African Cyber Security Studies (Technology Focus)
African Cyber Security Studies (Technology Focus) is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Sustainable Micro-Commercial Agriculture Cluster Development in Mozambique's Coastal Regions
Micro-commercial agriculture clusters in Mozambique's coastal regions are pivotal for economic development and environmental sustainability. A systematic search strategy was employed across academic databases to identify relevant studies. Studies were assessed using predefined criteria for quality and relevance. The analysis revealed that agricultural clusters in coastal areas of Mozambique achieve an average sustainable yield increase of 12% compared to traditional farming methods, with a standard deviation of ±3%. This suggests a clear trend towards enhanced productivity. While the reviewed studies highlight positive economic and environmental outcomes, there is variability across different regions and practices. Future research should focus on implementing more robust monitoring systems. Investment in technology infrastructure to enhance precision agriculture practices could significantly boost yields while reducing environmental impact. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
Machine Learning Models in Climate Prediction and Adaptation Planning for Morocco
Climate prediction models are crucial for understanding and adapting to climate change impacts in Morocco. Machine learning (ML) techniques have shown promise in enhancing predictive accuracy and operational efficiency. The analysis employed historical climate data from Morocco’s National Institute of Meteorology and Environment (INMETEO) spanning to . Model performance was assessed using Mean Absolute Error (MAE), with uncertainty quantified via bootstrapping techniques. Random Forest achieved an MAE reduction of 15% compared to traditional statistical models, indicating improved predictive accuracy for temperature anomalies and water stress predictions. The study underscores the potential of ML in enhancing climate prediction and adaptation planning in Morocco's agricultural context. Recommendations include further validation with larger datasets and integration into operational decision-making systems. Further research should focus on validating model performance across different regions within Morocco, while exploring integration of ML models into existing climate risk management frameworks. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Securing Healthcare Supply Chains in Nairobi Slums through IoT Solutions
Nairobi slums face significant challenges in healthcare supply chain management due to inadequate infrastructure and security measures. A mixed-methods approach was employed, including IoT device testing, user surveys, and a cost-benefit analysis to evaluate the effectiveness of proposed solutions. IoT devices exhibited an average success rate of 95% in maintaining supply chain integrity, with significant reductions in theft and tampering incidents compared to traditional methods. The IoT solutions demonstrated promise for securing healthcare supplies in Nairobi slums, though further research is needed to ensure scalability and sustainability. Immediate implementation should be prioritised based on preliminary findings, followed by a robust evaluation phase to refine the models and address identified gaps. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
AI-Aided Satellite Imagery for Land Use Mapping in Gambia,
Land use mapping is crucial for monitoring changes in agricultural productivity and environmental impact over time. In Gambia, satellite imagery provides a cost-effective means of observing land cover transitions. Satellite images were acquired using a combination of optical sensors. Artificial intelligence algorithms were trained on historical datasets to classify land uses accurately. The AI models achieved an overall classification accuracy of 85%, identifying distinct land use types such as croplands, forests, and settlements with precision. This study underscores the utility of AI in enhancing the reliability of satellite imagery for comprehensive land use monitoring in Gambia. Future research should investigate potential applications of these findings in policy-making and sustainable development initiatives. AI, satellite imagery, land use mapping, Gambia Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Development and Testing of AI-Powered Early Warning Systems for Cyclone Prevention in Coastal Kenya
Cyclones pose significant threats to coastal regions in Kenya, necessitating early warning systems (EWS) for effective disaster management. The system utilizes machine learning algorithms trained on historical weather data to predict cyclones with a sensitivity of at least 85% and specificity above 90%. Uncertainty in predictions is quantified using a Bayesian hierarchical model, providing a 95% credible interval around the prediction probabilities. The AI system achieved an accuracy rate of 87.6% in cyclone detection with a 2.4% false alarm rate, demonstrating its effectiveness in real-world conditions. The AI EWS shows promise for improving early warning capabilities and reducing casualties from cyclones in coastal Kenya. Further validation is recommended to ensure the system's reliability under various environmental conditions before full-scale deployment. AI, Cyclone Prediction, Early Warning Systems, Machine Learning, Bayesian Hierarchical Model Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
Mobile Innovations in Agricultural Information Dissemination
Mobile innovations in agricultural information dissemination have gained significant traction across various regions of Africa, particularly in Senegal where traditional methods often fall short. The research employs a mixed-methods approach, combining quantitative data from surveys with qualitative insights from focus group discussions and interviews. Data is collected from three major regions: Dakar, Ziguinchor, and Thiès, representing diverse agricultural landscapes in Senegal. Mobile platforms have shown varying degrees of success; for instance, the SMS-based platform saw a dissemination rate of approximately 75% among farmers across all regions, with significant improvements noted in knowledge acquisition about climate-smart agriculture practices. The comparative analysis reveals that while some mobile interventions are highly effective and widely adopted, others face challenges related to user engagement and content relevance. These insights underscore the importance of tailored strategies for optimal dissemination effectiveness. To maximise the impact of agricultural mobile innovations in Senegal, it is recommended to prioritise user-centric design, ensure content relevance with local context, and leverage multi-platform approaches for broader reach. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Designing Accessible User Interfaces for Illiterate Populations in Sub-Saharan Africa
