Browsing by Author "Siunduh, Eric Sifuna"

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Adoption of Machine Learning Technologies in Mitigation of Climate Change Risks in North Rift, Kenya
(International Journal of Applied Science and Engineering Review, 2025-07-07) Siunduh, Eric Sifuna; Ikoha, Peters Anselemo; Konje, Martha Muthoni
This study examines the implementation and effectiveness of Machine Learning (ML) technologies in addressing climate change risks within Kenya's North Rift region. The research investigates how ML applications are being utilized to enhance climate resilience, improve agricultural practices, and support decision-making processes in climate risk management. Through a mixed-methods approach combining quantitative data analysis and qualitative stakeholder interviews, this study evaluates the current state of ML adoption, identifies key challenges, and assesses the impact on local communities. Findings indicate that while ML adoption is still in its early stages, there is significant potential for these technologies to improve climate risk prediction, optimize resource allocation, and enhance adaptation strategies. The study reveals that successful implementation requires addressing infrastructure limitations, building local capacity, and ensuring community engagement. This research contributes to the growing body of knowledge on technological solutions for climate change adaptation in developing regions and provides practical recommendations for policymakers and practitioners.
Evaluating Learning Analytics Usability Factors Towards Learner Performance Assessment in Virtual Environment in Kenyan Universities
(International Journal of Scientific Research & Engineering Trends, 2025-08-28) Mohammed, Mohammed Swaleh; Asenahabi, Bostley Muyembe; Wechuli, Alice Nambiro; Siunduh, Eric Sifuna
The purpose of the study was to evaluate the learning analytics usability factors towards e-learning learner performance assessment in Kenyan Universities. The study used quantitative methodology toward achieving the purpose of the study. Quantitative approach was attained through using five- point Likert scale distributed through random sampling to eight universities in Kenya. A focus on those students using e-learning whether blended or virtual learning. The findings revealed two factors: Perceived Usefulness and Perceived Ease of Use.
Modelling AI Technologies towards Prediction of Disasters Related to Climate Change: Case Study of North Rift, Kenya
(International Journal of Applied Science and Engineering Review, 2025-08-07) Siunduh, Eric Sifuna; Ikoha, Peters Anselemo; Konje, Martha Muthoni
The study explores the application of artificial intelligence (AI) technologies for predicting climate change-induced disasters in Kenya's North Rift region. The North Rift, characterized by diverse topography including highlands, valleys, and arid plains, has experienced increasing frequency and severity of climate-related disasters such as floods, droughts, and landslides over the past decade. These events have significantly impacted agricultural productivity, water resources, infrastructure, and community livelihoods. The study employs machine learning algorithms, including random forests, convolutional neural networks, and long short-term memory (LSTM) networks, to analyze historical meteorological data, satellite imagery, and ground-based observations. This multi-modal approach enables the integration of traditional climate indicators with novel predictive features derived from remote sensing. The research leverages data from Kenya Meteorological Department stations, climate analysis products, and Earth observation satellites to develop regionally calibrated prediction models. Preliminary findings demonstrate that AI-based systems outperform conventional statistical methods in predicting the onset, intensity, and spatial distribution of climate disasters in the region. Notably, the LSTM models achieved 78% accuracy in forecasting drought conditions three months in advance, while CNN-based image analysis shows promising results in identifying flood-prone areas with 82% precision. The research addresses challenges related to data availability and quality through novel data fusion techniques and transfer learning approaches that adapt global climate models to local contexts. The study further examines the integration of AI predictions into existing early warning systems and disaster management frameworks. Stakeholder interviews with local government officials, community representatives, and disaster management agencies reveal both opportunities and barriers for effective implementation. Key recommendations include capacity building for local meteorological services, development of user friendly prediction interfaces, and community-based participatory approaches for validation and refinement of AI outputs. This research contributes to the growing field of climate AI and demonstrates the potential of machine learning in enhancing disaster preparedness and resilience in vulnerable regions. The findings provide a foundation for developing scalable AI-based early warning systems that can be adapted to similar ecological contexts across East Africa