Browsing Computational and Communication Science Engineering by Author "Mduma, Neema"
Now showing items 21-27 of 27
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Machine learning model for predicting Peste des Petits Ruminants
Nyambo, Devotha; Ngulumbi, Nguse; Mduma, Neema; Sinde, Ramadhani; Lyimo, Tumaini (IEEE, 2023-11-16)Peste des petits ruminants (PPR) is a viral disease that affects small ruminants and is prevalent in many developing countries, particularly in Africa and Asia. It can spread through direct contact, air, and contaminated ... -
Mobile-Based convolutional neural network model for the early identification of banana diseases
Elinisa, Christian; Mduma, Neema (Elsevier, 2024-02-29)This study aimed to deploy a deep learning model in a mobile application for the early identification of Fusarium Wilt and Black Sigatoka in bananas. In this paper, a Convolutional Neural Network (CNN) model for the ... -
Mobile-Based convolutional neural network model for the early identification of banana diseases
Elinisa, Christian; Mduma, Neema (Elsevier, 2024-02-29)This study aimed to deploy a deep learning model in a mobile application for the early identification of Fusarium Wilt and Black Sigatoka in bananas. In this paper, a Convolutional Neural Network (CNN) model for the ... -
Poultry diseases diagnostics models using deep learning
Machuve, Dina; Nwankwo, Ezinne; Mduma, Neema; Jimmy, Mbelwa (Frontiers in Artificial Intelligence, 2022-08-01)Coccidiosis, Salmonella, and Newcastle are the common poultry diseases that curtail poultry production if they are not detected early. In Tanzania, these diseases are not detected early due to limited access to agricultural ... -
Review of Sources of Uncertainty and Techniques Used in Uncertainty Quantification and Sensitivity Analysis to Estimate Greenhouse Gas Emissions from Ruminants
Kimei, Erica; Nyambo, Devotha; Mduma, Neema; Kaijage, Shubi (MDPI, 2024-03-06)Uncertainty quantification and sensitivity analysis are essential for improving the modeling and estimation of greenhouse gas emissions in livestock farming to evaluate and reduce the impact of uncertainty in input ... -
A Survey of Machine Learning Approaches and Techniques for Student Dropout Prediction
Mduma, Neema; Kalegele, Khamisi; Machuve, Dina (Data Science Journal, 2019-04-17)School dropout is absenteeism from school for no good reason for a continuous number of days. Addressing this challenge requires a thorough understanding of the underlying issues and effective planning for interventions. ... -
Updating “machine learning imagery dataset for maize crop: A case of Tanzania” with expanded data to cover the new farming season
Mduma, Neema; Mayo, Flavia (Elsevier Inc., 2024-03-23)Maize Lethal Necrosis (MLN) and Maize Streak Virus (MSV) are among maize diseases which affect productivity in Tan- zania and Africa at large. These diseases can be detected early for timely interventions and minimal ...