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Advancements in Gas Turbine Fault Detection: A Machine Learning Approach Based on the Temporal Convolutional Network–Autoencoder Model machine learning (ML) tools. For this purpose, an advanced Temporal Convolutional Network (TCN)–Autoencoder
DruGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico-of-concept of implementing deep generative adversarial autoencoder (AAE) to identify new molecular fingerprints
Time-frequency analysis and autoencoder approach for network traffic anomaly detection transform (STFT), and autoencoders to identify anomalous network behaviour. It conducts time- frequency
Development of Molecular Autoencoders as Generators of Protein Inhibitors: Application for Prediction of Potential Drugs Against Coronavirus SARS-CoV-2A generative autoencoder for the rational design of potential inhibitors of the SARS-CoV-2 main
AutoEncoders for Denoising and Classification ApplicationsSeveral structures of autoencoders used for
the efficient data coding with unsupervised learning
Anomaly detection using autoencoder for data quality monitoring in cloud the original project Autoencoder that focuses on the technology of analysis, detection and forecasting poor
Smile biometric imprint creation with the use of autoencoder was proposed, which is obtained from smile video using stacked autoencoder that allows to build a biometric
Development of a method for using autoencoder to search for anomaliesin cloud data an autoencoder is proposed. The technique was developed using an example and for use in analyzing the
telemetry
Anomaly detection using autoencoder for data quality monitoring in cloud the original project Autoencoder that focuses on the technology of analysis, detection and forecasting poor
Lung image quality assessment and diagnosis using generative autoencoders in unsupervised ensemble learning autoencoders with attention mechanisms (GAME). By including attentional mechanisms in the generative
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