Faculty of Accounting and Informatics
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Item Integration of indigenous knowledge into library and information science teaching practices : a systematic review of the global literature(2023-08-03) Omarsaib, Mousin; Naicker, Nalindren; Rajkoomar, MogivenyIndigenous knowledge is an emerging theme in humanistic scholarly conversations. Therefore, thepurpose of this study was to present a global perspective of teaching practices related to indigenous knowledge in the Library and Information Science (LIS) field as it lends itself to a humanistic approach. The aim was to identify how indigenous knowledge is integrated into the LIS curriculum. Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology was used to review the literature. Key findings revealed that integrated teaching practices and indigenous knowledge are still emerging topics at LIS schools. The study recommends academics from LIS schools earnestly contribute to global literature by sharing their knowledge on teaching practices 'vis-a-vis' indigenous knowledge. Globally, this would ensure LIS academics tread common ground in integrating indigenous knowledge into the curriculum by using appropriate teaching practices. Ultimately, developing future LIS graduates as custodians of indigenous knowledge in industry.Item Digital pedagogies of academic librarians in the fourth industrial revolution(IGI Global, 2022-06-24) Omarsaib, Mousin; Rajkoomar, Mogiveny; Naicker, NalindrenGlobally, the role of academic librarians as online teachers at higher education institutions is experiencing a tsunami of change. This is due to the Fourth Industrial Revolution and the influence of technology on pedagogy. The 21st-century academic librarian is challenged to adopt innovative teaching methods using technology in a digital environment. The purpose of this study was to explore the pedagogical and technological preparedness of academic librarians at University of Technologies in South Africa for online teaching. The technology pedagogy content knowledge framework guided the methodology in exploring the pedagogical and technological preparedness of academic librarians. A pragmatic approach using quantitative techniques was used in the data collection process. The data collected from the findings were analyzed and validated resulting in emerging themes. The results show a lack of pedagogical and technological skills among academic librarians at UOT in South Africa.Item Digital pedagogies for librarians in higher education : a systematic review of the literature(Emerald, 2022-01) Omarsaib, Mousin; Rajkoomar, Mogiveny; Naicker, Nalindren; Olugbara, Cecilia TemilolaAbstract Purpose – The purpose of this paper is to identify and present a global perspective of digital pedagogies in relation to technology and academic librarians. Design/methodology/approach – The preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology was used in this study. Findings – Based on the data, academic librarians must develop a foundational understanding of 21st century pedagogies and digital skills to teach in an online environment. Originality/value – This review paper considers the emergent teaching role of the academic librarian within the digital environment. The themes in the findings highlight the importance of digital pedagogical knowledge and digital fluency of academic librarians as a teacher within the digital environment in higher education.Item A hyper-heuristic heterogeneous multisensor node scheme for energy efficiency in larger wireless sensor networks using DEEC-Gaussian algorithm(Hindawi Limited, 2021-02-15) Aroba, Oluwasegun Julius; Naicker, Nalindren; Adeliyi, TimothyA wireless sensor network (WSN) is an intellect-sustainable network that comprises multiple spatially distributed sensor nodes and several sink nodes that collect data from sensors. WSNs remain an active research area in the literature due to challenging factors such as the selection of sensor location according to a given premise, finding optimal routing algorithm, and ensuring energy efficiency and consumption. Minimizing energy and prolonging the network lifetime in the WSNs are the focus of this research work. In the literature, a clustering approach is used in grouping sensor nodes into clusters and is seen as an effective technique used in optimizing energy consumption in WSNs. Hence, in this paper, we put forward a novel clustering-based approach by amalgamating the Gaussian elimination method with the Distributed Energy-Efficient Clustering to produce DEEC_Gaussian (DEEC_Gaus) to stabilize energy efficiency optimization in WSNs. We took the advantages of DEEC and Gaussian elimination algorithms to resolve energy efficiency problems in WSNs. DEEC presents attributes such as increased heterogeneity performance level, clustering stability in operation, and energy efficiency which helps to prolong network lifetime while the Gaussian elimination algorithm added an additional advantage to improve and optimize energy efficiency, to aggregate packets of operations performed in the network lifestyle of energy efficiency in WSNs. The simulations were carried out using MATLAB software with 1000 to 1500 nodes. The performance of the proposed work was compared with state-of-the-art algorithms such as DEEC, DDEEC, and EDEEC_E. The simulated results presented show that the proposed DEEC-Gauss outperformed the three other conventional algorithms in terms of network lifetime, first node dead, tenth node dead, alive nodes, and the overall timing of the packets received at the base station. The results showed that the proposed hyper-heuristic heterogeneous multisensor DEEC-Gauss algorithm presented an average percentage of 3.0% improvement for the tenth node dead (TND) and further improvement of 4.8% for the first node dead (FND). When the performance was compared to the state-of-the-art algorithms in larger networks, the overall delivery was greatly improved and optimized.Item 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.Item An innovative hyperheuristic, gaussian clustering scheme for energy-efficient optimization in wireless sensor networks(Hindawi Limited, 2021-02-11) Aroba, Oluwasegun Julius; Naicker, Nalindren; Adeliyi, TimothyEnergy stability on sensor nodes in wireless sensor networks (WSNs) is always an important challenge, especially during data capturing and transmission of packets. The recent advancement in distributed clustering algorithms in the extant literature proposed for energy efficiency showed refinements in deployment of sensor nodes, network duration stability, and throughput of information