3. Electronic Theses and Dissertations (ETDs) - All submissions
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Item Preparing Grade 9 technology learners for the terrain of the fourth industrial revolution(2024) Ngcobo, ZimeThe fourth industrial revolution (4IR) represents a complete shift from manual and conventional methods of operation to a more computerized one. It is crucial to get ready for this environment so that civilization can survive and manage it throughout this period. The goal of this research was to find out if technology students in grade 9 are being adequately prepared for the 4IR environment. The study examined the pedagogies, subject-matter expertise, and 4IR knowledge that potential grade 9 technology teachers may possess to achieve this goal. Due to the nature of the focus of the study, it remains important to target individuals who are relevant to the study and may contribute relevant findings, purpose sampling was done, hence, only well experienced Grade 9 Technology teachers were requested to participate. Five South African public schools in Gauteng province participated in the study. In these five South African public schools, only grade 9 Technology teachers were requested to participate. Learners did not form part of the research as the research dwells much on the teaching strategies and type of content presented in the classroom. Data was collected using interviews through Google Meets virtual platform with ten Technology teachers in grade 9. The study is qualitative and used a case study. The conceptual framework for the development of 4IR skills, which was modified from (Kamaruzaman, Hamid, Mutalib & Rasul, 2019), served as the basis for the data analysis. The data analysis approach of this study was entirely based on inductive reasoning as all the findings and conclusions were based on evident information. The link between educational institutions, graduates, and workplaces was explained in detail and in broad strokes by this paradigm. This demonstrates how crucial it is for the educational system to deliver high-quality instruction that is compatible with the demands of the 4IR workplace. The 4IR skills are anticipated to be taught through the educational system utilizing digital tools and effective pedagogies. The research revealed that the majority of technology educators in public schools lacked adequate digital teaching and learning resources, which impede them from using digital technology-driven instruction in the classroom. This further prevent students from using digital resources to acquire 4IR skills like digital fluency. Participants also mentioned that the majority of them lacked digital technology abilities and were unable to use these educational tools because of this. Participants are thus unable to get learners ready for the 4IR. Learning the skills required for the 4IR workplace are being hampered by learners' lack of exposure to digital technology. Some participants also expressed their lack of familiarity with the 4IR. They are unaware of and do not think that 4IR is a possibility. Because of this, it is impossible to prepare students for this period. To ensure for protection and reliability of the study, ethical procedures were followed according to the expectations of the university. Personal information of all the participants was protected at all times.Item Industrial change detection using deep learning applied to DInSAR and optical satellite imagery(2020) Karim, ZainoolabadienIn order to detect industrial change in satellite imagery binary classification is investigated using traditional ML with feature extractors (HOG and LBP) with SVM, a simple CNN with only 2 convolutional layers called Simple ConvNet, state-of-the-art Deep Learning (DL) and Transfer Learning (TL) methods. Differential Interferometric Synthetic Aperture Radar (DInSAR) is used to generate interferograms and Terrain Corrected Displacement maps. A Sentinel-1B Synthetic Aperture Radar (SAR) image data stack from 28th Nov. 2017 to 5th Dec. 2018 is used with the images 12 days apart, where imagery was available. Blobs are detected in a displacement map which is generated using DInSAR and the Laplacian of Gaussian algorithm. The blobs are qualitatively verified using optical images from the Sentinel-2 satellite. If subsidence or uplift has indeed occurred then the blob is classified as positive. However, if uplift or subsidence has not occured then the blob is classified as negative which refers to noise. The blob detection algorithm has a high false positive rate. However, true positive blobs are detected corresponding to quarries, mines, construction, etc. A novel dataset is developed comprising of DInSAR processed Sentinel-1 displacement, coherence and phase georeferenced imagery and the corresponding Sentinel-2 RGB optical satellite images of the blobs. A variety of DL architectures that are pretrained on ImageNet, a computer vision performance benchmark dataset, are used for implementing TL with Feature Extraction (FE). For the EfficientNet B4 and ResNet-50 architectures TL with Fine-Tuning (FT) as well as classic DL i.e. training from scratch, Random Initialisation (RI), are also evaluated. Ensemble performance containing certain architectures is also evaluated. The best performing architecture and method (84.34%) is the ResNet-50 with TL via FE applied. It outperformed all the other models and methods including newer, deeper (due to more data being needed to train deeper networks) and ensemble models from an accuracy perspective. Two of the ensembles evaluated using FE have an accuracy of 84.40% and 84.27% respectively with the same F1 score as ResNet50. It is concluded that a correlation between an increase in model size inferring a lower FE accuracy depends on architecture and holds for ResNet and EfficientNet but not for ResNetV2 architectures. The ResNet50 which has slightly more parameters than the EfficientNet B4 performs better with RI and FT respectively. Most models using TL with FE independent of network size outperformed RI and FT with ResNet50 and EfficientNet B4 respectively on this dataset. Most of the DL and TL models outperformed the traditional ML models except for the EfficientNet B4 models for RI and FT. Simple ConvNet (83.37%) outperformed most of the models except for FE with ResNet101, ResNet50 and the ensemble models. FE with the ResNet50 only outperformed Simple ConvNet by 0.97%. Thus, simple CNNs should not be overlooked for small datasets. Displacement Time-series has been developed for all the pixels in the study area. Although there is some noise upward and downward trends can be seen corresponding to change. A velocity map was developed with uplift of 20cm to subsidence of 60cm over the 1 year period noting that the subsidence sometimes occurred in areas of low coherence where it is not accurate.Item Using ensemble learning for the network intrusion detection problem(2019-08-01) Kalonji, Roland MpoyiNowadays, most organizations and platforms employ an intrusion detection system (IDS) to enhance their network security and protocol systems. The IDS has therefore become an essential component of any network system; it is a tool with several applications that can be tuned to specific content in a network by identifying various accesses (normal or attack). However, network intrusion detection system (NIDS) that focuses on revealing suspicious activities, is not effective in solving various problems such as identifying false IP packets and encrypted traffic. Hence, this work investigates the use of ensemble learning to solve these types of network intrusion detection problems (NIDPs). Random forest (RF), Decision Tree (DT) and Support Vector Machine (SVM) are introduced as classifiers based on Boruta and Principal Component Analysis (PCA) algorithms. In general, the main difficulties in using ensemble for the intrusion problem are to minimize false alarms and to maximize detection accuracy (Anuar et al., 2008). Additionally, the NIDP is divided into five categories, namely the detection of probe attacks, denial of service, remote to local, user to root and normal instances. Each problem is examined by one of the three aforementioned classifiers. In tackling these problems, the three classifiers achieved competitive results comparing to the works conducted by Balon-Perin (2012), Zainal et al. (2009) and Kevric et al. (2017). The results revealed that ensemble learning achieved more than 99% accuracy in demarcating attacks from normal connections. Particularly, RF, DT and SVM allowed to safeguard the NIDS from known and unknown attacks by developing reliable techniques. The KDD99 and NSL KDD datasets have been used to implement and measure the system performance (Fan et al., 2000; Dhanabal and Shantharajah, 2015).