Department of Computer Sciencehttps://hdl.handle.net/20.500.12504/2082024-03-25T17:15:15Z2024-03-25T17:15:15ZConsumer segmentation and profiling using demographic data and spending habits obtained through daily mobile conversationsSamuel, W. KamandeEvans, A. K. MiritiEmmanuel, Ahishakiyehttps://hdl.handle.net/20.500.12504/13102023-07-19T00:09:32Z2018-08-01T00:00:00ZConsumer segmentation and profiling using demographic data and spending habits obtained through daily mobile conversations
Samuel, W. Kamande; Evans, A. K. Miriti; Emmanuel, Ahishakiye
Knowledge of customer behaviour helps organizations to continuously re-evaluate their strategies with the consumers and plan to improve and expand their application of the mosteffective strategies. Using expenditure data collected through daily mobile conversations with consumers in Kenya, this study sought to compare various clustering algorithms and establish one that best segments consumers, and subsequently providing profiles that provide a basis for marketing and brand strategy based on existing demographic data – age, gender, region and primary income source. K-Means, Hierarchical and Partitioning around Medoids (PAM) clustering algorithms were compared using internal and stability validation tests. Hierarchical clustering with four clusters had the best Connectivity (0.847) and Silhouette width (0.924) measures. Stability validation compares the results by removing a column, one at a time. Average Proportion of Non-overlap (APN), Average Distance (AD), Average Distance Between Means (AND) and Figure of Merit (FOM) were used to compare the algorithms. Again, Hierarchical clustering with four clusters was found to partition the data best. The study forms a basis for the use of additional profile descriptors once available to provide a firmer understanding of the customer segments built on expenditure data in Kenya.
2018-08-01T00:00:00ZComparative Performance of Machine Leaning Algorithms in Prediction of Cervical CancerEmmanuel, AhishakiyeWaweru, MwangiPetronilla, MuthoniLawrence, NderuRuth, Wariohttps://hdl.handle.net/20.500.12504/13082023-05-24T00:49:02Z2021-10-01T00:00:00ZComparative Performance of Machine Leaning Algorithms in Prediction of Cervical Cancer
Emmanuel, Ahishakiye; Waweru, Mwangi; Petronilla, Muthoni; Lawrence, Nderu; Ruth, Wario
Cervical cancer is among the most common types of cancer affecting women around the world despite the advances in prevention, screening, diagnosis, and treatment during the past decade. Cervical cancer can be treated if diagnosed in its early stages. Machine learning algorithms like multi-layer perceptron, decision trees, random forest, K-Nearest Neighbor, and Naïve-Bayes have been used for the prediction of cervical cancer to aid in its early diagnoses. In this study, we compare the performance of ensemble methods (AdaBoost, Stochastic Gradient Boosting, Random Forests, and Extra Trees), and classification algorithms (K-Nearest Neighbor and Support Vector Machine) in the prediction of cervical cancer basin g risk factors. Ensemble methods and classification algorithms were used during this study. Ensemble methods were selected because they combine several machine learning techniques into one model to decrease variance, bias, or improve performance while the classification methods were selected because our dataset was generally categorical and therefore could work well with our problem domain. Experimental results revealed that all the algorithms did not perform well on the “imbalanced” dataset. Experiments on balanced revealed an improved performance. The performance metrics used include Fl-score, Area Under Curve (AUC), and Recall. Extra Trees performed better than the rest when using the Fl-score metric, Stochastic Gradient Boosting and Random Forest performed better than the rest when using the AUC metric, K-Nearest Neighbors outperformed the rest using the recall metric, and Extra Trees had the best accuracy 0.96. The application of machine learning methods in the prediction of cervical cancer using risk factors may lead to early detection of the disease which can be treated if diagnosed early. Six algorithms have been considered in this study. The general performance reveals that ensemble methods performed better than classification methods using both imbalanced and balanced datasets.
2021-10-01T00:00:00ZA dictionary learning approach for joint reconstruction and denoising in low field magnetic resonance imagingEmmanuel, AhishakiyeMartin, Bastiaan VAN GijzenXiujie, ShanJulius, TumwiineJohnes, Obungolochhttps://hdl.handle.net/20.500.12504/13072023-05-24T00:44:17Z2021-10-01T00:00:00ZA dictionary learning approach for joint reconstruction and denoising in low field magnetic resonance imaging
Emmanuel, Ahishakiye; Martin, Bastiaan VAN Gijzen; Xiujie, Shan; Julius, Tumwiine; Johnes, Obungoloch
Currently, many children with hydrocephalus in East Africa and other resource-constrained countries do not have access to Magnetic Resonance Imaging (MRI) scanners, the preferred imaging tool during the disease administration and treatment. Conventional MRI scanners are costly to buy and manage, which limits their utilization in low-income countries. Low-field MRI scanners can offer an affordable, sustainable, and safe imaging alternative to high-field MRI. However, they are associated with a low signal-to-noise ratio (SNR), and therefore the images obtained are noisy. In this study, we propose an algorithm that may help to alleviate the drawbacks of low-field MRI by improving the quality of images obtained. The proposed algorithm combines our previous proposed algorithm known as AS-DLMRI for image reconstruction and a nonlinear diffusion filter for image denoising. The formulation is capable of removing additive zero-mean white and homogeneous Gaussian noise, as well as other noise types that could be present in the original signal. Experiments on visual quality revealed that the proposed algorithm is effective in denoising images during reconstruction. The proposed algorithm effectively denoised a noisy phantom, and a noisy MRI image, and had better performance when compared to DLMRI and AS-DLMRI in terms of Peak Signal to Noise ratio (PSNR) and High-Frequency Error Norm (HFEN). Integrating AS-DLMRI and the nonlinear diffusion filter proved to be effective in improving the quality of the images during the experiments performed. The hybrid algorithm may be of great use in imaging modalities like low-field MRI that are associated with low SNR.
2021-10-01T00:00:00ZA secure web based records management system for prisons: a case of Kisoro prison in UgandaEmmanuel, AhishakiyeDanison, TaremwaElisha, Opiyo Omulohttps://hdl.handle.net/20.500.12504/13062023-05-24T00:37:55Z2017-01-01T00:00:00ZA secure web based records management system for prisons: a case of Kisoro prison in Uganda
Emmanuel, Ahishakiye; Danison, Taremwa; Elisha, Opiyo Omulo
Most Prisons in the developing countries are still using the traditional system – pen and papers, to keep track of
their records. This system takes long to finish a single transaction; this has led to loss of information of some
cases (crimes files), insecurity and data redundancy. Similarly, some cases have been reported where some
prison staff connives with clients (victims) to change and hide some information or files hence leading to
compromising the evidence of the matter. This has consequently resulted in time wastage to handle cases,
increased corruption and insecurity of important files hence making the whole process costly. Also when reports
are needed especially about prisoners, it takes a long time and therefore makes it hard for Prison Management to
take urgent decisions. This has created a lot of loopholes in the system because there is no tracking and/or
monitoring of the information available in the different Departments and there are no security measures in place
to safe guard the available information. This necessitated automating the system to make it more efficient and
effective. There was close study of the existing manual file based system that was in use, it was compared to the
proposed system. A prototype of a proposed system was developed to ease data access, security and retrieval for
instant report production by the prison management. The prototype was developed using MySql database, PHP,
CSS, JavaScript and HTML.
2017-01-01T00:00:00Z