Research Articles (Computer Science)

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https://hdl.handle.net/10566/52

The research papers in this collection represent the work of several projects.

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A multifactor comparative assessment of augmented reality frameworks in diverse computing settings
(Institute of Electrical and Electronics Engineers, 2023) Maneli, Mfundo A.; Isafiade, Omowunmi E.
Research and development on different augmented reality (AR) frameworks have come a long way when it comes to image tracking, object tracking, plane tracking and light estimation. However, there might be trade-offs and varying results obtained from different AR frameworks, depending on the use cases, and this is critical for consideration during immersive application development. Besides the current literature effort, this research proposes a multifactor comparative analysis of two core AR frameworks, which aims to analyze and evaluate ARKit and ARCore in diverse computing settings. This research developed a structural application which evaluated three major test parameters across ten devices spanning ARKit and ARCore. The first parameter relates to evaluating AR measurements using four different distance criteria. The second parameter evaluated resource utilization, relating to the central processing unit (CPU) and random access memory (RAM), while the last parameter evaluated plane detection based on light estimation.
Clustered data muling in the internet of things in motion
(MDPI, 2019) Tuyishimire, Emmanuel; Bagula, Antoine; Ismail, Adiel
This paper considers a case where an Unmanned Aerial Vehicle (UAV) is used to monitor an area of interest. The UAV is assisted by a Sensor Network (SN), which is deployed in the area such as a smart city or smart village. The area being monitored has a reasonable size and hence may contain many sensors for efficient and accurate data collection. In this case, it would be expensive for one UAV to visit all the sensors; hence the need to partition the ground network into an optimum number of clusters with the objective of having the UAV visit only cluster heads (fewer sensors). In such a setting, the sensor readings (sensor data) would be sent to cluster heads where they are collected by the UAV upon its arrival. This paper proposes a clustering scheme that optimizes not only the sensor network energy usage, but also the energy used by the UAV to cover the area of interest. The computation of the number of optimal clusters in a dense and uniformly-distributed sensor network is proposed to complement the k-means clustering algorithm when used as a network engineering technique in hybrid UAV/terrestrial networks. Furthermore, for general networks, an efficient clustering model that caters for both orphan nodes and multi-layer optimization is proposed and analyzed through simulations using the city of Cape Town in South Africa as a smart city hybrid network engineering use-case.
Community healthcare mesh network engineering in white space frequencies
(Institute of Electrical and Electronics Engineers, 2019) Bagula, Antoine
The transition from analog to digital television has availed new spectrum called white space, which can be used to boost the capacity of wireless networks on an opportunistic basis. One sector in which there is a need to use white space frequencies is the healthcare sector
An economic feasibility model for sustainable 5G networks in rural dwellings of South Africa
(MDPI, 2022) Maluleke, Hloniphani; Bagula, Antoine; Ajayi, Olasupo
Numerous factors have shown Internet-based technology to be a key enabler in achieving the sustainable development goals (SDG), as well as narrowing the divide between the global north and south. For instance, smart farming, remote/online learning, and smart grids can be used to, respectively, address SDGs 1 and 2 (ending poverty and hunger), 3 (quality education), and 7 and 9 (energy and infrastructure development). Though such Internet-based solutions are commonplace in the global north, they are missing or sparsely available in global south countries. This is due to several factors including underdevelopment, which dissuades service providers from investing heavily in infrastructure for providing capable Internet solutions such as 5G networks in these regions. This paper presents a study conducted to evaluate the feasibility of deploying 5G networks in the rural dwellings of South Africa at affordable rates, which would then serve as a pre-cursor for deploying solutions to improve lives and achieve the SDGs.
