http://localhost/xmlui/handle/123456789/1281 COPAS 2026-08-26T21:07:26Z http://localhost/xmlui/handle/123456789/7105 Adoption of Shallow Neural Networks in Pneumonia Classification Chacha, Josephine Mweyeli In low-resource healthcare environments, limited access to radiologists and a high computational burden of conventional deep learning models are significant challenges for pneumonia diagnosis. The current Convolutional Neural Networks (CNNs) for chest X ray classification demand high memory, computation and specific hardware, which is not feasible in under-resourced hospitals and clinics. In this work, the authors explored the possibility of a lightweight shallow CNN producing reliable pneumonia classification without being too computationally expensive. The architecture proposed was comprised of three convolutional layers to reduce the amount of computational and memory resources without compromising the diagnostic performance. The model was tested on a set of common experimental settings, with the other popular lightweight and deeper CNN models. To compare the performances fairly, all the models were optimized in the same way, trained by the same augmentation methods and assessed by the same evaluation metrics. It involved two data sets: the first was a secondary benchmark data set from publicly available chest X-ray repositories, and the second was a Kenyan primary data set collected from health care facilities. The accuracy, precision, recall, F1-score, and Type I and Type II error rate were used as evaluation metrics. The proposed model was found to be 91% accurate on the secondary benchmark data set and 95% accurate on the primary data set of the Kenyan. The model showed better stability of convergence, reduced overfitting and reduced majority-class bias in comparison with deeper architectures. The study proposes a framework for CNN that is efficient in terms of computation and scalable, which is suitable for resource-limited healthcare systems, where there are limited GPUs, memory, and a lack of digital infrastructure in low-resource clinical environments. Keywords: Pneumonia, Lightweight, Imaging, Resource-Constrained, Artificial Intelligence. MSc in Computer Systems 2026-08-11T00:00:00Z http://localhost/xmlui/handle/123456789/7094 Sustainable Intensification of Smallholder Farming Systems Using Push-Pull Technology as a Template Buleti, Sylvia Imbuhila Sustainable intensification is essential for increasing food production on shrinking smallholder farms while reducing environmental risks. In western Kenya, farmers use practices such as intercropping, crop rotation, agroforestry, crop-livestock integration, and push-pull technology to address constraints such as low soil fertility, pests, diseases, and limited land for production. Push-pull technology, which combines cereal crops with repellent and trap companion crops, is effective in managing striga weed, stem borer, and fall armyworm, but its adoption remains limited because it is mainly cereal-based and its companion crops are not edible. This study aimed to identify farmers’ preferred sustainable intensification options for integration into push-pull systems and to assess the effects of the selected option on soil fertility, pests, natural enemies, and maize productivity in Kisumu, Siaya, and Vihiga counties. Farmers’ preferred intensification practices were identified through participatory research involving focus group discussions, key informant interviews, and validation with farmers. Integration of Cajanus cajan (pigeon pea) into push-pull technology was selected as a priority option because pigeon pea provides additional benefits, including food, feed, and fuelwood. Field trials were then established on farmers’ plots during the long and short rainy seasons of 2021, 2022, and 2023 using four treatments: push-pull, push-pull with pigeon pea, maize with pigeon pea, and maize monocrop. Subplots were used to monitor crop growth, striga weed density, fall armyworm, stem borer, and natural enemies. Equally, soil physical and chemical properties, maize grain and stover yield, pigeon pea productivity, Brachiaria biomass, and desmodium biomass were measured in the same plot. Data were analyzed using analysis of variance, and treatment means were separated using Tukey’s Honestly Significant Difference test. Farmers identified several sustainable intensification options for integration into push-pull systems, with intercropping, crop rotation, crop-livestock integration, and agroforestry ranked as the most preferred. Their main motivations were food diversification, fuelwood and fodder provision, soil improvement, and pest management. Soil fertility varied across counties, ranging from low in Vihiga to moderate in Kisumu and Siaya. Major nutrients, particularly nitrogen, phosphorus, and potassium, were generally below critical levels, while micronutrients such as zinc and manganese were sufficient. Integrating pigeon pea improved nitrogen, phosphorus, and potassium levels in the tested soils, indicating its potential to support soil fertility improvement in maize-based systems. Maize monocrop recorded the highest striga density and the greatest infestation by fall armyworm and stem borer, whereas maize with pigeon pea had significantly lower striga density. Maize grain yield did not differ significantly for treatments across sites and seasons, indicating that intensified systems-maintained productivity. However, maize monocrop produced the highest stover yield, followed by push-pull, maize + pigeon pea, and push-pull + pigeon pea. Pigeon pea also contributed additional benefits, including food, fodder, and fuelwood, while supporting striga suppression and maintaining maize productivity. The findings show that integrating pigeon pea into push-pull and other maize-based farming systems can diversify farm benefits without compromising crop productivity. The study recommends the integration of pigeon pea into push-pull and maize-based systems in Kisumu, Siaya, and Vihiga counties as one of the practical strategies for improving soil