Browsing by Subject "Machine Learning"
Now showing items 1-12 of 12
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An application of Data Mining Classification Techniques to Electricity Fraud Detection; A Case of Umeme (U) Ltd
(2021-11)The main objective of this research was to develop a suitable classification model that will be able to identify and predict customers with fraudulent consumption. This research addresses the lack of effective methods for ... -
Assessing the effect of crop production drivers on maize yield in Lake Kyoga Basin Using Machine Learning
(Makerere University, 2024-12)Agriculture is a crucial economic activity in Uganda, with maize serving as a priority crop for both food security and income generation. Even though over 80% of households in Lake Kyoga Basin are cultivating maize, ... -
Automated refund fraud detection in mobile money transactions using Machine Learning
(Makerere University, 2023-08-08)In an era marked by the widespread adoption of mobile money systems, the need to secure financial transactions against fraudulent activities has never been more pressing. This research addresses the crucial issue of ... -
Availability Estimation of Power Generating Units at Nalubaale Power Station.
(Makerere University, 2023-01)The main aim of an electric power system is to reliably provide electrical power supply to customers. However, in 2020, Uganda’s grid experienced an average of 62.4% forced outages. The forced outages due to generator ... -
A detection model for user-to-root attacks using the AdaBoost classifier
(Makerere University, 2021-10-06)Intrusion detection in enterprise networks is a key area of interest in computer security today because of its importance and vast application, such as detection of attacks by legal users. Current attack ... -
Developing a machine learning algorithm to predict prostate cancer risk in men aged 40 and above at risk of prostate cancer
(Makerere University, 2024)Introduction – Prostate cancer is the second most common cancer among men globally and the leading cause of cancer-related deaths in Uganda. Despite the availability of screening tools, the accuracy of prostate cancer ... -
Estimating Carbon Stock using field data, Satellite Imagery, and Cloud-Based Machine Learning Algorithms: case study of Mubende District
(Makerere University, 2023-01)Quantifying carbon stock is a good step in pursuit to mitigate GreenHouse Gases (GHGs). The purpose of this research was to estimate carbon stock changes in Mubende District as a consequence of Land Cover Change (LCC) ... -
Machine learning and orthology-based design of synergistic drug combinations against aspergillus fumigatus
(Makerere University, 2024)Background: Aspergillus fumigatus is a common fungal species that can cause invasive aspergillosis, particularly in immunocompromised individuals. In Uganda, around 9% of the population suffers from fungal diseases. Drug ... -
A machine learning approach to predict E. coli antibacterial resistance using whole-genome sequencing data
(Makerere University, 2023)Background: Antimicrobial resistance (AMR) is a significant global health threat, particularly impacting low- and middle-income countries(LMICS) such as Uganda, where reliable and rapid methods for detecting AMR in E. coli ... -
Predicting suicidality in people living with HIV in Uganda: A machine learning approach
(Makerere University, 2024-10)Background Suicidality is a major risk factor for future suicide attempts and completed suicide. People living with HIV (PLWH) are at a higher risk of fatal suicide attempts compared to the general population due to the ... -
Predicting switch to second-line antiretroviral therapy regimen: A comparison of the traditional linear classification methods and advance nonlinear machine learning algorithms
(Makerere University, 2022)Introduction: Due to changes in data patterns, self-learning approaches have been adopted in research which is commonly known as Machine Learning (ML). ML has been used previously to predict health outcomes such as early ... -
A unified spatiotemporal sleep mode approach for energy efficient dense HetNet’s using machine learning
(Makerere University, 2024-11)Future cellular networks are characterized by dense deployment of heterogeneous networks due to the ever increasing data traffic demand. However, the dense deployment of small base stations in a heterogeneous network ...