Cyber security education is as essential as “the three R's”
(CELLPRESS, 2019) Venter, Isabella M.; Blignaut, Renette J.; Renaud, Karen; Venter, Anja
Smartphones have diffused rapidly across South African society and constitute the most dominant information and communication technologies in everyday use. That being so, it is important to ensure that all South Africans know how to secure their smart devices. Doing so requires a high level of security awareness and knowledge. As yet, there is no formal curriculum addressing cyber security in South African schools. Indeed, it seems to be left to universities to teach cyber security principles, and they currently only do this when students take computingrelated courses. The outcome of this approach is that only a very small percentage of South Africans, i.e. those who take computing courses at university, are made aware of cyber security risks and know how to take precautions. In this paper we found that, because this group is overwhelmingly male, this educational strategy disproportionately leaves young South African women vulnerable to cyber-attacks. We thus contend that cyber security ought to be taught as children learn the essential “3 Rs”—delivering requisite skills at University level does not adequately prepare young South Africans for a world where cyber security is an essential skill. Starting to provide awareness and knowledge at primary school, and embedding it across the curriculum would, in addition to ensuring that people have the skills when they need them, also remove the current gender imbalance in cyber security awareness.
A Comparison of deep learning architectures for optical galaxy morphology classification
(International Conference on Electrical, Computer and Energy Technologies (ICECET), 2021) Fielding, Ezra; Nyirenda, Clement N.; Vaccari, Mattia
The classification of galaxy morphology plays a crucial role in understanding galaxy formation and evolution. Traditionally, this process is done manually. The emergence of deep learning techniques has given room for the automation of this process. As such, this paper offers a comparison of deep learning architectures to determine which is best suited for optical galaxy morphology classification. Adapting the model training method proposed by Walmsley et al in 2021, the Zoobot Python library is used to train models to predict Galaxy Zoo DECaLS decision tree responses, made by volunteers, using EfficientNet B0, DenseNet121 and ResNet50 as core model architectures. The predicted results are then used to generate accuracy metrics per decision tree question to determine architecture performance. DenseNet121 was found to produce the best results, in terms of accuracy, with a reasonable training time. In future, further testing with more deep learning architectures could prove beneficial.
3D forensic crime scene reconstruction involving immersive technology: A systematic literature review
(Institute of Electrical and Electronics Engineers, 2022) Maneli, Mfundo A.; Isafiade, Omowunmi E.
Recreation of 3D crime scenes is critical for law enforcement in the investigation of serious crimes for criminal justice responses. This work presents a premier systematic literature review (SLR) that offers a structured, methodical, and rigorous approach to understanding the trend of research in 3D crime scene reconstruction as well as tools, technologies, methods, and techniques employed thereof in the last 17 years. Major credible scholarly database sources, Scopus, and Google Scholar, which index journals and conferences that are promoted by entities such as IEEE, ACM, Elsevier, and SpringerLink were explored as data sources. Of the initial 17, 912 papers that resulted from the first search string, 258 were found to be relevant to our research questions after implementing the inclusion and exclusion criteria.
A Novel Epidemic Model for the Interference Spread in the Internet of Things
(MDPI, 2022) Tuyishimire, E; Niyigena, J; Tubanambazi, F; Bagula, A
: Due to the multi-technology advancements, internet of things (IoT) applications are in high demand to create smarter environments. Smart objects communicate by exchanging many messages, and this creates interference on receivers. Collection tree algorithms are applied to only reduce the nodes/paths’ interference but cannot fully handle the interference across the underlying IoT. This paper models and analyzes the interference spread in the IoT setting, where the collection tree routing algorithm is adopted. Node interference is treated as a real-life contamination of a disease, where individuals can migrate across compartments such as susceptible, attacked and replaced. The assumed typical collection tree routing model is the least interference beaconing algorithm (LIBA), and the dynamics of the interference spread is studied. The underlying network’s nodes are partitioned into groups of nodes which can affect each other and based on the partition property, the susceptible–attacked–replaced (SAR) model is proposed. To analyze the model, the system stability is studied, and the compartmental based trends are experimented in static, stochastic and predictive systems. The results shows that the dynamics of the system are dependent groups and all have points of convergence for static, stochastic and predictive systems.
