Search Results - stacked supervised autoencoder (SSAE)

Dual‐noise autoencoder combining pseudo‐labels and consistency regularization for process fault classification by Guo, Xiaoping, Guo, Qingyu, Li, Yuan

“… A stacked supervised autoencoder (SSAE) network is trained using a small amount…”
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Semi-supervised denoising autoencoder with multiple consistency regularization for process fault classification by Guo, Xiaoping, Guo, Qingyu, Li, Yuan

“… To address these issues, this paper proposes a semi-supervised denoising autoencoder method with multi-consistency regularization (MCR-SSDAE…”
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A Semi-Supervised Stacked Autoencoder Using the Pseudo Label for Classification Tasks by Lai, Jie, Wang, Xiaodan, Xiang, Qian, Quan, Wen, Song, Yafei

“… Thus, by introducing the pseudo-labeling method into the SAE, a novel pseudo label-based semi-supervised stacked autoencoder (PL-SSAE…”
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An Anthocyanin Prediction Model of Blueberry Pomace Based on Stacked Supervised Autoencoders by Siqi LIU, Guohong FENG, Zhongshen LIU, Yujie ZHU

“…Based on the visible and near-infrared reflectance spectroscopy technique, stacked supervised autoencoders (SSAE…”
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Handling partially labeled network data: A semi-supervised approach using stacked sparse autoencoder by Aouedi, Ons, Piamrat, Kandaraj, Bagadthey, Dhruvjyoti

“…Network traffic analytics has become a crucial task in order to better understand and manage network resources, especially in the network softwarization era…”
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Discriminative Feature Learning With Distance Constrained Stacked Sparse Autoencoder for Hyperspectral Target Detection by Shi, Yanzi, Lei, Jie, Yin, Yaping, Cao, Kailang, Li, Yunsong, Chang, Chein-I

“… Unlike supervised networks, unsupervised stacked sparse autoencoders (SSAEs) can learn deep and nonlinear features without any labeled data…”
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Cervical cancer classification using sparse stacked autoencoder and fuzzy ARTMAP by Liaw, Lawrence Chuin Ming, Tan, Shing Chiang, Goh, Pey Yun, Lim, Chee Peng

“… sparse stacked autoencoder (SSAE) and fuzzy adaptive resonance theory MAP (FAM), respectively, and is denoted as SSAE-FAM…”
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Prognostic prediction of lung adenocarcinoma based on transcriptomic data and stacked supervised autoencoder by LI Pengpeng, CHEN Xicheng, HUANG Jinyu, WU Yazhou

“…Objective To build a stacked supervised autoencoder (SSAE) model based on transcriptomic data, so as to improve the prognostic prediction of lung adenocarcinoma (LUAD…”
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Mask-based Self-supervised Network Intrusion Detection System by Lu, Xiaoya, Liu, Yifan, Feng, Fan, Liu, Yi, Liu, Zhenpeng

“… (Mask-based Self-supervised Network Intrusion Detection System), which employs the techniques of mask shielding and Stacked Sparse Autoencoder (SSAE…”
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SSAE‐MLP: Stacked sparse autoencoders‐based multi‐layer perceptron for main bearing temperature prediction of large‐scale wind turbines by Xiao, Xiaocong, Liu, Jianxun, Liu, Deshun, Tang, Yufei, Dai, Juchuan, Zhang, Fan

“… To achieve the goal, this paper proposes a novel deep learning approach named stacked sparse autoencoder multi‐layer perceptron (SSAE‐MLP…”
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Improved Human Activity Recognition Using Stacked Sparse Autoencoder (SSAE) Algorithm by Aziz, Firman, Mustamin, Nurul Fathanah, Rijal, Muhammad, Tanniewa, Adam M

“…This study aims to enhance the performance of Human Activity Recognition (HAR) systems by implementing the Stacked Sparse Autoencoder (SSAE…”
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Automatic digital modulation recognition based on stacked sparse autoencoder by Bouchou, Mohamed, Wang, Hua, El Hadi Lakhdari, Mohammed

“…, the stacked sparse autoencoder benefits from both, unsupervised and supervised learning approaches. In fact, the main advantage of the SSAE is that it…”
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SSAE - DeepCNN Model for Network Intrusion Detection by Lee, Jong-Hwa, Kim, Jong-Wouk, Choi, Mi-Jung

“… Our proposed model is a semi-supervised learning model that combines stacked sparse autoencoder (SSAE…”
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Assessment of PD severity in gas-insulated switchgear with an SSAE by Tang, Ju, Jin, Miao, Zeng, Fuping, Zhang, Xiaoxing, Huang, Rui

“… Hence, a deep-learning neural network model called stacked sparse auto-encoder (SSAE) is proposed to realise feature extraction from the middle layer with a small number of nodes…”
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Automated gleason grading on prostate biopsy slides by statistical representations of homology profile by Yan, Chaoyang, Nakane, Kazuaki, Wang, Xiangxue, Fu, Yao, Lu, Haoda, Fan, Xiangshan, Feldman, Michael D., Madabhushi, Anant, Xu, Jun

“…•A new Statistical Representations of Homology Profile (SRHP) and its statistical representation was presented to capture the topological arrangement of nuclei…”
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A Semi-supervised Stacked Autoencoder Approach for Network Traffic Classification by Aouedi, Ons, Piamrat, Kandaraj, Bagadthey, Dhruvjyoti

“… However, most of them are based on supervised learning where only labeled data are used…”
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Intelligent evaluation method for identifying favorable shale oil areas based on improved stacked sparse autoencoder by Xu, Rui, Yan, Tie, Sun, Shihui, Qu, Jingyu, Hou, Zhaokai

“… This study proposes an intelligent method for identifying favorable shale oil areas under semi-supervised learning (SSAE-plus…”
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A Sleep Apnea Detection Method Based on Unsupervised Feature Learning and Single-Lead Electrocardiogram by Feng, Kaicheng, Qin, Hengji, Wu, Shan, Pan, Weifeng, Liu, Guanzheng

“… However, these methods are based on feature engineering or supervised and semisupervised learning techniques, and the feature sets are always incomplete, subjective, and highly dependent on labeled data…”
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Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning by Sun, Jiedi, Yan, Changhong, Wen, Jiangtao

“…Effective intelligent fault diagnosis has long been a research focus on the condition monitoring of rotary machinery systems. Traditionally, time-domain…”
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Stacked Sparse Autoencoders for EMG-Based Classification of Hand Motions: A Comparative Multi Day Analyses between Surface and Intramuscular EMG by Zia ur Rehman, Muhammad, Gilani, Syed Omer, Waris, Asim, Niazi, Imran Khan, Slabaugh, Gregory, Farina, Dario, Kamavuako, Ernest Nlandu

“… The aim of this study was to quantify the performance of stacked sparse autoencoders (SSAE), an emerging deep learning technique used to improve myoelectric control and to compare multiday surface electromyography…”
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