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COVID-19 has impacted the way many people go about their daily lives, but what are the main factors driving the changes in the housing market, particular house prices?

The global issue of water quality has led to the use of machine learning models, like ANN and SVM, to predict water potability. However, these models can be complex and resource-intensive. This research aimed to find a simpler, more efficient model for water quality prediction.

The authors looked at the relationship between unemployment and minimum wage in Turkey (Türkiye). They found that there is a positive correlation between minimum wage and unemployment.

The authors looked at unionization petitions from Starbucks workers between August 2021 and July 2024 to determine what factors influence votes for or against unionization.

The authors looked at the ability of different machine learning algorithms to predict the level of financial corruption in different countries.

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.

The authors looked at different factors, such as age, pre-existing conditions, and geographic region, and their ability to predict what an individual's health insurance premium would be.

The authors used machine learning to predict the presence of anxiety and burnout in students based on facial expressions and demographic information.

Authors examine the impact of environmental noise on cognitive processes in teenagers, focusing on five different noise conditions: two types of noise (aircraft and construction) at two different decibel levels (30 dBA and 60 dBA) and a quiet condition.

Asthma is one of the most prevalent chronic conditions in the United States. But not all people experience asthma equally, with factors like healthcare access and environmental pollution impacting whether children are likely to be hospitalized for asthma's effects. Li, Li, and Ruffolo investigate what demographic and environmental factors are predictive of childhood asthma hospitalization rates across Pennsylvania.