Browsing by Author "Dennis Kariuki Muriithi"

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Application of Response Surface Methodology for Optimization of Potato Tuber Yield
(Science Publishing Group, 2015-07-12) Dennis Kariuki Muriithi
The Author investigates the operating conditions required for optimal production of potato tuber yield in Kenya. This will help potato farmers to safe extra cost of input in potato farming. The potato production process was optimized by the application of factorial design 23 and response surface methodology. The combined effects of water, Nitrogen and Phosphorus mineral nutrients were investigated and optimized using response surface methodology. It was found that the optimum production conditions for the potato tuber yield were 70.04% irrigation water, 124.75Kg/Ha of Nitrogen supplied as urea and 191.04Kg/Ha phosphorus supplied as triple super phosphate. At the optimum condition one can reach to a potato tuber yield of 19.36Kg/plot of 1.8meters by 2.25 meters. Increased productivity of potatoes can improve the livelihood of smallholder potato farmers in Kenya and safe the farmers extra cost of input. Finally, i hope that the approach applied in this study of potatoes can be useful for research on other commodities, leading to a better understanding of overall crop production.
Explainable Artificial Intelligence Models for Predicting Malaria Risk in Kenya
(European Journal of Artificial Intelligence and Machine Learning, 2025-02-12) Dennis Kariuki Muriithi; Victor Wandera Lumumba; Olushina Olawale Awe; Daniel Mwangi Muriithi
The article aims to develop interpretable Machine Learning models using R statistical programming language for malaria risk prediction in Kenya, emphasizing leveraging Explainable AI (XAI) techniques to support targeted interventions and improve early detection mechanisms. The methodology involved using synthetic data with 1000 observations, employing over-sampling to address class imbalance, utilizing two machine learning algorithms (Random Forest and Extreme Gradient Boosting), applying cross-validation techniques, Hyper-parameter tuning and implementing feature importance and SHAP (Shapley Additive Explanations) for model interpretability. The findings revealed that Random Forest outperformed Extreme Gradient Boosting with 98% accuracy. Critical prediction features included clinical symptoms such as nausea, muscle aches, and fever, plasmodium species identification, and environmental factors like rainfall and temperature. Both models demonstrated strong sensitivity in detecting malaria cases. This promotes trust in model predictions by clearly outlining the decision process for individual outcomes. The research concluded that integrating Explainable AI into malaria risk prediction represents a transformative approach to public health management. Through providing transparent, interpretable models, the research offers a robust, data-driven approach to predicting malaria risks, potentially empowering healthcare providers and policymakers to deploy resources more effectively and reduce the disease burden in endemic regions.