https://repository.kcau.ac.ke/handle/123456789/8 2026-06-04T11:09:56Z https://repository.kcau.ac.ke/handle/123456789/1603 Context awareness vulnerabilities detection model in byod environment using a linear regression technique Wanjiru, Jeremiah N. The purpose of this study was to examine context-awareness vulnerabilities in Bring Your Own Device (BYOD) environments within large SACCOs in Kenya. Adoption of BYOD practices, enable employees of an organization to use personal devices for work. In the recent past there has been incidents of financial vulnerabilities, including losses attributed to both internal collusion and external cyber-attacks, which showed the urgent need for solutions focused on vulnerability detection mechanisms. The analysis of past literature revealed gaps in existing models, which inadequately address SACCO-specific risks such as the role-based access and dynamic access patterns, often relying on a narrow set of data points or reliance of static approaches. The study employed a descriptive survey design, using structured questionnaires to collect data from 86 employees of Mwalimu SACCO’s head office in Nairobi. As the largest SACCO in Kenya, Mwalimu SACCO provided a suitable context to analyse BYOD-related vulnerabilities in a high-risk, resource-constrained environment. Descriptive techniques and multivariate regression analysis were employed to determine the influence of the identified factors on the vulnerability index. The study findings showed that access time, location, and role risk factors significantly wielded and affect vulnerability in BYOD environments. Access time emerged as the most critical determinant, with increased risks observed during non-standard work hours. Location vulnerabilities were heightened in remote settings due to limited security measures, while role risk factors indicated that employees with elevated access privileges, particularly in ICT and finance roles, posed greater risks. The study formulated a multivariate regression model which demonstrated high predictive accuracy, with an R² value of 0.89 and a mean absolute error of 0.12. These results validated its reliability in identifying and predicting context-awareness vulnerabilities in SACCO BYOD environments. The study concludes that; there is increased use of personal devices by SACCO staff to undertake both personal and official engagements. Further, the study concludes that, there is lack of comprehensive BYOD policies that conforms to prevailing vulnerabilities. Through adoption of robust access controls, organization centered BYOD policies, and role-specific security measures, SACCOs can upscale their defenses. These measures would enable SACCOs to mitigate vulnerabilities, reduce insider fraud and external threats, and strengthen their cyber-security posture. This research fills a critical gap in understanding and managing context-aware vulnerabilities in BYOD environments, offering a practical framework for enhancing the security of SACCO operations in Kenya. 2025-01-01T00:00:00Z https://repository.kcau.ac.ke/handle/123456789/1600 An ensemble deep learning judgement prediction model for civil Cases in Kenya Amagoye, Jeremy S This study develops and evaluates an ensemble deep learning model combining Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM) networks, and an Attention Mechanism (AM) to predict judgments in Kenyan civil cases. With Kenya's judiciary facing a backlog exceeding 400,000 cases, this research addresses critical efficiency and consistency challenges. The CNN+BiLSTM+AM architecture extracts key textual features from legal documents, captures sequential dependencies in legal arguments, and prioritizes relevant information through attention weighting, providing both accurate predictions and interpretable results. Using stratified sampling across court levels, the study analyzes civil cases to identify influential predictors of judicial outcomes, including legal representation disparities, citation patterns, and procedural factors. Results demonstrate the model's superior performance compared to baseline approaches, with implications for case management, resource allocation, and access to justice. By providing data-driven insights into judicial decision-making, this research contributes to addressing systemic inefficiencies in Kenya's legal system while establishing a methodological framework applicable across similar jurisdictions. The findings support Kenya's judicial reform efforts by offering an innovative, technologically driven approach to enhancing transparency, consistency, and efficiency in civil litigation. 2025-01-01T00:00:00Z https://repository.kcau.ac.ke/handle/123456789/1587 N-beats Deep Learning Transformer Model For Nowcasting Consumer Price Index Mwangi, Julius Maina Accurate modelling of time-series data is vital across various domains, particularly in economic forecasting, such as predicting inflation rates. With inflation data typically released monthly, the limited number of observations poses a challenge for traditional modelling techniques. This study explores the applicability of the Neural Basis Expansion Analysis for Interpretable Time Series Forecasting (N-BEATS) transformer architecture to predict the Consumer Price Index (CPI). Transformers, commonly pre-trained on extensive datasets, offer promising capabilities for fine-tuning to specific tasks, even with limited data. In this research, we aim to replicate the N-BEATS transformer model architecture, utilizing monthly CPI data from the Kenya National Bureau of Statistics (KNBS). The analysis includes exploratory data analysis (EDA) to uncover patterns and trends, followed by model evaluation using Root Mean Squared Error (RMSE) and Mean Squared Error (MSE). This research endeavours to provide an alternative approach for inflation predictions to conventional deep learning and the traditional statistical modelling methods. 2025-01-01T00:00:00Z https://repository.kcau.ac.ke/handle/123456789/1575 Model For Detecting Common Bean Fungal Leaf Disease Using Deep Convolutional Neural Network Addikah, Sydney M Agriculture forms the basis of food security and economic growth in most countries. Pest and diseases remain to be a significant challenge and a big hindrance to the success of small-scale farming. Pest and diseases are responsible for heavy losses through death of crops and reduced productivity. In Kenya, common bean is the most important pulse and is the third most important food crop. Fungal based angular leaf spot and rust are two major diseases of common beans in the tropics and sub-tropics. Therefore, there is a need to provide a reliable and accessible technical solution for farmers to detect early detection of common bean leaf fungal diseases in Kenya. The main objective of the current study is to develop a deep convolution neural networks model for detection of common bean fungal leaf diseases in Kenya. The data for training was extracted from the GitHub data (Al. Lab. Makerere, 2020). Testing was done using SoftMax activation function in the output layer to provide a range of probabilities to the various output options. The initial TensorFlow model was built using the CRISP-DM methodology. The ResNet-50 model was adopted and custom layers were built using transfer learning. The TensorFlow Lite framework was used to convert and optimize the model. Float16 quantization was used to optimize the model. Performance metrics, including accuracy, precision, and recall, were used to evaluate the model. 2024-01-01T00:00:00Z