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Twenty years of machine-learning-based text classification: A systematic review
Machine-learning-based text classification is one of the leading research areas and has a
wide range of applications, which include spam detection, hate speech identification …
wide range of applications, which include spam detection, hate speech identification …
Few-shot learning for facial expression recognition: a comprehensive survey
Facial expression recognition (FER) is utilized in various fields that analyze facial
expressions. FER is attracting increasing attention for its role in improving the convenience …
expressions. FER is attracting increasing attention for its role in improving the convenience …
Nature vs. nurture: Feature vs. structure for graph neural networks
Graph neural networks take node features and graph structure as input to build
representations for nodes and graphs. While there are a lot of focus on GNN models …
representations for nodes and graphs. While there are a lot of focus on GNN models …
Data augmentation for rolling bearing fault diagnosis using an enhanced few-shot Wasserstein auto-encoder with meta-learning
Despite the advance of intelligent fault diagnosis for rolling bearings, in industries, data-
driven methods still suffer from data acquisition and imbalance. We propose an enhanced …
driven methods still suffer from data acquisition and imbalance. We propose an enhanced …
Few-shot short-text classification with language representations and centroid similarity
W Liu, J Pang, N Li, F Yue, G Liu - Applied Intelligence, 2023 - Springer
Aiming at the problems of insufficient labelled samples and low-generalization performance
in text classification tasks, this paper studies text classification problems under the condition …
in text classification tasks, this paper studies text classification problems under the condition …
Deep meta-learning and variational autoencoder for coupling fault diagnosis of rolling bearing under variable working conditions
Considering the characteristics of rolling bearing such as variable working conditions,
unbalanced fault sample size, and multiple coupling fault types, it is a great challenge to …
unbalanced fault sample size, and multiple coupling fault types, it is a great challenge to …
Transfer learning and source domain restructuring-based BiLSTM approach for building energy consumption prediction
Y Yan, F Wang, C Tian, W Xue, L Lin… - International Journal of …, 2025 - Taylor & Francis
Currently, building energy consumption prediction typically relies on vast amounts of
historical data. However, for newly constructed buildings, the scarcity of data leads to …
historical data. However, for newly constructed buildings, the scarcity of data leads to …
IDA-NET: Individual Difference aware Medical Image Segmentation with Meta-Learning
Individual differences in organ size and spatial distribution can lead to significant variations
in the content of medical images at similar anatomical locations. These case-level …
in the content of medical images at similar anatomical locations. These case-level …
A Chinese few-shot text classification method utilizing improved prompt learning and unlabeled data
T Hu, Z Chen, J Ge, Z Yang, J Xu - Applied Sciences, 2023 - mdpi.com
Insufficiently labeled samples and low-generalization performance have become significant
natural language processing problems, drawing significant concern for few-shot text …
natural language processing problems, drawing significant concern for few-shot text …
HDGN: Heat diffusion graph network for few-shot learning
A heat diffusion graph network (HDGN) is proposed in this paper, which retains more similar
graph signals in the spectral domain, for few-shot learning. Convolution on the graph is …
graph signals in the spectral domain, for few-shot learning. Convolution on the graph is …