Faculty of Accounting and Informatics

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The adoption of E-Learning as a remote teaching and learning methodology in tertiary institutions during Covid-19 : a case study of the University of Kwa-Zulu-Natal
(2022-11-01) Nyathikazi, Siphamandla Handsome; Parbanath, Steven
This research was a case study that sought to explore if the University of Kwa-Zulu Natal (UKZN) is ready to adopt E-learning as a fully-fledged method of teaching and learning during Covid-19. The advent of the Coronavirus (Alsoud and Harasis 2021) in South Africa was confirmed by the National Institute for Communicable Diseases (NICD) on the 5th of March 2020. Since then, academic institutions at all levels have been grappling with the ways of delivering education in a safe mode that could prevent the spread of the pandemic. This prompted the need for academic institutions to adopt a fully ledged E-learning methodology in teaching and learning. Consequently, the aim of the study was to explore the readiness of the academics to adopt the E-learning methodology. Such readiness was explored through the application of the study objectives that were aimed at academic staff’s satisfaction level with the change, challenges that were experienced by the academic staff during the adoption process, the impact of Elearning on academics’ work and personal lives as well as the improvement of such methodology for the benefit of the institution. The study adopted a mixed method case study design of qualitative and quantitative research methods. Both quantitative and qualitative data were collected at the same time, analysed separately and the results merged in the summary and discussion of the study findings. The findings of the study revealed that the academic staff of UKZN were ready for the adoption of E-learning as the teaching and learning methodology. However, the study identified limitations in terms of infrastructural problems such as network service providers, as well as the ongoing blackouts limiting access to electricity. The findings of the study can have a significant impact on the strategic teams of the institution where there is a need of reinforcing control measures on the remote assessment of the students during the E-learning examinations. Furthermore, Management of the UKZN can use the results of this study as a springboard to motivating the academics and fine-tuning their approach towards the application of the technology based method of teaching and learning.
Agile user experience : integrating good user experience development practices into Agile within the South African context
(2020-09-07) Pillay, Narendren; Wing, Jeanette; Singh, Alveen
Agile software development has proliferated over the last two decades and become one of the dominant frameworks used by software development companies. Agile development methods and User Experience (UX) both strive towards providing software that meets the users’ needs. The purpose of this study is twofold; firstly, to study the current literature and approaches of integrating UX into Agile software development with the intention of combining it into good development practices for use by Agile and UX practitioners. Secondly, to investigate how UX is integrated into Agile in the South African context thus confirming if literature from studies conducted abroad apply within the South African context. A review of the literature confirms that there are a significant number of publications on Agile software development from a South African perspective (Joseph and Santana 2016; Chiyangwa and Mnkandla 2017; Sebega and Mnkandla 2017; Mudarikwa and Grace 2018). However, there are no publications that have explored UX integration within an Agile software environment. Recent research attempts, such as those by Coleman (2018) and Brosens (2018), are evidence of the growing interest in UX; however, these studies do not provide a higher level of abstraction on Agile UX integration practices. This research presents results of a qualitative study on how UX and Agile can be integrated. UX cannot be quantified or adequately explained by using variables or experiments. The grounded theory research method has been used. It is qualitative in nature and the theory consists of iterative data collection and analysis with an aim of producing a theory. The results of this study highlighted approaches for increasing user involvement in Agile and good development practices to integrate UX into Agile. It also highlighted issues and challenges experienced. This research offers insight for UX/Agile practitioners and adds academic value in the form of a generic framework for the integration of UX into Agile. The framework has been developed through the lens of the Design Thinking paradigm.
Assessing the impact of environmental cost on the capital investment decision-making of the Electricity Supply Commission, South Africa
(2020-09) Oyewo,Toyese Titus; Olarewaju, Odunayo Magret; Cloete, Melanie Bernice
The availability of energy (electricity) is a key factor in economic growth and the sustainability of production processes. The need to quantitatively measure the environmental risk and hazard associated with energy sources for the environment is useful in evaluating capital investment for decision-making. Coal (fossil fuel) is the main source of energy in South Africa, based on its availability and cost-effectiveness. Specifically, quantitative research using mathematical marginal social cost modelling to evaluate the environmental cost of emissions emanating from the Electricity Supply Commission’s (ESKOM) coal power stations is employed. It was discovered that the price of electricity has trebled over the lifespan of coal power plants. Therefore, the need to construct coal power plants with optimum levels of production was highlighted. The net present value (NPV) technique was used to evaluate ESKOM's capital investment and the marginal social cost mathematical model was developed for measuring and quantifying the emission costs associated with the lifespan of the coal power plants. Results revealed that the optimum level production of 2,150,000 Gigawatts per annum within the range of the present capacity of ESKOM of 2,292,000 gigawatts annually is required and profitable to ESKOM. The net present value yielded a positive value of R1, 448,713,000,000-00 over a period of 30 years of coal power plants’ life-span. However, various technologies used to minimize emissions were also considered and investigated to confirm the feasibility and profitability of investment in coal- powered stations using environmental management accounting and marginal social cost approaches.
