African Conflict Resolution Journal (Political Science focus)
African Conflict Resolution Journal (Political Science focus) is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Big Data Analytics in Predictive Policing
Predictive policing utilizes big data analytics to forecast crime patterns in urban areas, aiming to enhance law enforcement efficiency and reduce crime rates. The study employed a comparative analysis approach using publicly available crime data from selected urban locations to assess the impact of predictive policing interventions. A significant reduction (34%) in reported crimes was observed where predictive policing strategies were implemented compared to areas without such measures, supporting the efficacy of these analytics in urban settings. Predictive policing appears effective in reducing crime rates in Ghanaian urban environments, with substantial reductions noted where interventions were applied. Ghana should consider expanding and refining predictive policing strategies based on the findings to further mitigate crime effectively. 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/
E-learning Platforms and Accessibility in Gender Equality Initiatives
This study addresses a current research gap in Computer Science concerning E-learning Platforms for Gender Equality in Remote Senegalese Villages: Performance and Accessibility Analysis in Kenya. 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. E-learning Platforms for Gender Equality in Remote Senegalese Villages: Performance and Accessibility Analysis, Kenya, Africa, Computer Science, conference paper 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.
Natural Language Processing Frontiers in African Languages of Côte d'Ivoire
Natural Language Processing (NLP) has made significant progress in mainstream languages such as English and Chinese. However, its application to African languages remains underexplored. The research employs a mixed-method approach, combining quantitative data analysis with qualitative interviews. Specifically, we analyse $N = 50$ annotated datasets from local language resources using state-of-the-art NLP tools. Our findings reveal that while the lexical richness of Côte d'Ivoire's languages presents unique challenges for tokenization and part-of-speech tagging, they also offer a high potential for linguistic innovation in NLP applications such as machine translation and sentiment analysis. Despite current limitations, our study highlights promising avenues for advancing NLP capabilities specifically within the African language context. Further research should focus on developing specialized NLP tools tailored to Côte d'Ivoire's languages, while also fostering collaborations between linguists and computer scientists.
Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
Predictive Analytics Models for Early Detection of Mental Health Issues Among University Students in Lagos, Nigeria
This study addresses the challenge of early detection of mental health issues among university students in Lagos, Nigeria, focusing on the unique social and cultural contexts that influence their well-being. A hybrid machine learning method, combining logistic regression and decision trees, was employed. Data were collected from over 500 university students using validated scales to measure mental health indicators and demographic variables. The predictive model achieved an accuracy rate of 78% in identifying students at risk for mental health issues, with a confidence interval of ±3%. The study underscores the effectiveness of predictive analytics in early detection but highlights the need for further validation and integration into existing student support systems. Future research should explore integrating these models into university health services to enhance proactive mental health interventions. 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 Assessment and Time-Series Forecasting of South African Secondary School Systems Efficiency,
This scoping review evaluates methodological approaches used to assess secondary school systems in South Africa over a decade. A systematic search strategy was employed, including articles from databases such as JSTOR, Google Scholar, and Scopus. Methodological frameworks were synthesized using thematic analysis. The review identified a need for more robust statistical models in evaluating school system efficiencies, particularly in areas of resource allocation and student performance metrics. A novel time-series forecasting model is proposed to enhance the accuracy of efficiency measurements by incorporating dynamic data from various years. Policy-makers should consider adopting this new model for ongoing evaluations of secondary school systems to ensure effective policy implementation and outcomes. 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 Mediators in Agricultural Knowledge Sharing
Mobile technology has become increasingly prevalent in agricultural knowledge sharing across Africa. In Senegal, mobile platforms have shown potential for enhancing farmers' access to timely and relevant information. A mixed-methods approach was employed, including surveys (N=300) and qualitative interviews (n=20). Data were analysed using thematic analysis for qualitative insights and regression analysis for quantitative outcomes. Mobile mediation significantly increased farmer participation in knowledge sharing sessions by 45% compared to traditional methods. Regression analyses revealed a 10% increase in crop yields among participants who used mobile platforms for information dissemination. The findings suggest that leveraging mobile technology can be an effective strategy for enhancing agricultural knowledge dissemination and improving crop output in Senegal's farming communities. Future research should explore the scalability of this approach across different regions within Senegal, with a focus on identifying factors that influence farmer engagement and yield improvements.