On the Evolution Equation for Modelling the Covid-19 Pandemic
(2021) Blackledge, J
The paper introduces and discusses the evolution equation, and, based exclusively on this equation, considers random walk models for the time series available on the daily confirmed Covid-19 cases for different countries. It is shown that a conventional random walk model is not consistent with the current global pandemic time series data, which exhibits non-ergodic properties. A self-affine random walk field model is investigated, derived from the evolutionary equation for a specified memory function which provides the non-ergodic fields evident in the available Covid-19 data. This is based on using a spectral scaling relationship of the type 1/ωα where ω is the angular frequency and α ∈ (0, 1) conforms to the absolute values of a normalised zero mean Gaussian distribution. It is shown that α is a primary parameter for evaluating the global status of the pandemic in the sense that the pandemic will become extinguished as α → 0 for all countries. For this reason, and based on the data currently available, a study is made of the variations in α for 100 randomly selected countries. Finally, in the context of the Bio-dynamic Hypothesis, a parametric model is considered for simulating the three-dimensional structure of a spike protein which may be of value in the development of a vaccine.
Waternet: A network for monitoring and assessing water quality for drinking and irrigation purposes
(IEEE, 2022) Ajayi, Olasupo O.; Bagula, Antoine B.; Maluleke, Hloniphani C.
Water is a fundamental requirement for human, animal, and plant survival. Despite its importance, quality water is not always fit for drinking, domestic and/or industrial use. Numerous factors such as industrialization, mining, pollution, and natural occurrences impact the quality of water, as they introduce or alter various parameters present therein, thus, affecting its suitability for human consumption or general use. The World Health Organization has guidelines which stipulate the threshold levels of various parameters present in water samples intended for consumption or irrigation. The Water Quality Index (WQI) and Irrigation WQI (IWQI) are metrics used to express the level of these parameters to determine the overall water quality.
A novel immersive anatomy education system (anat_hub): Redefining blended learning for the musculoskeletal system
(MDPI, 2022) Boomgaard, Ayesha; Fritz, Kaylyn A.; Isafiade, Omowunmi E.
Immersive technologies are redefining ways of interacting with 3D objects and their environments. Moreover, efforts in blended learning have presented several advantages of incorporating educational technology into the learning space. The advances in educational technology have in turn helped to widen the choice of different pedagogies for improving learner engagement and levels of understanding. However, there is limited research in anatomy education that has considered the use and adoption of immersive technologies for the musculoskeletal system, despite its immense advantage. This research presents a practical immersive anatomy education system (coined Anat_Hub) developed using the agile scrum and participatory design method at a selected tertiary institution in Cape Town, South Africa, which promotes learner engagement through an asynchronous technological means using augmented reality (AR). The aim of the study was to develop an immersive AR mobile application that will assist learners and educators in studying and teaching the names, attachments, and actions of muscles of the human musculoskeletal system (upper and lower limbs). The Anat_Hub application offers a wide range of useful features for promoting active and self-regulated learning, such as 3D and AR modes, glossary, and quiz features.
Africa 3: A continental network model to enable the African fourth industrial revolution
(IEEE, 2020) Ajayi, Olasupo O; Bagula, Antoine B.; Maluleke, Hloniphani C.
It is widely recognised that collaboration can help fast-track the development of countries in Africa. Leveraging on the fourth industrial revolution, Africa can achieve accelerated development in health care services, educational systems and socio-economic infrastructures. While a number of conceptual frameworks have been proposed for the African continent, many have discounted the Cloud infrastructure used for data storage and processing as well as the underlying network infrastructure upon which such frameworks would be built. This work therefore presents a continental network model for interconnecting nations in Africa through its data centres. The proposed model is based on a multilayer network engineering approach, which first groups African countries into clusters of data centers using a hybrid combination of clustering techniques; then utilizes Ant Colony Optimisation with Stench Pheromone, that is modified to support variable evaporation rates, to find ideal network path(s) across the clusters and the continent as a whole. The proposed model takes into consideration the geo-spatial location, population sizes, data centre counts and intercontinental submarine cable landings of each African country, when clustering and routing. For bench-marking purposes, the path selection algorithm was tested on both the obtained clusters and African Union’s regional clusters.
Article comparing three countries’ higher education students’ cyber related perceptions and behaviours during COVID-19
(MPDI, 2021) Tick, Andrea; Cranfield, Desireé J.; Renaud, Karen V.