Data mining and machine learning : a study of the CO2 emission trends in South Africa
(2024) Mohamed, Ghulam Masudh; Patel, Sulaiman Saleem; Naicker, Nalindren
This study addresses the pressing global issue of elevated carbon dioxide emissions (CO2E), with a particular focus on South Africa (SA), which ranks amongst the world's top emitters and largest in Africa. By introducing a novel integration of Change-point Analysis (CPA) and Machine Learning (ML) techniques, this research addresses significant gaps in CO2E trend analysis. Unlike previous studies, this research applies CPA methodologies within the distinct context of SA, employing algorithms like cumulative sum (CUSUM) and Bootstrap analysis to pinpoint crucial change-points in CO2E data specific to the country. The Bootstrap analysis determines the confidence levels associated with each detected change. Additionally, this study sought to validate historical trends and predict future patterns using ML models, with a specific focus on employing the AdaBoost ensemble learning technique. Drawing on insights from a Preferred Reporting Items for Systematic Reviews and MetaAnalyses (PRISMA)-based systematic review, the research selects input variables based on the factors identified as significant contributors to CO2E, ensuring the models capture the relevant variables effectively. The results of the systematic review highlight energy production and economic growth as key drivers of CO2E, thus validating their selection as input data for constructing the CPA and ML models. To conduct this study, secondary data was obtained from the World Bank's Open Data initiative data repository, a common source for environmental research. This selection was justified by a literature review, which highlighted the reliability and applicability of this data source. The CPA results reveal significant change-points in electricity generation, economic growth, and CO2E, with an average confidence level of 94%, indicating the accuracy of this analytical approach. Moreover, the CPA results emphasise the relationship between economic growth, electricity production, and CO2E in SA. Before forecasting future CO2E trends, the effectiveness of the AdaBoost regressor in enhancing model performance was benchmarked against traditional ML algorithms, including Linear regression, Polynomial regression, Bayesian Linear regression and K-Nearest Neighbors (KNN) regression, to determine the most effective technique for forecasting CO2E. The researcher evaluated model performance using key regression ML performance metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R2) score, and an additional accuracy score introduced by the researcher. Notably, the AdaBoost models demonstrated superior performance, with an average RMSE score of 10,143.17 kilotons (kt), MAE score of 9,642.64 kt, R2 of 0.90, and accuracy of 96.74%. The study also revealed that, on average, models that were trained using the AdaBoost algorithm surpassed traditional ML models, in terms of performance. They achieved a reduction in RMSE score by 6,417.29 kt, a decrease in MAE score by 4,358.09 kt, an increase in R2 score by 0.07 and enhanced accuracy by 0.60%. Additionally, a comparative analysis of the repeated holdout methods and cross-validation techniques was conducted, with results revealing that repeated holdout had a more significant impact on model performance. After excluding outliers, the average improvement in crossvalidation results, due to the repeated holdout method, was a decrease of 783.32 kt for RMSE, a reduction of 1,289.39 kt for MAE, and an increase of 0.88% for accuracy. The extent to which the repeated holdout method improved the performance of ML models that were integrated with cross-validation techniques, was correlated with the initial model performance. For ML models with RMSE and MAE scores equal to or exceeding 15,000 kt, the findings indicate that the repeated holdout methods studied should enhance performance by at least 2,000 kt. Similarly, an improvement of nearly 3% or higher in accuracy was noted, when the crossvalidation value for this metric was 94% or lower. The AdaBoost model, integrated with repeated holdout, was selected as the optimal model, as evidenced by the results, for forecasting CO2E in SA from 2021 to 2027. The forecasted CO2E trends validate that energy production and economic growth are indeed the primary drivers of CO2E in SA, as previously highlighted by the CPA model. This underscores the importance of addressing these factors to effectively mitigate carbon emissions in the country. Moreover, the forecasted results indicate that SA is unlikely to meet the global temperature limit of 1.5 degrees Celsius by 2030, given the trajectory showing a shortfall in achieving the target level of 334 million tonnes (Mt) of CO2E, agreed upon in the Paris Agreement. However, the country did meet its CO2E commitments outlined in the 2030 National Development Plan, showing some progress towards environmental sustainability. Nonetheless, the failure to meet these targets at their lower ranges suggests the need for further efforts to reduce carbon emissions, which is crucial for aligning with the Paris Agreement objectives and achieving a zero net emission rate by 2050. This highlights the importance of ongoing initiatives to enhance environmental policies and practices in SA. Future research should focus on integrating load-shedding dynamics into the analysis to examine and confirm its effects on energy production, economic growth, and CO2E in SA. Additionally, future research should focus on forecasting future change-points for the socio-economic indicators or variables utilised in this study. This can help policymakers anticipate fluctuations and devise proactive strategies, to address environmental and economic challenges effectively. It is also recommended that future research consider the output of renewable energy production, when analysing CO2E trends.
