EDL combines the outputs of several machine learning (ML) models to enhance their generalization performance. The traditional approach to building an ensemble uses deep neural networks (DNNs) in a ...

A research team has developed a novel approach combining refined spatial competition mapping with powerful ensemble learning ...

The global shift toward digital banking has been dramatic, with the volume of cashless transactions increasing year over year. While this growth signals progress in financial technology, it has also ...

Ensemble learning algorithms include stacking, boosting such as Gradient Boosting and AdaBoost, and bagging such as Random Forest.

Researchers have tested eight stand-alone deep learning methods for PV cell fault detection and have found that their accuracy was as high as 73%. All methods were trained and tested on the ELPV ...

Gynecological cancers, including breast, ovarian, and cervical malignancies, account for a significant global health burden among women. The review outlines how a spectrum of machine learning (ML) ...

In the paper "UCB-Type Learning Algorithms with Kaplan–Meier Estimator for Lost Sales Inventory Models with Lead Times," Lyu, Zhang, and Xin propose an upper confidence bound–type learning framework.