: In 2020, a global pandemic led to lockdowns, and subsequent social and business restrictions. These required overnight implementation of emergency measures to permit continued functioning of vital industries. Digital technologies and platforms made this switch feasible, but it also introduced several cyber related vulnerabilities, which students might not have known how to mitigate. For this study, the Global Cyber Security Index and the Cyber Risk literacy and education index were used to provide a cyber security context for each country. This research project—an international, cross-university, comparative, quantitative project—aimed to explore the risk attitudes and concerns, as well as protective behaviours adopted by, students at a South African, a Welsh and a Hungarian University, during the pandemic. This study’s findings align with the relative rankings of the Oliver Wyman Risk Literacy and Education Index for the countries in which the universities reside. This study revealed significant differences between the student behaviours of students within these universities.
Basic statistical estimation outperforms machine learning in monthly prediction of seasonal climatic parameters
(MPDI, 2021) Hussein, Eslam A.; Ghaziasgar, Mehrdad; Thron, Christopher
Machine learning (ML) has been utilized to predict climatic parameters, and many successes have been reported in the literature. In this paper, we scrutinize the effectiveness of five widely used ML algorithms in the monthly prediction of seasonal climatic parameters using monthly image data. Specifically, we quantify the predictive performance of these algorithms applied to five climatic parameters using various combinations of features. We compare the predictive accuracy of the resulting trained ML models to that of basic statistical estimators that are computed directly from the training data. Our results show that ML never significantly outperforms the statistical baseline, and underperforms for most feature sets. Unlike previous similar studies, we provide error bars for the relative performance of different predictors based on jackknife estimates applied to differences in predictive error magnitudes. We also show that the practice of shuffling data sequences which was employed in some previous references leads to data leakage, resulting in over-estimated performance. Ultimately, the paper demonstrates the importance of using well-grounded statistical techniques when producing and analyzing the results of ML predictive models.
A review of the fractal market hypothesis for trading and market price prediction
(MPDI, 2022) Blackledge, Jonathan; Lamphiere, Marc
This paper provides a review of the Fractal Market Hypothesis (FMH) focusing on financial times series analysis. In order to put the FMH into a broader perspective, the Random Walk and Efficient Market Hypotheses are considered together with the basic principles of fractal geometry. After exploring the historical developments associated with different financial hypotheses, an overview of the basic mathematical modelling is provided. The principal goal of this paper is to consider the intrinsic scaling properties that are characteristic for each hypothesis. In regard to the FMH, it is explained why a financial time series can be taken to be characterised by a 1/t 1−1/γ scaling law, where γ > 0 is the Lévy index, which is able to quantify the likelihood of extreme changes in price differences occurring (or otherwise). In this context, the paper explores how the Lévy index, coupled with other metrics, such as the Lyapunov Exponent and the Volatility, can be combined to provide long-term forecasts. Using these forecasts as a quantification for risk assessment, short-term price predictions are considered using a machine learning approach to evolve a nonlinear formula that simulates price values. A short case study is presented which reports on the use of this approach to forecast Bitcoin exchange rate values.
Comparison of phenolic content and antioxidant activity for fermented and unfermented rooibos samples extracted with water and methanol
(MPDI, 2022) Hussein, Eslam A.; Thron, Christopher; Ghaziasgar, Mehrdad
Rooibos is brewed from the medicinal plant Aspalathus linearis. It has a well-established wide spectrum of bio-activity properties, which in part may be attributed to the phenolic antioxidant power. The antioxidant capacity (AOC) of rooibos is related to its total phenolic content (TPC). The relation between TPC and AOC of randomly selected 51 fermented (FR) and 47 unfermented (UFR) rooibos samples was studied after extraction using water and methanol separately. The resulted extracts were assessed using two antioxidant assays, trolox equivalent antioxidant capacity (TEAC) and ferric reducing antioxidant power (FRAP). The results were analyzed using both simple statistical methods and machine learning. The analysis showed different trends of TPC and AOC correlations of FR and UFR samples, depending on the solvent used for extraction. The results of the water extracts showed similar TPC and higher AOC of FR than UFR samples, while the methanolic extracted samples showed higher TPC and AOC of UFR than FR. As a result, the methanolic extracts showed better agreement between TPC and AOC than water extracts.