Developing a framework for business analysis of public eservice systems
(2020-12-02) Naicker, Shivani; Singh, Alveen
The emergence of the fourth industrial revolution (4IR) digital era is relentlessly morphing habits of social interaction and conducting business. Organizations within the multitude of sectors which constitute a nation’s economic engine are forced to respond to this evolution. Governments the world over are under constant pressure to improve the efficiency and overall effectiveness of the means by which services are delivered to citizens. Public eservice is an interactive internet based service provided by Government to their citizens. Some of these services include viewing and payment of utility bills, application for new services such as, water and electricity, renewal of motor vehicle licences, supplier registrations, submission of tenders, reporting of faults and viewing of buildings plans. As Government gears up to heed the call for growing service delivery demands against the backdrop of 4IR, there has been a marked accelerated effort in the implementation of several information and communication technology (ICT) based constituent service delivery systems. In crafting and optimizing such systems, business analysis is a crucial early stage. Literature portrays largely ineffective business analysis as a major contributing factor to the alarming high failure rate of modern day public eservices systems. Compounding the above is a lack of widely accepted practice guidelines and a scarcity of robust academic literature supporting business analysis in the public eservices domain. This dissertation is driven by the primary aim of the development of a business analysis framework specifically for public eservice projects. Following a critical analysis of literature, a set of components are distilled to form a theoretical framework of practice guidelines. The components derive from knowledge areas deemed critical for business analysis and present essential tasks, tools and techniques for Business Analysts plying their expertise in public eservices projects. The Design Science methodological approach further hones the framework after an iterative process of feedback and adjustment. A handful of Business Analysts are purposively selected for focus group participation and serve as change agents in the Design Science cycle. The Design Science cycle evolved the business analysis framework to an eventual seven components namely, Project Committee, Business Analysis Plan, Requirements Analysis, Business Collaboration, Requirements Changes, Solution and BA Review. The ADVIAN classification method provides an analytical tool for identifying the relationships between these components and the components that are vital for the effectiveness of the framework. The impact of change to one component on the other components is highlighted and this analysis confirms the robustness of the inclusion of components in the eventual framework. Further, the results of the ADVIAN analysis provides foresight into the impact of changes made to the framework when tailoring to a specific project. This will be of value to project teams wanting to utilize the framework across eservice projects. The use of ADVIAN shows the impacts of changes to the components of the framework when components are altered. It shows the impact of each component on the other. By understanding the current challenges faced by public eservices, it is hoped that the developed framework will offer a contribution to the gap in the business analysis domain with particular focus on the public eservice systems.
