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Using machine learning to predict the risk of algae bloom

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 analyze political endorsement patterns and impacts from the 2018 and 2020 midterm elections and find that such endorsements may be predictable based on the ideological and demographic factors of the endorser.

This study explores global trends in average total cholesterol levels among adults from 1980 to 2018, finding a significant decline in both men and women, with a sharper decrease among men. While these improvements likely reflect advances in public health and medical treatment, the persistent gender gap highlights the need for more targeted approaches to cardiovascular disease prevention.

The authors use machine learning to develop an evidence-based detection tool for identifying human trafficking.

We investigated whether dietary calcium intake affects how adolescents handle lead internally. Using paired blood and urinary lead data, we assessed whether calcium status is associated with differences in lead clearance. The results point to subgroups of adolescents—particularly those with lower calcium intake—who may carry a higher internal lead burden than their blood levels alone would suggest.

The authors assess the relationship between supportive family environments and internalized stigma in adolescents.

Tax incentives for sustainable technology are a key part of the push for a greener future. However, these incentives may not reach all income strata equally. Using a machine learning approach, this study analyzed the distributional effects of residential energy tax credits across different income levels in the United States.