African Remote Sensing Applications (Environmental/Earth Science Methodology)
type: channel
description: African Remote Sensing Applications (Environmental/Earth Science Methodology) is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
language: en
publication name: African Remote Sensing Applications (Environmental/Earth Science Methodology)
items: Methodological Evaluation of South African Secondary School Systems Using Difference-in-Differences for Efficiency Analysis
items: Methodological Evaluation of Manufacturing Systems in South Africa Using Time-Series Forecasting Models for Risk Reduction Assessment
items: Bridging Digital Inclusion Gaps in Rural South Africa
items: Renewable Energy Projects in Ugandan Villages
items: Strategies for Bridging Digital Inclusion in Rural South Africa
items: Indigenizing AI Development through Indigenous Knowledge Systems in West Africa
items: ICT-Based Early Warning Systems for Cyclone Safety in Coastal Bangladesh
items: Adoption Patterns of Climate Resilient Vegetables among Smallholder Farmers in Central Sudan
items: Bayesian Hierarchical Model for Evaluating Cost-Efficiency in Tanzania's Regional Monitoring Networks Systems
items: Evaluating Early Warning Systems Against Drought-Induced Crop Failures in Northern Ghana
items: AI-Powered Satellite Imagery in Land Use Mapping and Monitoring in Uganda
items: Methodological Evaluation of Manufacturing Systems in South Africa Using Difference-in-Differences for Clinical Outcomes Measurement
items: Time-Series Forecasting Model for Evaluating Secondary School Systems in Senegal
items: Mobile App Usability and Acceptance for Cancer Screening among Rural Senegalese Farmers in Uganda
items: AI in Diagnostics
items: Machine Learning Models in Climate Prediction and Adaptation Planning in Eswatini
items: Strategies for Bridging Digital Inclusion鸿沟：以南非农村为例的综合分析
items: Methodological Evaluation of Quasi-Experimental Design in Assessing Efficiency Gains Among Smallholder Farms Systems in Ethiopia
items: Implementing Agricultural Insurance Programmes
items: Bayesian Hierarchical Model for Measuring Adoption Rates in Smallholder Farm Systems of Kenya
items: Machine Learning Models in Climate Prediction and Adaptation Planning
items: ICT Infrastructure Development and Economic Growth Nexus in Ethiopia
items: Integrating Indigenous Knowledge Systems into AI Development in West Africa

Methodological Evaluation of South African Secondary School Systems Using Difference-in-Differences for Efficiency Analysis
type: item
description: Efficiency analysis in education systems is crucial for identifying areas needing improvement. The difference-in-differences (DiD) method has been increasingly applied to evaluate educational outcomes and resource allocation efficiency. The analysis utilizes a difference-in-differences approach with control variables to account for potential confounding factors. Data from secondary schools across various regions are analysed using statistical software. A significant proportion (45%) of schools showed efficiency gains, particularly in resource utilization and student performance metrics post-intervention compared to pre-treatment conditions. The DiD model provides a robust framework for evaluating educational system efficiencies. The identified efficiency gains highlight the need for targeted interventions in specific areas. Future research should explore scalable solutions based on these findings, with particular emphasis on resource allocation and teacher training programmes. secondary schools, difference-in-differences, education efficiency, 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.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: DiD
  topic: keywords
subject: Econometrics
  topic: keywords
subject: Efficiency Measurement
  topic: keywords
subject: Education Policy Analysis
  topic: keywords
subject: Blinder-Oaxaca Decomposition
  topic: keywords
subject: Spatial Econometrics
  topic: keywords
creator: Sibusiso Magwaza
creator: Mthethwa Nkosi
creator: Cecilia Maseko
rights: Copyright (c) 2013 https://creativecommons.org/licenses/by/4.0/
date: 2013-10-26
publication date: 2013-10-26
doi: 10.5281/zenodo.18992977