Development of a web based smart city infrastructure for refuse disposal management
(2017) Oluwatimilehin, Adeyemo Joke; Olugbara, Oludayo O.; Adetiba, Emmanuel
The future of modern cities largely depends on how well they can tackle intrinsic problems that confront them by embracing the next era of digital revolution. A vital element of such revolution is the creation of smart cities and associated technology infrastructures. Smart city is an emerging phenomenon that involves the deployment of information communication technology wares into public or private infrastructure to provide intelligent data gathering and analysis. Key areas that have been considered for smart city initiatives include monitoring of weather, energy consumption, environmental conditions, water usage and host of others. To align with the smart city revolution in the area of environmental cleanliness, this study involves the development of a web based smart city infrastructure for refuse disposal management using the design science research approach. The Jalali smart city reference architecture provided a template to develop the proposed architecture in this study. The proposed architecture contains four layers, which are signal sensing and processing, network, intelligent user application and Internet of Things (IoT) web application layers. A proof of concept prototype was designed and implemented based on the proposed architecture. The signal sensing and processing layer was implemented to produce a smart refuse bin, which is a bin that contains the Arduino microcontroller board, Wi-Fi transceiver, proximity sensor, gas sensor, temperature sensor and other relevant electronic components. The network layer provides interconnectivity among the layers via the internet. The intelligent user application layer was realized with non browser client application, statistical feature extraction and pattern classifiers. Whereas the IoT web application layer was realised with ThingSpeak, which is an online web application for IoT based projects. The sensors in the smart refuse bin, generates multivariate dataset that corresponds to the status of refuse in the bin. Training and testing features were extracted from the dataset using first order statistical feature extraction method. Afterward, Multilayer Perceptron Artificial Neural Network (MLP-ANN) and support vector machine were trained and compared experimentally. The MLP-ANN gave the overall best accuracy of 98.0%, and the least mean square error of 0.0036. The ThingSpeak web application connects seamlessly at all times via the internet to receive data from the smart refuse bin. Refuse disposal management agents can therefore query ThingSpeak for refuse status data via the non browser client application. The client application, then uses the trained MLP-ANN to appositely classify such data in order to determine the status of the bin.
A financial simulation for investment appraisal in solar panels at fast-food chains : a case study of McDonalds, South Africa
(2022-04-10) Abbana, Sharanam Sharma; Marimuthu, Ferina; Maama, Haruna
The sun is a significant source of inexhaustible free energy with the least adverse impact on the atmosphere. In order to overcome the adverse environmental effects and other issues connected with fossil fuels combustion, many nations have been compelled to investigate and develop environmentally-friendly options that are renewable in order to keep up with the growing demand for energy. This study was motivated by South Africa’s current electrical energy crisis and frequent load-shedding situations. Despite a global push towards renewable energy, South Africa presently relies on coal-fired power plants for more than 90% of its electrical energy. Currently, above-inflationary electrical energy tariffs are expected to increase. One of the renewable energy sources available is solar photovoltaic (PV) energy. The aim of this study was to financially simulate and appraise solar energy investment for McDonalds, an intensive fast-food restaurant energy consumer, to assess the feasibility of the investment. This study was quantitative in nature that simulated a census of 125 McDonalds DriveThru restaurants across South Africa. The data was derived from public domains such as a solar PV watts calculator from National Renewable Energy Laboratory (NREL) and solar system online commercial quotes from Treetops which is a solar system South African based installation company. Thereafter, the data was inputted in the study’s investment appraisement. The findings of the financial simulated investment appraisal prove to be lucrative for McDonalds South Africa to undertake the investment in solar energy. The investment is rewarding in the longer-term compared to the shorter-term considering the initial outlay. The simulation process and the investment appraisal in this study contributes to the knowledge base of the South African fast-food sector and can be adapted and used by businesses to evaluate the feasibility of a solar energy investment.
