Suchergebnisse - Stacked denoising variational autoencoder model

Stacked Denoising Variational Auto Encoder Model for Extractive Web Text Summarization von Yadav, Madhuri, Katarya, Rahul

“… of a lot of storage and time. To solve this issue, the continuous bag of words text vectorization model has been used that reduce the processing time by producing a distributed combination of words in vector arrangement …”
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Robust prediction of remaining useful lifetime of bearings using deep learning von Magadán, L., Granda, J.C., Suárez, F.J.

“… With the technological advances of Industry 4.0, physical models for prognostics and RUL prediction have been replaced by data-driven models that require no expert feature extraction …”
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Reliable Fault Diagnosis of Bearings Using an Optimized Stacked Variational Denoising Auto-Encoder von Yan, Xiaoan, Xu, Yadong, She, Daoming, Zhang, Wan

“… Therefore, in order to improve the anti-noise performance of the VAE model and adaptively select its parameters, this paper proposes an optimized stacked variational denoising autoencoder (OSVDAE …”
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Support Vector Machine – Recursive Feature Elimination for Feature Selection on Multi-omics Lung Cancer Data von Azman, Nuraina Syaza, A Samah, Azurah, Lin, Ji Tong, Abdul Majid, Hairudin, Ali Shah, Zuraini, Wen, Nies Hui, Howe, Chan Weng

“… Biological data obtained from sequencing technologies is growing exponentially. Multi-omics data is one of the biological data that exhibits high …”
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Comparative Analysis of Deep Learning Algorithm for Cancer Classification using Multi-omics Feature Selection von Azmi, Nur Sabrina, A Samah, Azurah, Sirgunan, Vivekaanan, Ali Shah, Zuraini, Abdul Majid, Hairudin, Howe, Chan Weng, Wen, Nies Hui, Azman, Nuraina Syaza

“… This study aims to investigate how deep learning algorithms, namely stacked denoising autoencoder (SDAE …”
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A review on autoencoder based representation learning for fault detection and diagnosis in industrial processes von Qian, Jinchuan, Song, Zhihuan, Yao, Yuan, Zhu, Zheren, Zhang, Xinmin

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Augmenting deviation of faults from the normal using fault assistant Gaussian mixture prior variational autoencoder von Lee, Yi Shan, Chen, Junghui

“… •Abnormal data are used to augment the deviation of the fault from the normal.•Non-negative information sharing and transferring improve model accuracy …”
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VMD-SEAE-TL-Based Data-Driven soft sensor modeling for a complex industrial batch processes von Ren, Jun-Chao, Liu, Ding, Wan, Yin

“… •A stack enhanced autoencoder algorithm based on VMD is proposed in this paper. Here, VMD is implemented by decomposing and reconstructing the original data to eliminate the noise in the data …”
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A comprehensive review on encoder–decoder architectures in ECG signal compression and denoising: opportunities, challenges, and prospects von Das, Maumita, Sahana, Bikash Chandra

“… For effective real-time ECG signal transmission via wearables or telemetry systems, the denoising autoencoder (DAE …”
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Deep Autoencoder Architectures For Foreground Object Detection In Video Sequences Based On Probabilistic Mixture Models von Garcia-Gonzalez, Jorge, Molina-Cabello, Miguel A., Luque-Baena, Rafael M., Ortiz-de-Lazcano-Lobato, Juan M., Lopez-Rubio, Ezequiel

“… Therefore, different types of autoencoders, deterministic and variational, with different architectures, activation functions and number of layers, are analyzed …”
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A Three-Stage Ensemble Short-Term Wind Power Prediction Method Based on VMD-WT Transform and SDAE Deep Learning von Xiaosheng, Peng, Zuowei, Zhang, Qiyou, Xu, Bo, Wang, Jianfeng, Che, Fan, Yang, Wenze, Li, Zian, Huang

“… Second, in stage two, multiple stacked denoising auto-encoders (SDAE) are constructed based on the subsequences to perform WPPs separately …”
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Stochastic Gradient Variational Bayes for deep learning-based ASR von Tjandra, Andros, Sakti, Sakriani, Nakamura, Satoshi, Adriani, Mirna

“… Many successful methods for training deep neural networks (DNN) rely on an unsupervised pretraining algorithm. It is particularly effective when the number of …”
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Variational Discriminative Stacked Auto-Encoder: Feature Representation Using a Prelearned Discriminator, and Its Application to Industrial Process Monitoring von Huang, Jian, Sun, Xiaoyang, Ding, Steven X., Yang, Xu, Ersoy, Okan K.

“… In deep-learning-based process monitoring, obtaining an effective feature representation is a critical step in constructing a reliable deep-learning monitoring model …”
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A short‐term wind power prediction method based on deep learning and multistage ensemble algorithm von Peng, Xiaosheng, Li, Cong, Jia, Shiyuan, Zhou, Liangsong, Wang, Bo, Che, Jianfeng

“… In the second stage, based on the decomposition sequences, the stacked denoising autoencoder (SDAE), long short‐term memory (LSTM), and bidirectional long short …”
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Forecasting regional carbon prices in china with a hybrid model based on quadratic decomposition and comprehensive feature screening von Yi, Yaoyang

“… This work presents a hybrid model incorporating comprehensive feature screening, optimized quadratic decomposition, and the Optuna-Attention-LSTM prediction method, aiming to improve the accuracy …”
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Depth feature extraction-based deep ensemble learning framework for high frequency futures price forecasting von Wang, Jujie, Chen, Yu, Zhu, Shuzhou, Xu, Wenjie

“… ), an improved denoising variational mode decomposition (VMD) is proposed to extract the fluctuation characteristics of futures price signal and eliminate the interference of complex components …”
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