Computer Science Publications

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Computer Science Publications

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    An overview of cybersecurity in Zimbabwe’s financial services sector [version 1; peer review: 2 approved with reservations]
    (F1000 Research, 2023-09-29) Maphosa, V.
    Background: As nations, businesses, and individuals rely on the Internet for everyday use, so are cybercriminals manipulating systems to access information illegally and disrupting services for financial gain. The global cost of cybercrime eclipsed one trillion US Dollars in 2020, with Africa losing US $3.5 billion. Methods: A quantitative research methodology was adopted to investigate factors affecting cybercrime in Zimbabwean financial institutions. The study focused on the technical aspects of cybersecurity. Data were collected from July 2022 to October 2022, targeting technology experts in the financial services sector. Participants were recruited from 13 institutions to rank cybersecurity constructs, frameworks, and challenges associated with cybersecurity. Data was collected using a questionnaire distributed to participants. Descriptive statistics were used to extract meanings from the responses that measure mean and standard deviation. Results: Network and data security were the most highly ranked cybersecurity constructs, while physical security was the least. The top three barriers are increasing sophistication of threats, limited skills and emerging technologies, while lack of executive support was the least. The top frameworks used are the Information Technology Infrastructure Library (ITIL) and Control Objectives for Information and Related Technologies (COBIT), while a fifth is yet to adopt cybercrime frameworks. Conclusions: The study proposes that financial institutions establish a cybersecurity culture to fight cybercrime, addressing cybersecurity barriers and following best practices. Financial institutions should invest in cybersecurity technologies, train security specialists, and employ a Chief Information Security Officer (CISO). The study’s small sample may affect the generalisability of the results. Financial institutions should implement strategies to raise awareness and collaborate with institutions to train cybersecurity security specialists to close the skills gap.
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    AI-based Drought Forecasting for Parametric Insurance
    (IEOM Society International, USA, 2024-05-07) Mathende, M.T.; Ndlovu, B.; Dube, S.; Muduva, M.; Kiwa, F.J.
    In drought-prone African countries like Zimbabwe, the uptake of parametric insurance has been low due to the absence of localized models. Guided by the CRISP-DM model, the present study proposes an AI-based approach to drought prediction in parametric insurance. The study’s paramount objectives are establishing trigger thresholds for drought events, assessing their significance, identifying the most effective machine learning models for drought modeling based on the Standardized Precipitation Index (SPI), and forecasting future drought occurrences and their magnitudes. Historical weather data, including temperature and rainfall, are utilized and a range of machine learning modelsneural networks, random forest, and support vector machines are employed for drought prediction. The performance of these models is evaluated based on accuracy, reliability, and interpretability, with continuous refinement based on feedback from stakeholders. The significance of this research lies in promoting data-driven decisions, incentivizing preparedness, enabling risk transfer, facilitating rapid insurance payouts, and enhancing financial stability. With accurate drought predictions driving parametric insurance, policyholders can make well-informed choices, adopt proactive measures, transfer the risk of drought-related losses, receive swift insurance payouts, and improve their financial resilience during drought events.
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    A Blockchain-based Patient Portal for Mental Health Management
    (IEOM Society International, USA, 2024-04-23) Jhamba, P.; Ndlovu, B.; Dube, S.; Muduva, M.; Jacqueline, F.; Maguraushe, K.
    Mental health is an important aspect of well-being as it encompasses emotional, psychological and social well-being. The use of patient portals in mental health care has gained attention as a potential tool to improve access to care for individuals with mental illness. Patient portals may be vulnerable to unauthorized access if appropriate security measures are not put in place. This study leverages blockchain technology to create tamper-proof patient records. The proposed solution uses an on-chain database that stores hashes and the actual medical record of a patient as well as an off-chain solution that handles encryption of each user’s medical record using their respective keys in a trustless manner before they are uploaded on-chain. A secure smart contract hosted on Ethereum and the Byzantine Fault Tolerance consensus algorithm was used to ensure patient privacy. The research employed the Comparative Analysis Research Methodology as the research methodology and the Kanban methodology as the software development methodology. The research project concludes that the proposed solution addresses the current security issues and data privacy concerns in patient data. The decentralized nature of blockchain ensures security, transparency, and tamper-proof storage of information. Further research is needed for future advancements, like integrating blockchain-based patient portals with wearable devices and IoT.
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    An ontology-based framework for mobile learning in rural secondary schools
    (2015) Ngwenya, S; Mangena, S.B.; Trimble, J.; Hlatywayo, D.; Chilumani, K.R.
    In some countries mobile learning is becoming an important issue in academic institutions, as teachers and students get connected to networks through smart phones that combine telephony, computing, messaging and multimedia. However, in rural areas the process of designing, communicating and presenting learning resources, content services and learning content for mobile learners poses challenges. Teachers and students are not able to connect to networks for the purposes of learning and teaching. Therefore an enabler framework for this purpose becomes necessary. Those who connect to the Internet are not able to get precise and relevant content that meets their requirements and needs. This is due to poor internet connectivity, lack of semantics on content, inaccurate searches and information overload. This paper proposes a solution to some of the challenges by designing a conceptual ontology-based framework for mobile learning which could be used in rural secondary schools. The framework takes into account the following: a knowledge base, ontology, software agents, learning resources and learning/teaching content. Agents search for learning objects and extract knowledge according to learner and teacher/instructor profiles. The proposed framework would facilitate collaboration, sharing of ideas, instruction flow and access to learning and teaching content with accuracy, anytime from anywhere.
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    Climate Variability Forecasting Using Bat Algorithm Optimised Artificial Neural Network.
    (Zimbabwe Journal of Science & Technology, 2015) Kokera, N.; Chilumani, Khesani, R.; Mzelikahle, Kenman
    This paper presents a summary and results of a study that was conducted in an attempt to forecast climate variability in Zimbabwe using the BAT Algorithm optimised Artificial Neural Network (BAT-ANN) analysis technique. Forecasts of climate ahead of time can potentially allow governments, farmers and other players in private and/or public sectors to make decisions to reduce unwanted impacts or take advantage of expected favourable climate. However, potential benefits of climate forecasts vary considerably because of many physical, biological, economic, social, and political factors. In a developing country, like Zimbabwe where agriculture is the base of the national economy, climate conditions play leading role for progressive and sustainable development, therefore climate variability forecasts are very important. The BAT-ANN was adapted and tested using the Zimbabwean meteorological dataset and results confirm that our proposed model has the potential for reliable climate forecasting for a 25 year period. The mean percentage accuracy was used to evaluate the performance of the trained climate forecasting neural network and proved sufficient. Therefore, in this paper, we present a new technique to climate variability assessment namely; the BAT-ANN. In this study, the approach employed to achieve objectives was; collecting quantitative data, adapting a BAT-ANN for analysis, and developing a Java program that employs the BAT-ANN for forecasting. The objectives of the study were met.