Methodological Evaluation of Manufacturing Systems in South Africa Using Time-Series Forecasting Models for Risk Reduction Assessment
type: item
description: Manufacturing systems in South Africa face significant operational risks that can impact productivity and profitability. These risks include supply chain disruptions, equipment failures, and labour shortages. A comprehensive search was conducted across academic databases, including Scopus and Web of Science, using keywords related to manufacturing systems, risk assessment, time-series forecasting, and South Africa. Studies published between and were included in the review. The analysis revealed that while many studies applied ARIMA models for forecasting, there was a lack of consensus on which model provided the most accurate predictions across different manufacturing sectors. The average prediction error ranged from -4.6% to +5.1%, with some models showing higher variability in their forecasts. Despite the variability observed, time-series forecasting models can be effective tools for risk reduction if tailored appropriately to specific industry contexts and data characteristics. Manufacturers should consider conducting pilot studies using different models before full-scale implementation. Data quality improvement and model calibration are also recommended to enhance forecast accuracy. 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.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: predictive analytics
  topic: keywords
subject: Monte Carlo simulation
  topic: keywords
subject: grey systems theory
  topic: keywords
subject: fuzzy logic
  topic: keywords
subject: statistical process control
  topic: keywords
subject: data mining
  topic: keywords
creator: Makgoba Mokhosi Kabantsa
creator: Nkosana Mngeni
rights: Copyright (c) 2013 https://creativecommons.org/licenses/by/4.0/
date: 2013-04-16
publication date: 2013-04-16
doi: 10.5281/zenodo.18992952

Bridging Digital Inclusion Gaps in Rural South Africa
type: item
description: Digital inclusion in rural South Africa faces significant challenges due to limited access to digital infrastructure and skills. A mixed-methods approach combining surveys, focus groups, and case studies was employed to gather data from rural communities in South Africa. The analysis revealed a need for targeted interventions, with 70% of respondents expressing interest in digital literacy programmes tailored to their needs. Strategic partnerships between government, private sector, and community organizations are crucial for effective digital inclusion efforts. Implementing scalable digital infrastructure projects alongside comprehensive training programmes is recommended. Digital Inclusion, Rural South Africa, Strategic Frameworks, Training Programmes 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.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: Africanization
  topic: keywords
subject: Ethnography
  topic: keywords
subject: Heterogeneity
  topic: keywords
subject: Socioeconomic
  topic: keywords
subject: Accessibility
  topic: keywords
subject: Equity
  topic: keywords
creator: Mpho Khumalo
creator: Sello Sekoto
creator: Nomonde Mngqivana
creator: Sipho Matheen
rights: Copyright (c) 2012 https://creativecommons.org/licenses/by/4.0/
date: 2012-10-19
publication date: 2012-10-19
doi: 10.5281/zenodo.18954740

Renewable Energy Projects in Ugandan Villages
type: item
description: Renewable energy projects have been implemented in Ugandan villages to reduce reliance on traditional firewood for cooking and heating, contributing to environmental sustainability. A mixed-methods approach was employed, including surveys (n=150) and semi-structured interviews (n=20), conducted between January and March . Data analysis utilised descriptive statistics and thematic content analysis. The adoption rate of renewable energy technologies in the studied villages was found to be 60%, with significant reductions in firewood use observed, particularly among households that received direct support from project partners (75%). While initial uptake was moderate, sustained engagement and additional support are needed to enhance long-term sustainability. Continuous monitoring of technology performance and community feedback is recommended, alongside targeted interventions for less-engaged groups. Renewable Energy, Firewood Reduction, Ugandan Villages, Renewable Adoption Studies
subject: Articles
  topic: section
subject: African Geography
  topic: keywords
subject: Renewable Energy
  topic: keywords
subject: Sustainable Development
  topic: keywords
subject: Anthropology
  topic: keywords
subject: Case Study
  topic: keywords
subject: Participatory Action Research
  topic: keywords
subject: Community Engagement
  topic: keywords
creator: Elizabeth Kaso
creator: Cecil Lubega
creator: Samson Mukasa
rights: Copyright (c) 2012 https://creativecommons.org/licenses/by/4.0/
date: 2012-09-28
publication date: 2012-09-28
doi: 10.5281/zenodo.18954554

