AIMC Topic: Egypt

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Modeling regional mean sea level based on climate measurements using a stacked ensemble approach.

Environmental monitoring and assessment
Assessing changes in mean sea level (MSL) has become increasingly critical due to the significance of climate changes. Soft computing techniques are now widely used to reduce the time and cost associated with traditional MSL estimation methods. Histo...

Comparison of machine learning classification and regression models for prediction of academic performance among postgraduate public health students.

Scientific reports
Machine learning (ML) is an artificial intelligence tool that focuses on learning by generating models using established algorithms that represent a given dataset. It can be used as a predictive tool for students' academic performance (AP) at both un...

End-to-end deep SAE-DNN model for predicting Egyptian buffalo calf sex, weight, and daily milk yield.

Tropical animal health and production
In the present study, a novel stacked Sparse Autoencoder-Deep Neural Network (SAE-DNN) learning prediction model was applied to predict calf sex, weight, and daily milk yield for dairy buffalo. First, SAE stage extracts the unique statistical feature...

Machine learning-driven geochemical fingerprinting and risk characterization of mineral dust across different operational settings in El-Gedida Iron Mine, Egypt.

Environmental geochemistry and health
Investigating mineral dust emitted from mining activities enables the assessment of environmental risks posed by potentially toxic elements (PTEs) and the discrimination of geochemical fingerprints characteristic of distinct operational settings. Acc...

An interpretable machine learning approach based on SHAP, Sobol and LIME values for precise estimation of daily soybean crop coefficients.

Scientific reports
Increasing water scarcity and climate variability have intensified the need for precise agricultural irrigation management. Accurate estimation of crop coefficients (Kc) is critical for determining crop water requirements, especially in arid and semi...

Clinical, biochemical, and molecular characterization of a cohort of Egyptian patients with Sanfilippo B syndrome (MPS IIIB): Bayesian Gaussian mixture model.

Molecular biology reports
BACKGROUND: Lysosomal storage diseases (LSDs) are a group of genetically heterogeneous inherited metabolic disorders that affect the functions of the lysosomes in different human tissues. Mucopolysaccharidosis IIIB (MPS IIIB), Sanfilippo B syndrome, ...

Macroeconomic-aware forecasting of construction costs in developing countries: Using gated recurrent unit and long short-term memory deep learning framework.

PloS one
Cost overruns are common on long-term construction projects. This is mostly because of inaccurate early estimates and unexpected changes in the economy and finances. In Egypt, the costs of materials like steel, cement, bricks, sand, and aggregates ma...

A deep learning approach to gender equality: Forecasting educational indicators with 1D-CNN aligned with SDG 5.

PloS one
Sustainable development goal (SDG) 5 focuses on gender equality and empowerment and it is considered as one of the most important SDGs. Therefore, this article presented a time series prediction model that predicts gender-related educational results ...

A hybrid machine learning model for predicting agricultural production costs: Integrating economic sensitivity analysis and environmental factors in Egypt.

Journal of environmental management
Accurate prediction of agricultural production costs is crucial for sustainable development in Egypt, where productivity is highly sensitive to fluctuating economic and environmental conditions. This study introduces a hybrid machine learning model t...

The impact of digital competence on pedagogical innovation among nurse educators: The moderating role of artificial intelligence readiness.

Nurse education in practice
AIM: To investigate the relationships between digital competence, AI readiness and pedagogical innovation among nurse educators, with a specific focus on the moderating role of AI readiness.