Transport inequalities and the adoption of intelligent transportation systems in Africa: A research landscape
(MDPI, 2021) Ajayi, Olasupo O; Bagula, Antoine B.; Maluleke, Hloniphani C; Odun-Ayo, Isaac A
Intelligent Transportation Systems (ITS), also known as Smart Transportation, is an infusion of information and communication technologies into transportation. ITS are a key component of smart cities, which have seen rapid global development in the last few decades. This has in turn translated to an increase in the deployment and adoption of ITS, particularly in countries in the Western world. Unfortunately, this is not the case with the developing countries of Africa and Asia, where dilapidated road infrastructure, poorly maintained public/mass transit vehicles and poverty are major concerns. However, the impact of Westernization and “imported technologies” cannot be overlooked; thus, despite the aforementioned challenges, ITS have found their way into African cities. In this paper, a systematic review was performed to determine the state of the art of ITS in Africa. The output of this systematic review was then fed into a hybrid multi-criteria model to analyse the research landscape, identify connections between published works and reveal research gaps and inequalities in African ITS. African peculiarities inhibiting the widespread implementation of ITS were then discussed, followed by the development of a conceptual architecture for an integrated ITS for African cities.
Improving quality-of-service in cloud/fog computing through efficient resource allocation
(MPDI, 2019) Akintoye, Samson Busuyi; Bagula, Antoine
Recently, a massive migration of enterprise applications to the cloud has been recorded in the IT world. One of the challenges of cloud computing is Quality-of-Service management, which includes the adoption of appropriate methods for allocating cloud-user applications to virtual resources, and virtual resources to the physical resources. The effective allocation of resources in cloud data centers is also one of the vital optimization problems in cloud computing, particularly when the cloud service infrastructures are built by lightweight computing devices. In this paper, we formulate and present the task allocation and virtual machine placement problems in a single cloud/fog computing environment, and propose a task allocation algorithmic solution and a Genetic Algorithm Based Virtual Machine Placement as solutions for the task allocation and virtual machine placement problem models. Finally, the experiments are carried out and the results show that the proposed solutions improve Quality-of-Service in the cloud/fog computing environment in terms of the allocation cost.
Carbon futures trading and short-term price prediction: An analysis using the fractal market hypothesis and evolutionary computing
(MPDI, 2021) Lamphiere, Marc; Blackledge, Jonathan; Kearney, Derek
This paper presents trend prediction results based on backtesting of the European Union Emissions Trading Scheme futures market. This is based on the Intercontinental Exchange from 2005 to 2019. An alternative trend prediction strategy is taken that is predicated on an application of the Fractal Market Hypothesis (FMH) in order to develop an indicator that is predictive of short term future behaviour. To achieve this, we consider that a change in the polarity of the Lyapunov-toVolatility Ratio precedes an associated change in the trend of the European Union Allowances (EUAs) price signal. The application of the FMH in this case is demonstrated to provide a useful tool in order to assess the likelihood of the market becoming bear or bull dominant, thereby helping to inform carbon trading investment decisions. Under specific conditions, Evolutionary Computing methods are utilised in order to optimise specific trading execution points within a trend and improve the potential profitability of trading returns. Although the approach may well be of value for general energy commodity futures trading (and indeed the wider financial and commodity derivative markets), this paper presents the application of an investment indicator for EUA carbon futures risk modelling and investment trend analysis only.
Cyber physical systems dependability using cps-iot monitoring
(MPDI, 2021) Bagula, Antoine; Ajayi, Olasupo; Maluleke, Hloniphani
Recently, vast investments have been made worldwide in developing Cyber-Physical Systems (CPS) as solutions to key socio-economic challenges. The Internet-of-Things (IoT) has also enjoyed widespread adoption, mostly for its ability to add “sensing” and “actuation” capabilities to existing CPS infrastructures. However, attention must be paid to the impact of IoT protocols on the dependability of CPS infrastructures. We address the issues of CPS dependability by using an epidemic model of the underlying dynamics within the CPS’ IoT subsystem (CPS-IoT) and an interferenceaware routing reconfiguration. These help to efficiently monitor CPS infrastructure—avoiding routing oscillation, while improving its safety. The contributions of this paper are threefold. Firstly, a CPS orchestration model is proposed that relies upon: (i) Inbound surveillance and outbound actuation to improve dependability and (ii) a novel information diffusion model that uses epidemic states and diffusion sets to produce diffusion patterns across the CPS-IoT.

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