School of Computing and Information Science

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Adaptive-size dictionary learning using information theoretic criteria for image reconstruction from undersampled k-space data in low field magnetic resonance imaging
(BMC Medical Imaging, 2020-06) Emmanuel, Ahishakiye; Martin, Bastiaan Van Gijzen; Julius, Tumwiine; Johnes, Obungoloch
Background Magnetic resonance imaging (MRI) is a safe non-invasive and nonionizing medical imaging modality that is used to visualize the structure of human anatomy. Conventional (high-field) MRI scanners are very expensive to purchase, operate and maintain, which limit their use in many developing countries. This study is part of a project that aims at addressing these challenges and is carried out by teams from Mbarara University of Science and Technology (MUST) in Uganda, Leiden University Medical Center (LUMC) in the Netherlands, Delft University of Technology (TU Delft) in the Netherlands and Pennsylvania State University (PSU) in the USA. These are working on developing affordable, portable and low-field MRI scanners to diagnose children in developing countries with hydrocephalus. The challenges faced by the teams are that the low-field MRI scanners currently under development are characterized by low Signal-to-Noise Ratio (SNR), and long scan times. Methods We propose an algorithm called adaptive-size dictionary learning algorithm (AS-DLMRI) that integrates information-theoretic criteria (ITC) and Dictionary learning approaches. The result of the integration is an adaptive-size dictionary that is optimal for any input signal. AS-DLMRI may help to reduce the scan time and improve the SNR of the generated images, thereby improving the image quality. Results We compared our proposed algorithm AS-DLMRI with adaptive patch-based algorithm known as DLMRI and non-adaptive CSMRI technique known as LDP. DLMRI and LDP have been used as the baseline algorithms in other related studies. The results of AS-DLMRI are consistently slightly better in terms of PSNR, SNR and HFEN than for DLMRI, and are significantly better than for LDP. Moreover, AS-DLMRI is faster than DLMRI. Conclusion Using a dictionary size that is appropriate to the input data could reduce the computational complexity, and also the construction quality since only dictionary atoms that are relevant to the task are included in the dictionary and are used during the reconstruction. However, AS-DLMRI did not completely remove noise during the experiments with the noisy phantom. Our next step in our research is to integrate our proposed algorithm with an image denoising function.
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.
Burden of cumulative risk factors associated with non-communicable diseases among adults in Uganda: evidence from a national baseline survey
(International Journal for Equity in Health, 2016-12-01) Wesonga, Ronald; Guwatudde, David; Bahendeka, Silver K.; Mutungi, Gerald; Nabugoomu, Fabian; Muwonge, James
Modification of known risk factors has been the most tested strategy for dealing with noncommunicable diseases (NCDs). The cumulative number of NCD risk factors exhibited by an individual depicts a disease burden. However, understanding the risk factors associated with increased NCD burden has been constrained by scarcity of nationally representative data, especially in the developing countries and not well explored in the developed countries as well. Methods: Assessment of key risk factors for NCDs using population data drawn from 3987 participants in a nationally representative baseline survey in Uganda was made. Five key risk factors considered for the indicator variable included: high frequency of tobacco smoking, less than five servings of fruit and vegetables per day, low physical activity levels, high body mass index and raised blood pressure. We developed a composite indicator dependent variable with counts of number of risk factors associated with NCDs per participant. A statistical modeling framework was developed and a multinomial logistic regression model was fitted. The endogenous and exogenous predictors of NCD cumulative risk factors were assessed. Results: A novel model framework for cumulative number of NCD risk factors was developed. Most respondents, 38 · 6% exhibited one or two NCD risk factors each. Of the total sample, 56 · 4% had at least two risk factors whereas only 5.3% showed no risk factor at all. Body mass index, systolic blood pressure, diastolic blood pressure, consumption of fruit and vegetables, age, region, residence, type of residence and land tenure system were statistically significant predictors of number of NCD risk factors (p < 0 · 05). With exception to diastolic blood pressure, increase in age, body mass index, systolic blood pressure and reduction in daily fruit and vegetable servings were found to significantly increase the relative risks of exhibiting cumulative NCD risk factors. Compared to the urban residence status, the relative risk of living in a rural area significantly increased the risk of having 1 or 2 risk factors by a multiple of 1.55. Conclusions: The non-communicable disease burden is on the increase, with more participants reporting to have at least two risk factors. Our findings imply that, besides endogenous factors, exogenous factors such as region, residence status, land tenure system and behavioral characteristics have significant causal effects on the cumulative NCD risk factors. Subsequently, while developing interventions to combat cumulative risk factors of NCDs, the Ministry of Health needs to employ a more holistic approach to facilitate equitable health and sensitization across age, residence and regional divide.