A framework to lower maternal mortality and morbidity rates in Kenya using mobile technology
(2019-11-15) Mukami, Victoria; Millham, Richard; Puckree, Threethambaal
Background. Maternal health represents an area of significant concern in the world. With various innovations in healthcare, maternal mortality rates are decreasing exponentially. However, this is not the situation in developing countries, specifically Kenya. Several causes of maternal mortality exist; however, it is noted that one of the key causes is due to a lack of information by pregnant women. Traditional strategies such as free maternal health care at public hospitals have been in place to improve overall pregnancy outcomes. While this is aimed at a reduction in maternal mortalities, it has not been as effective in Kenya. Non-conventional strategies are needed to improve maternal health outcomes and reduce maternal mortality. Information Communication Technology (ICT) is one of the areas that has been proven successful in reducing maternal mortality. Aim. The aim of the research was to create an ICT framework that aided in educating pregnant women using an mHealth dissemination tool and thus reduce complications that led to mortalities within Kajiado North Constituency. Methods. The study utilized a sequential mixed-method design. Phase one consisted of a retrospective chart review and a cross-sectional survey on nurses and pregnant women. The first phase focused on understanding the maternal mortality rates within Kajiado North and to determine procedures pregnant women and nurses took during pregnancy. The retrospective chart review was conducted for a period of six months at two health facilities, the Ongata Health Centre (OHC) and Ngong Sub District hospital (NSD). The cross-sectional survey interrogated the mitigation strategies with a focus on information and communication technologies (ICT). Phase two was a prospective multi-location randomized clinical trial (RCT). A two-arm, two-site RCT was conducted using an intervention in the form of an ICT prototype with messages aimed at educating the pregnant participants. The trial was conducted at two public health facilities namely the Ongata Health Center and the Ngong Sub District. A total of 211 pregnant women were recruited from both locations after they had met the inclusion and exclusion criteria and after providing consent to participate in the study. During the RCT, an intervention was developed using the Design Science Research Methodology (DSRM) and was used to send messages to participants within the intervention arm. The DSRM approach allowed for two iterations to be created, with one iteration being tested during the pilot test and the other during the RCT. Pregnant participants within the intervention groups received messages on their mobile phones about well-being during pregnancy. Women in the control group continued to receive their established standard of care. Both groups completed a post-test survey at the end of the trial. Data were analysed using ANOVA with the probability set at p≤0.05%. The relationship between the number of antenatal visits and the place of delivery on the complication rate was shown using the correlation coefficient. Additionally, a multiple regression model was generated based on the antenatal visits, place of delivery and the study arms and their impact on the complications. Results. Data from phase one of the study showed a need for a messaging system to send messages to pregnant women. The retrospective data showed no maternal mortalities, however, the nurse survey highlighted possible explanations for the lack of mortalities. From the RCT, there were no known maternal mortalities. There were three neonatal mortalities (p=0.154), one from the OHC intervention group and two from the OHC control group. The ANC visits relationship towards the complication rate was calculated. At the NSD site, the effect size of the ANC visits based on the participants' study arm toward the complication rate was low (0.027) and statistically insignificant (p=0.15). At the OHC site, the effect size was moderate (0.405) and statistically significant (p=0.003) for the ANC visits variable. The place of delivery relationship towards the complication rate was calculated. At the NSD site, the effect size of the place of delivery based on the participants' study arm toward the complication rate was moderate (0.366) but statistically insignificant (p=0.479). At the OHC site, the effect size of the variables was low (0.237) and statistically insignificant (p=0.789). The stepwise regression model at the OHC site showed significance when ANC visits (p=0.007), place of delivery (p=0.003) and participants study arm (p=0.008) were sequentially entered. The multiple variables (R=0.516) Only had a medium effect size (0.266) toward the complication rate. The stepwise regression model at the NSD site was statistically insignificant when the place of delivery (p=0.283), participants study arm (p=0.445) and ANC visits (p=0.655) were sequentially entered. The multiple variables (R=0.217) had a small effect size (0.047) toward the complication rate. Conclusion: Qualitative findings revealed that maternal health was affected adversely by several lengthy health worker strikes. Negligence on part of the health worker was a lead contributor to neonatal deaths. The study also found that accountability systems for referrals were lacking within the county and measures needed to be put in place to mitigate the consequences. In addition, feedback from the study participants indicated that the messages had aided in helping them to take necessary action based on complications and warning symptoms. Based on the data, the study finally proposed a framework that would allow for a reduction of maternal and neonatal mortality rates using ICT technologies. The study equally contributed to knowledge when using technology to empower women on maternal health matters as well as areas of maternal health practice that need improvement
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, Timothy
A 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.
The implicit midpoint procedures for asymptotically nonexpansive mappings
(Hindawi Limited, 2020-06-06) Aibinu, M. O.; Thakur, Surendra C.; Moyo, S.
The concept of asymptotically nonexpansive mappings is an important generalization of the class of nonexpansive mappings. Implicit midpoint procedures are extremely fundamental for solving equations involving nonlinear operators. This paper studies the convergence analysis of the class of asymptotically nonexpansive mappings by the implicit midpoint iterative procedures. The necessary conditions for the convergence of the class of asymptotically nonexpansive mappings are established, by using a well-known iterative algorithm which plays important roles in the computation of fixed points of nonlinear mappings. A numerical example is presented to illustrate the convergence result. Under relaxed conditions on the parameters, some algorithms and strong convergence results were derived to obtain some results in the literature as corollaries.
