Faculty of Accounting and Informatics
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Item 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, NalindrenDriver 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%Item A multiple‐input, multiple‐output broadcasting system with space, time, polarization, and labeling diversity(Wiley, 2022) Patel, Sulaiman Saleem; Quazi, TahmidEnhancing the reliability of wireless links plays an important role in addressing the digital divide between under-developed and developed nations. Diversity techniques are used in wireless communication to improve link reliability. This article presents a broadcasting system that incorporates space, time, polarization and labeling diversity. The key challenge in incorporating labeling diversity into a system is the design of appropriate bit-to-symbol mappers. The latest technique to designing bit-to-symbol mappers is to use a genetic algorithm approach, which is applicable to any generic modulation scheme. This article presents a modified genetic algorithm mapper design algorithm based on recent advances in labeling diversity evaluation theory. The proposed system is studied under ideal (uncorrelated) conditions, as well as in the presence of inter-beam and inter-antenna inference (correlated conditions). Analytical expressions are presented to model both the correlated and uncorrelated systems, and are verified via Monte Carlo simulations. When compared to the best comparable scheme at a bit-error-rate of 10−6, results show that the proposed system improves performance by ≈7 dB for a 2 × 2 16APSK system configuration, and by ≈5 dB for both 2 × 4 32APSK and 2 × 2 64APSK system configurations. Results also show that the proposed system is highly sensitive to correlation at the transmitting node. In particular, transmit-side correlation degrades link reliability by 4 orders of magnitude for the 2 × 3 8APSK configuration studied at 25 dBItem 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