AIMC Topic: Machine Learning

Clear Filters Showing 7971 to 7980 of 34417 articles

Can " Zero waste city" policy promote green technology? Evidence from econometrics and machine learning.

Journal of environmental management
The promotion of green technology innovation (GTI) is regarded as an effective way to protect the environment and achieve sustainable development. The "Zero waste city" construction pilot policy (ZWCP), is an important policy for the promotion of was...

Leveraging explainable machine learning for enhanced management of lake water quality.

Journal of environmental management
Freshwater lakes worldwide suffer from eutrophication caused by excessive nutrient loads, particularly nitrogen (N) and phosphorus (P) from wastewater and runoff, affecting aquatic life and public health. Using a large (1800 km) subtropical lake as a...

Spatial patterns of rural opioid-related hospital emergency department visits: A machine learning analysis.

Health & place
As opioid-related overdose emergency department visits continue to rise in the United States, there is a need to understand the location and magnitude of the crisis, especially in at-risk rural areas. We analyzed sets of ZIP code level electronic hea...

Prediction of urban surface water quality scenarios using hybrid stacking ensembles machine learning model in Howrah Municipal Corporation, West Bengal.

Journal of environmental management
In the pursuit of understanding surface water quality for sustainable urban management, we created a machine learning modeling framework that utilized Random Forest (RF), Cubist, Extreme Gradient Boosting (XGB), Multivariate Adaptive Regression Splin...

Prediction of Social Engagement in Long-Term Care Homes by Sex: A Population-Based Analysis Using Machine Learning.

Journal of applied gerontology : the official journal of the Southern Gerontological Society
The objective of this study was to use population-based clinical assessment data to build and evaluate machine-learning models for predicting social engagement among female and male residents of long-term care (LTC) homes. Routine clinical assessment...

Assessing prospective molecular biomarkers and functional pathways in severe asthma based on a machine learning method and bioinformatics analyses.

The Journal of asthma : official journal of the Association for the Care of Asthma
BACKGROUND: Severe asthma, which differs significantly from typical asthma, involves specific molecular biomarkers that enhance our understanding and diagnostic capabilities. The objective of this study is to assess the biological processes underlyin...

Machine learning assisted rapid approach for quantitative prediction of biochemical parameters of blood serum with FTIR spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
This study develops regression models for predicting blood biochemical data using Fourier-transform infrared spectroscopy (FTIR) analysis. Absorption at specific wavelengths of blood serum is revealed to have strong correlations with biochemical para...

A machine learning algorithm for creating isotropic 3D aortic segmentations from routine cardiac MR localizers.

Magnetic resonance imaging
BACKGROUND: The identification and measurement of aortic aneurysms is an important clinical problem. While specialized high-resolution 3D CMR sequences allow detailed aortic assessment, they are time-consuming which limits their use in screening rout...

Prediction of electron-solid interaction parameters using machine learning.

Medical physics
BACKGROUND: Electron backscattering coefficient and electron-stopping power are essential concepts in many disciplines, from radiation to materials science, semiconductor manufacturing, and space exploration. They enable precise calculations, measure...

Data-augmented machine learning scoring functions for virtual screening of YTHDF1 mA reader protein.

Computers in biology and medicine
Machine learning is rapidly advancing the drug discovery process, significantly enhancing speed and efficiency. Innovation in computer-aided drug design is primarily driven by structure- and ligand-based approaches. When the number of known inhibitor...