The sonogram is currently an effective cancer screening and diagnosis way due to the
convenience and harmlessness in humans. Traditionally, lesion boundary segmentation is …

In this study, deep learning method coupled with near-infrared (NIR) hyperspectral imaging
(HSI) technique was used for nondestructively determining total viable count (TVC) of …

When confronted with limited labelled samples, most studies adopt an unsupervised feature
learning scheme and incorporate the extracted features into a traditional classifier (eg …

Recently, deep convolutional neural network (DCNN)-based methods have achieved much
success in hyperspectral image (HSI) classification, when sufficient labeled samples are …

This paper presents an end-to-end methodology that can be used in the disaster response
process. The core element of the proposed method is a deep learning process which …

In-line anomaly detection (AD) not only identifies the needs for semiconductor equipment
maintenance but also indicates potential line yield problems. Prompt AD based on available …

The integration of Artificial Neural Networks (ANNs) and Feature Extraction (FE) in the
context of the Sample-Partitioning Adaptive Reduced Chemistry approach was investigated …

In this paper, we propose an end-to-end (E2E) neural network model to detect autism
spectrum disorder (ASD) from children's voices without explicitly extracting the deterministic …

Cross-domain few-shot hyperspectral image classification focuses on learning prior
knowledge from a large number of labeled samples from source domains and then …

In online learning, students' learning behavior might change as the course progresses. How
students adjust learning behaviors aligned with course requirements reflects their self …