Browsing by Author "Sibanda, K."
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Item A framework for evaluating the reliability of health monitoring technologies that are based on ambient intelligence(University of Fort Hare, 2024-12) Scott, Mfundo Shakes; Jere, N.R.; Sibanda, K.The advancement of health monitoring technologies rooted in Ambient Intelligence (AmI) has created innovative solutions for improving healthcare delivery and supporting independent living. However, a critical challenge persists because of the absence of a standardised, integrated framework to evaluate the reliability of these systems. This gap is particularly significant in resource-constrained environments, where operational uncertainties and infrastructure limitations exacerbate reliability issues. To address this problem, this study proposes a comprehensive framework for evaluating the reliability of AmI-based health monitoring technologies. The framework encompasses key dimensions, including data accuracy, system robustness, reliability and context or environment considerations. It was developed using a combination of simulation-based modelling, reliability block diagrams, and Monte Carlo Markov Chain (MCMC) techniques to systematically quantify and analyse reliability metrics. These methods were applied to case studies such as continuous glucose monitoring and heart rate monitoring in elderly care to validate the framework's practical relevance. Results from these applications demonstrate the framework's efficacy in identifying and addressing reliability challenges. The findings highlight its ability to facilitate systematic evaluation, enhance fault tolerance, and ensure adaptability across diverse operational contexts. The presented framework contributes significantly to various stakeholders as it offers researchers a structured methodology for reliability assessment, provides healthcare practitioners with tools to ensure dependable patient monitoring, and guides policymakers in the adoption of reliable health technologies. By bridging the gap between technological innovation and practical application, this research advances the field of AmI-based health monitoring, fostering improved healthcare outcomes and greater trust in intelligent systems. Software developers, researchers, Health Monitoring devices manufacturers, Internet of Things (IOT) experts and patients on chronic medication who require regular monitoring, and the health sector are the targeted beneficiaries of the framework. Using two case studies which are Continuous Glucose Monitoring and Heart Rate Monitoring in elderly care the main limitations was the effectiveness of reliability in addressing these issues. This lays the groundwork for further refinement and application of the framework in broader contexts. Thus, future research ought to focus on addressing operational challenges, improving explainability in AI models, and discovering innovative applications of AI in healthcare monitoring systems.Item A Kmeans-LSTM hybrid model for optimising spectrum sensing in wireless networks(University of Fort Hare, 2024-03) Tamuka, Nyashadzashe Everson; Sibanda, K.Due to the increasing demand for the wireless spectrum, cognitive radio (CR) technology has gained attention. Spectrum sensing, an essential task of CR, involves detecting vacant frequency bands for unlicensed users when licensed users are not fully utilising them. Traditional techniques like energy detection and matched-filter detection have limitations, including poor performance at low signal-to-noise ratios (SNR) and the need for prior knowledge of licensed user signal characteristics. Furthermore, most machine learning (ML) and hybrid techniques rely on simulated datasets, lacking real-world validation. This research proposed a novel hybrid Kmeans-LSTM model, robust at low SNR and independent of prior licensed user information. The model was validated with real-world spectrum datasets collected using an RTL-SDR dongle, unlike most of previous studies that depend on simulated datasets. Guided by design-science research methodology, the study involved iterative training, testing, and evaluation. The spectrum data was collected and labelled using K-means, followed by training the LSTM algorithm. Performance metrics, including accuracy, detection probability, false-alarm probability, precisionrecall curves, and ROC curves, showed that the Kmeans-LSTM model outperformed other models such as Support Vector Machine, Artificial Neural Network, and Random Forest. The real-world dataset provided a realistic assessment of the model’s performance, confirming its robustness and potential for optimizing spectrum sensing in wireless networks. This study demonstrated that the Kmeans-LSTM model can optimise spectrum sensing at low Signal-Noise-Ratio conditions without simulated and labelled spectrum datasets.Item Database Management and Design: CSC 224, Supplementary Examinations January 2019(University of Fort Hare, 2019-01) Gurajena, C.; Dyakalashe, S.; Sibanda, K.Item Human Computer Interaction: CSC 522, Supplementary Examinations January 2019(University of Fort Hare, 2019-01) Sibanda, K.; Mabanza, N.Item Mathematical Economics 2B: ECO 222/E, Degree Examinations November 2017(University of Fort Hare, 2017-11) Sibanda, K.; Khumalo, S.; Ngonyama, N.Item Mathematical Economics 2B: ECO 222/E, Degree Examinations November 2019(University of Fort Hare, 2019-11) Jiza, A.; Mgxekwa, B.; Sibanda, K.Item Monetary Economics: ECO 516/E, Degree Examinations November 2018(University of Fort Hare, 2018-11) Ngonisa, P.; Sibanda, K.; Maredza, A.Item Monetary Economics: ECO 516/E, Supplementary Examinations January 2019(University of Fort Hare, 2019-01) Ngonisa, P.; Sibanda, K.; Maredza, A.