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.
Read More...Environmental contributors of asthma via explainable AI: Green spaces, climate, traffic & air quality
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.
Read More...Deep dive into predicting insurance premiums using machine learning
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.
Read More...Using two-step machine learning to predict harmful algal bloom risk
Using machine learning to predict the risk of algae bloom
Read More...Class distinctions in automated domestic waste classification with a convolutional neural network
Domestic waste classification using convolutional neural network
Read More...Forecasting air quality index: A statistical machine learning and deep learning approach
Here the authors investigated air quality forecasting in India, comparing traditional time series models like SARIMA with deep learning models like LSTM. The research found that SARIMA models, which capture seasonal variations, outperform LSTM models in predicting Air Quality Index (AQI) levels across multiple Indian cities, supporting the hypothesis that simpler models can be more effective for this specific task.
Read More...PID and fuzzy logic optimization of the pitch control of wind turbines
Wind turbines are a valuable source of renewable energy, but face challenges related to unpredictable wind speed. The turbine must be able to control its angle to catch enough wind to generate electricity, while avoiding excess wind that may damage the turbine. Zhou and Wang explore different types of smart turbine controllers to see which appears optimal for electricity generation.
Read More...A natural language processing approach to skill identification in the job market
The authors looked at using machine learning to identify skills needed to apply for certain jobs, specifically looking at different techniques to parse apart the text. They found that Bidirectional Encoder Representation of Transforms (BERT) performed best.
Read More...Cardiovascular Disease Prediction Using Supervised Ensemble Machine Learning and Shapley Values
The authors test the effectiveness of machine learning to predict onset of cardiovascular disease.
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...Statistical models for identifying missing and unclear signs of the Indus script
This study utilizes machine learning models to predict missing and unclear signs from the Indus script, a writing system from an ancient civilization in the Indian subcontinent.
Read More...