RP-Department of Computing & Information Technology

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https://repository.ku.ac.ke/handle/123456789/5396

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Browsing RP-Department of Computing & Information Technology by Author "Kandiri, John"

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    Enhancing Service Delivery in Public Sector by Leveraging on Digital Transformation: A Case of Kiambu Level Five Hospital
    (Reviewed Journal of Social Science & Humanities, 2024-09) Wachira, Purity Wamuyu; Kandiri, John
    In the publ ic sector, the main goal of service delivery is to make sure that people can get to and use important tools and services in a way that meets their needs. Service delivery is very important in the healthcare field, especially in state hospitals, because it has a direct effect on how well patients do and on the health of the people as a whole. Digital change has a big effect on service delivery because it lets companies use flexible organizational structures and connect digital business communities. The goal of this study was to look into how going digital has changed the way services are provided at Kenya's Kiambu Level 5 hospital. The study looked at how customer relationships, growing skills, and constant improvement affect the digital change process. Resou rce - based view, identify, distinguish, connect, and modify theories, as well as dynamic capability theories, were used to guide the study. Based on a detailed study approach, the study was carried out. The target group was made up of 282 Kiambu Level 5 hos pital workers. A group of 165 individuals was used as a sample. Structured surveys and an interview plan were used to gather the data. The study looked at how accurate and reliable research tools were. Descriptive statistics and regression analysis were us ed to look at quantitative data. Content analysis, on the other hand, was used to look at qualitative data. A strong link (r=0.822, p<.001) was found between the digital change factors and service performance. The study found that the three digital transfo rmation factors can explain about 66.9% of the differences in service performance. Continuous growth (p<.001) and customer interaction (p<.001) were both important. The study found that going digital has a big and good impact on service delivery at Kenya's Kiambu Level 5 Hospital, mainly through bettering relationships with customers and always making things better. The study said that Kiambu Level 5 Hospital should use digital tools to try to keep customers they already have. It was also suggested that the hospital spend money on digital technology to make the hospital more productive and find technology that guarantees on-ti me delivery of health services.
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    Hybrid Machine Learning Techniques for Comparative Opinion Mining
    (IJAIDM, 2023-08) Ondara, Bernard; Waithaka, Stephen; Kandiri, John; Muchemi,Lawrence
    Comparative opinion mining has lately gained traction among individuals and businesses due to its growing range of applications in brand reputation monitoring and consumer decision making among others. Past research in sub-field of opinion mining have mostly explored single-entity opinion mining models and the mining of comparative sentences suing single classifiers. Most of these studies relied on a limited number of comparative opinion labels and datasets while applying the techniques in limited domains. Consequently, the reported performances of the techniques might not be optimal in some cases like working with big data. In this study, however, we developed four hybrid machine learning techniques, with which we performed multi-class based comparative opinion mining using three datasets from different domains. From our results, the best-performing hybrid machine learning technique for comparative opinion mining using a multi-layer perceptron as the base estimator was the Multilayer Perceptron + Random Forest (MLP + RF). This technique had an average accuracy of 93.0% and an F1-score of 93.0%. These results show that our hybrid machine learning techniques could reliably be used for comparative opinion mining to support business needs like brand reputation monitoring.
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    YOLO-APD: Enhancing YOLOv8 for Robust Pedestrian Detection on Complex Road Geometries
    (International Journal of Computer Trends and Technology, 2025-06) Joctum, Aquino; Kandiri, John
    - Autonomous vehicle perception systems require robust pedestrian detection, particularly on geometrically complex roadways like Type-S curved surfaces, where standard RGB camera-based methods face limitations. This paper introduces YOLO-APD, a novel deep learning architecture enhancing the YOLOv8 framework specifically for this challenge. YOLO-APD integrates several key architectural modifications: a parameter-free SimAM attention mechanism, computationally efficient C3Ghost modules, a novel SimSPPF module for enhanced multi-scale feature pooling, the Mish activation function for improved optimization, and an Intelligent Gather & Distribute (IGD) module for superior feature fusion in the network's neck. The concept of leveraging vehicle steering dynamics for adaptive region-of-interest processing is also presented. Comprehensive evaluations on a custom CARLA dataset simulating complex scenarios demonstrate that YOLO-APD achieves state-of-the-art detection accuracy, reaching 77.7% mAP@0.5:0.95 and exceptional pedestrian recall exceeding 96%, significantly outperforming baseline models, including YOLOv8. Furthermore, it maintains real-time processing capabilities at 100 FPS, showcasing a superior balance between accuracy and efficiency. Ablation studies validate the synergistic contribution of each integrated component. Evaluation on the KITTI dataset confirms the architecture's potential while highlighting the need for domain adaptation. This research advances the development of highly accurate, efficient, and adaptable perception systems based on cost-effective sensors, contributing to enhanced safety and reliability for autonomous navigation in challenging, less-structured driving environments.