Research Publications (Accounting and Informatics)

Permanent URI for this collectionhttp://ir-dev.dut.ac.za/handle/10321/212

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Computer vision: the effectiveness of deep learning for emotion detection in marketing campaigns
(The Science and Information Organization, 2022-05) Naidoo, Shaldon Wade; Naicker, Nalindren; Patel, Sulaiman Saleem; Govender, Prinavin
—As businesses move towards more customer-centric business models, marketing functions are becoming increasingly interested in gathering natural, unbiased feedback from customers. This has led to increased interest in computer vision studies into emotion recognition from facial features, for application in marketing contexts. This research study was conducted using the publicly-available Facial Emotion Recognition 2013 data-set, published on Kaggle. This article provides a comparative study of four deep learning algorithms for computer vision application in emotion recognition, namely, Convolution Neural Network (CNN), Multilayer Perceptron (MLP), Recurring Neural Network (RNN), Generative Adversarial Networks (GAN) and Long Short-Term Memory (LSTM) models. Comparisons between these models were done quantitatively using the metrics of accuracy, precision, recall and f1-score; as well and qualitatively by determining goodness-of-fit and learning rate from accuracy and loss curves. The results of the study show that the CNN, GAN and MLP models surpassed the data, and the LSTM model failed to learn at all. Only the RNN adequately learnt from the data. The RNN was found to exhibit a low learning rate, and the computational intensiveness of training the model resulted in a premature termination of the training process. However, the model still achieved a test accuracy of up to 72%, the highest of all models studied, and it is possible that this could be increased through further training. The RNN also had the best F1-score (0.70), precision (0.73) and recall (0.73) of all models studied
Data augmentation for deep learning algorithms that perform driver drowsiness detection
(The Science and Information Organization, 2023-01) Mohamed, Ghulam Masudh; Patel, Sulaiman Saleem; Naicker, Nalindren
Driver drowsiness is one of the main causes of driver-related motor vehicle collisions, as this impairs a person’s concentration whilst driving. With the enhancements of computer vision and deep learning (DL), driver drowsiness detection systems have been developed previously, in an attempt to improve road safety. These systems experienced performance degradation under real-world testing due to factors such as driver movement and poor lighting. This study proposed to improve the training of DL models for driver drowsiness detection by applying data augmentation (DA) techniques that model these real-world scenarios. This paper studies six DL models for driver drowsiness detection: four configurations of a Convolutional Neural Network (CNN), two custom configurations as well as the architectures designed by the Visual Geometry Group (VGG) (i.e. VGG16 and VGG19); a Generative Adversarial Network (GAN) and a Multi-Layer Perceptron (MLP). These DL models were trained using two datasets of eye images, where the state of eye (open or closed) is used in determining driver drowsiness. The performance of the DL models was measured with respect to accuracy, F1-Score, precision, negative class precision, recall and specificity. When comparing the performance of DL models trained on datasets with and without DA in aggregation, it was found that all metrics were improved. After removing outliers from the results, it was found that the average improvement in both accuracy and F1 score due to DA was +4.3%. Furthermore, it is shown that the extent to which the DA techniques improve DL model performance is correlated with the inherent model performance. For DL models with accuracy and F1-Score ≤ 90%, results show that the DA techniques studied should improve performance by at least +5%
A meta-analysis of educational data mining for predicting students performance in programming
(The Science and Information Organization, 2021-02) Moonsamy, Devraj; Naicker, Nalindren; Adeliyi, Timothy T.; Ogunsakin, Ropo E.
An essential skill amid the 4th industrial revolution is the ability to write good computer programs. Therefore, higher education institutions are offering computer programming as a module not only in computer related programmes but other programmes as well. However, the number of students that underperform in programming is significantly higher than the non-programming modules. It is, therefore, crucial to be able to accurately predict the performance of students pursuing programming since this will help in identifying students that may underperform and the necessary support interventions can be timeously put in place to assist these students. The objective of this study is therefore to obtain the most effective Educational Data Mining approaches used to identify those students that may underperform in computer programming. The PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analysis) approach was used in conducting the meta-analysis. The databases searched were, namely, ACM, Google Scholar, IEEE, Pro-Quest, Science Direct and Scopus. A total of 11 scientific research publications were included in the meta-analysis for this study from 220 articles identified through database searching. The residual amount of heterogeneity was high (τ2 = 0.03; heterogeneity I2 = 99.46% with heterogeneity chi-square = 1210.91, a degree of freedom = 10 and P = >0.001). The estimated pooled performance of the algorithms was 24% (95% CI (13%, 35%). Meta-regression analysis indicated that none of the moderators included have influenced the heterogeneity of studies. The result of effect estimates against its standard error indicated publication bias with a P-value of 0.013. These meta-analysis findings indicated that the pooled estimate of algorithms is high.