As a common technology in social network, clustering has attracted lots of research interest
due to its high performance, and many clustering methods have been presented. The most …

The aim of the present study is to characterize the damage in bi-material steel-to-composite
double-lap adhesively-bonded joints using Acoustic Emission (AE). Two different structural …

Aiming at the problems of long time, high cost, invasive sampling damage, and easy
emergence of drug resistance in lung cancer gene detection, a reliable and non-invasive …

Fully connected (FC) layers are used in almost all neural network architectures ranging from
multilayer perceptrons to deep neural networks. FC layers allow any kind of …

Outlier detection is extensively adopted in machine learning applications to identify rare but
significant objects in a data distribution that deviate from the majority of objects. However …

Traditional Chinese medicine (TCM) has become popular and been viewed as an effective
clinical treatment across the world. Accordingly, there is an ever-increasing interest in …

Objective. While Electroencephalography (EEG)-based driver fatigue state classification
models have demonstrated effectiveness, their real-world application remains uncertain …

In tabular data, certain challenges can negatively affect the quality of machine learning
models, such as high dimensionality, noisy, irrelevant, or repetitive features, interactions …

Most machine learning algorithms rely on having a sufficient amount of labeled data to train
a reliable classifier. However, labeling data is often costly and time-consuming, while …

The primary objective of this research work is to build a binary classifier for categorizing the
input brain magnetic resonanceimaging (MRI) images as either demented or nondemented …