Browsing by Author "Owoche, Patrick Oduor"

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A Fuzzy Ontological Model for Semantic Interoperability in Distributed Healthcare Information Systems
(International Journal of Research and Innovation in Applied Science, 2025-01-17) Okemwa, Joshua; Owoche, Patrick Oduor; Mbuguah, Samuel Mungai
The exchange of patient or care data across heterogeneous health systems is crucial in the modern healthcare ecosystem yet remains daunting. Integration and interoperability in siloed health applications require robust health information exchange (HIE) and a pragmatic ontological model to be successful. This paper details a prototype development endeavor and systematic review of literature that has pioneered the development of a robust, practical, and tested fuzzy ontological model to enhance semantic interoperability in siloed, distributed health systems. Leveraging the tenets, standards, LOINC codes, and developed model from this study will enable robust and flexible data mapping and sharing health data in an environment marred with ambiguities and uncertainties but requiring the sophistry of interoperability
Assessing Preparedness for Smart Farming and Technology Adoption among Kenyan Farmers
(International Journal of Research and Innovation in Applied Science, 2024-09-16) Gichuki, Dennis Karugu; Mbuguah, Samuel Mungai; Owoche, Patrick Oduor; Oyile, Paul Oduor
Agriculture is crucial in reducing poverty, promoting economic prosperity, and ensuring food security for the world’s growing population, which is expected to reach 9.7 billion by 2050. This sector is vital to the global economy, contributing significantly to GDP and providing jobs for a large workforce. Precision agriculture and e-commerce advances have proven beneficial, boosting crop yields and rural incomes. Sub-Saharan Africa faces similar agricultural challenges as it anticipates a population of 2.1 billion by 2050. Although the region has made strides in expanding farmland and labour, improvements in crop yields have been limited. The digital revolution offers new opportunities to tackle issues such as undernutrition by improving access to information and technology. In Kenya, with a population projected to reach 95 million by 2050, expanding food production is a pressing challenge. Significant hurdles include declining soil fertility, inadequate water management, and a lack of technical support. While technologies like Wireless Sensor Networks (WSNs) and Machine Learning (ML) have the potential to enhance agricultural productivity, their adoption is constrained by infrastructure issues, high costs, and a shortage of technical expertise. Addressing these barriers and improving farmer education is essential to fully realising these technologies' benefits.
Evaluating total cost of ownership for university enterprise resource planning: case of Maseno university
(2015) Owoche, Patrick Oduor; Gregory, Wanyembi; Juma, Kilwake Humphrey
Higher learning institutions (HEI) are investing in IT to attain operational excellence. Organizations looking to reduce technology costs typically look for ways to reduce the Total Cost of Ownership (TCO). TCO captures all direct and indirect costs related to deploying a particular system. Understanding life-cycle costs provides opportunity to save scarce resources, improve IT and increase productivity. The study sought to analyze the TCO of university ERP system. The TCO analysis was based on Gartner’s TCO model, the Distributed Computing Chart of Account as the theoretical framework. Modified Gartner TCO model was used to determine the total cost of owning Maseno University ERP system. The aim of the study was to create awareness on life-cycle costs of ERP solutions. The results obtained indicate that operations and maintenance account for 51%, end-user usage 23% while technology acquisition costs accounted for 16 %, implementation costs with 7% and the least cost was taken by procurements costs with 3%. Case study was used in gathering both qualitative and quantitative data from Maseno University. Interviews, document analysis, and questionnaires were used to gather the research data. It is hoped that the results of this study will help HEIs improve management of their ERP systems.
Performance Evaluation of Machine Learning Algorithms in Smart Agriculture
(International Journal of Advanced Research in Computer and Communication Engineering, 2024-08-26) Gichuki, Dennis Karugu; Owoche, Patrick Oduor; Mbuguah, Samuel Mungai
This study explores the integration of Wireless Sensor Networks (WSN) and Machine Learning (ML) in smart farming to address critical agricultural challenges. By leveraging real-time data collection and advanced analytical tools, the research demonstrates the potential of ML algorithms—Decision Trees, Naive Bayes, Support Vector Machines (SVM), Logistic Regression, and Random Forests—in enhancing crop management, including yield prediction, soil quality assessment, and pest and disease detection. The study finds that Naive Bayes achieves the highest accuracy and balanced precision-recall metrics, while ensemble methods like Random Forests effectively reduce overfitting and improve prediction accuracy. Despite the promising results, the research identifies challenges such as data accessibility, model integration, and user interface design that must be addressed to fully realize the potential of smart farming technologies. Overall, the findings provide valuable insights into optimizing resource utilization, reducing crop losses, and promoting sustainable farming practices, thereby supporting global food security and economic stability.