Conference Papers/Proceedings

Permanent URI for this collectionhttp://localhost:4000/handle/20.500.12504/625

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    A dictionary learning approach for joint reconstruction and denoising in low field magnetic resonance imaging
    (2021 IST-Africa Conference (IST-Africa), 2021-10) 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.
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    Blockchain technology needs for sustainable mineral supply chains: A framework for responsible sourcing of Cobalt.
    (Elsevier, 2022) Godfrey, Mugurusi; Emmanuel, Ahishakiye
    Blockchain technology has recently become the go-to solution for companies and industries that seek to enhance value chain traceability of their products, and transparency in their supply chains. Because of these benefits, it's been proposed for monitoring environmental, social, and governance (ESG) performance and compliance in industries that have weak regulatory and formal structures. The cobalt mining industry especially in the Democratic Republic of Congo, the world’s biggest producer of cobalt ore used in the manufacturing of lithium-ion batteries, is one such environment that’s characterized by conflict, and serious human rights abuses. The key actors in the cobalt supply chain, therefore, face the tradeoff involving maintaining long-term supply versus reducing the risks associated with cobalt sourced from locations with poor environmental and human rights records. Most of such problems emerge from Artisanal and small-scale mining. This paper presents an attempt to tightly link existing blockchain technology frameworks in the cobalt industry with ESG performance of companies to enable them to audit the chain of custody journeys for their components and ultimately sustainability performance. We present a responsible sourcing framework to connect blockchain source data needs to ESG metrics to help companies build interoperable but understandable blockchain architectures.
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    Prediction of cervical cancer basing on risk factors using ensemble learning
    (IEEE, 2020-05-22) Ahishakiye, Emmanuel; Wario, Ruth; Mwangi, Waweru; Taremwa, Danison
    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 used an ensemble learning technique in the prediction of cervical cancer using risk factors. This technique was selected because it combines several machine learning techniques into one model to decrease variance, bias, and improvement in performance. K-Nearest Neighbor, Classification and Regression Trees, Naïve Bayes Classifier, and Support Vector Machine. Classification methods were selected because the interest of this study was to solve a classification problem. Therefore these algorithms could work well within our problem domain. The final prediction model was trained and validated, and our experimental results revealed that our model had an accuracy of 87.21%.
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    Evaluation of requirements for the design of water resource management ICT model for integrated water resources management: the case of management of lake Victoria basin
    (IEEE, 2020-05-18) Odongtoo, Godfrey; Lating, Peter O.; Ssebugwawo, Denis
    The paper addresses the use of partial least square-structural equation modelling (PLS- SEM) technique to evaluate the requirements for a design of a water resource management (ICT) model for an integrated water resource management. Researchers employed a quantitative approach using smart-PLS version 3. The sample size of 152 was computed from a population size of 245 across some districts within LVB. This study revealed the perceptions of different experts based on their experiences in water resource sectors. The findings of the study discovered that distribution and management, efficient use, pollution reduction, water conservation & storage factors had a significantly positive effect on the design of an effective water resource management ICT model. Pollution reduction had the highest path coefficient (beta=0.536) thus having the highest influence on the design of water resource management ICT model. The four exogenous latent constructs wholesomely explained 65.2% of the variance in the design of an effective water resource management ICT model that was also confirmed by the value of R 2 being 0.652. The study recommends putting a special attention on a pollution reduction related requirement to achieve an effective design of water resource management ICT model. These findings can support practitioners and decision makers engaged in the management of LVB and other water bodies in designing an effective water resource management ICT model.