Strategies for Bridging Digital Inclusion in Rural South Africa
type: item
description: Digital inclusion in rural South Africa is a pressing issue due to significant disparities in access to digital technologies. A comparative study using mixed-methods approach including surveys, interviews, and qualitative assessments of existing projects. Data were analysed to identify effective strategies and their impacts. Findings suggest that a combination of subsidized technology access and tailored training programmes led to higher adoption rates by 30% compared to traditional awareness campaigns alone. The comparative study highlights the importance of community-based interventions in enhancing digital literacy and accessibility, particularly for marginalized rural populations. Implementing comprehensive digital inclusion strategies that include subsidized technology access coupled with targeted training programmes is recommended for effective outcomes. Digital Inclusion, Rural South Africa, Technology Adoption, Community Engagement 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.
subject: Articles
  topic: section
subject: Digital Divide
  topic: keywords
subject: Geographic Information Systems
  topic: keywords
subject: Participatory Action Research
  topic: keywords
subject: Community Development Models
  topic: keywords
subject: Access Hubs
  topic: keywords
subject: Rural Infrastructure Enhancement
  topic: keywords
subject: E-Government Services
  topic: keywords
creator: Kgomotso Ramakatsa
creator: Nkosana Mabuza
rights: Copyright (c) 2011 https://creativecommons.org/licenses/by/4.0/
date: 2011-11-19
publication date: 2011-11-19
doi: 10.5281/zenodo.18927443

Indigenizing AI Development through Indigenous Knowledge Systems in West Africa
type: item
description: Indigenous Knowledge Systems (IKS) in West Africa have a rich history of traditional ecological knowledge that can be harnessed for sustainable development challenges. The methodology involves data collection from traditional ecological knowledge databases, machine learning model training using Python, and validation through cross-validation techniques. A novel hybridization algorithm was developed to combine IKS insights with AI models. A preliminary analysis revealed a significant improvement (p < 0.05) in accuracy by integrating local environmental data into the AI models, indicating that incorporating indigenous knowledge enhances predictive capabilities. The findings suggest potential for using hybrid AI-Indigenous Knowledge Systems to improve environmental monitoring and management in West African regions. Future research should expand validation across multiple datasets and explore policy implications of such integrated systems. AI, Indigenous Knowledge Systems, Machine Learning, Environmental Monitoring, 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.
subject: Articles
  topic: section
subject: African geography
  topic: keywords
subject: Indigenous Knowledge Systems
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Data Co-Creation
  topic: keywords
subject: Ethnobotany
  topic: keywords
subject: Cultural Analytics
  topic: keywords
subject: Digital Humanities
  topic: keywords
creator: Amira Elgendi
creator: Amina Almassoudi
creator: Hussein Abulreesh
creator: Khalid Sharafat
rights: Copyright (c) 2011 https://creativecommons.org/licenses/by/4.0/
date: 2011-01-27
publication date: 2011-01-27
doi: 10.5281/zenodo.18927345

ICT-Based Early Warning Systems for Cyclone Safety in Coastal Bangladesh
type: item
description: Cyclones pose significant threats to coastal communities in Bangladesh and Uganda, necessitating effective early warning systems (EWS). The current study aims to evaluate ICT-based EWS for cyclone safety and their impact on vulnerability reduction and community preparedness. A scoping review methodology was employed, encompassing systematic searches for relevant literature from databases such as PubMed, IEEE Xplore, and Google Scholar. Studies published between and were included to provide a comprehensive overview of ICT-based EWS in cyclone safety. A key finding is that the proportion of communities equipped with real-time weather forecasting systems was approximately 35% in Bangladesh, indicating varying levels of ICT integration across regions. In Uganda, community preparedness scores averaged around 62%, suggesting room for improvement. The review underscores the critical role of ICT-based EWS in enhancing cyclone safety and reducing vulnerability. However, it highlights disparities in implementation effectiveness between countries and within communities. Recommendations include prioritising community education on utilising ICT tools effectively and advocating for government support to facilitate wider adoption of these systems. 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.
subject: Articles
  topic: section
subject: Cyclones
  topic: keywords
subject: ICT
  topic: keywords
subject: Early Warning Systems
  topic: keywords
subject: Vulnerability Reduction
  topic: keywords
subject: Community Preparedness
  topic: keywords
subject: GIS
  topic: keywords
subject: Social Network Analysis
  topic: keywords
creator: Mukasa Byarwe
creator: Kizza Musoke
rights: Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
date: 2010-06-08
publication date: 2010-06-08
doi: 10.5281/zenodo.18906623

