School of Technology

Permanent URI for this collectionhttps://repository.kcau.ac.ke/handle/123456789/70

Browse

Search Results

Now showing 1 - 10 of 37
  • Thumbnail Image
    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, Amit
    Data 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.
  • Thumbnail Image
    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.
  • Thumbnail Image
    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.
  • Thumbnail Image
    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.
  • Thumbnail Image
    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.
  • Thumbnail Image
    Item
    Dynamics of technology transfer for innovation processes in a constrained resource settings :
    (Scientific & Academic Publishing Co., 2018) Kiarie, Peter; Mwangi, Henry; Rong, Chunming
    Abstract Technology transfer, defined as the movement of scientific inventions from an enterprise to the market place, is often a difficult and frustrating process. Stakeholders in this area of study are usually at different levels of understanding due to many factors involved and speak different languages. There are number of problems associated with technology transfer processes in constrained resource settings such as lack of researchers in specific domains, motivation, bureaucratic climate, inability to make effective public investments, funding and inappropriate infrastructure, culture among many others. This research explores the above problems and others discussed by varies researchers in technology transfer and particularly those in Technology-Organization-Environment (TOE) framework using Data analytics and System Dynamics modeling approaches. Data analytics will facilitate in developing a more promising and data rich System Dynamics model. The study will shed light on technical and social factors that lead to formulation of policies which enable accelerated technology transfers in constrained resource settings.
  • Thumbnail Image
    Item
    Supporting e-learning in computer-poor environments by combining oer, cloud services and mobile learning
    (ICCE2014, 2014) Mwendia, Simon Nyaga; Hoppe, H. Ulrich
    Abstract Research supervision is an important type of support for advanced students when engaged in study projects or in writing their final theses. One of the most common complaints from research students is erratic or infrequent contact with supervisors, who might be too busy with other responsibilities or are not present frequently enough . High proliferation of mobile phones(i.e. 'mobile-rich') but no computer prevalence (i.e. 'computer-poor') in African countries calls for using mobile technologies to address this challenge. However, limitations of mobile devices (such as usage cost, memory capacity and small screen) are some of the barriers for mobile learning adoption. In this paper, we combine mobile learning with OER and Cloud Computing Services to enhance supervisors’ availability to their research students, who are in 'mobile-rich' but 'computer-poor ' learning settings typical for African universities.
  • Thumbnail Image
    Item
    Environmental risk factors influencing bicycle theft:
    (PubMed Central, 2016) Mburu, Lucy W.; Helbich, Marco
    Abstract Urban authorities are continuously drawing up policies to promote cycling among commuters. However, these initiatives are counterproductive for the targeted objectives because they increase opportunities for bicycle theft. This paper explores Inner London as a case study to address place-specific risk factors for bicycle theft at the street-segment level while controlling for seasonal variation. The presence of certain public amenities (e.g., bicycle stands, railway stations, pawnshops) was evaluated against locations of bicycle theft between 2013 and 2016 and risk effects were estimated using negative binomial regression models. Results showed that a greater level of risk stemmed from land-use facilities than from area-based socioeconomic status. The presence of facilities such as train stations, vacant houses, pawnbrokers and payday lenders increased bicycle theft, but no evidence was found that linked police stations with crime levels. The findings have significant implications for urban crime prevention with respect to non-residential land use.
  • Thumbnail Image
    Item
    Contextual factors and public value of e-government services in kenya
    (Global Scientific Journals, 2017) Kamau, Gabriel; Wausi, Agnes; Njihia, James
    Abstract E-government research has been skewed towards technological deterministic perspective mainly centering on technological issues. This provides no explicit guidance to the design and practice of e-government programs that result to increased uptake of e-government services. Theoretical discourse reveals undisputed consensus among e-government researchers that e-government uptake may be influenced by others contextual factors such as administrative and political consequences and should not be overlooked as they are valued. This study filled this gap by conducting an empirical investigated of the influence of contextual factors: ICT infrastructure, human capital and governance and the public value of e-government services. The study employed a mixed method exploratory, descriptive cross-sectional approach to realize the research objectives. Structural Equation Modeling was used to conduct statistical analysis of data collected. The study findings demonstrated that ICT infrastructure insignificantly contributed to public value of e-government services. However, the study revealed significantly contribution of human capital as well as governance to public value of e-government services.
  • Thumbnail Image
    Item
    Modeling spatial interactions between areas to assess the burglary risk
    (MDPI, 2016) Mburu, Lucy W.; Bakillah, Mohamed
    Abstract It is generally acknowledged that the urban environment presents different types of risk factors, but how the structural effects of areas influence the risk levels in neighboring areas has been less widely investigated. This research assesses the local effects of burglary contributory factors on burglary over small areas in a large metropolitan region. A comparative framework is developed for analyzing the effects of geographic dependence on burglary rates, and for assessing how such dependence conditions the community context and the urban land use. A local indicators spatial autocorrelation analysis assesses burglaries over five years (2011–2015) to identify risk clusters. Thereafter, effects of different variables (e.g., unemployment, building density) on burglary frequency are estimated in a series of regression models while controlling for changes in the risk levels of nearby surrounding areas. Results uncover strong evidence that the configuration of the surroundings influences risk. After controlling for area-based interaction, patterns are identified that contrast with the previous literature, such as lower burglary frequency in areas with higher tenancy in social housing units. Together the findings demonstrate that the spatial arrangement of areas is as crucial as contextual crime factors, particularly when assessing the risk for small areas.