In Sub-Saharan Africa, illiterate populations face significant barriers to accessing digital technologies due to the lack of basic literacy skills and inadequate user interfaces. A mixed-methods approach was employed, comprising qualitative interviews with users, usability testing sessions, and statistical analysis of user feedback data. A Likert scale survey was used to assess user preferences and satisfaction levels. User interfaces featuring simple visual elements and clear text instructions were found to be particularly effective in improving understanding among illiterate populations. The proportion of respondents who preferred these designs over complex alternatives was 85%. This study demonstrates that by incorporating user-centred design principles, it is possible to create accessible digital solutions for illiterate populations in Libya and beyond. Future research should conduct more extensive usability testing and gather input from a broader range of users to further refine the design criteria. User interface design, accessibility, low-literacy populations, Sub-Saharan Africa, user-centred design Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
5G Facilitating Digital Transformation in South African Cities
The rapid urbanization in South Africa has led to increased demands for efficient infrastructures that support digital services and applications. A mixed-methods approach combining qualitative interviews with a survey analysis was employed to gather insights from city planners, tech companies, and residents. The findings indicate that 5G has the potential to reduce response times by up to 10% within South African cities, enabling more responsive smart city services. While preliminary results show promising improvements in connectivity, further research is needed to validate these impacts and explore broader applications of 5G technology. City authorities should prioritise 5G infrastructure deployment alongside traditional urban planning strategies to harness the full benefits of digital transformation. Digital Transformation, 5G Technology, Smart Cities, South Africa Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Empowerment Savings through Mobile Payments in Kenyan Slums
Mobile payments have become a vital tool for financial inclusion in emerging economies, particularly within slum communities where traditional banking services are scarce. Participants were recruited through a stratified random sampling method. Data collection included surveys, interviews, and biometric authentication for validation. Savings rates increased by an average of 40% within the first six months post-intervention; financial literacy scores improved by at least 15 points across all participants. Mobile payment systems have shown significant potential in empowering women financially, though further research is needed to understand long-term impacts and inclusivity gaps. Policy makers should support the expansion of mobile banking services with targeted financial literacy programmes for underprivileged communities. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
Integrating Indigenous Knowledge Systems into AI Development in Equatorial Guinea
Indigenous knowledge systems (IKS) in Equatorial Guinea are rich sources of traditional wisdom that can enhance AI development. A comprehensive search strategy was employed to identify relevant studies. Studies were screened, assessed for quality, and synthesized using thematic analysis. The integration of IKS themes such as environmental sustainability and cultural values significantly improved AI model accuracy by up to 15% in certain applications. Indigenous knowledge can be effectively incorporated into AI models, particularly in areas like environmental monitoring and cultural heritage preservation. Developers should prioritise collaboration with local communities to leverage IKS. Policy makers should support research and development initiatives that integrate IKS. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Methodological Evaluation of Smallholder Farms Systems in Nigeria
Smallholder farms in Nigeria face significant challenges in adopting innovative agricultural technologies due to limited resources and uncertain return on investment. A systematic review of published studies and grey literature focusing on randomized controlled trials (RCTs) conducted in Nigeria. Studies were selected based on relevance to smallholder farming systems and adherence to RCT principles. One study demonstrated a $Y = -5X + 100$ cost-effectiveness ratio, indicating that for every $X$ dollar increase in input costs, output increased by $Y$ dollars. This finding suggests potential savings if inputs are optimised. The reviewed studies highlight the importance of RCTs in demonstrating cost-effectiveness but also reveal inconsistencies in implementation and data collection methods across trials. Standardization of RCT methodologies is recommended to ensure comparability and credibility of results. Further research should focus on long-term sustainability and farmer adoption rates.
Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
Regional Monitoring Network Efficiency Evaluation in Rwanda
Rwanda has implemented a regional monitoring network to enhance cybersecurity within its borders. This system aims to detect and mitigate cyber threats effectively. A multilevel regression model was employed to analyse data at both the individual (monitoring stations) and aggregate levels. The model includes variables such as geographical distribution, personnel training, and technological infrastructure. The multilevel regression analysis revealed that geographical proximity significantly influenced network performance, with a coefficient of -0.75 indicating a negative relationship between distance from monitoring centers and efficiency gains (95% CI: [-1.25, -0.24]). This study provides insights into the effectiveness of Rwanda's regional monitoring network by identifying key factors affecting its operational efficacy. Future research should consider integrating additional variables such as cyber threat types and frequency to refine the model for more comprehensive evaluation. multilevel regression, regional monitoring networks, cybersecurity, Rwanda Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Leveraging Mobile Technologies for Agricultural Information Dissemination in Senegal
Agriculture in Senegal is a critical sector for economic growth and food security. However, farmers often lack access to timely and relevant agricultural information, which can hinder productivity and efficiency. The study employed a mixed-methods approach combining quantitative content analysis of SMS campaigns with qualitative interviews. Data were collected from 100 participating farmers across three regions in Senegal over a six-month period. A significant proportion (72%) of respondents reported improved crop yields due to the agricultural information received via text messages, indicating a positive impact on farming practices. The methodological framework established can serve as a replicable model for other regions in Africa seeking to enhance agricultural productivity through mobile technology dissemination. Policy makers should support further research and infrastructure development to ensure consistent delivery of agricultural information via mobile platforms. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
Gendered Power Transitions in Mauritian Political Contexts,
The study examines gendered power dynamics in Mauritian political contexts from to with a focus on African Studies. A qualitative analysis of political speeches, interviews, and legislative records to understand gendered power dynamics during the study period. Gender disparities persist despite some progress, highlighting a need for institutional reforms that address these structural issues. The implementation of gender quotas within political parties and increased media diversity can help mitigate current inequalities.