Classification of cassava leaf diseases using deep Gaussian transfer learning model
(Engineering Reports, 2023-03) Emmanuel, Ahishakiye; Ronald, Waweru Mwangi; Petronilla, Murithi; Fredrick, Kanobe; Danison, Taremwa
In Sub-Saharan Africa, experts visually examine the plants and look for disease symptoms on the leaves to diagnose cassava diseases, a subjective method. Machine learning algorithms have been employed to quickly identify and classify crop diseases. In this study, we propose a model that integrates a transfer learning approach with a deep Gaussian convolutional neural network model. In this study, two pre-trained transfer learning models were used, that is, Mobile Net V2 and VGG16, together with three different kernels: a hybrid kernel (a product of a squared exponential kernel and a rational quadratic kernel), a squared expo-nential kernel, and a rational quadratic kernel. In experiments using MobileNet V2 and the three kernels, the hybrid kernel performed better, with an accuracy of 90.11%, compared to 86.03% and 85.14% for the squared exponential kernel and a rational quadratic kernel, respectively. Additionally, experiments using VGG16 and the three kernels showed that the hybrid kernel performed better, with an accuracy of 88.63%, compared to the squared exponential kernel’s accuracy of 84.62% and the rational quadratic kernel’s accuracy of 83.95%, respectively. All the experiments were done using a traditional computer with no access to GPU and this was the major limitation of the study.
Comparative Performance of Machine Leaning Algorithms in Prediction of Cervical Cancer
(IEEE xplore, 2021-10) 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.
A comparative study of some pre-trained models on transfer learning approach in detection and classification of cassava leaf diseases
(SSRN, 2022) Emmanuel, Ahishakiye; Waweru, Mwangi; Petronilla, Muthoni; Danison, Taremwa; Fredrick, Kanobe
Cassava diseases affect cassava harvest posing the greatest danger to the food security and livelihoods of more than 200 million people. To identify cassava diseases, government professionals visit various sections of the country and visually score the plants by looking for disease indicators on the leaves. This procedure is notoriously subjective; it is not uncommon for specialists to differ on a plant's diagnosis. Automating the detection and classification of crop diseases could help professionals diagnose diseases more accurately and allow farmers in remote locations to monitor their crops without the help of specialists. Machine learning algorithms have been used in the early detection and classification of crop diseases. Motivated by the current developments and many influential studies in the field of deep learning and transfer learning models in the detection and classification of crop diseases, this study evaluates the performance of VGG16, VGG19, ResNet50, InceptionV3, DenseNet201, and MobileNetV2 in detection and classification of cassava leaf diseases. Fine-tuning of the hyperparameters was done during training to improve the accuracy of the models. Experimental results on the cassava dataset revealed that InceptionV3, DenseNet201, and MobileNetV2 models had high training accuracy but low validation accuracy with various epochs which means that they had issues with over fitting while ResNet50 had issues with underfitting. Moreso, VGG16 and VGG19 models performed well on both training and validation datasets, though VGG16 performed relatively well compared to VGG19.
Consumer segmentation and profiling using demographic data and spending habits obtained through daily mobile conversations
(American Journal of Modeling and Optimization, 2018-08) 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.
Data literacy: a catalyst for improving research publication productivity of kyambogo university academic staff
(Journal of eScience Librarianship (JeSLIB), 2023-11) Robert, Stalone Buwule; State, Eliz Nassali; Edward, Mukiibi
Objective: The aim of this study is to explore how data literacy can influence the research and publications productivity of Kyambogo University academic staff. Methods: The study employed a literature review to collect detailed information. It observed lessons, and studied patterns of the phenomenon to explore data literacy initiatives that can be used by Kyambogo University academic staff to improve their research publications productivity and also to mitigate the accrued challenges. Results: The paper explored eight initiatives through which data literacy skills could enhance the research productivity of Kyambogo University academic staff. These were awareness and promoting freedom of using open data, engaging students in data literacy activities, pedagogical reflection, datafication of single and joint academic staff publications, visualization of data, storytelling, ethical use of data, and preservation of research data. Conclusions: While this paper relies on the context of the Kyambogo University academic staff, the authors posit that these data literacy skills can be embraced by universities in developing economies; especially those struggling with poor research and publications productivity. The paper further identifies areas where universities in developing economies, in conjunction with their libraries, can improve the academic staff pedagogy and compliance to eScience through polishing their data literacies.
