Accurate and reliable Gross Domestic Product (GDP) forecasting is indispensable for informed economic policymaking and risk management. Autocorrelation, a prevalent characteristic of macroeconomic time series, poses significant challenges to traditio...
Drought is a climate risk that affects access to safe water, crop development, ecological stability, and food production. Therefore, developing drought prediction methods can lead to better management of surface and groundwater resources. Similarly, ...
Neural networks : the official journal of the International Neural Network Society
40068497
Minimum error entropy with fiducial points (MEEF) has gained significant attention due to its excellent performance in mitigating the adverse effects of non-Gaussian noise in the fields of machine learning and signal processing. However, the original...
Nitrogen-fixing microorganisms play a critical role in the global nitrogen cycle by converting atmospheric nitrogen into ammonia through the action of nitrogenase (EC 1.18.6.1). In this study, we employed six machine learning algorithms to model the ...
A framework for parameter estimation and uncertainty quantification is crucial for understanding the mechanisms of biological interactions within complex systems and exploring their dynamic behaviors beyond what can be experimentally observed. Despit...
COVID-19 is a disease in which early prognosis of severity is critical for desired patient outcomes and for the management of limited resources like intensive care unit beds and ventilation equipment. Many prognostic statistical tools have been devel...
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
40117723
PURPOSE: This study aims to develop a comprehensive simulation framework to connect radiation effects from the microscopic to the nanoscopic scale.
Neural networks : the official journal of the International Neural Network Society
40112635
Typical machine learning regression applications aim to report the mean or the median of the predictive probability distribution, via training with a squared or an absolute error scoring function. The importance of issuing predictions of more functio...
Random forest (RF) regression is popular machine learning method to develop prediction models for continuous outcomes. Variable selection, also known as feature selection or reduction, involves selecting a subset of predictor variables for modeling. ...
PURPOSE: Clinical management of pediatric chronic kidney disease requires estimation of glomerular filtration rate (eGFR). Currently, eGFR is determined by two endogenous markers measured in blood: serum creatine (SCr) and cystatin C (CysC). Machine ...