Browsing by Author "Barasa, Peter Wawire"

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E-learning Transforming Economies
(International Journal of Trend in Research and Development, 2016-12-28) Barasa, Peter Wawire; Owoche, Patrick Oduor; Nambiro Alice Wechuli,
as E-learning technology has quickly evolved into more sophisticated forms, it is opening the options for educators and business professionals to expand learning opportunities and transform economies globally. The ability to transform economies from low income, to more vibrant growing economies which can generate employment and growing incomes to citizens generally, has been described as economic development. It is recognized that „Human Capital,‟ a term attributed to economist Theodore Schultz, is a reflection on the human capacities. Schultz believed human capital was like any form of capital. It could be invested in through education, training and enhanced benefits that would lead to an improvement on the quality and level of production. In this paper the authors make a case of how a nation‟s education system that is E-learning relates to its economic performance
Enhancing climate resilience: A data-driven north rift weather prediction system for real-time forecasting and agricultural decision support
(Heliyon, 2025-02-07) Makokha, John Wanjala; Barasa, Peter Wawire; Khamala, Geoffrey W.
This study presents the development and integration of predictive models for the Normalized Difference Vegetation Index (NDVI) and Bare Soil Index (BSI) using the XGBoost algorithm within the North Rift Weather Prediction System (NRWPS) to enhance ecosystem monitoring in Kenya’s North Rift region. Trained on a comprehensive dataset spanning 1995 to 2020, which includes precipitation (from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS)), temperature (TerraClimate), historical NDVI (Landsat 4–5 Thematic Mapper (from 1995 to 2013) and Landsat 7 Enhanced Thematic Mapper plus (ETM+) (from 2014 to 2020)), and BSI (SoilGrids) data, the models effectively capture the complex relationships between environmental factors and vegetation health. The BSI model achieved an MSE of 0.029, an MAE of 0.019, and an R-squared score of 0.93, while the NDVI model yielded an MSE of 0.002, an MAE of 0.024, and an R-squared score of 0.945. These results demonstrate the models’ strong predictive accuracy, enabling precise assessments of vegetation health and bare soil exposure. By analyzing temporal variations in vegetation health and land degradation from 1995 to 2020, the study identifies a significant inverse relationship between NDVI and BSI, where increasing bare soil exposure corresponds to declining vegetation health. The analysis also reveals that climatic factors particularly temperature (minimum and maximum) and precipitation play a critical role in shaping these trends, with high temperatures after 2000 associated with reduced NDVI, while regions with higher precipitation show healthier vegetation and lower BSI. The successful development of the NRWPS model provides significant opportunities for informing land management strategies, conservation efforts, and agricultural practices, enabling data-driven decision- making. Moreover, its integration into larger decision support systems allows for proactive interventions to mitigate land degradation and climate change stressors. This study emphasizes the importance of sustainable land-use practices and climate adaptation strategies to preserve vegetation health and manage ecosystem vulnerabilities effectively in the wake of regional climate change with the North Rift region most affected.
Multi-Agent Based M-Voting System
(International Journal of Trend in Research and Development, 2016-12-28) Barasa, Peter Wawire; Wechuli, Alice Nambiro; Savatia, Edward
Multi-agent based m-voting system is capable of saving time, minimizing errors in voting and making voting easier. M-voting is an emerging area with wide application in all sectors of the Economy; m- voting is given a new dimension. M-voting is considered a brand new model which is based on use of mobile phone for voting in elections. The whole of M-voting in computing technology may be viewed as distributed, complex, dynamic because it has attributes such as network, popularization, personalization and lifelong. The object oriented design methodologies have been used in solving the voting problem. The voting problem is also being approached from artificial intelligence point of view. Many questions therefore arise about M-voting. One general question is how modern artificial intelligence models can be applied to the voting problem. An open direction of inquiry into this problem is the investigation of how multi-agents can be used to solve M-Voting. In this study, we focus on design of a multi-agent systems model, where the components in the Mvoting scenario are intelligent and can reactively and proactively participate in solving the voting problem. An agent oriented methodology –Prometheus- was used in the analysis and design of the multi agent based M-voting system We see the overall solution to the multi agent based M-voting system as the settlement resulting from communications and negotiations of individual agents in the M- voting process. This is a multi-agent scenario.
Multi-agent Based Surveillance System for Diseases
(International Journal of Trend in Research and Development, 2016-12-28) Barasa, Peter Wawire; Wechuli, Alice Nambiro
In this paper the authors make a case for medical care practitioners and governments to harness the power of technology to improve surveillance of diseases. Thus, using the multi-agent technology based surveillance system for diseases as a solution to the surveillance problems facing the healthcare sector in Kenya and the entire continent of Africa. The current surveillance problem is as a result of disease cases being carried out in a manual, inefficient and ineffective manner. An agent oriented methodology –Prometheus was used in the analysis, design, implementation and evaluation of a program designed to assist medical care practitioners..
Report of the Baseline Study on Technology-Enabled Learning at Kibabii University
(Commonwealth of Learning, 2020-04-28) Barasa, Peter Wawire; Anselemo, Peters Ikoha; Wechuli, Alice Nambiro; Wekesa, MacDonald