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?
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Comparative study of machine learning models for water potability prediction

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.
Read More...Determining the relationship between unemployment and minimum wage in Turkey

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.
Read More...The impact of environmental noise on the cognitive functions and mental workload of high school students

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.
Read More...Risk factors contributing to Pennsylvania childhood asthma

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.
Read More...Epileptic seizure detection using machine learning on electroencephalogram data

The authors use machine learning and electroencephalogram data to propose a method for improving epilepsy diagnosis.
Read More...Exploring the effects of diverse historical stock price data on the accuracy of stock price prediction models

Algorithmic trading has been increasingly used by Americans. In this work, we tested whether including the opening, closing, and highest prices in three supervised learning models affected their performance. Indeed, we found that including all three prices decreased the error of the prediction significantly.
Read More...Prediction of diabetes using supervised classification

The authors develop and test a machine learning algorithm for predicting diabetes diagnoses.
Read More...A machine learning approach to detect renal calculi by studying the physical characteristics of urine

The authors trained a machine learning model to detect kidney stones based on characteristics of urine. This method would allow for detection of kidney stones prior to the onset of noticeable symptoms by the patient.
Read More...Utilizing meteorological data and machine learning to predict and reduce the spread of California wildfires

This study hypothesized that a machine learning model could accurately predict the severity of California wildfires and determine the most influential meteorological factors. It utilized a custom dataset with information from the World Weather Online API and a Kaggle dataset of wildfires in California from 2013-2020. The developed algorithms classified fires into seven categories with promising accuracy (around 55 percent). They found that higher temperatures, lower humidity, lower dew point, higher wind gusts, and higher wind speeds are the most significant contributors to the spread of a wildfire. This tool could vastly improve the efficiency and preparedness of firefighters as they deal with wildfires.
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