Detecting the risk of customer churn in telecom sector: a comparative study
(Mathematical Problems in Engineering : Hindawi, 2022-07-18) Edwine, Nabahirwa; Wang, Wenjuan; Song, Wei; Ssebuggwawo, Denis
Churn rate describes the rate at which customers abandon a product or service. Identifying churn-risk customers is essential for telecom sectors to retain old customers and maintain a higher competitive advantage. The purpose of this paper is to explore an effective method for detecting the risk of customer churn in telecom sectors through comparing the advanced machine learning methods and their optimization algorithms. Based on two different telecom datasets, Mutual Information classifier was firstly utilized to select the most critical features relevant to customer churn. Next, the controlled-ratio undersampling strategy was employed to balance both minority and majority classes. Key hyperparameter optimization algorithms of Grid Search, Random Search, and Genetic Algorithms were then combined to fit the three promising machine learning models-Random Forest, Support Vector Machines, and K-nearest neighbors into the customer churn prediction problem. Six evaluation metrics-Accuracy, Recall, Precision, AUC, F1-score and Mean Absolute Error, were last used to evaluate the performance of the proposed models. The experimental results have revealed that the RF algorithm optimized by Grid Search based on a low-ratio undersampling strategy (RF-GS-LR) outperformed other models in extracting hidden information and understanding future churning behaviors of customers on both datasets, with the maximum accuracy of 99% and 95% on the applied dataset 1-2 respectively.
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.
The effectiveness of COVID-19 surveillance applications in Uganda: assessment of a medical rapid response system
(ScienceOpen Preprints, 2024-03-06) Goswami, Nandu; Acanit, Mary; Buwule, Robert Stalone; Schmid-Zalaudek, Karin; Brix, Bianca
Different Information Communication Technologies (ICTs) health-based innovations such as cloud computing, web and mobile surveillance applications were used by proactive governments to fight COVID-19. Contact tracing mobile applications were used by more than 100 countries. However, the extent to which these surveillance applications have been used to track Covid-19 in Uganda is not clear. This study aimed to establish the use of COVID-19 surveillance applications in Uganda. This was a purely qualitative study. Health practitioners managing these surveillance applications were interviewed from Kampala City, Mukono and Wakiso districts of Uganda. The assessment of the COVID-19 surveillance applications underscores the relevance of health-based ICTS. The surveillance applications provided accurate, authoritative and timely data. However, there were false alerts as result of inaccurate data supplied by the applications. The study recommends increased facilitation of the surveillance officers, continuous training of surveillance teams and integration of the applications for the management of other non-communicable diseases.
Enhancing African indigenous knowledge collection management in Ugandan public university libraries: lived experiences of senior library staff
(IGI Global, 2023) Rugambwa, Nina Olivia; Akena, Francis Adyanga; Nabutto, Claire Clement Lutaaya; Bugembe, Kamulegeya Grace
Various studies in library and information science have emphasized that indigenous knowledge management is still a neglected area and a challenge in the discipline of information management. However, the rationale for this neglect and driving challenges in university libraries has not been documented from the practitioner's perspective. This chapter shares lived experiences from experienced senior staff of public University libraries in Uganda regarding the management of African Indigenous knowledge collections. The study uses the theoretical lens of Wilson's information behavior model interpolated with participants' views to gain insight into the perspectives of the practitioners. The findings revealed challenges in lack of appropriate metadata descriptors to accommodate this knowledge, biased knowledge organization tools that are incompatible with African indigenous knowledge metadata characteristics, and limited funding in university libraries for research and indigenous knowledge collection development.