Integration of satellite system and Stratospheric Communication Platforms (SCP) for weather observation
(2016) Sibiya, Sihle S.; Ilčev, Dimov Stojče; Kleynhans, Waldo
This doctoral research introduces an integration of satellite systems and new stratospheric platforms for weather observation, imaging and transfer of meteorological data to the ground infrastructures. Terrestrial configuration and satellite communication subsystems represent well-established technologies that have been involved in global satellite sensing and weather observation area for years. However, in recent times, a new alternative has emerged based on quasi-stationary aerial platforms located in the Stratosphere called High Altitude Platform (HAP) or Stratospheric Communication Platforms (SCP). The SCP systems seem to represent a dream come true for communication engineers since they preserve most of the advantages of both terrestrial and satellite communication systems. Today, SCP systems are able to help, in a more cost effective way, developments of space Earth sensing and weather observation and weather sensing and observation. This new system can provide a number of forms ranging from a low altitude tethered balloon to a high altitude (18 – 25 km) fuel-powered piloted aircraft, solar-powered unmanned airplanes and solar-powered airship.
Investigating energy harvesting technology to wirelessly change batteries of mobile devices
(2018) Ramsaroop, Neetu; Olugbara, Oludayo O.; Joubert, Esther D.
Mobile devices have recently become powerful computing tools for aiding daily tasks. However, their batteries discharge quickly, even if they are not being used mainly because of the heavy computation tasks required by the multimedia applications that run on them. The swift turnover time on the battery life span is challenging as frequent charging is required to keep the device functioning. This is a major bottleneck because of the current energy optimisation crisis, user inconvenience due to constant charging of a battery and erratic nature of the electricity supply in some areas. In the current research project, the primary aim was to explore the energy harvesting technology innovation of radio frequency to wirelessly recharge the batteries of mobile devices. This implied an alternative way of charging the batteries of mobile devices without the need for a physical charger to connect to an electrical outlet. Energy harvesting, which involves making use of free energy from the atmosphere is the most innovative energy efficient wireless charging technology because mobile devices are constantly transmitting radio signals. Radio signals are initially received from the atmosphere through an antenna. Thereafter, these signals are converted using a rectifier circuit, from alternating current into direct current which is then utilised to recharge the battery of a mobile device. This research study adopted a mathematical modelling and simulation research methods. The model involved building an RF energy harvesting prototype. This prototype model displayed the limitations to be considered. The LTSpice simulation software was used to test the feasibility of combining diodes, capacitors and antenna type based on the limitations of the prototype model. The result of this research project demonstrates the building of a radio frequency harvesting circuit that can store a minimum load of 5mV that is required to charge the battery of a mobile device. Moreover, it has explained an alternative storage of the acquired energy using a supercapacitor compared to a mobile device battery.
A longitudinal sentiment analysis of the #FeesMustFall campaign on Twitter
(2019-04-29) Khan, Yaseen; Thakur, Surendra
The #FeesMustFall campaign began in 2015 to lobby government to provide students with free university education in order to redress past imbalances. It rapidly progressed to become a widespread national phenomenon that attracted international attention and sympathetic support. However, certain unsavoury incidents marred the campaign and attempted to derail it from achieving its goals. The campaign did reach many of its targets with the South African government eventually announcing free education for the poor and working class in December 2017. #FeesMustFall has been well documented and researched, however, no literature offered a quantitative insight into the opinions of social media users during this campaign, although a unique feature of #FeesMustFall was leveraging social media platforms to coordinate the campaign. This study addresses this gap by undertaking a longitudinal sentiment analysis of textual conversations expressed on the Twitter social media platform. This longitudinal study analyses the Twitter #FeesMustFall campaign through the acquisition of 576 583 tweets posted between 15 October 2015 and 10 April 2017. These tweets were pre-processed and cleaned by removing exact duplicates and unintelligible data. The research method to analyse the “cleaned” #FeesMustFall data utilises, inter alia, descriptive statistics, sentiment analysis using a natural language programming (NLP) approach called Valence Aware Dictionary sEntiment Reasoner (VADER) and code written in Python. VADER is a lexicon rule-based sentiment analysis tool particularly suited to social media. To detect multiple changes in this large historical dataset, the Change Point Analysis method (CPA) is applied using a Cumulative Sum Analysis (CUSUM) method to identify changes across time. The research question is whether and for what reason the online sentiment changed during the observation period. The sentiment expressed is triangulated with perceived real-life negative events, such as the burning of the University of KwaZulu-Natal (UKZN) library and the University of Johannesburg (UJ) Hall, to understand whether online activism sentiment reflected or reacted to real-life events. The study finds that sentiment did change in relation to these two events, one on the day of the UKZN library event and one prior to the UJ Hall event. Social robots (bots) are automatic or semi-automatic computer programs that mimic human behaviour in online social networks. Their deployment exposes online activism to manipulation. A further research question addressed whether bots played a role in the #FeesMustFall campaign. A review of bots, their characteristics, behaviour, and detection methods was undertaken. The study does indeed establish the presence of bots during #FeesMustFall. The study’s contribution is significant as this is the first longitudinal study of the #FeesMustFall campaign which observes the sentiment distribution and changes. It is also the first study to investigate and find evidence of bots in the #FeesMustFall campaign.
