DISCOVER, READ & SHARE
The Institutional Repository is a digital hub for scholarly communication and knowledge dissemination which serves as a vital resource for researchers, students and the wider academic community. Content comprises of research publications, theses, conference proceedings and much more.
Recent Submissions
Strategic Leadership and Incident Response Time Among Law Enforcement Agencies in Juba City, South Sudan
(Mount Kenya University, 2026-01) Ekidor,Paul; Kiprono,Wilson; Wekesa,Peter
The purpose of this study was to investigate the dynamics of strategic leadership on incident
response time among law enforcement agencies in Juba City, South Sudan. The contention of
the article is that incident response time is widely recognized as a critical indicator of law
enforcement effectiveness, particularly within fragile and post-conflict contexts where
institutional capacity and public trust remain limited. In South Sudan, persistent insecurity
and weak governance structures have heightened the demand for timely and effective law
enforcement responses, especially in urban centers such as Juba City. This paper examines
the influence of strategic leadership on incident response time among law enforcement
agencies in Juba City, South Sudan. Anchored in strategic leadership theory and the
organizational performance literature, the study posits that leadership practices including
strategic planning, coordination, resource allocation, and decision-making play a decisive
role in shaping operational responsiveness. Employing an exploratory qualitative approach
informed by a review of relevant empirical studies and contextual evidence, the paper finds
that deficiencies in strategic leadership contribute to delayed response times, fragmented
operations, and ineffective interagency coordination. Conversely, effective strategic
leadership is associated with improved preparedness, clearer command structures, and
enhanced operational agility. The study concludes that leadership-centered reforms are
essential for strengthening incident response capabilities, improving public safety, and
rebuilding public trust in law enforcement institutions in South Sudan.
Strategic Leadership and Case Resolution Performance in Police Departments in Juba City, South Sudan
(Mount Kenya University, 2026-01) Ekidor, Paul
The purpose of this study was to investigate the association between strategic leadership and
crime clearance performance, measured through the number of cases resolved per quarter,
within police divisions in Juba City, South Sudan. The contention of the study is that crime
clearance performance remains a central indicator of policing effectiveness, particularly in
post conflict urban environments where institutional capacity, coordination, and leadership
practices directly influence investigative outcomes. A mixed methods explanatory sequential
design was adopted, integrating quantitative survey data from 235 law enforcement personnel
with qualitative insights from key informant interviews. Structured questionnaires measured
perceptions of leadership practices and investigative performance, while interviews provided
contextual explanations of supervisory processes and operational challenges. Descriptive
findings indicated moderately strong clearance performance, with a composite mean score of
3.81. Leadership contribution to case clearance recorded the highest mean of 4.05, suggesting
that supervisory direction and oversight are perceived as central drivers of investigative
productivity. Reliability analysis confirmed strong internal consistency of the measurement
scale, with Cronbach alpha values exceeding 0.79. Pearson correlation analysis revealed
statistically significant positive relationships between case resolution and all leadership
dimensions, with inclusive leadership showing the strongest association (r = .659), followed
by accountable leadership (r = .570), visionary leadership (r = .552), and adaptive leadership
(r = .516). Multiple regression results demonstrated that leadership variables collectively
explained 48.0 % of the variance in case resolution performance (R² = .480, F = 53.141, p <
.001). Inclusive leadership emerged as the strongest independent predictor (β = .462, p <
.001), while visionary leadership also showed a significant positive effect (β = .226, p =
.001). The findings confirm that collaborative leadership practices and clear strategic
direction substantially enhance investigative throughput and case completion within policing
institutions operating under complex operational conditions. The study recommends
strengthening participatory supervisory structures, improving communication of investigative
targets, and institutionalizing routine performance monitoring systems to enhance crime
clearance performance in Juba City.
Multimodal CNN–LSTM Framework for Real-Time Maize Disease Detection
(Mount Kenya University, 2026-06-03) Tonui,Mercy Chepkoech; Kamau , John; Ongus,Raymond Wafula
Maize diseases present a major challenge to agricultural productivity and
food security, particularly in low-resource settings in sub-Saharan Africa.
