AIMC Topic: Ethiopia

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Optimizing machine learning models for predicting health service access and determinants among pregnant women in rural Ethiopia.

Scientific reports
Pregnant women in rural Ethiopia face substantial barriers to accessing adequate healthcare services, contributing to adverse maternal and neonatal health outcomes. Traditional statistical approaches often fall short in capturing the complex, nonline...

A novel integrated framework for long-term assessment of ecosystem service degradation and restoration prioritization in a semi-arid rift valley landscape.

Environmental monitoring and assessment
Wetland ecosystems in Africa's semi-arid rift valleys are crucial for supporting biodiversity, regulating water systems, and sustaining livelihoods; however, they are rapidly deteriorating due to agricultural expansion and urbanization. Previous asse...

Machine learning predictions of climate change effects on nearly threatened bird species (Crithagra xantholaema) habitat in Ethiopia for conservation strategies.

Scientific reports
Endemic and endangered bird species, such as Salvadori serin (C. xantholaema), are vulnerable to environmental and anthropogenic changes. Understanding the impact of climate change on ecological niches is essential for effective conservation. This st...

An artificial neural network approach for predicting infant mortality status in Ethiopia.

BMC public health
Infant mortality is a major public health issue that is rooted in the larger problem of socio-economic and healthcare disparities. Deep learning techniques were employed in this study to predict infant mortality using data gathered via 2019 Ethiopia ...

A deep learning framework for Ethiopian sign language recognition using skeleton-based representation.

Scientific reports
This study proposes an environment- and signer-invariant sign language recognition model. The model first extracts skeletal key-points from the signer via MediaPipe, which is Google's cross-platform pipeline framework that helps to detect and track h...

Haplotype stacking to improve stability of stripe rust resistance in wheat.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Genotype-by-environment interaction analysis and haplotype-level characterisation provide novel insights into the stability of stripe rust resistance. Breeding selection strategies are proposed to achieve rapid and stable genetic gains across environ...

Predicting malnutrition in PLWHIV using machine learning in gondar, Ethiopia.

BMC public health
BACKGROUND: Human Immunodeficiency Virus (HIV) continues to be a major global public health challenge, affecting 39.9 million people globally by the end of 2023. Sub-Saharan Africa bears a significant burden, contributing to 67% of cases. Malnutritio...

Detection of weeds in teff crops using deep learning and UAV imagery for precision herbicide application.

Scientific reports
In Ethiopia, Teff is a vital staple crop, yet its productivity is significantly challenges due to inefficient weed and fertilizer management, threatening food security. Traditional weed control methods rely on manual labor and the indiscriminate appl...

Application of causal forest double machine learning (DML) approach to assess tuberculosis preventive therapy's impact on ART adherence.

Scientific reports
Adherence to antiretroviral therapy (ART) is critical for HIV treatment success, yet the impact of tuberculosis preventive therapy (TPT) remains inadequately understood. Using observational data from 4152 HIV patients in Ethiopia (2005-2024), we appl...

Predicting stunting status among under five children in ethiopia using ensemblemachine learning algorithms.

Scientific reports
Childhood stunting is a persistent public health challenge in Ethiopia, significantly impacting children's physical growth, cognitive development, and overall well-being. This study overcame a key limitation in previous stunting prediction models by ...