https://ir.cut.ac.zw/xmlui/handle/123456789/46 2026-07-16T23:21:11Z 2026-07-16T23:21:11Z Mafuhure, Tirivashe Kabanda, Gabriel Tsvere, Maria https://ir.cut.ac.zw/xmlui/handle/123456789/772 2026-05-28T08:34:33Z 2021-12-12T00:00:00Z

The Effect of Massification on Student Experiences: A Case of Higher and Tertiary Students Studying Introduction to Computer Programming Course in A Large Class Set-Up Mafuhure, Tirivashe; Kabanda, Gabriel; Tsvere, Maria This paper explored current experiences of computer students learning an introductory computer programming course in a large class set-up. The study utilized the descriptive survey design and data was collected by making use of a structured questionnaire which was distributed to students in the school in introduction to C programming language at Chinhoyi university of technology. Data that was gathered in this study was quantitative in nature and SPPS version 20 was used to analyze it. Results obtained in the study show that large class environments are not suitable for complex and hands on modules such as computer programming. The majority of student find it complicated to grasp concepts in computer programming and as a result fail the module or memorize content instead of engaging into critical thinking. Since enrolling students in large numbers to fund operations in Zimbabwean universities is the order of the day, this study recommended the development and use of teaching tools that will assist students develop skills such as Self-regulated learning strategies (SRL) to navigate through challenges of large numbers. Despite the challenges that computer programming students face SRL strategies will enable them to be critical thinkers and problem solvers.

2021-12-12T00:00:00Z Charamba, Anesu Nicholas Kumba, Hagreaves Makepa, Denzel Christopher https://ir.cut.ac.zw/xmlui/handle/123456789/696 2026-03-06T10:19:06Z 2024-11-10T00:00:00Z

Assessing the opportunities and obstacles of Africa’s shift from fossil fuels to renewable sources in the southern region Charamba, Anesu Nicholas; Kumba, Hagreaves; Makepa, Denzel Christopher This study presents a comprehensive analysis of the current energy landscape and the imperative transition toward renewable energy. It begins with an overview of current energy sources and trends, highlighting the disparity between supply and increasing demand. Adverse impacts of reliance on fossil fuels such as environmental degradation, economic volatility, and health hazards underscore the urgent need for a transition. The study then explores the vast potential of renewable energy sources (RES) such as solar, wind, hydrogen, and hydro, emphasizing their feasibility in the Southern African context. The positive impacts of integrating renewables are examined, including reduced greenhouse gas emissions, enhanced energy security, and economic diversification. Through case studies of regional examples, the success and failures of transitioning efforts are analyzed, providing valuable insights into best practices and pitfalls. The study identifies significant challenges in transitioning, particularly in grid-tied and off-grid scenarios, and discusses infrastructural, financial, and regulatory obstacles. The recommendations section outlines strategic steps for achieving a feasible transition, proposing either a full transition or specific percentages of renewable energy integration to meet energy demands. In conclusion, the study emphasizes the critical importance of adopting these strategies for sustainable development and global climate goals, advocating for continuous innovation and localized solutions to maximize the benefits of renewable energy. Key findings are that the environmental and economic effects of fossil fuel usage strain economies by increasing fossil fuel subsidies. RES are abundant in the Southern African region, and some projects have already been successfully implemented, especially in South Africa. Economic growth and technological advancement are some of the benefits of fully transitioning to renewables, but lack of skilled labor, infrastructure, necessary technology, and most importantly, high capital requirements, etc., are some challenges being faced. Hence, the need for regional cooperation, policy frameworks, and infrastructure enhancement, and investment mobilization for an accelerated transition.

2024-11-10T00:00:00Z Mayeni, Remalia Dube, Sibusisiwe Ndlovu, Belinda Maduva, Martin Kiwa, Fungai Jacqueline https://ir.cut.ac.zw/xmlui/handle/123456789/641 2025-07-24T07:50:41Z 2024-07-01T00:00:00Z

A Novel Ensemble-based Machine Learning Model for Anomaly Detection in CDRs to Identify International Revenue Share Fraud Mayeni, Remalia; Dube, Sibusisiwe; Ndlovu, Belinda; Maduva, Martin; Kiwa, Fungai Jacqueline Mobile network operators in developing countries often rely on traditional fraud detection systems, overlooking the potential of advanced machine learning techniques. This study addresses this gap by developing an International Revenue Share Fraud (IRSF) detection model using ensemble learning with random forest and support vector machine algorithms. The model analyzes Call Detail Records (CDRs) to identify fraudulent call patterns. CDRs contain call attributes like time, duration, source and destination numbers and completion status, providing valuable data for anomaly detection. Random Forest is chosen for its effectiveness in handling complex and imbalanced datasets, common in telecom fraud scenarios. Its ability to address imbalanced data is crucial, as fraudulent calls are typically rare compared to legitimate ones. This research aims to develop a machine learning model that leverages call logs to detect fraudulent international account takeover. Our results advance descriptive analysis and improve knowledge of the traits and patterns of IRSFs. In the end, this produces a picture of IRSF operations that is more accurate. The model demonstrates good predictive performance on the testing set with a Mean Absolute Error (MAE) of 1.1208, indicating a low average absolute difference between predicted and actual values and the R-squared value of 0.9828 signifying strong overall predictive accuracy

2024-07-01T00:00:00Z Murere, Isaac Ndlovu, Belinda Dube, Sibusisiwe Muduva, Martin Kiwa, Fungai Jacqueline https://ir.cut.ac.zw/xmlui/handle/123456789/640 2025-07-24T07:49:42Z 2024-07-01T00:00:00Z

Comparative Analysis of Machine Learning Techniques for Predicting Diabetes Murere, Isaac; Ndlovu, Belinda; Dube, Sibusisiwe; Muduva, Martin; Kiwa, Fungai Jacqueline Diabetes, a chronic illness causing serious health problems, affects millions of people globally. With cases expected to rise, effective strategies for managing, detecting, and preventing the disease are essential. Artificial intelligence (AI) and machine learning (ML) have become powerful allies in this fight. These advancements aid in the automated detection of eye complications (retinopathy), supporting clinical decisions, identifying high-risk populations, and empowering patients to manage their health. The significant public health challenge of diabetes in Zimbabwe, impacting all demographics, highlights the need for better solutions. This research aims to develop a precise predictive model for diabetes using the CRISP-DM methodology. Machine learning techniques like random forest, Naive Bayes, XGBoost, decision trees, and support vector machines, were used to predict the presence of diabetes. The results revealed that the random forest approach outperformed other models, demonstrating a larger area under the curve (AUC).

2024-07-01T00:00:00Z