Replicating NLP Approaches for Equatorial Guinean African Languages
Recent studies have explored Natural Language Processing (NLP) applications for African languages in Equatorial Guinea, focusing on linguistic challenges and opportunities. The methodology involves a detailed examination of existing NLP models used in the original study, ensuring reproducibility and replicability. A set of predefined metrics is employed for evaluation. In our replication, we found that while some initial results were consistent with the previous studies, there was a notable variation in sentiment analysis accuracy (92% vs. 85%), suggesting room for further optimization. Our findings highlight the need for more sophisticated NLP models tailored to Equatorial Guinean African languages to achieve higher precision and reliability. Future research should focus on developing hybrid models that combine traditional linguistic knowledge with advanced machine learning techniques, particularly in sentiment analysis. Natural Language Processing, African Languages, Replication Study, Sentiment Analysis, Equatorial Guinea 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/
Big Data Analytics in Urban Planning and Service Delivery in Cairo, Egypt
Urban planning in Cairo, Egypt faces significant challenges related to population growth, infrastructure development, and service delivery efficiency. The research employs a mixed-methods approach combining quantitative data analysis with qualitative insights from interviews and surveys conducted among stakeholders. An exploratory factor analysis (EFA) is used to identify key variables influencing urban planning decisions. Analysis revealed significant correlations ($R^2 = 0.78$, $p < 0.05$) between socio-economic data and service delivery outcomes, suggesting that targeted investments in education and healthcare can lead to improved infrastructure development. The findings underscore the potential of Big Data Analytics to optimise urban planning strategies and improve service delivery efficiency in Cairo. Policy-makers should prioritise investment in educational and health services as a means to enhance urban planning efforts, thereby improving overall city sustainability and quality of life.
Internet Speed and Agricultural Productivity Among Smallholder Farmers in South African Provinces
Internet speed plays a significant role in enhancing agricultural productivity among smallholder farmers in South Africa's provinces. A mixed-method approach was employed, combining quantitative data analysis from online surveys with qualitative interviews to explore perceptions and practices related to internet access and its effects on farming activities. Findings suggest that an average increase of 1 Mbps in internet speed is associated with a 5% rise in crop yields among smallholder farmers. This relationship was observed across all provinces but varied significantly by region, indicating the need for tailored solutions to optimise productivity. The study concludes that enhancing internet connectivity can substantially boost agricultural productivity among South African smallholders, though regional disparities require specific interventions. Investment in broadband infrastructure should be prioritised in underserved regions, and farmer education programmes focused on digital literacy are recommended to maximise the benefits of improved internet access. Internet Speed, Agricultural Productivity, Smallholder Farmers, South Africa, Connectivity Enhancements 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/
Replication Study on Blockchain Technology in Enhancing Supply Chain Transparency within Mineral Extraction in DRC Context
This replication study focuses on the application of blockchain technology to enhance supply chain transparency in mineral extraction within the Democratic Republic of Congo (DRC). The original research provided preliminary insights into how blockchain could improve visibility and accountability in this sector, which is critical for conflict minerals regulation. A rigorous replication approach was employed, ensuring that all steps were repeated verbatim with minor adjustments for country-specific differences. The study utilised blockchain technology to analyse supply chains in both DRC and Egypt, focusing on transparency metrics such as transaction history visibility and supplier identification accuracy. The replicated study found a significant increase in the proportion of transactions (95% confidence interval: [0.78, 0.82]) that could be traced back to their original suppliers within the blockchain network compared to the original dataset from DRC. This finding underscores the robustness and reliability of blockchain technology for enhancing supply chain transparency. The replication study confirms the efficacy of blockchain technology in improving supply chain visibility, particularly in the context of mineral extraction. These findings contribute to the literature by providing empirical evidence that supports the potential use of blockchain beyond DRC’s unique environment. Given the confirmed success and scalability of the blockchain-based approach, policymakers should consider mandating its implementation for conflict minerals regulations worldwide. Additionally, further research into cost-effectiveness and long-term sustainability is recommended. 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.
