The convergence of ensemble deep learning and machine learning has become a critical
strategy for tackling intricate challenges across diverse fields such as healthcare, finance …

This study addresses the research gap in materials science by developing an integrated
predictive model for Ultimate Tensile Strength (UTS), Maximum Hardness (MH), and Heat …

The interaction between Ribonucleic Acids (RNAs) and proteins, also called RNA Protein
Interaction (RPI), plays an important role in the life activities of organisms, including in …

The precise determination of battery state of charge (SoC) holds paramount significance and
has garnered considerable attention across diverse sectors, including academia. Accurate …

Circular RNAs (circRNAs) have surfaced as important non-coding RNA molecules in
biology. Understanding interactions between circRNAs and RNA-binding proteins (RBPs) is …

Slope deformation, a key factor affecting slope stability, has complexity and uncertainty. It is
crucial for early warning of slope instability disasters to master the future development law of …

A convolutional neural network (CNN) model by deep-learning single channel data from a
serpentine carbon nanotube sensor (S-CNT) with gradient distributed CNTs is proposed for …

Accurate estimation of the state of health (SOH) is crucial for ensuring the stable operation of
lithium-ion batteries. A new heterogeneous learner fusion SOH estimation method, PCA …

The design of the mixtures of ternary geopolymers is challenging due to the need to balance
multiple objectives, including cost, strength, and carbon emissions. In order to address this …

In this study, we present an innovative approach to improve the prediction of protein–protein
interactions (PPIs) through the utilization of an ensemble classifier, specifically focusing on …