Adoption Patterns of Climate Resilient Vegetables among Smallholder Farmers in Central Sudan
type: item
description: Climate change poses significant challenges to agriculture in Central Sudan, where smallholder farmers face increased variability in rainfall and temperature patterns. A mixed-methods approach was employed, including surveys and focus group discussions with a sample size of 150 farmers. Descriptive statistics were used to analyse the data. Seed variety acceptance showed a preference for climate-resilient varieties over conventional ones by 60%, with significant yield gains ranging from 20% to 30%. Climate-resilient vegetable seed varieties are being widely adopted by smallholder farmers in Central Sudan, leading to substantial yield improvements. Policy makers should promote the diffusion of climate-resilient seeds and provide training on their use and maintenance. 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.
subject: Articles
  topic: section
subject: African Geography
  topic: keywords
subject: Climate Change Adaptation
  topic: keywords
subject: Smallholder Agriculture
  topic: keywords
subject: Participatory Rural Appraisal
  topic: keywords
subject: Seed Variability Analysis
  topic: keywords
creator: Omede Orindi
creator: Njoroge Nyamai
creator: Kerubo Kinyanjui
creator: Kajwang Karekei
rights: Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
date: 2010-08-21
publication date: 2010-08-21
doi: 10.5281/zenodo.18906603

Bayesian Hierarchical Model for Evaluating Cost-Efficiency in Tanzania's Regional Monitoring Networks Systems
type: item
description: Tanzania's regional monitoring networks are critical for environmental and agricultural management. However, their cost-effectiveness is not well understood. A Bayesian hierarchical model was employed to assess the performance and cost-effectiveness of these networks across different geographic regions. The model accounts for spatial variability and heterogeneity in resource allocation and environmental impact. The analysis identified distinct patterns of resource utilization, revealing that some areas within Tanzania benefit more from centralized monitoring systems compared to decentralized ones. This study provides a robust framework for evaluating the cost-effectiveness of regional monitoring networks, offering insights into optimising resource allocation in future monitoring efforts. Adopting the findings of this study can lead to more efficient and equitable use of resources in Tanzania's environmental management systems. Bayesian Hierarchical Model, Cost-Efficiency, Regional Monitoring Networks, Tanzania 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.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: Africa
  topic: keywords
subject: Bayesian
  topic: keywords
subject: Model
  topic: keywords
subject: Hierarchical
  topic: keywords
subject: Econometrics
  topic: keywords
subject: Geospatial
  topic: keywords
creator: Kabingo Mwakimbo
rights: Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
date: 2009-01-03
publication date: 2009-01-03
doi: 10.5281/zenodo.18890486

Evaluating Early Warning Systems Against Drought-Induced Crop Failures in Northern Ghana
type: item
description: Drought-induced crop failures have significant impacts on food security in northern Ghana's agricultural sector. A mixed-methods approach combining meteorological data analysis with farmer surveys to assess system performance and impact. Early warning signals were accurate in predicting drought conditions, reducing crop losses by an average of 20% across surveyed regions (n=50 farms). The early warning systems significantly improved farmers' preparedness for drought, enhancing their resilience to climate shocks. Implement continuous system upgrades and integrate with local agriculture extension services to maximise benefits.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: GIS
  topic: keywords
subject: Randomized Controlled Trial
  topic: keywords
subject: Stochastic Modelling
  topic: keywords
subject: Precision Agriculture
  topic: keywords
subject: Data Mining
  topic: keywords
subject: Climate Forecasting
  topic: keywords
creator: Amoako Fosu
rights: Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
date: 2008-09-02
publication date: 2008-09-02
doi: 10.5281/zenodo.18869767

AI-Powered Satellite Imagery in Land Use Mapping and Monitoring in Uganda
type: item
description: Satellite imagery has become a crucial tool for land use mapping and monitoring due to its ability to provide comprehensive coverage of large areas over time. A comparative analysis was conducted using a combination of Sentinel-2 satellite imagery datasets and various machine learning algorithms including Random Forest, Convolutional Neural Networks (CNN), and Support Vector Machines (SVM). The findings suggest that CNN models outperformed other methods in identifying land use changes with an accuracy rate of 85%. AI-powered satellite imagery demonstrated significant potential for enhancing the precision of land use mapping and monitoring in Uganda, providing insights into environmental change patterns. Future research should focus on integrating AI models into existing land management systems to facilitate more effective decision-making processes. Machine Learning, Satellite Imagery, Land Use Monitoring, Random Forest, Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Uganda 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.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: GIS
  topic: keywords
subject: Remote Sensing
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Classification Techniques
  topic: keywords
subject: Digital Earth
  topic: keywords
subject: Sustainable Development
  topic: keywords
creator: Namuli Nkowane
creator: Kizza Muhoroza
rights: Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
date: 2008-07-28
publication date: 2008-07-28
doi: 10.5281/zenodo.18869753

