Browsing by Author "Murindanyi, Sudi"

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    Machine Learning Analysis of Radio Data to Uncover Community Perceptions on the Ebola Outbreak in Uganda
    (ACM Journal on Computing and Sustainable Societies, 2024-09-16) Nakatumba-Nabende, Joyce; Mukiibi, Jonathan; Bateesa, Tobius Saul; Murindanyi, Sudi; Katumba, Andrew; Mutebi, Chodrine
    Radio is vital for people, especially in rural areas, to share their concerns through interactive talk shows. Understanding public perceptions of pandemics is crucial because they influence people’s attitudes and health-seeking behaviors. This study used machine learning to analyze English and Luganda radio broadcast data to understand public perceptions and perspectives on the Ebola outbreak in Uganda. Our findings revealed three main speaker categories: media personalities, community guests and listeners, and government officials. The government made the most significant effort to educate the public about the Ebola outbreak. The analysis showed that the community was hesitant to use Ebola vaccines, believing that they had not been tested on other populations where the Ebola virus had originated. The community was also concerned about the effects of the lockdown measures imposed during the COVID-19 pandemic. The analysis of the radio broadcast data revealed differences in the timing and content of the conversations between male and female speakers. These experiences can inform population-specific policies for handling ongoing and future pandemics.
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    Predicting Sweepotato Sensory Attributes Using DigiEye and Image Analysis as a Breeding Tool
    (RTBfoods, 2022) Nakatumba-Nabende, Joyce; Nabiryo, Ann Lisa; Babirye, Claire; Tusubira, Jeremy Francis; Katumba, Andrew; Murindanyi, Sudi; Mutegeki, Henry; Nantongo, Judith; Sserunkuma, Edwin; Nakitto, Mariam; Ssali, Reuben; Davrieux, Fabrice
    The objective of the work was to develop, test and evaluate a color and mealiness classification model based on images of sweetpotato roots. A total of 3018 images were collected from 950 samples from October 2021 to November 2022. The captured image data samples were harvested from several sites, including Namulonge, Arua, Bulindi, Nassari, Serere, Rwebitaba, Iganga, Kabarole, Mbale, Mpigi, Busia, Kamuli, Hoima, Kabale and Kenya. Calibrations were done using reference data collected by a sensory panel. Up to twelve cooked roots per genotype were used for sensory evaluation of traits per session. Calibrations used various linear and non-linear models. Using linear regression, high performances were observed of the calibration for orange color intensity (R2 = 0.92, Mean Squared Error (MSE) =0.58), suggesting that the model is sufficient for field application. For mealiness-by-hand and positive area, the best model has a Mean Absolute Error (MAE) of 2.16 and 9.01 respectively