http://localhost:8080/xmlui/handle/123456789/4546 2026-05-07T23:46:06Z 2026-05-07T23:46:06Z Joe, Munashe P. http://localhost:8080/xmlui/handle/123456789/4626 2025-11-10T13:26:08Z 2025-01-01T00:00:00Z

Title: Exploring the Application of Internet of Things (IoT) in the Private Healthcare Sector: A Case of PSI Authors: Joe, Munashe P. Abstract: This study investigates the adoption of Internet of Things (IoT) technologies in the private healthcare sector, focusing on clinics, hospitals, and private practices. It aims to enhance patient outcomes and improve organizational efficiency through IoT integration. Utilizing literature analysis, case studies, and surveys of IT and healthcare professionals, the research identifies benefits, challenges, and effective IoT implementations. The literature review encompasses empirical findings from the past four years, while case studies provide insights into current IoT applications in private facilities. Data collected via self constructed questionnaires will be analyzed using statistical and qualitative methods to reveal patterns and trends. This research contributes to understanding the barriers to IoT adoption, showcases best practices, and proposes strategies to maximize its advantages in private healthcare. The findings will inform governments, health technology vendors, and healthcare facilities in making evidence-based decisions regarding IoT technologies, ultimately enhancing service quality and operational efficiency in the sector.

2025-01-01T00:00:00Z Chama, Joshua http://localhost:8080/xmlui/handle/123456789/4625 2025-11-10T13:08:49Z 2025-01-01T00:00:00Z

Title: The Impact of Progressive Web Apps (PWAs) on User Experience and Performance: A Case Study of Family Legacy Mission Zambia (FLMZ), in Lusaka, Zambia. Authors: Chama, Joshua Abstract: In the past few years, there is a new trend has emerged in the IT landscape, the adoption of progressive web applications (PWAs), Most companies are shifting away from native applications to PWAs due to the better user experience benefits they offer. PWAs closes the gap between traditional websites and mobile apps, they provide enhanced and inclusive user experience, offline access, interactivity, and engagement. PWAs are web applications that offer a seamless, app-like experience to users. They combine the best of both web pages and mobile application. The features that PWAs provide offline access, faster load times, and cross device compatibility. Unlike native apps, which require installation from app stores, PWAs are accessed directly through web browsers, eliminating the need for installation and enabling instant access. However, PWAs do have limitations, including restricted access to certain device features and potential performance variations across different platforms. PWAs have changed web development by making web apps work more like mobile apps. They enhance user experiences, provide offline functionality, improve performance, and offer cost-effectiveness

2025-01-01T00:00:00Z MakonI, Anesu S. http://localhost:8080/xmlui/handle/123456789/4624 2025-11-10T13:02:38Z 2024-01-01T00:00:00Z

Title: Design and Implementation of a Customer Churn Prediction Model for Zimbabwean Banks Using Machine Learning Techniques Authors: MakonI, Anesu S. Abstract: The loss of customers in the banking industry due to account closures or the termination of banking services is known as customer churn. This could have serious repercussions for banks, such as decreased revenue, increased expenses to draw in new customers, and harm to their reputation. This study explores the problem of customer churn in Zimbabwean banks and looks into the efficacy of traditional machine learning algorithms in order to forecast and analyze customer churn. The study uses a dataset of customer data that includes demographic and banking-related features to train and assess a number of models, including logistic regression, decision trees, random for ests, and XGBoost. The results show that every model was able to accurately forecast customer attrition. The study illustrates the value of anticipating customer attrition in the banking industry and shows how well machine learning algorithms perform in this regard. The study does point out certain limitations, though, such as the dataset's limited feature set and small sample size, which could limit how broadly the results can be applied. The study's conclusion addresses how the results might be applied to banks in Zimbabwe and offers possible avenues for further investigation.

2024-01-01T00:00:00Z Ngwarai, Trish http://localhost:8080/xmlui/handle/123456789/4623 2025-11-07T13:06:19Z 2025-01-01T00:00:00Z

Title: AI and Education: Personalised Learning and Adaptive Systems. Authors: Ngwarai, Trish Abstract: This dissertation investigates the transformative role of Artificial Intelligence (AI) in enhancing educational experiences through personalised learning and adaptive systems. In an era where AI reshapes learning environments globally, there is a notable research gap in understanding its practical impacts within African academic settings. This study employs a mixed-methods approach—integrating quantitative surveys of 40 students and qualitative interviews with educators and students experienced in AI-driven education— to explore the effectiveness of AI tools in tailoring content, providing adaptive feedback, and improving student outcomes. Key findings indicate that AI technologies significantly contribute to personalised learning: 55% of respondents reported substantial improvements in understanding complex subjects, while adaptive feedback and learning recommendations were identified as the most valuable features by 65% and 53% of participants, respectively. Despite challenges such as contextual accuracy and ethical concerns, the research recommends targeted initiatives including AI literacy programs for educators and the development of institutional frameworks to support AI integration. This work not only enriches the dialogue on technology-enhanced learning in African higher education but also offers actionable strategies to optimise AI adoption for improved teaching and learning outcomes.

2025-01-01T00:00:00Z