Rainfall prediction has advanced rapidly with the adoption of machine learning, but most models remain optimized for overall ...

Researchers have employed Bayesian neural network approaches to evaluate the distributions of independent and cumulative ...

Researchers have developed a hybrid CFD-neural network model for predicting TAIs in hydrogen-fueled turbines, improving ...

Modern neural networks, with billions of parameters, are so overparameterized that they can "overfit" even random, structureless data. Yet when trained on datasets with structure, they learn the ...

ABSTRACT: The accurate prediction of backbreak, a crucial parameter in mining operations, has a significant influence on safety and operational efficiency. The occurrence of this phenomenon is ...

Tumor Site–Specific Radiation-Induced Lymphocyte Depletion Models After Fractionated Radiotherapy: Considerations of Model Structure From an Aggregate Data Meta-Analysis Lymphocytes play critical ...

Accurate disaster prediction combined with reliable uncertainty quantification is crucial for timely and effective decision-making in emergency management. However, traditional deep learning methods ...

Abstract: Bayesian networks are widely used for causal discovery and probabilistic modeling across diverse domains including healthcare, multi-dimensional data analysis, environmental modeling, and ...

ABSTRACT: The rapid proliferation of the Internet of Things (IoT) and Industrial IoT (IIoT) has revolutionized industries through enhanced connectivity and automation. However, this expansion has ...

Abstract: The machine learning method for surrogate modeling is a keystone in surrogate model-assisted evolutionary algorithms (SAEAs). The current arguably most widely used surrogate modeling methods ...