et al. (2023). Data augmentation and random multi-model deep learning for data classification. Computers, Materials & Continua, 74(3), 5191-5207.
Vancouver Style
Harby F, Thaljaoui A, Nayab D, Aladhadh S, Khediri SE, Khan RU. Data augmentation and random multi-model deep learning for data classification. Comput Mater Contin. 2023;74(3):5191-5207
IEEE Style
F. Harby, A. Thaljaoui, D. Nayab, S. Aladhadh, S.E. Khediri, and R.U. Khan "Data Augmentation and Random Multi-Model Deep Learning for Data Classification," Comput. Mater. Contin., vol. 74, no. 3, pp. 5191-5207. 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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