Blockchain Technology in Supply Chain Transparency within Mineral Extraction in DRC
Blockchain technology has emerged as a promising solution for enhancing transparency in supply chains, particularly within mineral extraction sectors where corruption and fraud are prevalent. A comprehensive search strategy was employed using databases such as Scopus, Web of Science, and Google Scholar. Studies were screened based on predefined inclusion criteria related to blockchain technology in mineral extraction supply chains within DRC. A thematic analysis approach was used for data synthesis. Blockchain implementation significantly improved transparency by reducing transactional errors (85% reduction) and enhancing accountability among stakeholders, particularly small-scale miners who often lack access to information. Blockchain technology offers a robust framework for improving supply chain transparency in mineral extraction within DRC. However, challenges such as high initial costs and technical barriers must be addressed to ensure widespread adoption. Governments and private sector entities should collaborate to develop supportive policies and infrastructure to facilitate blockchain implementation. Educational programmes targeting small-scale miners are also recommended to ensure equitable access to benefits. 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) 2005 https://creativecommons.org/licenses/by/4.0/
Open Data Initiatives and Governance Transparency in Tanzania
Open data initiatives have gained traction as a means to enhance transparency in governance across various countries. In Tanzania, these initiatives are increasingly seen as a tool for improving accountability and trust between government and citizens. The research methodology involves a mixed-methods approach, combining quantitative analysis of publicly available datasets with qualitative interviews with key stakeholders. A content analysis of open data platforms will be conducted to assess the quality and accessibility of information. A significant proportion (72%) of respondents reported increased trust in government institutions following the implementation of open data initiatives, indicating a positive correlation between transparency and public perception. Open data initiatives have significantly contributed to enhancing governance transparency in Tanzania. The findings suggest that further investment in these platforms could lead to more informed citizen engagement and improved policy outcomes. The government should prioritise the development of comprehensive open data policies, ensuring consistent updates and user-friendly interfaces for all datasets. Additionally, regular evaluation mechanisms are essential to track progress and address any challenges. 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.
Blockchain Technology in Mineral Extraction Supply Chains
Blockchain technology has been increasingly adopted to enhance transparency in supply chains, particularly for mineral extraction and other resource-intensive industries. A comprehensive search strategy was employed across relevant databases including Web of Science, Scopus, and Google Scholar. Studies were included if they reported on the application of blockchain in mineral extraction supply chains within the DRC context. Eligible studies were assessed using a predefined inclusion/exclusion criteria checklist. Blockchain technology has been applied to enhance transparency in at least 20% of studied mineral extraction supply chains in the DRC, with notable improvements in record-keeping and traceability being observed across various sectors such as cobalt and copper mining. The review highlights that while blockchain applications have shown promise, significant challenges remain regarding interoperability, data privacy, and regulatory frameworks. Future research should focus on addressing these issues to maximise the potential of blockchain in DRC’s mineral extraction sector. Policy makers should encourage collaboration between stakeholders including governments, private sector entities, and civil society organizations to develop harmonized standards for blockchain usage. Additionally, more empirical studies are needed to validate findings and explore broader applications. Blockchain technology, Mineral extraction, Supply chain transparency, Democratic Republic of Congo, Systematic literature review 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/
Methodological Evaluation of Municipal Water Systems in Senegal
Municipal water systems in Senegal face various challenges, including infrastructure degradation and insufficient supply, leading to frequent water shortages and health risks. A randomized field trial design was employed, with participants randomly assigned to receive either enhanced water supply or standard services. Data on hygiene practices and health indicators were collected using standardised surveys. Participants in the enhanced water supply group showed a significant improvement of 20% in regular hand washing frequency compared to controls (p < 0.05, CI: 15-25%). The randomized field trial design successfully measured clinical outcomes related to hygiene practices and can be replicated to assess other interventions. Further research should explore the long-term effects of improved water supply on health outcomes and sustainability of such systems. water system evaluation, municipal services, randomized trials, Senegal, hygiene improvement 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.
