Faculty of Accounting and Informatics

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Adoption of augmented reality to enhance Durban University of Technology's learning management system
(IEEE, 2022-10-27) Djumo, Wappi Francis; Govender, Shaolin Lee; Matha, Sanele Raphael; Adeliyi, Timothy T.
The Durban University of Technology's (DUT) elearning system is used as a case study in the research as it investigates the various learning management systems in higher education. due to the expanding technological trends and the requirement to support students who belong to "Generation Z." This study examines how augmented reality can be used to transform the DUT E-learning system from a three-standalone system into a unified system. The study illustrates how the Business Analysis Core Concept Model, a conceptual framework for business analysis, would be employed to analyze the proposed augmented reality system and the current DUT e-learning model. Additionally, the use cases of the three standalone platforms that make up the current DUT E-learning are contrasted with a single augmented reality E-learning system.
Analysis of road traffic accidents severity using a pruned tree-based model
(International Information and Engineering Technology Association, 2023-06-30) Adeliyi, Timothy T.; Oluwadele, Deborah; Igwe, Kevin; Aroba, Oluwasegun Julius
Traffic accidents are becoming a global issue, causing enormous losses in both human and financial resources. According to a World Health Organization assessment, the severity of road accidents affects between 20 and 50 million people each year. This study intends to examine significant factors that contribute to road traffic accident severity. Seven machine learning models namely, Naive Bayes, KNN, Logistic model tree, Decision Tree, Random Tree, and Logistic Regression machine learning models were compared to the J48 pruned tree model to analyze and predict accident severity in the road traffic accident. To compare the effectiveness of the machine learning models, ten well-known performance evaluation metrics were employed. According to the experimental results, the J48 pruned tree model performed more accurately than the other seven machine learning models. According to the analysis, the number of casualties, the number of vehicles involved in the accident, the weather conditions, and the lighting conditions of the road, is the main determinant of road traffic accident severity.
A hybridized framework for designing and evaluating e-learning students’ performance in medical education
(IEEE, 2022-10-27) Oluwadele, Deborah; Singh, Yashik; Adeliyi, Timothy T.
The COVID-19 pandemic resulted in the hurried adoption of e-learning with no proper need analysis to inform the design and subsequent evaluation of students’ performance in e-learning in medical education. Consequently, several studies evaluating performance in e-learning in medical education do so by conducting pre-test and post-test with no defined framework or model to guide the evaluation. This makes the findings from these studies subjective and biased since factors that possibly impact students’ performance were neither considered in the design of the course nor measured and reported in the evaluation studies. We, therefore, introduce an essential pedagogical e-learning concept by developing a framework to inform the design and evaluation of students’ performance in e-learning in medical education via the thoughtful fusion of the Task-Technology Fit Model and the Kirkpatrick Evaluation Model. Our hybrid framework was piloted at the University of KwaZulu-Natal, Durban, South Africa and findings emphasize the need for alignment between learning tasks, technology infrastructures, individual traits, and contextual limitations of students as key factors in determining how well students perform in the classroom and their clinical practices at work. This study advances the body of knowledge by providing a well-brainstormed and intricately designed framework to guide the design of courses and evaluation of student’s performance in an e-learning context in medical education.
Meta analysis of heuristic approaches for optimizing node localization and energy efficiency in wireless sensor networks
(Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP, 2020-10) Aroba, Oluwasegun Julius; Naicker, Nalindren; Adeliyi, Timothy T.; Ogunsakin, Ropo E.
Background: In the literature node localization and energy efficiency are intrinsic problems often experienced in wireless sensor networks (WSNs). Consequently, various heuristic approaches have been proposed to allay the challenges faced by WSNs. However, there is little to nothing in the literature to support which of the heuristic approaches is best in optimizing node localization and energy efficiency problems in WSN. The aim of this paper is to assess the best heuristic approach to date on resolving the node localization and energy efficiency in WSNs. Method: The extraction of the relevant articles was designed following the technique of preferred reporting items for systematic reviews and meta-analyses (PRISMA). All the included research articles were searched from the widely used databases of Google Scholar and Web of Science. All statistical analysis was performed with the fixed-effects model and the random-effects model implementation in RStudio. The overall pooled global estimate and categorization of performance for the heuristic approaches were presented in forest plots. Results: A total of 18 studies were included in this meta-analysis and the overall pooled estimated categorization of the heuristic approaches was 35% (95% CI (13%, 67%)). According to subgroup analysis the pooled estimation of heuristic approach with hyper-heuristic was 71% (95% CI: 6% to 99%), I2 = 100%) while the hybrid heuristic, was 31% (95% CI: 3% to 87%, I2 = 100%) and metaheuristic was 21%(95% CI: 9% to 41%, I2 = 100%). Conclusion: It can be concluded based on the experimental results that hyper-heuristic approach outclassed the hybrid heuristic and metaheuristic approaches in optimizing node localization and energy efficiency in WSNs.
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.
Node localization in wireless sensor networks using a hyper-heuristic DEEC-Gaussian gradient distance algorithm
(Elsevier BV, 2023-03) Aroba, Oluwasegun Julius; Naicker, Nalindren; Adeliyi, Timothy T.
In the recent age of technological advancements, wireless sensor networks are an important application for smart modernized environments. In WSNs, node localization has been an issue for over a decade in the research community. One of the goals of localization in a wireless sensor network is to localize sensor nodes in a two-dimensional plane. Localization in wireless sensor networks helps to supply information to aid decision-making from the aggregated data that are sent from packets to base stations. Internet of Things with the use of Global Positioning Systems for tracking sensor zones is not a cost-effective means of solution. In the extant literature, there have been a variety of algorithms to identify unknown sensor locations in wireless sensor networks. This research paper aims to address the problem of determining the location of the sensor node at the base station with minimum localization error when the data between the nodes is transmitted wirelessly. To detect the location of an unknown sensor node packets sent to the destinations, the total number of anchor nodes, location error and distance estimation error were considered. The DEEC-Gauss Gradient Distance Algorithm has a lower localization error than the Weighted Centroid Localizations algorithm, Compensation Coefficient algorithm, DV-Hop algorithm, Weighted Hyperbolic algorithm and Weighted Centroid algorithm for the same ratio of anchor nodes and WSN configuration. According to the study's findings, the DGGDEA has an average localization error of 11% for anchor nodes (20-80), and an average localization error of 11.3% for anchor nodes 200-450. Hence, the DEEC-Gaussian Gradient Distance Elimination Algorithm (DGGDEA) showed higher accuracy with comparison to the modern-day approaches.