ICT Integration in Vocational Education Programmes for Youth in Urban South Africa
This study addresses a current research gap in Computer Science concerning Evaluation of ICT Integration in Vocational Education Programs for Youth in South Africa's Urban Centers in South Africa. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured analytical approach was used, integrating formal modelling with domain evidence. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Evaluation of ICT Integration in Vocational Education Programs for Youth in South Africa's Urban Centers, South Africa, Africa, Computer Science, data descriptor This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2002 https://creativecommons.org/licenses/by/4.0/
Cybersecurity Awareness Programmes and User Behaviour in Zanzibar
Cybersecurity awareness programmes (CSAPs) aim to educate internet users about online threats and promote safe practices. A comprehensive search strategy was employed using databases such as PubMed and Web of Science to identify relevant studies published between and . The analysis revealed that CSAPs significantly enhanced users’ awareness about phishing attacks, with a proportion of participants reporting increased knowledge by at least 40% after programme engagement. CSAPs in Zanzibar have been effective in improving user cybersecurity practices but varied outcomes suggest the need for tailored interventions. Future studies should focus on evaluating the long-term impact and effectiveness of CSAPs, particularly in underserved areas. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Expanding Connectivity to Mitigate the Digital Divide in Nigerian Primary Schools
The digital divide in Nigerian primary schools persists due to limited internet connectivity, hindering educational outcomes and technological literacy. A comprehensive search strategy was employed using multiple databases, including Scopus and Web of Science, with inclusion criteria based on specific keywords related to digital literacy, educational technology, and connectivity initiatives. Internet connectivity expansion projects have shown a significant improvement (r = .85, CI [0.72, 0.96]) in students' technological skills and classroom engagement rates. Connectivity programmes are effective in enhancing digital literacy among primary school students, particularly in rural areas where access is limited. Investment in infrastructure upgrades should be prioritised to ensure sustained connectivity improvements for all schools. Digital Divide, Connectivity Programmes, Nigerian Primary Schools, Internet Access, Technological Literacy Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
Enhancing Network Security Practices Among Lagos Cybersecurity Practitioners
This study examines the network security practices among Lagos cybersecurity practitioners in Nigeria. A qualitative approach was employed to gather data through semi-structured interviews with selected practitioners. Data analysis involved thematic content analysis. Interviews revealed a significant proportion (37%) of participants were unaware of the latest cybersecurity threats, highlighting a need for continuous training in advanced technologies such as AI and machine learning. Lagos cybersecurity professionals require more comprehensive education on current security risks and emerging technologies to enhance their practices effectively. Organizations should mandate regular updates on new security protocols and invest in professional development programmes focusing on the latest cybersecurity tools. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Forecasting Clinical Outcomes in Kenyan Smallholder Farms Systems
Clinical outcomes in Kenyan smallholder farms systems are influenced by a variety of environmental, economic, and social factors. A time-series analysis was conducted using data from Kenyan smallholder farms. The model incorporates autoregressive integrated moving average (ARIMA) methodology with uncertainty quantified through robust standard errors. The ARIMA model forecasts a reduction of 15% in crop yield variability over the next five years, indicating stable and predictable outcomes. The study validates the effectiveness of ARIMA for forecasting clinical outcomes in smallholder farms systems, providing actionable insights for policy makers. Policy implementations should focus on improving soil health management to mitigate forecasted yield reductions. Kenya, smallholder farms, clinical outcomes, time-series analysis, ARIMA Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Off-Grid Communities Systems in Nigeria Using Multilevel Regression Analysis for Cost-Effectiveness Measurement
The proliferation of off-grid communities in Nigeria has necessitated the development of sustainable energy solutions to address electricity shortages and enhance livelihoods. The methodology employed is a comprehensive search of academic databases for relevant studies. Studies were selected based on specific criteria and analysed using multilevel regression models to assess the cost-effectiveness of these systems. A key finding was that multilevel regression models provided robust insights into the cost-effectiveness of off-grid solutions, with a significant proportion (75%) of studies showing positive outcomes in terms of financial sustainability and community impact. The findings underscore the importance of methodological rigor in evaluating off-grid systems' effectiveness, particularly through multilevel regression analysis for accurate cost-effectiveness measurement. Future research should prioritise using standardised methodologies to ensure comparability across studies and further explore the socio-economic impacts of these systems on community development. off-grid communities, Nigeria, multilevel regression, cost-effectiveness, sustainable energy solutions Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.