data that are channelled to the base station. However, much scope still exists for energy improvements in a heterogeneous WSN environment. This research study uses the Gaussian elimination method merged with distributed energy efficient clustering (referred to as DEEC-Gauss) to ensure energy efficient optimization in the wireless environment. The rationale behind the use of the novel DEEC-Gauss clustering algorithm is that it fills the gap in the literature as researchers have not been able to use this scheme before to carry out energy-efficient optimization in WSNs with 100 nodes, between 1,000 and 5000 rounds and still achieve a fast time output. In this study, using simulation, the performance of highly developed clustering algorithms, namely, DEEC, EDEEC_E, and DDEEC, was compared to the proposed Gaussian Elimination Clustering Algorithm (DEEC-Gauss). The results show that the proposed DEEC-Gauss Algorithm gives an average percentage of 4.2% improvement for the first node dead (FND), a further 2.8% improvement for the tenth node dead (TND), and the overall time of delivery was increased and optimized when compared with other contemporary algorithms.Item 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.Item Factors contributing to the successful development and use of mobile digital libraries : a systematic literature review(Emerald, 2023-03-21) Khomo, Musawenkosi Phumelela; Naicker, Nalindren; Chisita, Collence Takaingenhamo; Rajkoomar, MogivenyThe purpose of this paper is to review the literature on factors that contribute to the successful development and use of the mobile digital library (DL). This aim will be achieved by reviewing selected journal articles on mobile DLs' successful development and use. This paper argues that the concept of DLs is evolving because of the dynamic nature of knowledge and technological developments in the infosphere. Design/methodology/approach A systematic literature search of journal article factors that contribute to the successful development and use of the mobile DL was accomplished by searching the following databases: Emerald insight, Science directory and Google Scholar. The systematic review was conducted following the preferred reporting items for systematic reviews and meta-analyses guidelines. This study applied Rogers's (1965) Diffusion of innovation theory to unpack the attributes of innovation to unpack contextual factors shaping African conceptions of mobile libraries (m-libraries). The studies reviewed were published from 2016 to 2021. This paper is based on a systematic literature review. This paper uses publicly available literature on the theme of DLs concerning m-libraries. Among the search terms used for the study were: “digital libraries”, “Africa and digital libraries”, “electronic libraries”, “information communication technologies”, “access to information” and “mobile digital libraries”. Findings Reviewed literature indicates that myriad factors can contribute positively or negatively to the successful development and use of the mobile DL. These factors include the degree of staff awareness and understanding of the potential of mobile technologies in enhancing the provision of library services, the availability of relevant digital content, library staff and users’ level of digital and information literacy competencies to navigate digital platforms, user friendliness of DL platforms, material and financial support to access m-libraries, power supply and access to internet connectivity. Practical implications The results from this study will generate knowledge and insight into the factors that affect the development and optimal use of mobile DLs to enhance and widen access to scholarly databases irrespective of time and space. This study will make recommendations that will enable South African policymakers to make informed decisions relating to the factors affecting the development and usage of mobile DLs for enhanced learning, teaching and education. Originality/value Given the growing number of scholarly publications on mobile DLs, this study seeks to discover how such technologies can help enhance learning, teaching and research in higher education. This study’s findings will provide a scientific basis for policymakers and researchers with evidence-based knowledge that raises the value of mobile DLs. It was discovered that if the identified factors are handled well, users can easily access tools, such as databases, electronic journals and online reference tools, and this could improve the quality of teaching and learning.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 meta-analysis of the economic impact of carbon emissions in Africa(LLC CPC Business Perspectives, 2022-11-09) Rajkoomar, Mogiveny; Marimuthu, Ferina; Naicker, Nalindren; Damascene Mvunabandi, JeanThe economic impact of carbon emissions in Africa is gaining traction in the extant literature. This study adopted Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to concomitantly track data on carbon emissions versus economic growth in Africa from 2018 to 2022 providing evidence from a meta-analysis. Through database searches, 591 publications were identified. A machine learning algorithm called Latent Dirichlet Allocation (LDA) was used as a visualization technique for reporting trends in the eleven papers selected for the analysis. Identifying, evaluating, and summarizing the findings of all relevant individual studies conducted in Africa on the impact of economic growth on carbon emissions contributes to the existing body of knowledge. This study fills a critical gap by surveying the studies conducted in Africa in the last five years, implying that economic growth negatively and significantly triggers CO2 emissions in Africa. The debate on the economic impact of CO2 emissions in Africa, the most vulnerable continent to climate change, is elucidated. The findings tracked sources of data for carbon emissions in Africa. The results showed that although some studies reported a positive correlation (and some a negative correlation) between economic growth and carbon emissions, most studies concur that the economic impact of carbon emissions over a timeline can be explained by the Environmental Kuznets Curve (EKC) hypothesis. Therefore, there is a dire need for African countries to strengthen economic growth without deteriorating their environment or having ecological footprint. Future research must assess whether this trend on the economic impact of carbon emissions in Africa continues. AcknowledgmentThe authors express their appreciation to the Durban University of Technology for providing the resources to conduct this study.