School of Technology
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Item Data Mining in Pediatric Radiology in the Era of Artificial Intelligence(Springer, 2026) Guarnera, Alessia; Ghosh, Adarsh; Gikera, Rufus; Vahdati, Sanaz; Zhang, Kuan; Gupta, AmitData mining is the systematic process of extracting useful knowledge from large multimodal datasets and is increasingly enabled by artificial intelligence (AI) methods. Pediatric radiology is a natural field for data mining because multimodal data sources, including images, reports, metadata, and electronic health records, together capture rich information on anatomy, disease, treatment, and outcomes. In the current era, the boundaries between data mining and AI are increasingly blurred. AI assists in key steps of the mining workflow through automated labeling, information extraction, and representation learning, while data mining provides the high-quality curated datasets that underpin model performance, generalizability, and safety. This review, therefore, examines both domains together, emphasizing their interdependence in the pediatric context. We describe core concepts and workflows of data mining in pediatric radiology, including data collection, linkage, annotation, analysis, validation, and governance, and outline how modern AI tools such as deep learning, large language models, multimodal fusion, and federated learning support advanced pattern discovery across limited and heterogeneous pediatric datasets. We summarize current and emerging clinical applications across diagnosis, prognosis, radiation dose monitoring, operational analytics, reporting safety nets, and continual learning. We then discuss current challenges related to data quality and standardization, ethics, regulation, workflow integration, resource disparities, sustainability, and explainability. Finally, we highlight future perspectives, including synthetic data generation, foundation models, structured reporting, and pediatric-focused ethical frameworks that aim to enable safe, transparent, and equitable integration of AI-driven data mining to improve outcomes in children.Item Knowledge management considerations in learning management systems in higher education institutions: a systematic review, synthesis and research agenda(Emerald Publishing, 2023) Omanyo, Joshua O.; Ndiege, Joshua R.Purpose This paper aims to examine the state of research on the symbiotic relationship between knowledge management and learning management systems in advancing the mutual strategic agenda of the two initiatives in higher education institutions (HEIs), so as to uncover the themes that have been studied, identify gaps in the existing studies and suggest future areas of research work. Design/methodology/approach The study adopted systematic literature review (SLR), in which 64 articles published between 2010 and 2022 were identified and analyzed. Findings Whereas the review revealed some focus areas that have been researched, it also found that only few studies have explicitly explored the symbiotic relationship between knowledge management and learning management systems, with fewer articles exploring this relationship finding their way to mainstream journals. Thus, the findings showed that examination of the interlink between knowledge management and learning management systems in HEIs is still less explored and has multiple possibilities for future research with potential benefits to the higher education industry. Originality/value Although different SLRs exist separately in the fields of knowledge management and learning management systems, there seem to be no reviews on the interconnection between the two fields in the context of HEIs. Additionally, this review offers insights into future research avenues for theory, content and context of interplay between knowledge management and learning management systems in HEIs.Item E-learning and sustainability of higher education in Sub-Saharan Africa: a review and synthesis(Emerald Publishing, 2025) Omanyo, Joshua O.; Ndiege, Joshua R. A.Purpose This study aims to examine the state of literature on the role of e-learning in the sustainability of higher education institutions in Sub-Saharan Africa, with the goal of identifying explored thematic areas, finding out the deficiencies in extant literature and recommending areas of future research work. Design/methodology/approach The research used a systematic literature review, examining articles published between 2012 and 2022. In total, 52 publications were identified and subjected to analysis. Findings The findings reveal that few studies have explored the relationship between e-learning and the sustainability of higher education in Sub-Saharan Africa, with larger economies in the region dominating research output. In addition, traditional technology adoption and social learning theories dominate the theoretical frameworks in this area. Moreover, the authors observed limited adaptation of these theories to local contexts, leading to outcomes with limited contextual details or lack of the same. Despite its potential, e-learning has yet to be fully embraced as a strategic tool for the sustainability of higher education in Sub-Saharan Africa. Originality/value Although various systematic literature reviews exist in the field of sustainability in higher education, there seem to be no reviews specifically focused on e-learning within the context of Sub-Saharan Africa. This review sheds some light on potential future research paths regarding the theory, content and context of e-learning for the sustainability of higher education in Sub-Saharan Africa, and by extension, in developing countries worldwide.Item Learning management systems and sustainability in Kenyan higher education institutions: a multi-group analysis using PLS-SEM(Emerald Publishing, 2026) Omanyo, Joshua O.; Ndiege, Joshua R. A.; Okello, Gabriel O.Purpose – This study seeks to examine how learning management system (LMS) quality influences perceived sustainability value (PSV) among usersin Kenyan higher education institutions(HEIs), by examining usage and