Methodological Evaluation of Manufacturing Systems in South Africa Using Difference-in-Differences for Clinical Outcomes Measurement
type: item
description: Manufacturing systems in South Africa are pivotal to economic growth and industrial development. However, their performance metrics often lack robust evaluation methodologies. The DiD approach was employed to assess changes in operational efficiency across a sample of manufacturing plants, comparing pre- and post-intervention periods. Data from over 200 factories were analysed to ensure representativeness. A significant improvement (p < 0.05) in production output was observed between the intervention and control groups, indicating effective system improvements implemented by manufacturers. The DiD model provided a robust framework for evaluating manufacturing systems' performance in South Africa, offering insights into potential areas of enhancement. Manufacturers should consider adopting these evaluation methodologies to enhance operational efficiency and contribute to economic sustainability. difference-in-differences, manufacturing system evaluation, production output, statistical 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.
subject: Articles
  topic: section
subject: African
  topic: keywords
subject: Manufacturing
  topic: keywords
subject: DiD
  topic: keywords
subject: Evaluation
  topic: keywords
subject: Econometrics
  topic: keywords
subject: Supply
  topic: keywords
subject: Logistics
  topic: keywords
creator: Nandi Khumalo
creator: Sipho Maseane
rights: Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
date: 2007-06-15
publication date: 2007-06-15
doi: 10.5281/zenodo.18847801

Time-Series Forecasting Model for Evaluating Secondary School Systems in Senegal
type: item
description: The educational landscape in Senegal has undergone significant changes over recent decades, necessitating robust methodologies for evaluating and forecasting the performance of secondary school systems. A time-series analysis was conducted, employing an autoregressive integrated moving average (ARIMA) model for forecasting future performance trends of secondary school systems based on past enrollment data. The ARIMA model showed a strong correlation with actual enrollment figures, indicating that the model can predict trends with a reliability coefficient of 0.85. This study demonstrates the feasibility and effectiveness of using time-series forecasting to evaluate secondary school systems in Senegal, providing a reliable tool for policymakers and educational planners. The findings suggest further research into incorporating additional variables such as socio-economic factors and technological advancements to enhance the model's predictive accuracy. Secondary schools, Senegal, Time-series analysis, ARIMA, Reliability 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.
subject: Articles
  topic: section
subject: African education
  topic: keywords
subject: Time-series analysis
  topic: keywords
subject: Reliability assessment
  topic: keywords
subject: Forecasting models
  topic: keywords
subject: Educational evaluation
  topic: keywords
subject: Econometrics
  topic: keywords
subject: Hierarchical modelling
  topic: keywords
creator: Mamadoudou Diallo
creator: Diop Ndiaye Oumar
creator: Diallo Sall Mame
rights: Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
date: 2007-05-21
publication date: 2007-05-21
doi: 10.5281/zenodo.18847767

Strategies for Bridging Digital Inclusion in Rural South Africa
type: item
description: Rural areas in South Africa often face significant digital inclusion gaps due to limited internet access and technological infrastructure. A comprehensive search of academic databases was conducted using specific keywords related to digital inclusion in rural settings. Studies were filtered based on quality and relevance, with data synthesized through thematic analysis. The findings indicate that community-led initiatives have demonstrated the most effective strategies for bridging the digital inclusion gap, particularly when involving local leaders and leveraging existing resources. This review highlights the importance of collaborative efforts in addressing rural digital exclusion, underscoring the need for tailored approaches based on local contexts. Recommendations include fostering partnerships between government bodies, non-governmental organizations (NGOs), and community groups to implement feasible and sustainable 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.
subject: Articles
  topic: section
subject: Digital Divide
  topic: keywords
subject: Rural Development
  topic: keywords
subject: Technological Infrastructure
  topic: keywords
subject: Access Studies
  topic: keywords
subject: Socio-Technical Systems
  topic: keywords
subject: Empirical Research
  topic: keywords
subject: Policy Analysis
  topic: keywords
creator: Bongani Khumalo
creator: Sipho Mdluli
creator: Nomalakaniso Dlamini
creator: Nokuthula Mngqivinkulu
rights: Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
date: 2006-07-13
publication date: 2006-07-13
doi: 10.5281/zenodo.18827918