fertility, suppressing striga weed, maintaining crop production, and providing additional farm products such as food, fodder, and fuelwood. PhD in Plant Science 2026-08-07T00:00:00Z http://localhost/xmlui/handle/123456789/7093 A Likelihood-Based Multiple Change Point Algorithm for Count Data with Allowance for Over-dispersion: A Case of COVID-19 Infections in Kenya Nyambura, Shalyne Gathoni Count data frequently exhibit over-dispersion, where the variance exceeds the mean, limiting the effectiveness of conventional changepoint detection methods that assume equi-dispersion. ThisstudyaddressesthislimitationbydevelopingahybridLikelihood Based Negative Binomial Multiple Changepoint Algorithm (NBMCPA) capable of detecting multiple changepoints in both equi-dispersed and over-dispersed count pro cesses within a unified framework. The algorithm exploits the limiting relationship between the Negative Binomial and Poisson distributions, allowing a single likelihood formulation for both data types. It integrates Stepwise Recursive Binary Segmentation, maximum likelihood estimation, and likelihood ratio testing to identify statistically significant changepoints, while Monte Carlo simulation provides critical values for reliable statistical inference. Performance is evaluated using simulated datasets with varying sample sizes and changepoint locations. Results show that the algorithm accu rately detects true changepoints with low false detection rates, with detection accuracy improving as sample size increases. Application to daily averaged COVID-19 infection data from Kenya (March 2020–August 2021) identified four statistically significant changepoints corresponding to major epidemiological developments and public health interventions. Lag analysis showed that observable changes in infection trends oc curred, on average, approximately 38 days after policy implementation, while infection peaks followed interventions by about one week. The study introduces a novel hybrid likelihood-based framework thatextendsexistingchangepointmethodologybyunifying the analysis of equi-dispersed and over-dispersed count data within a single Negative Binomial likelihood approach. The proposed algorithm provides a robust, flexible, and computationally efficient tool for detecting structural changes in count data, with applications in epidemiology, public health surveillance, environmental monitoring, finance, and industrial quality control. PhD in Applied Statistics 2026-08-07T00:00:00Z http://localhost/xmlui/handle/123456789/7092 Assessment of Occupational Risk Factors and Health Effects of Computer Use amongst Bankers in Nairobi County Kenya Nyaga, Anthony Njiru The use of computers has increased significantly during the 21st century. Most financial institutions have integrated computer devices in their day-to-day operations. This has improved the productivity and efficiency of most employees. However, the use of computers has also been associated with adverse health effects. Some of the documented health issues caused by computers include visual disorders and Musculoskeletal disorders (MSDs). This study aimed to assess the occupational risk factors and health effects of computer use amongst bankers in Nairobi County Kenya. Specifically, the study aimed to determine the individual risk factors of computer use and their health effects amongst bankers in Nairobi County Kenya, to evaluate the environmental risk factors of computer use and their health effects amongst bankers in Nairobi County Kenya and to assess the level of knowledge and awareness on risk factors and health effects of computer use amongst bankers in Nairobi County Kenya. This study was anchored on the health brief theory and the Bio- psychosocial Model. The study applied proportionate stratified random sampling method. A sample size of 368 bank workers from across the 17 sub counties in Nairobi was enrolled into the study. Data was collected using semi structured questionnaires, observational checklist and taking actual measurement on the employees and the work environment. It was then analyzed using SPSS software Version 25 and then summarized using descriptive statistics. It was presented in form of pie charts, tables and bar graphs. Chi square was applied to demonstrate the strength of association between independent and dependent variables. Results showed prevalence of visual disorders with 54.1% of bank workers experiencing watery eyes while 50.3% of the workers experience burning senstaions. Results also indicated prevalence of musculoskeletal symptoms amongst the bankers with workers experiencing neck pain taking up 19.9% of the population and a significant 19% having shoulder problems. From the study, individual risk factors such as body mass index (p=0.028), working duration before taking a break(p=0.048), working hours with computers(p=0.038) and number of hours spent sitting(p=0.037) were significantly associated with occurrence of musculoskeletal and visual symptoms. Ergonomic considerations such as use of adjustable seat heights(p=0.003) and well cushioned seats(p=0.002) were significantly associated with occurrence of musculoskeletal symptoms. Results also showed that eye-related health effects were influenced by factors such as break frequency(p=0.004) and distance from the screen(p=0.045). The study recommends that it is imperative for organizations to implement tailored ergonomic guidelines and promote adherence to recommended break intervals so as to safeguard the musculoskeletal and visual health of employees engaged in prolonged computer use. Additionally, educational initiatives aimed at raising awareness about the importance of maintaining healthy weight, taking regular breaks, and optimizing workstation ergonomics could further enhance employee well being in the workplace. The study also recommends that organizations should incorporate ergonomics in the design of work places so as to promote health, comfort and efficiency at work place while preventing strain and injuries. MSc in Occupational Safety and Health 2026-08-07T00:00:00Z