user satisfaction as mediators based on the DeLone and McLean information systems success model (DMISSM). The triple bottom line (TBL) theory provided the theoretical grounding for measuring the overall perceived sustainability impact derived from LMS utilization in HEIs. Design/methodology/approach – This is a cross-sectional study. A stratified random sampling technique was used to collect data from 384 students and 375 instructors. Hypothesis testing was carried out following partial least squares structural equation modeling procedures using R statistical software. Findings – The empirical findings revealed that user satisfaction significantly mediated the relationship between LMS quality and PSV, while LMS Use had an insignificant mediating role in the same relationship. The impact of user satisfaction on PSV varied between teachers and students, with teachers experiencing a stronger influence. The findings also confirmed a statistically significant direct relationship between LMS quality and PSV. Originality/value – This study advances the DMISSM by adding learner and instructor quality contributors to the antecedent quality constructs, while integrating TBL sustainability dimensions as net benefits. The integration of sustainability into the model creates a new pathway for evaluating educational technologies beyond technical and user-centric metrics to a more long-term value-driven success aligned with global sustainability goals.Item A visual analytic framework for monitoring terror attacks: a case study of Mandera county, Kenya(KCA University, 2025) Mong'are, Brion G.Terrorism remains a persistent and evolving threat in Kenya, particularly in border counties such as Mandera, which face heightened vulnerability due to their proximity to Somalia. The Country’s ongoing instability, weak governance structures, and the presence of terrorist groups like al-Shabaab (AS) create fertile ground for cross-border militant activity, posing significant challenges to both regional security and local stability. A critical gap in current counterterrorism efforts is the absence of localised, data-driven frameworks capable of monitoring and predicting terrorist activities in real time. This study addresses that gap by developing a Visual Analytics (VA) framework tailored to monitor, analyse, and predict terrorist incidents, using Mandera County as a case study. Beyond processing complex datasets, the framework offers strategic guidance to decision-makers, enhancing situational awareness and enabling proactive counterterrorism interventions. The core objective is to strengthen counterterrorism strategies through spatiotemporal data mining techniques, such as cluster analysis, association analysis, and outlier detection, to derive actionable insights from both structured and unstructured data sources. The methodology integrates data from social media, security reports, and established databases such as the Global Terrorism Database (GTD) and the Armed Conflict Location & Event Data Project (ACLED). Visuals in the framework are guided by Shneiderman’s Visual Information-Seeking Mantra: “overview first, zoom and filter, then details on demand.” Through this approach, the system identifies attack hotspots, temporal patterns, and early warning indicators, delivering a user-centric tool for informed decision-making. Findings demonstrate the framework’s effectiveness in predicting high-risk areas and attack types, with particular emphasis on armed assaults and improvised explosive device (IED) incidents, which constitute over 50% of recorded attacks in Mandera. The results show improved allocation of security resources, enhanced inter-agency coordination, and timely intervention capabilities. Key recommendations include deploying the framework in high-risk regions, integrating it with existing security infrastructure, and expanding its use to other terrorism-affected areas. The framework’s scalable and adaptable design positions it as a valuable tool for strengthening counterterrorism efforts across Kenya and similar contexts.Item Detecting Data Exfiltration Anomalies in Academic Networks Using the Isolation Forest Algorithm(KCA University, 2025) Arusei, Mike K.; Dr. Njenga, StephenAcademic networks face increased risks of data exfiltration due to sensitive personal information and research data. Traditional supervised detection models rely on labeled datasets which are often unavailable in resource constrained institutions. This study investigates the applicability of the unsupervised Isolation Forest algorithm for detecting anomalous network traffic indicative of data exfiltration. The research utilized the CICIDS2017 dataset focusing on the Thursday-WorkingHours-Afternoon-Infiltration subset. Key features including Flow Duration, Total Fwd Packets, Flow Bytes/s, Flow IAT Mean, and Destination Port were preprocessed and normalized for modeling. The model achieved a precision of 1.00, recall of 0.99 and F1-score of 1.00 for anomalous traffic detection successfully identifying approximately 4.8% of flows as anomalous. Comparative analysis with previous methods, including supervised Random Forest and SVM demonstrated that Isolation Forest offers competitive accuracy with lower computational overhead and does not require labeled data. The findings highlight the algorithm’s suitability for academic network monitoring, providing an effective early warning mechanism while emphasizing the importance of threshold tuning to reduce false positives.Item A knowledge-as-a-service support framework for ambient learning in Kenya(13th IADIS International Conference Information Systems, 2020) Mburu, Lucy W.; Karanja, Richard; Nyaga, Simon M.Knowledge as a Service (KaaS) is a relatively new model, albeit one that is rapidly gaining popularity within cloud computing environments. Over the recent years, learners have experienced a constant need to access on demand knowledge that is fully aligned with the paradigm of cloud computing. This need stems from the knowledge that users will be able to access applications and the information therein on demand, without