An ensemble model based on learning vector quantization algorithms for early detection of cassava diseases using spectral data
(SpringerLink, 2023-03) Emmanuel, Ahishakiye; Waweru, Mwangi; Petronilla, Murithi; Ruth, Wario; Fredrick, Kanobe; Taremwa, Danison
In Sub-Saharan Africa, cassava is the second most significant food crop after maize. Cassava brown streak disease (CBSD) and cassava mosaic virus disease (CMD) combined account for nearly 90% of productivity losses. Automating the detection and classification of crop diseases could help professionals diagnose diseases more accurately and allow farmers in remote locations to monitor their crops without the help of specialists. Machine learning algorithms have been used in the early detection and classification of crop diseases. Previous research has used plant image data captured with smartphones. However, disease symptoms must be observable to use this strategy (using image data). Unfortunately, once symptoms appear on the aerial part of the plant, the root, which is the edible part of the plant, is destroyed. In this study, we used spectral data in a three-class classification challenge for diagnosing cassava diseases. We propose an ensemble model based on Generalized Learning Vector Quantization (GLVQ), Generalized Matrix LVQ (GMLVQ), and Local Generalized Matrix LVQ (LGMLVQ). Experimental results revealed that the LGMLVQ model had the best overall performance on the precision, recall, and F1-score followed by our proposed ensemble model, the GMLVQ model performed third, and finally GLVQ model. Also, using an accuracy performance metric, LGMLVQ had overfitting issues even though it had the highest accuracy of 100%, followed by our proposed ensemble model with an accuracy of 82%, and then the third in performance was the GMLVQ model with an accuracy of 74% and the least performed model on accuracy was GLVQ model with an accuracy of 56%.
Evaluation of Information literacy training for enhanced teaching, learning, and research competence for academic staff and students at the University of Rwanda: A descriptive mixed-method study
(Qualitative and Quantitative Methods in Libraries, 2024-07-09) Namuleme, Robinah Kalemeera; Umutesi, Annonciatte
This study evaluates the effectiveness of Information Literacy Training (ILT) for academic staff and postgraduate students in the digital era. A descriptive mixed-method approach was used, with data collected from 87 postgraduate students, academic staff, and librarians from nine Campuses across the University of Rwanda. The results showed that providing ILT at the beginning of student’s study program significantly enhanced their capacity to identify, access, evaluate, and use information effectively. It also improved students’ competencies in research and scholarly publishing. The study highlights the importance of ILT evaluation in training, providing critical insights into program effectiveness, efficiency, and long-term impact. It recommends ILT as a mandatory component in the curriculum for all students. Keywords: Information Literacy, Critical thinking, Reference Management, Digital literacy, Literacy proficiency.
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.
Factors influencing the use of e-library resources by postgraduate engineering students at Kyambogo University in Uganda
(Sage Journals, 2024-10) Acanit, Mary; Ngulube, Patrick; Mojapelo, Samuel Maredi
The aim of this study was to investigate the factors influencing the use of e-library resources among postgraduate engineering students at Kyambogo University in Uganda to make suggestions on how to improve access to and use of e-library resources. The study adopted a survey research design to collect data from postgraduate engineering students. Following a census sampling strategy, data was obtained from 58 out of 80 registered postgraduate engineering students using online self-administered questionnaires. The findings revealed that the use of e-library resources by postgraduate engineering students was influenced by ease of use, convenience, level of awareness, and information search skills. However, access restrictions and high internet costs negatively affected e-library resources usage. It is the conclusion of the study that the use of e-library resources was largely influenced by personal factors. This study has implications on e-library resources collection development, policy development, and service delivery in academic libraries. The study bridges the knowledge gap in the use of e-library resources among postgraduate engineering students.
Fostering a Culture of Quality Research at a Young Institution: Insights from Kyambogo University.
(Library waves, 2023-12) Mukiibi, Edward; Buwule, Robert Stalone; State, Eliz Nassali
The study explored the trends and quality of research output of academic staff at Kyambogo University, Uganda for the period 2003 to 2020. Using desk research content analysis, the findings showed 199 (47%) out of 425 staff had published 440 articles of which 266 (60%) were credible. The three most productive Faculties were: Science 110, Education, 106, and Arts and Social Sciences with 90 publications. The most prolific author produced 35 articles 6 of which were the first author. This productivity was attributed to factors commended for leveraging the identified niche in science, education, and humanities. The study is instrumental in advancing strategies that could foster a culture of quality research through deliberate policy actions.