Timely detection plays an important role in reducing yield losses and
enabling effective farm management. This research introduces and validates
a multimodal machine learning–based system for real-time maize disease
detection in Bomet County, Kenya. The system integrates maize leaf image
data, environmental sensor data, and farmer-reported observations to
develop a hybrid Convolutional Neural Network–Long Short-Term Memory
(CNN–LSTM) model designed to automatically identify and categorize
maize diseases. A mixed-methods research design was adopted, combining
machine learning experiments with surveys and interviews involving
farmers and agricultural officers. The findings revealed that Maize Lethal
Necrosis (MLN) was the most prevalent disease (41%), followed by Gray
Leaf Spot (33%) and Northern Leaf Blight (26%). Environmental variables
such as humidity and temperature demonstrated strong associations with
disease occurrence. The proposed multimodal CNN–LSTM framework
integrates maize leaf images, environmental sensor data, and farmer
observations, achieving an accuracy of 94.2%, which outperforms
conventional image-only CNN models (87.5%) and environmental-data
based LSTM models (81.3%). Additionally, 78% of farmers reported faster
disease diagnosis using the developed system. The findings demonstrate that
the proposed system supports real-time maize disease detection through an
edge-enabled architecture, enabling deployment on mobile devices and
facilitating practical intelligent system integration in agricultural
environments.
Multimodal Machine Learning for Maize Disease Detection: A Systematic Review of Architectures and Deployment Challenges
(Mount Kenya University, 2026-02-24) Tonui,Mercy Chepkoech; Kamau,John Wachira; Ongus,Raymond Wafula
Maize diseases continue to threaten agricultural productivity and food
security, particularly in developing regions where early diagnosis remains
constrained by limited expert access. While deep learning has enabled
automated disease detection systems, most existing approaches rely on
unimodal image datasets and cloud-dependent architectures, limiting
robustness and deployment feasibility in resource-constrained environments.
This study presents a structured systematic review of 38 peer-reviewed studies
published between 2020 and 2025, focusing on multimodal machine learning
approaches integrating visual and environmental data for maize disease
detection. Quantitative synthesis reveals that 58% of reviewed studies employ
image-only deep learning models, 26% implement multimodal frameworks,
and only 29% conduct validation under real or semi-real field conditions.
Furthermore, 32% explicitly address deployment considerations, including
edge computing and mobile inference. The findings demonstrate that
multimodal architectures improve robustness and contextual modeling
compared to unimodal systems by integrating phenotypic and environmental
drivers of disease expression. However, increased computational complexity,
synchronization challenges, and limited edge optimization remain barriers to
scalable deployment. This review advances scientific knowledge by providing
a computing-centered synthesis of multimodal architectures, fusion strategies,
deployment constraints, and explainability gaps, identifying key research
priorities in edge efficiency, real-world validation, and interpretable intelligent
systems.
Accuracy of Machine Learning Models for Anomaly Detection in Online Proctored Examinations: A Review of the Potential of Haar Cascades for Mobile Based Proctoring
(International Transactions on Electrical Engineering and Computer Science, 2026-03) Oganda Bartholomew Mogoi; John Kamau; Raymond Ongus
The rapid shift toward digital learning in higher education has made online examinations an everyday reality, increasing the need for reliable and trustworthy anomaly detection systems powered by machine learning (ML). While recent advances in deep learning have improved the technical capabilities of automated proctoring tools, many existing solutions remain difficult to deploy fairly and effectively; particularly in mobile-first environments that are common in low resource settings. Challenges related to computational demands, accessibility, and algorithmic bias continue to limit their practical impact. This review explores the accuracy and suitability of ML models used for anomaly detection in online proctoring, with particular attention given to Haar cascade classifiers; a classical yet computationally efficient approach that remains widely supported on mobile platforms. A structured literature search across seven major databases identified 150 relevant studies, from which 20 met the defined inclusion criteria. The findings show that deep learning models, especially convolutional neural networks (CNNs), consistently achieve the highest detection accuracy but often require substantial computational resources. In contrast, Haar cascades offer fast, low latency detection suitable for mobile devices, although their performance declines under challenging conditions such as poor lighting, pose variation, and facial occlusion. Notably, hybrid approaches that combine Haar cascades with lightweight CNNs emerge as a promising middle ground, balancing efficiency with improved robustness. However, the review also highlights important research gaps, including the scarcity of mobile-centric datasets, limited real world field evaluations, and insufficient testing for fairness across diverse demographic groups. Addressing these gaps will require future research to prioritize mobile optimized model design, standardized evaluation benchmarks, privacy aware computation strategies, and broader empirical validation in authentic exam settings.
Discover
Top 5 Subjects
- Education 643
- Management 109
- Organization 108
- school of education 104
- Educational planning 93
Top 5 Authors