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Generative adversarial networks in time series: A systematic literature review
Generative adversarial network (GAN) studies have grown exponentially in the past few
years. Their impact has been seen mainly in the computer vision field with realistic image …
years. Their impact has been seen mainly in the computer vision field with realistic image …
Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review
Background The electrocardiogram (ECG) is one of the most commonly used diagnostic
tools in medicine and healthcare. Deep learning methods have achieved promising results …
tools in medicine and healthcare. Deep learning methods have achieved promising results …
An empirical survey of data augmentation for time series classification with neural networks
In recent times, deep artificial neural networks have achieved many successes in pattern
recognition. Part of this success can be attributed to the reliance on big data to increase …
recognition. Part of this success can be attributed to the reliance on big data to increase …
Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM
More and more photovoltaic (PV) power generation is incorporated into the grid. However,
the intermittence and fluctuation of solar energy have brought huge challenges to the safe …
the intermittence and fluctuation of solar energy have brought huge challenges to the safe …
A novel LSTM–CNN–grid search-based deep neural network for sentiment analysis
As the number of users getting acquainted with the Internet is escalating rapidly, there is
more user-generated content on the web. Comprehending hidden opinions, sentiments, and …
more user-generated content on the web. Comprehending hidden opinions, sentiments, and …
Deep learning in mining biological data
Recent technological advancements in data acquisition tools allowed life scientists to
acquire multimodal data from different biological application domains. Categorized in three …
acquire multimodal data from different biological application domains. Categorized in three …
Faulty rolling bearing digital twin model and its application in fault diagnosis with imbalanced samples
Y Qin, H Liu, Y Mao - Advanced Engineering Informatics, 2024 - Elsevier
The simulation signals generated by the bearing dynamics model have a big gap with the
actual signals, which limits their efficacy in bearing fault diagnosis. Therefore, it is valuable …
actual signals, which limits their efficacy in bearing fault diagnosis. Therefore, it is valuable …
Brain-on-a-chip: Recent advances in design and techniques for microfluidic models of the brain in health and disease
Recent advances in biomaterials, microfabrication, microfluidics, and cell biology have led to
the development of organ-on-a-chip devices that can reproduce key functions of various …
the development of organ-on-a-chip devices that can reproduce key functions of various …
Diffusion-based conditional ECG generation with structured state space models
Generating synthetic data is a promising solution for addressing privacy concerns that arise
when distributing sensitive health data. In recent years, diffusion models have become the …
when distributing sensitive health data. In recent years, diffusion models have become the …
TAnoGAN: Time series anomaly detection with generative adversarial networks
Anomaly detection in time series data is a significant problem faced in many application
areas such as manufacturing, medical imaging and cyber-security. Recently, Generative …
areas such as manufacturing, medical imaging and cyber-security. Recently, Generative …