[HTML][HTML] A survey on GANs for computer vision: Recent research, analysis and taxonomy
In the last few years, there have been several revolutions in the field of deep learning,
mainly headlined by the large impact of Generative Adversarial Networks (GANs). GANs not …
mainly headlined by the large impact of Generative Adversarial Networks (GANs). GANs not …
Deep learning-based animal activity recognition with wearable sensors: Overview, challenges, and future directions
Animal behavior, as one of the most crucial indicators of animal health and welfare, provides
rich insights into animal physical and mental states. Automated animal activity recognition …
rich insights into animal physical and mental states. Automated animal activity recognition …
Minority-weighted graph neural network for imbalanced node classification in social networks of internet of people
K Wang, J An, M Zhou, Z Shi, X Shi… - IEEE Internet of Things …, 2022 - ieeexplore.ieee.org
Social networks are an essential component of the Internet of People (IoP) and play an
important role in stimulating interactive communication among people. Graph convolutional …
important role in stimulating interactive communication among people. Graph convolutional …
Generalized multiscale feature extraction for remaining useful life prediction of bearings with generative adversarial networks
Bearing is a key component in industrial machinery and its failure may lead to unwanted
downtime and economic loss. Hence, it is necessary to predict the remaining useful life …
downtime and economic loss. Hence, it is necessary to predict the remaining useful life …
[HTML][HTML] Review on generative adversarial networks: focusing on computer vision and its applications
The emergence of deep learning model GAN (Generative Adversarial Networks) is an
important turning point in generative modeling. GAN is more powerful in feature and …
important turning point in generative modeling. GAN is more powerful in feature and …
SMOTified-GAN for class imbalanced pattern classification problems
Class imbalance in a dataset is a major problem for classifiers that results in poor prediction
with a high true positive rate (TPR) but a low true negative rate (TNR) for a majority positive …
with a high true positive rate (TPR) but a low true negative rate (TNR) for a majority positive …
On IoT intrusion detection based on data augmentation for enhancing learning on unbalanced samples
Y Zhang, Q Liu - Future Generation Computer Systems, 2022 - Elsevier
Internet of things (IoT) security is a prerequisite for the rapid development of the IoT to
enhance human well-being. Machine learning-based intrusion detection systems (IDS) have …
enhance human well-being. Machine learning-based intrusion detection systems (IDS) have …
Ten years of generative adversarial nets (GANs): a survey of the state-of-the-art
Generative adversarial networks (GANs) have rapidly emerged as powerful tools for
generating realistic and diverse data across various domains, including computer vision and …
generating realistic and diverse data across various domains, including computer vision and …
Two-stage generative adversarial networks for binarization of color document images
Document image enhancement and binarization methods are often used to improve the
accuracy and efficiency of document image analysis tasks such as text recognition …
accuracy and efficiency of document image analysis tasks such as text recognition …
An effective data enhancement method for classification of ECG arrhythmia
S Ma, J Cui, CL Chen, X Chen, Y Ma - Measurement, 2022 - Elsevier
Our blood vessels show signs of aging as we grow older, which leads to various
cardiovascular diseases. Arrhythmia is usually the symptom of patients with early …
cardiovascular diseases. Arrhythmia is usually the symptom of patients with early …