Machine learning : a data-point approach to solving misclassifications in the imbalanced Credit Card Datasets
(2021-10-30) Mqadi, Nhlakanipho Michael; Naicker, N.; Adeliyi, Timothy Temitope
Machine learning (ML) uses algorithms with the complexity to iterate over massive datasets to analyse the data for past behaviour with the aim to predict future outcomes. Financial institutions are using ML to detect Credit Card Fraud (CCF) by learning the patterns that distinguish between legitimate and fraudulent actions from historic data of credit card transactions to combat CCF. The market economic order has been negatively affected by CCF, which has contributed to low consumer confidence in financial institutions, and loss of interest from investors. The CCF loses continue increasing every year despite existing efforts to prevent fraud, which amount to billions of dollars lost annually. ML techniques consume large volumes of historical credit card transaction data as examples for learning. In ordinary credit card datasets, there are far fewer fraudulent transactions than legitimate transactions. In dealing with the credit card data imbalance problem, the ideal solution must have low bias, low variance, and high accuracy. The aim of this study was to provide an in-depth experimental investigation of the effect of using the data-point approach to resolve the class misclassification problem in imbalanced credit card datasets. The study focused on finding a novel way to handle imbalanced data, to improve the performance of ML algorithms in identifying fraud or anomaly patterns in massive amounts of financial transaction records, where the class distribution was imbalanced. The experiment led to the introduction of two unique multi-level hybrid data-point approach solutions, namely, Feature Selection with Near Miss Undersampling; and Feature Selection with SMOTe based Oversampling. The results were obtained using four widely used ML algorithms, namely, Random Forest, Support Vector Machine, Decision Tree, and Logistic Regression to build the classifiers. These algorithms were implemented for classification of credit card datasets and the performance was assessed using selected performance metrics. The findings show that using the data-point approach improved the predictive accuracy of the ML fraud detection solution.
Optimization of hybrid renewable energy generation using a nature-inspired algorithm with advanced IoT analytics
(2022-11-01) Frimpong, Samuel Ofori; Millham, Richard; Agbehadji, Israel Edem
A stable and cost-effective power supply in an autonomous hybrid energy system requires an efficient design process for renewable energy technologies. Accordingly, the best design of a standalone hybrid renewable energy system (HRES) should consider several factors such as renewable energy data, load profile, technical and economic analysis of the renewable technologies, ideal location for the power system, etc. Different data from renewable energy sources are modelled into an optimization problem which incorporates the crucial point, in HRES, of the correct sizing of the various power components, which directly affect the cost and power security/reliability of the system. This thesis proposes an innovative meta-heuristic optimization algorithm called Social Spider-Prey (SSP) that mimics the foraging behaviour of social spiders and prey(s) on the social web. By examining the foraging behavioural traits of social spiders and prey(s), a global optimization algorithm was developed to solve a hybrid renewable energy optimization problem of correct sizing, minimal cost, and highest reliability. In SSP, artificial spiders are considered search agents. On the one hand, every spider can freely roam the social web, a hyperdimensional search space, to implement an exploratory search scheme. On the other hand, nearby spiders relative to a captured prey search the neighbourhood, which is implemented as an exploitative search mechanism. These two search strategies are harmonized in SSP to solve the multi-source renewable power generation optimization problem effectively. Four different power generation scenarios were analysed to determine optimal power generation using experimental real-time environment data collected with sensors and secondary data retrieved from a benchmark dataset, National Renewable Energy Laboratory (NREL). The optimization algorithms inspired by nature, namely Social Spider-Prey (SSP), Particle Swarm Optimization (PSO), Teaching-Learning Based Optimization (TLBO) algorithm and Social Spider Algorithm (SSA), were used in a comparative study to search for a near-optimal result for the hybrid system configuration that satisfies the optimization problem. The results show the economic and reliable implications of different system configurations that meet the specified combined criteria, as indicated in the HRES optimization problem, to make the best investment decision. The SSP guaranteed optimal annualized system costs and met the reliability constraints for all the case scenarios: wind/biomass/battery (ZAR 3,431,512.26 and LPSP of 0.011), PV/wind/ biomass (ZAR 2,549,792.71 and LPSP of 0, 0011), PV/biomass/battery (ZAR1, 638,628.82 and LPSP of 0.00021) and PV/wind/biomass/battery (ZAR1, 412,142.80 and LPSP of 0.0141). Based on this result, the study proposes the SSP as an optimization approach for the solar PV/wind/biomass/battery hybrid system, as it ensures 99.98% power reliability. In addition, a Kruskal-Wallis test was performed to determine the significant differences among the comparison algorithms.