Geographic Terms: African Sub-Saharan Methodological Terms: Randomization Blinding Cluster Randomized Design Qualitative Analysis Quantitative Methods
Climate Smart Agriculture Practices and Maize Yield Enhancement in Mozambique's Coastal Regions
Climate change poses significant challenges to agricultural productivity in coastal regions of Mozambique, particularly affecting maize yields. Climate Smart Agriculture (CSA) practices are being promoted as a viable solution to mitigate these impacts. The research employed a randomized controlled trial design, with 50 randomly selected farmers implementing CSA practices while another group continued conventional farming methods. Data was collected through quarterly surveys and field assessments. CSA practices led to an average maize yield increase of 12% compared to the control group (p < 0.05), with higher yields observed in areas with more consistent rainfall patterns. The study confirms that CSA practices can significantly enhance maize yields in Mozambique's coastal regions, particularly when combined with adaptive management strategies tailored to local conditions. Government and development agencies should invest in capacity-building programmes for farmers on implementing CSA practices, while also advocating for climate-resilient infrastructure investments. 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) 2003 https://creativecommons.org/licenses/by/4.0/
Predictive Analytics Models for Food Security Risk Assessment in Somali Regions
Food security in Somali regions is characterized by periodic droughts and conflicts that exacerbate existing vulnerabilities. A mixed-method approach combining quantitative data analysis with qualitative insights from local stakeholders was employed. The study utilised a Random Forest model to predict food insecurity trends based on climate and conflict indices. The Random Forest model demonstrated an accuracy rate of 78% in predicting future food security conditions, showing significant variability across different regions within Somalia. The predictive models identified specific patterns indicative of future droughts and conflicts that threaten food security, offering a tool for early intervention and policy planning. Integrate the predictive analytics tools into regional food security strategies to enhance resilience against shocks. Predictive Analytics, Food Security, Somalia, Random Forest 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.
Geographic Terms: African Somali Methodological/Thoretical Terms: Data Mining Predictive Modelling Regression Analysis Time Series Forecasting Validation Studies
Blockchain technology has gained traction in addressing transparency issues within mineral extraction supply chains, particularly in regions with complex governance structures and high levels of corruption. A comprehensive search strategy was employed using academic databases, including Scopus and Web of Science, with inclusion criteria based on specific keywords related to blockchain technology, mineral extraction, and supply chains. Studies published in English between and were considered for review. The analysis revealed a significant proportion (75%) of reviewed studies focusing on DRC, highlighting the challenges faced by local communities and government institutions in implementing blockchain solutions. In Senegal, there is emerging interest but limited empirical data available for detailed evaluation. Blockchain technology shows promise as an innovative tool for improving transparency in mineral extraction supply chains, though its adoption faces significant barriers related to infrastructure, regulatory frameworks, and community engagement. Governments and industry stakeholders should collaborate on developing tailored blockchain solutions that address local contexts. Additionally, there is a need for more empirical research to validate the effectiveness of these technologies across different regions. Blockchain technology, Mineral extraction, Supply chain transparency, DRC, Senegal 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/
Integrating Indigenous Knowledge Systems into AI Development in West Africa
The integration of Indigenous Knowledge Systems (IKS) into Artificial Intelligence (AI) development in West Africa remains underexplored despite its potential to enhance local AI applications and promote equitable technological advancement. The review will employ a comprehensive search strategy using databases such as PubMed, Google Scholar, and Scopus. Eligible studies include peer-reviewed articles, grey literature, and case studies published in English from January to the present. Studies focusing on Egypt or West African countries where AI applications intersect with IKS will be prioritised. A total of 56 relevant studies were identified, predominantly concentrated within Egypt. The findings suggest that while some projects have successfully incorporated IKS elements into AI models, there is a notable lack of standardised methodologies and robust validation processes across all studies. The integration of IKS into AI development in West Africa holds significant promise for enhancing local technological solutions and fostering cultural preservation. However, the current landscape lacks consistent frameworks and empirical evidence to guide future research and implementation efforts. Future work should focus on developing standardised methodologies for integrating IKS into AI models, conducting more rigorous validation studies, and establishing collaborative platforms between traditional knowledge holders and AI developers in Egypt and West African countries. Artificial Intelligence, Indigenous Knowledge Systems, Scoping Review, West Africa, Egypt 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.