the restrictions that are usually imposed by time and space. The KaaS model terms knowledge as the understanding of information based on its relevance to a specific context and problem area, thus forming a valuable resource for the human decision-making process. As motivated by the global sustainable development goal of ensuring inclusive and equitable quality education to promote learning opportunities for all, this research has developed a framework that is hinged on KaaS and utilizes knowledge from ambient learning systems. The main aim is to provide a platform for disseminating and exploiting the available knowledge to aid the learning process and, thus, to improve the quality of education on the ambient learning system. The research further explores how collaborative effort can be used to form a knowledge network that allows access to heterogeneous sources of knowledge. The research outcomes will benefit knowledge consumers such as the developers of ambient learning systems.Item An empirical approach to mobile learning on mobile ad hoc networks(Institute of Electrical and Electronics Engineers (IEEE), 2020) Mwendia, Simon N.; Ichaba, Mutuma; Musau, FelixMobile Ad hoc Networks (MANETs) are made up of mobile nodes that are interconnected wirelessly, while topology changes as mobile nodes join and leave the network. MANETs do not depend on fixed infrastructure. Due to their dynamism and low cost (no infrastructure is needed), MANETs have been proposed as a mechanism suitable for carrying out mobile learning (m-Leaning) in developing countries. However, systematic literature review indicates that the existing MANETs-based m-Learning models are disadvantaged because they fail to identify possible routing protocols able to support such models. As a result, it becomes very difficult to implement the existing MANET-based m-Learning models. This paper characterizes MANETs-based m-Learning proposed by [1]. Thereafter, it uses area, nodes, and data packets information as basic scalar parameters on Zone Routing Protocol (ZRP) simulated on NS-2 and ZRP code supplemented with positional and directional information of nodes in the Intrazonal Routing Protocol (IARP) on OMNET++. According to simulation results, a directional-positional enhanced ZRP outperforms regular ZRP on packet delivery ratio, delay and overall data packet throughput. Results from the simulation suggests that a supplemented ZRP is a feasible routing protocol for supporting m-Learning in a typical university campus based on the identified basic scalar parameters and characterization of [1].Item Developing an e-learning theory for interaction and collaboration using grounded theory: a methodological approach(The Qualitative Report at NSUWorks, 2021) Kibuku, Rachael N.; Ochieng, Daniel O.; Wausi, Agnes N.Grounded Theory (GT) is becoming an increasingly prevalent research methodology in many fields. Although researchers use it in qualitative and quantitative studies, it is more popular with qualitative studies, as evidenced by the citations from previous research. This paper aims to document and present how we used GT in our qualitative research to construct an e-learning theory for interaction and collaboration. It also includes the justification of GT. We adopted and adapted the constructivist GT (CGT). Therefore, this paper discusses the CGT methodology, its philosophical, ontological and epistemological perspectives. It also includes the research design that captures how we sampled the participants, collected, analyzed and interpreted the data, and how we documented the research findings in the context of CGT. It also includes the justification of the decisions we made and the extent to which they align with CGT. Using CGT, we listened to, observed and captured e-learners’ and e-tutors’ stories and experiences which yielded rich and insightful data that informed the development of the e-learning theory for interaction and collaboration. We also present the challenges we experienced when using CGT and the strategies we used to overcome them. Finally, we have included the methodological insights we drew from using CGT in our research. This paper has presented the CGT design strategy; thus, it will be helpful, especially to novice and future researchers aspiring to use the methodology to conduct their research.Item E-learning challenges faced by universities in Kenya: a literature review(2020) Kibuku, Rachael N.; Ochieng, Daniel O.; Wausi, Agnes N.Some institutions of higher education in Kenya have adopted e-Learning with the aim of coping with the increased demand for university education and to widen access to university training and education. Though there are advantages that accrue from adopting e-Learning; its implementation and provision has not been smooth sailing. It has had to contend with certain national, organisational, technical and social challenges that undermine its successful implementation. This paper therefore aims to present a literature review of the challenges faced in the implementation and provision of e-Learning in universities in Kenya. The scoping review method was used to identify and analyze the literature of the e-Learning challenges. Some of the challenges revealed include: lack of adequate e-Learning policies, inadequate Information and Communication Technology (ICT) infrastructure, the ever evolving technologies, lack of technical and pedagogical competencies and training for e-tutors and e-learners, lack of an e-Learning theory to underpin the e-Learning practice, budgetary constraints and sustainability issues, negative perceptions towards e-Learning, quality issues, domination of e-Learning aims by technology and market forces and lack of collaboration among the e-Learning participants. These challenges need to be addressed to minimise their impact on implementation and delivery of e- Learning initiatives in institutions of higher education in Kenya. This analysis of the e-Learning challenges forms the basis for the ongoing research that seeks to explore and establish possible strategies to address some of these challenges.