Browsing by Author "Samson Otieno Ooko"

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A Machine Learning Model for Prediction of Malaria from Microscopic Blood Cell Images
(IEEE Xplore, 2024) Samson Otieno Ooko; Charles Theuri Kagwi
Machine learning (ML), a pivotal subset of artificial intelligence (AI), has been instrumental in developing systems that learn from and enhance their performance based on data. This technology has facilitated the creation of predictive models for a range of diseases, including pneumonia, heart disease, and COVID-19, allowing for diagnosis based on specific data parameters. Traditionally, malaria detection has relied on the microscopic examination of blood cells, a method that is not only time-consuming but also prone to misdiagnosis, leading potentially to the incorrect treatment of patients. This study introduces a machine-learning model designed to identify patterns in microscopic images of blood cells to differentiate between healthy and malaria-infected cells. Utilizing publicly available datasets from Kaggle for both training and testing, we evaluated the performance of K-Nearest Neighbor (KNN) and Convolutional Neural Network (CNN) algorithms. Our findings show that the CNN model significantly outperformed the KNN model, achieving an accuracy of 94%, and requiring less memory and computational time. Therefore, the CNN model is recommended for further development and use. The implementation of this model represents a significant step forward in achieving the sustainable development goals related to health and wellness, offering a more reliable, efficient, and scalable tool for malaria diagnosis.
Challenges and Opportunities of Mobile Cloud Computing
(IEEE, 2022) Grace Kwangalakwe; Samson Otieno Ooko; Rosemary Nalwanga
With the continued advancement in technology mobile phone are increasingly becoming accessible. A wide range of mobile application are continually being developed to perform tasks with capabilities that were only possible with powerful computers. The emerging concept of Mobile Cloud Computing (MCC) is fast being adopted to help overcome storage and compute limitation in mobile devices enabling more advance applications. With MCC storage and processing of data is done on the cloud reducing on costs and saving on energy consumption among other benefits. Motivated by the importance of reviewing its adoption, the objective of this study is to provide a state of the art of MCC and to establish the challenges and opportunities presented by MCC. This paper systematically reviewed previous publications following the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) checklist guidelines. From the systematic review outcome, a layered architecture for MCC is proposed, its application and benefits are presented. The challenges and opportunities resulting from MCC are then highlighted and proposed.
Use of Machine Learning for Realtime Water Quality Prediction
(IEEE Xplore, 2023) Samson Otieno Ooko; Elaine Kansiime Pamela; Grace Kwagalakwe
Water is an essential resource that is a foundation not only for people’s lives but also for economic development. According to statistics from the World Health Organization (WHO) in 2021, over 2 billion people across the globe use water from contaminated sources leading to life-threatening diseases with over 485,000 deaths as a result of waterborne diseases being reported annually. There is therefore a need for tools and techniques that can be used to ensure water quality. Traditional water quality index calculations consume time and are often prone to errors. In the recent past use of Machine Learning (ML) in predicting water quality has generated interest among researchers as a real-time solution. From the reviewed literature, existing models only consider a few chemical properties of water while ignoring others. In addition, there is a need to compare the performance of different models. This study thus presents a supervised learning model for predicting water quality. Our hypotheses were that (i) most of such parameters are all important and none can be ignored and (ii) with supervised learning one can predict water quality based on its physio-chemical properties. Open data sets are explored as inputs with different models being evaluated. The results show that the Random Forest algorithm gives the best results with a 79 percent accuracy. The use of this model will go a long way towards ensuring water quality and thus reducing related diseases and fatalities.