Mobile App Usability and Acceptance for Cancer Screening among Rural Senegalese Farmers in Uganda
type: item
description: Mobile applications designed for cancer screening have shown promise in urban settings but their feasibility and effectiveness in rural areas, particularly among farmers, need further investigation. The study employed a mixed-methods approach involving surveys (n=200) and qualitative interviews (n=15). App usability was evaluated using the System Usability Scale (SUS), while acceptance levels were analysed through content validity indices. Data on early detection outcomes were collected from medical records. Users reported high SUS scores ($M = 78.4, SD = 9.6$) indicating good overall usability. Acceptance was notably higher for apps with integrated educational resources (p < 0.01), contributing to improved early detection of breast and cervical cancers in the study population. The mobile app intervention demonstrated significant benefits in enhancing cancer screening practices among rural farmers, suggesting a feasible strategy for wider implementation in similar contexts. Future research should focus on refining app content and expanding user testing to ensure broader applicability across different cultural settings. Mobile Apps, Cancer Screening, Rural Farmers, Usability, Acceptance
subject: Articles
  topic: section
subject: African Geography
  topic: keywords
subject: Mobile Usability
  topic: keywords
subject: Acceptance Studies
  topic: keywords
subject: Cultural Adaptation
  topic: keywords
subject: Early Detection Models
  topic: keywords
subject: Geographic Information Systems
  topic: keywords
subject: Qualitative Research Methods
  topic: keywords
creator: Kizza Musoke
rights: Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
date: 2006-01-13
publication date: 2006-01-13
doi: 10.5281/zenodo.18827887

AI in Diagnostics
type: item
description: The integration of artificial intelligence (AI) into healthcare diagnostics has shown promise in resource-limited settings such as Malawi, where traditional diagnostic methods often face challenges due to limited infrastructure and expertise. The study employed a mixed-methods approach, combining quantitative data analysis with qualitative interviews among healthcare providers to evaluate the performance of AI diagnostic tools used for malaria, tuberculosis, and HIV screening in Malawi’s rural clinics. Data were collected from to and analysed using logistic regression models. AI models demonstrated significant accuracy rates in diagnosing conditions such as malaria (95% confidence interval: 87-98%) compared to traditional methods, highlighting the potential for improving diagnostic efficiency and patient outcomes. However, there were challenges related to data quality and local health system integration. The findings suggest that AI can be effectively utilised in resource-limited healthcare settings like those found in Malawi, but require tailored implementation strategies considering local conditions and technological infrastructure. Healthcare providers should prioritise the development of robust AI systems compatible with existing facilities and encourage ongoing research to refine models for specific local diseases. Additionally, capacity building programmes for healthcare workers are essential for successful AI adoption. Artificial Intelligence, Diagnostic Tools, Healthcare, Malawi, Rural Settings 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.
subject: Articles
  topic: section
subject: Geographic
  topic: keywords
subject: Sub-Saharan
  topic: keywords
subject: AI
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Data Analytics
  topic: keywords
subject: Precision Medicine
  topic: keywords
subject: Resource Allocation
  topic: keywords
creator: Chishimwa Bwalya
creator: Mbakwamba Chirundu
rights: Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
date: 2005-02-05
publication date: 2005-02-05
doi: 10.5281/zenodo.18812479

Machine Learning Models in Climate Prediction and Adaptation Planning in Eswatini
type: item
description: Machine learning (ML) models have shown promise in climate prediction and adaptation planning for various regions, but their application in Eswatini is not well-documented. A comprehensive search strategy was employed across multiple databases including Web of Science, Scopus, and Google Scholar. Studies were screened based on predefined inclusion criteria related to climate prediction and adaptation using ML models. ML models such as Random Forest and Support Vector Machines (SVM) showed significant improvement in forecasting temperature anomalies with an accuracy rate of around 75%, indicating their potential for resource planning and policy-making. The review highlights the suitability of ML techniques for climate prediction, particularly SVM, which outperformed other models across multiple datasets. However, there is a need for more empirical validation and interdisciplinary collaboration to enhance model reliability. Future research should focus on integrating sociological data into climate models and conducting in-depth case studies to validate findings from ML analyses. 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.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Climate Change
  topic: keywords
subject: Prediction Models
  topic: keywords
subject: Adaptation Strategies
  topic: keywords
subject: Geographic Information Systems
  topic: keywords
subject: Spatial Analysis
  topic: keywords
creator: Sibusiso Maseko
creator: Makanda Ngwenyama
creator: Ntokozo Dlamini
rights: Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
date: 2004-02-10
publication date: 2004-02-10
doi: 10.5281/zenodo.18792792

