@Article{cmc.2022.029420,
AUTHOR = {Fatma Harby, Adel Thaljaoui, Durre Nayab, Suliman Aladhadh, Salim EL Khediri, Rehan Ullah Khan},
TITLE = {Data Augmentation and Random Multi-Model Deep Learning for Data Classification},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {74},
YEAR = {2023},
NUMBER = {3},
PAGES = {5191--5207},
URL = {http://www.techscience.com/cmc/v74n3/50986},
ISSN = {1546-2226},
ABSTRACT = {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.},
DOI = {10.32604/cmc.2022.029420}
}