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Diagnosisformer: An efficient rolling bearing fault diagnosis method based on improved transformer
Y Hou, J Wang, Z Chen, J Ma, T Li - Engineering Applications of Artificial …, 2023 - Elsevier
Aiming at the problems of low accuracy and robustness of traditional deep learning fault
diagnosis methods, a novel attention-based multi-feature parallel fusion model …
diagnosis methods, a novel attention-based multi-feature parallel fusion model …
Diachronic word embeddings and semantic shifts: a survey
A Kutuzov, L Øvrelid, T Szymanski, E Velldal - ar** an intelligent cloud attention network to support global urban green spaces map**
Urban green spaces (UGS) play an important role in understanding of urban ecosystems,
climate, environment, and public health concerns. Satellite derived UGS maps provide an …
climate, environment, and public health concerns. Satellite derived UGS maps provide an …
Learning with recoverable forgetting
Life-long learning aims at learning a sequence of tasks without forgetting the previously
acquired knowledge. However, the involved training data may not be life-long legitimate due …
acquired knowledge. However, the involved training data may not be life-long legitimate due …
[PDF][PDF] Classification of Images of Childhood Pneumonia using Convolutional Neural Networks.
In this paper we describe a comparative classification of Pneumonia using Convolution
Neural Network. The database used was the dataset Labeled Optical Coherence …
Neural Network. The database used was the dataset Labeled Optical Coherence …
Leveraging contextual embeddings for detecting diachronic semantic shift
We propose a new method that leverages contextual embeddings for the task of diachronic
semantic shift detection by generating time specific word representations from BERT …
semantic shift detection by generating time specific word representations from BERT …
Class-incremental domain adaptation
We introduce a practical Domain Adaptation (DA) paradigm called Class-Incremental
Domain Adaptation (CIDA). Existing DA methods tackle domain-shift but are unsuitable for …
Domain Adaptation (CIDA). Existing DA methods tackle domain-shift but are unsuitable for …
Hierarchical graph transformer-based deep learning model for large-scale multi-label text classification
J Gong, Z Teng, Q Teng, H Zhang, L Du, S Chen… - IEEE …, 2020 - ieeexplore.ieee.org
Traditional methods of multi-label text classification, particularly deep learning, have
achieved remarkable results. However, most of these methods use word2vec technology to …
achieved remarkable results. However, most of these methods use word2vec technology to …
Hybrid covid-19 segmentation and recognition framework (hmb-hcf) using deep learning and genetic algorithms
Abstract COVID-19 (Coronavirus) went through a rapid escalation until it became a
pandemic disease. The normal and manual medical infection discovery may take few days …
pandemic disease. The normal and manual medical infection discovery may take few days …
SD-GAN: A style distribution transfer generative adversarial network for Covid-19 detection through X-ray images
The Covid-19 pandemic is a prevalent health concern around the world in recent times.
Therefore, it is essential to screen the infected patients at the primary stage to prevent …
Therefore, it is essential to screen the infected patients at the primary stage to prevent …