Misinformation, indigenous health information and HIV prevention among in- school adolescents, Uganda
(DigitalCommons@University of Nebraska - Lincoln Library Philosophy and Practice (e-journal), 2021-06) Olivia, Rugambwa Nina; Ruth, Nalumaga; Ikoja-Odongo, J.R; Otim, Onapa Maxwell
HIV/AIDS is still a major killer disease among adolescents in sub- Saharan Africa and Uganda in particular. There are many factors attributed to high HIV infections among young people in Uganda such as, multiple sexual partners, low condom use, those born with HIV and poverty. High level misinformation in the indigenous knowledge related to sexuality and HIV prevention remains an under investigated and under documented factor escalating the disease. Yet, the indigenous knowledge health information system is a major critical information source adolescents depend on for health information on HIV prevention in Uganda. Indigenous knowledge informs health interventions for HIV prevention among young people in many African communities and is relied on for decision making on health choices. This paper documents indigenous knowledge on practices for HIV prevention among secondary school adolescents in an urban context in Kampala District. A qualitative case study research design was employed. The findings revealed use of witchcraft, local herbs, male circumcision, elongation of labia menorah, abstinence, sexual taboos like not having sex with women in their menstrual periods and avoiding homosexuality as the key indigenous management practices for HIV prevention. Some of the findings were in agreement with existing biomedical information on prevention strategies while others were in contradiction. The findings also revealed that adolescents have a lot of misinformation on HIV prevention; such information may not support right healthy choices. The study contributes to the body of existing knowledge on HIV prevention using indigenous knowledge practices. The findings appeal to information science professionals to participate in ensuring that communities they serve have access to accurate and timely information to curb health emergencies and improve on health of societies they serve.
Ontology of plagiarism: the non-academic perspectives
(International Journal of Advanced Research, 2024-01) Ongaya, Kizito; Agatha, Alidri; Emily, Bagarukayo; Benedict, Oyo; Charles, Bazibu M.; Godfrey, Luyimbazi
Existence of plagiarism is an occupation of education in two dimensions: for learning and a mental process construct of lack of acknowledgement of innovations in learning. Unfortunately, the common concepts of plagiarism has not been clarified in learning process. This paper argues that plagiarism is an inherent natural process of learning. With the objectives; to examine the intrinsic nature of plagiarism and to explore the trans-disciplinary existence of plagiarism in human knowledge as categorised by Dewey Classification scheme 000-999. The study applied positivist paradigm and investigated the existence in relationship between learning processes and plagiarism. The study quantitatively measured opinions of 28 participants in these processes using the Likert scale. Dewey Decimal Classification Systems was used to examine epistemic harnessing of plagiarism in the advancement of different disciplines. The findings were that plagiarism is a natural, intrinsic process of learning through which research, innovations and evolution builds on. The paper concludes by putting a case that acknowledgement and development of referencing and citation technologies are evidence of ontological realities of plagiarism and evidence of the learning process.
Optimizing cervical cancer classification using transfer learning with deep gaussian processes and support vector machines
(Discover Artificial Intelligence, 2024-10-30) Ahishakiye, Emmanuel; Kanobe, Fredrick
Background Cervical cancer is the fourth most frequent cancer in women worldwide. Even though cervical cancer deaths have decreased significantly in Western countries, low and middle-income countries account for nearly 90% of cervical cancer deaths. While Western countries are leveraging the powers of artificial intelligence (AI) in the health sector, most countries in sub-Saharan Africa are still lagging. In Uganda, cytologists manually analyze Pap smear images for the detection of cervical cancer, a process that is highly subjective, slow, and tedious. Machine learning (ML) algorithms have been used in the automated classification of cervical cancer. However, most of the MLs have overfitting limitations which limits their deployment, especially in the health sector where accurate predictions are needed. Methods In this study, we propose two kernel-based algorithms for automated detection of cervical cancer. These algorithms are (1) an optimized support vector machine (SVM), and (2) a deep Gaussian Process (DGP) model. The SVM model proposed uses an optimized radial basis kernel while the DGP model uses a hybrid kernel of periodic and local periodic kernel. Results Experimental results revealed accuracy of 100% and 99.48% for an optimized SVM model and DGP model respectively. Results on precision, recall, and F1 score were also reported. Conclusions The proposed models performed well on cervical cancer detection and classification, and therefore suitable for deployment. We plan to deploy our proposed models in a mobile application-based tool. The limitation of the study was the lack of access to high-performance computational resources.

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