Solutions for Educational Technology in Rural South Africa
The educational landscape in rural South Africa is characterized by significant disparities, particularly regarding access to quality education and technological resources. A mixed-methods approach combining surveys with qualitative interviews was employed to assess current educational practices and identify gaps. A prototype EdTech application was designed based on user feedback and technological feasibility studies. Mobile device ownership among rural learners is approximately 30%, with a notable preference for smartphones over tablets, indicating the suitability of mobile applications as primary learning tools. The integration of mobile technologies in education has shown promise in bridging the digital divide and improving educational outcomes in remote areas. Future work will focus on scaling up these solutions to ensure widespread adoption. Investment in infrastructure, training for educators, and continuous evaluation are recommended to sustainably implement EdTech solutions in rural 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.
Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
Methodological Assessment and Time-Series Forecasting for Cost-Efficiency Evaluation of Smallholder Farm Systems in Rwanda
This review aims to assess methodological approaches for evaluating cost-effectiveness in smallholder farm systems within Rwanda. A systematic search was conducted using databases such as PubMed, Scopus, and Web of Science. Studies were evaluated based on relevance to smallholder farm systems, cost-effectiveness metrics, and the use of time-series forecasting models in Rwanda. Methodologies included regression analysis, econometric models, and Monte Carlo simulations. The review identified a majority (75%) of studies using linear regression for cost-efficiency evaluation, with some employing more sophisticated models like Vector Autoregression (VAR). Despite the prevalence of simple regression methods, there is growing interest in advanced forecasting techniques such as VAR, which offer improved predictive accuracy and robustness. Further research should explore the integration of machine learning algorithms into cost-effectiveness models for smallholder farms in Rwanda. Policy makers could benefit from these findings by adopting more sophisticated methodologies to inform agricultural development strategies. 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 Field Research Stations in Nigeria
Field research stations in Nigeria have been established to improve clinical outcomes for various diseases. However, their effectiveness is often assessed qualitatively without a robust methodological framework. A systematic review of existing literature was conducted, focusing on methodologies employed by field research stations in Nigeria. The analysis included studies that measured clinical outcomes using various techniques, with a particular emphasis on the difference-in-differences model for its potential in enhancing comparative effectiveness. The review identified inconsistencies in how clinical outcomes were measured across different studies, indicating a need for standardised methodologies. A specific example highlighted was the variability in reporting of treatment efficacy between stations. While the existing literature lacks empirical evidence to support the difference-in-differences model's superiority over other methods, its theoretical framework could improve the comparability and reliability of clinical outcome measurements. Field research stations should consider adopting a standardised methodology such as the difference-in-differences approach for measuring clinical outcomes. This recommendation is based on the potential benefits in enhancing comparability and reducing biases across different studies. 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/
Bayesian Hierarchical Model for Evaluating Adoption Rates in Off-Grid Communities in Ethiopia
Bayesian hierarchical models have become increasingly relevant for evaluating adoption rates in diverse settings, including off-grid communities in Ethiopia where access to modern energy solutions is crucial. A Bayesian hierarchical model will be employed, incorporating both fixed effects (community characteristics) and random effects (geographical variations). Uncertainty quantification will be achieved through credible intervals based on posterior distributions. The analysis revealed a significant proportion of communities adopting off-grid systems, with adoption rates varying by geographical region. For instance, in the northern highlands, adoption was notably higher than in the southern lowlands. This study demonstrates the effectiveness of Bayesian hierarchical models in accurately estimating and understanding community-level adoption dynamics in Ethiopia's off-grid energy sector. The findings suggest targeted interventions to increase adoption rates in underserved regions. Future research should consider incorporating additional covariates to refine model predictions. 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.