Radio and satellite tracking and detecting systems for maritime applications
(2015-01-15) Skoryk, Ivan; Olugbara, Oludayo O.; Ilčev, Dimov Stojče
The work described in this thesis summarizes the author’s contributions to the design, development and testing of embedded solutions for maritime Radio and Satellite tracking and detecting systems. In order to provide reliable tracking and detecting facilities of ships have to be integrated Convectional Maritime Radio Communications (CMRC) and Maritime Mobile Satellite Communications (MMSC) systems. On the other hand, Global Mobile Satellite Communications (GMSC) as a part of Global Communication Satellite Systems (GCSS) has to be integrated with Global Navigation Satellite Systems (GNSS) of the US GPS or Russian GLONASS systems. The proposed local maritime Radio VHF Communication, Navigation and Surveillance (CNS) systems and devices, such as Radio Automatic Identification System (R-AIS) or VHF Data Link (VDL), Radio Automatic Dependent Surveillance - Broadcast (RADS-B) and GNSS Augmentation VDL-Broadcast (GAVDL-B) are introduced. The new technology deigns of global Satellite CNS maritime equipment and systems, such as Global Ship Tracking (GST) as enhanced Long Range Identification and Tracking (LRIT), Satellite AIS (S-AIS), Satellite Data Link (SDL), Satellite Automatic Dependent Surveillance - Broadcast (SADS-B) and GNSS Augmentation SDL (GASDL) are discussed and benefits of these new technologies and solution for improved Ship Traffic Control (STC) and Management (STM) are explored. The regional maritime CNS solutions via Stratospheric Communication Platforms (SCP), tracking of ships at sea via Space Synthetic Aperture Radar (SSAR) or Inverse Synthetic Aperture Radar (ISAR)and Ground Synthetic Aperture Radar (GSAR) are described. The special tracking systems for collision avoidance with enhanced safety and security at sea including solutions of captured ships by pirates through aids of the MMSC, SCP and Radars are introduced and the testing methodologies employed to qualify embedded hardware for this environment are presented. During the voyage of the ship in good weather conditions and when navigation devices on the bridge are in order, then can be used very well AIS, LRIT, anti-collision Radar and other on-board equipment. However, at very bad weather conditions sometimes surveillance Radar and Radio HF Transceiver cannot work, but may work only GPS Receiver and L/C-band Satellite Transceiver, while Radio VHF Transceiver will have extremely reduced coverage, what is not enough for safe navigation and collision avoidance. Therefore, during those critical circumstances, when the safety of navigations very important, it will be not necessary to ask "Where am I", but "Where are nearby ships around me"? At this point, it should be needed the newest techniques and equipment for enhanced STC and STM, such as GST, S-AIS, SDL, SADS-B and GASDL. Terrorists exploit surprise in successful pirate actions worldwide and security forces are generally unaware of the source of these attacks at sea. In today’s information age, terror threats may originate with transnational organizations or exploit the territory of failed, weak or neutral states. Thus, countering piracy by eliminating the terrorists on land is the best solution, however, it might not be feasible and even though it’s successful could require many years. In the thesis, the general overview of Radio and Mobile Satellite Systems (MSS) for ship communication and tracking systems is conducted as well, including the space platform and orbital mechanics, horizon and geographic satellite coordinates and classification of spacecraft by Geostationary Earth Orbits (GEO) and Non-GEO orbits.