The transformer-based structure has an advantage in extracting global features and can make up for this deficiency. This paper proposes a hybrid multi-scale network model based on CNN and Swin ...

AdaBoost approaches have been used for multi-class imbalance classification with an imbalance ratio measured on class sizes. However, such ratio would assign each training sample of the same class ...

While deep convolutional neural networks (CNNs) have shown a great success in single-label image classification, it is important to note that real world images generally contain multiple labels, which ...

The classification of fashion cloth images is an important and challenging task in the field of computer vision. In recent years, deep learning (DL) techniques, especially Convolutional Neural ...

Cyberbullying is a pervasive issue across all forms of media, affecting various demographics and platforms indiscriminately. From social media networks to online forums and comment sections on news ...

Furthermore, many methods focus only on aligning marginal distributions while ignoring the consistency of inter-class features, which may lead to feature confusion and degraded classification accuracy ...

Global food security is seriously threatened by wheat leaf disease, which makes effective and precise disease detection and classification techniques necessary. For efficient disease control and the ...

In this paper, we present the different neural network models for multi-label classification of microblogging data. The proposed models are based on convolutional neural network (CNN) architectures, ...

Artificial Intelligence has greatly influenced healthcare, most particularly in medical imaging. This paper represents a review in large form that classifies fetal ultrasound images with the use of ...

News text classification is crucial for efficient information acquisition and dissemination. While deep learning models, such as BERT and BiGRU, excel in accuracy for text classification, their high ...

This research implements deep learning techniques to classify cardiac sounds based on mel-frequency spectrogram features, aiming to enhance diagnostic accuracy for heart-related abnormalities.

Bengali Multi-class Text Classification via Enhanced Contrastive Learning Techniques Abstract: Bengali, one of South Asia's most frequently spoken languages, poses substantial difficulties in tasks ...