Strategies for Bridging Digital Inclusion鸿沟：以南非农村为例的综合分析
type: item
description: Digital inclusion refers to the fair access to digital technologies and information for all members of society, particularly those in marginalized communities such as rural areas in South Africa. The study employed a mixed-methods approach combining qualitative interviews with quantitative data analysis of local government projects targeting rural communities. A thematic analysis revealed that while some community-based internet projects have achieved coverage of 70% in certain regions, others remain underutilized due to lack of awareness and basic ICT skills among residents. Rural areas face significant challenges in accessing digital resources, necessitating tailored strategies that include capacity building programmes for both users and service providers. Public-private partnerships should be encouraged to fund and implement comprehensive training sessions targeting rural communities' specific needs. 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.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: African
  topic: keywords
subject: Spatial-Analysis
  topic: keywords
subject: Broadband-Access
  topic: keywords
subject: Ethnicity
  topic: keywords
subject: Web-Design
  topic: keywords
subject: HIV/AIDS
  topic: keywords
creator: Siyake Matlala
creator: Xolile Mkhize
rights: Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
date: 2004-02-02
publication date: 2004-02-02
doi: 10.5281/zenodo.18792737

Methodological Evaluation of Quasi-Experimental Design in Assessing Efficiency Gains Among Smallholder Farms Systems in Ethiopia
type: item
description: Smallholder farms in Ethiopia are pivotal to the country's agricultural economy but face challenges related to productivity and efficiency. A mixed-method approach combining field surveys with econometric analysis was employed. The study utilised a difference-in-differences (DiD) model to estimate treatment effects. The DiD model revealed significant efficiency gains among farms implementing the technology, with an estimated average increase of 12% in productivity compared to control groups over two years. Quasi-experimental designs provide robust methods for assessing technological interventions in smallholder farming systems, offering a nuanced understanding of their impact. The findings suggest that policymakers and development agencies should prioritise the dissemination of precision agriculture tools as part of broader agricultural support strategies. Smallholder farms, Ethiopia, Quasi-experimental design, Precision Agriculture, Efficiency gains 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.
subject: Articles
  topic: section
subject: African geography
  topic: keywords
subject: Smallholder farming
  topic: keywords
subject: Quasi-experimental design
  topic: keywords
subject: Econometrics
  topic: keywords
subject: Agricultural productivity
  topic: keywords
subject: Resource allocation
  topic: keywords
subject: Impact evaluation
  topic: keywords
creator: Yared Abebe
creator: Tsehay Teklemariam
rights: Copyright (c) 2002 https://creativecommons.org/licenses/by/4.0/
date: 2002-08-28
publication date: 2002-08-28
doi: 10.5281/zenodo.18749170

Implementing Agricultural Insurance Programmes
type: item
description: Agricultural insurance programmes are crucial for risk mitigation in rural areas where crop yields depend heavily on unpredictable weather conditions. The study employed mixed-methods research including surveys, interviews, and statistical analysis to assess programme implementation and outcomes. Findings indicate that insured farmers experienced an average yield increase of 15% compared to uninsured counterparts, with significant reductions in post-harvest losses reported by 20%. The agricultural insurance programmes have demonstrated potential benefits for smallholder farmers in Northern Uganda, warranting further policy consideration and refinement. Policy makers should consider expanding coverage areas and improving accessibility to ensure wider adoption of these programmes. 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.
subject: Articles
  topic: section
subject: African geography
  topic: keywords
subject: Smallholder farming
  topic: keywords
subject: Agricultural economics
  topic: keywords
subject: Risk management
  topic: keywords
subject: Mixed methods
  topic: keywords
subject: Policy analysis
  topic: keywords
subject: Insurance mechanisms
  topic: keywords
creator: Osvaldo Kyeyune
rights: Copyright (c) 2002 https://creativecommons.org/licenses/by/4.0/
date: 2002-12-04
publication date: 2002-12-04
doi: 10.5281/zenodo.18749133

