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The authors studied the accuracy of two commonly used explainable AI techniques for detecting pneumonia in chest x-rays.

Autonomous drone imaging combined with machine learning offers a promising approach for early detection of invasive species. In this study, students built an autonomous drone and compared three models: CNN, SGDC, and XGBoost, to identify Brassica nigra from aerial footage. Their results show that CNNs most effectively recognize key visual features, demonstrating strong potential for supporting conservation and invasive plant management.

This study used machine learning models to examine which factors most influenced U.S. household energy consumption in 2020 using data from 18,496 households.

This study shows the efficacy of leveraging transfer learning, specifically from residual networks, to detect CVDs and possible signs of CVDs. The findings indicate that leveraging transfer learning from residual networks alongside medical professionals is a highly promising approach for CVD detection and diagnosis, warranting further investigation.

This study contributes to our understanding of how urban energy systems respond to climate variability and inform strategies for enhancing power grid resilience. The findings can help inform urban planners and infrastructure developers by identifying the factors that make regions within a power grid more vulnerable.

This study explored how green spaces, climate, traffic, and air quality (GCTA) collectively influence asthma-related emergency department visits in the U.S using machine learning models and explainable AI.

This study examined the impact of erosion on the performance of a triangular aerofoil at a low Reynolds number (Re = 10,000), relevant for harsh conditions like those on Mars.

Here, seeking to identify an optimal method to classify tree species through remote sensing, the authors used a few machine learning algorithms to classify forest tree species through multispectral satellite imagery. They found the Random Forest algorithm to most accurately classify tree species, with the potential to improve model training and inference based on the inclusion of other tree properties.

Here the author investigates how much heat energy is output and recovered from a conventional electric light bulb.

The authors looked at how soil temperature changes with fire to develop a sensor system that could aid in earlier detection of fires.