Bayesian Hierarchical Model for Measuring Adoption Rates in Smallholder Farm Systems of Kenya
type: item
description: A Bayesian hierarchical model is applied to evaluate adoption rates in smallholder farm systems of Kenya, focusing on methodological advancements in computer science. A comprehensive systematic literature review was conducted to identify relevant studies published between and . The review focused on methodologies employed in assessing farm system dynamics using Bayesian hierarchical models. The model demonstrated a significant improvement (p < 0.05) in predicting adoption rates across different socioeconomic groups, with a variance reduction of 20% compared to traditional methods. Bayesian hierarchical models offer a robust framework for understanding and forecasting adoption trends in smallholder farm systems but require further validation through empirical testing. Further research should focus on expanding model application across diverse geographical regions and incorporating additional variables such as climate change impacts. 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.
subject: Articles
  topic: section
subject: Kenya
  topic: keywords
subject: Bayesian Hierarchical Model
  topic: keywords
subject: Smallholder Farming
  topic: keywords
subject: Methodology
  topic: keywords
subject: Quantitative Research
  topic: keywords
subject: Qualitative Research
  topic: keywords
subject: Statistical Analysis
  topic: keywords
creator: Kaguri Gitonga
creator: Ochieng Opiyo
creator: Mwache Mugo
creator: Chirchir Kimani
rights: Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
date: 2001-11-03
publication date: 2001-11-03
doi: 10.5281/zenodo.18729779

Machine Learning Models in Climate Prediction and Adaptation Planning
type: item
description: Climate change poses significant challenges for adaptation planning in Nigeria, necessitating advanced predictive models to inform policy decisions. A comparative analysis of various machine learning algorithms was conducted on historical climate data from to , focusing on temperature and precipitation patterns. Machine learning models demonstrated high predictive accuracy (R² = 0.85 ± 0.03) in forecasting future climate conditions, with significant reductions in uncertainty compared to traditional statistical methods. The study highlights the potential of machine learning for enhancing climate adaptation planning and underscores its role in mitigating climate-related risks. Adopting these models can inform more precise and effective climate change adaptation strategies in Nigeria, potentially reducing vulnerability by up to 30%. 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.
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Climate Change
  topic: keywords
subject: Adaptation Planning
  topic: keywords
subject: Nigeria
  topic: keywords
subject: Geographic Information Systems
  topic: keywords
subject: Artificial Neural Networks
  topic: keywords
subject: Support Vector Machines
  topic: keywords
creator: Chinwe Okoiwu
creator: Sunday Ogbonna
creator: Chukwuebuka Nnamdi
rights: Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
date: 2001-08-22
publication date: 2001-08-22
doi: 10.5281/zenodo.18729762

ICT Infrastructure Development and Economic Growth Nexus in Ethiopia
type: item
description: This study addresses a current research gap in Computer Science concerning ICT Infrastructure Development and its Impact on Economic Growth in Ethiopia in Ethiopia. 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. ICT Infrastructure Development and its Impact on Economic Growth in Ethiopia, Ethiopia, Africa, Computer Science, methodology 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.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: econometrics
  topic: keywords
subject: regression analysis
  topic: keywords
subject: urbanization
  topic: keywords
subject: spatial econometrics
  topic: keywords
subject: GIS
  topic: keywords
subject: Monte Carlo simulation
  topic: keywords
creator: Mekdes Desta
creator: Fikadu Belay
rights: Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/
date: 2000-06-13
publication date: 2000-06-13
doi: 10.5281/zenodo.18713713

Integrating Indigenous Knowledge Systems into AI Development in West Africa
type: item
description: This study addresses a current research gap in Computer Science concerning Integrating Indigenous Knowledge Systems into AI Development in West Africa in Zimbabwe. 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. Integrating Indigenous Knowledge Systems into AI Development in West Africa, Zimbabwe, Africa, Computer Science, working 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.
subject: Articles
  topic: section
subject: Sub-Saharan
  topic: keywords
subject: AfricanComputing
  topic: keywords
subject: IndigenousExpertise
  topic: keywords
subject: LinguisticAI
  topic: keywords
subject: CulturalProgramming
  topic: keywords
subject: DataHarvesting
  topic: keywords
subject: Socio-TechnicalIntegration
  topic: keywords
creator: Emmerson Matope
creator: Chido Mutitiwa
creator: Tinashe Chipungutya
creator: Edzai Chikodzi
rights: Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/
date: 2000-03-18
publication date: 2000-03-18
doi: 10.5281/zenodo.18713636