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Journal articles on the topic 'Semi-autoencoder'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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1

Zemouri, Ryad. "Semi-Supervised Adversarial Variational Autoencoder." Machine Learning and Knowledge Extraction 2, no. 3 (2020): 361–78. http://dx.doi.org/10.3390/make2030020.

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We present a method to improve the reconstruction and generation performance of a variational autoencoder (VAE) by injecting an adversarial learning. Instead of comparing the reconstructed with the original data to calculate the reconstruction loss, we use a consistency principle for deep features. The main contributions are threefold. Firstly, our approach perfectly combines the two models, i.e., GAN and VAE, and thus improves the generation and reconstruction performance of the VAE. Secondly, the VAE training is done in two steps, which allows to dissociate the constraints used for the const

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2

Lai, Jie, Xiaodan Wang, Qian Xiang, Wen Quan, and Yafei Song. "A Semi-Supervised Stacked Autoencoder Using the Pseudo Label for Classification Tasks." Entropy 25, no. 9 (2023): 1274. http://dx.doi.org/10.3390/e25091274.

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The efficiency and cognitive limitations of manual sample labeling result in a large number of unlabeled training samples in practical applications. Making full use of both labeled and unlabeled samples is the key to solving the semi-supervised problem. However, as a supervised algorithm, the stacked autoencoder (SAE) only considers labeled samples and is difficult to apply to semi-supervised problems. Thus, by introducing the pseudo-labeling method into the SAE, a novel pseudo label-based semi-supervised stacked autoencoder (PL-SSAE) is proposed to address the semi-supervised classification t

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Yao, Shihong, Chuli Hu, Tao Wang, and Xinyou Cui. "Autoencoder-like semi-NMF multiple clustering." Information Sciences 572 (September 2021): 331–42. http://dx.doi.org/10.1016/j.ins.2021.04.080.

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Ahed, Mleih Al-Sbou, and Hafhizah Abd Rahim Noor. "An improved hybrid semi-stacked autoencoder for itemfeatures of recommendation system (iHSARS)." An improved hybrid semi-stacked autoencoder for itemfeatures of recommendation system (iHSARS) 30, no. 1 (2023): 481–90. https://doi.org/10.11591/ijeecs.v30.i1.pp481-490.

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In recent years, information overload has become a phenomenon where it makes people difficult to filter relevant information. To address issues such as high-dimensional data, cold start, and data sparsity, semi-autoencoder is one of the unsupervised deep learning methods used in the recommendation systems. It is particularly useful for reducing data dimensions, capturing latent representations, and flexibly reconstructing various parts of input data. In this article, we propose an improved hybrid semi-stacked autoencoder for itemfeatures of recommendation system (iHSARS) framework. This method

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5

Fu, Hongliang, Peizhi Lei, Huawei Tao, Li Zhao, and Jing Yang. "Improved semi-supervised autoencoder for deception detection." PLOS ONE 14, no. 10 (2019): e0223361. http://dx.doi.org/10.1371/journal.pone.0223361.

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Liu, Xingye, Bin Li, Jingye Li, Xiaohong Chen, Qingchun Li, and Yangkang Chen. "Semi‐supervised deep autoencoder for seismic facies classification." Geophysical Prospecting 69, no. 6 (2021): 1295–315. http://dx.doi.org/10.1111/1365-2478.13106.

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7

Yin, Wutao, Longhai Li, and Fang-Xiang Wu. "A semi-supervised autoencoder for autism disease diagnosis." Neurocomputing 483 (April 2022): 140–47. http://dx.doi.org/10.1016/j.neucom.2022.02.017.

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Deng, Yang, Wang Zhou, Amin Ul Haq, Sultan Ahmad, and Alia Tabassum. "Differentially private recommender framework with Dual semi-Autoencoder." Expert Systems with Applications 260 (January 2025): 125447. http://dx.doi.org/10.1016/j.eswa.2024.125447.

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Wu, Chuhan, Fangzhao Wu, Sixing Wu, Zhigang Yuan, Junxin Liu, and Yongfeng Huang. "Semi-supervised dimensional sentiment analysis with variational autoencoder." Knowledge-Based Systems 165 (February 2019): 30–39. http://dx.doi.org/10.1016/j.knosys.2018.11.018.

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Li, Ivy, Aarón Higuera, Shixiao Liang, Juehang Qin, and Christopher Tunnell. "Energy Reconstruction with Semi-Supervised Autoencoders for Dual-Phase Time Projection Chambers." EPJ Web of Conferences 295 (2024): 09022. http://dx.doi.org/10.1051/epjconf/202429509022.

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This paper presents a proof-of-concept semi-supervised autoencoder for the energy reconstruction of scattering particle interactions inside dualphase time projection chambers (TPCs), such as XENONnT. This autoencoder model is trained on simulated XENONnT data and is able to simultaneously reconstruct photosensor array hit patterns and infer the number of electrons in the gas gap, which is proportional to the energy of ionization signals in the TPC. Development plans for this autoencoder model are discussed, including future work in developing a faster simulation technique for dual-phase TPCs.

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Geng, Yishuai, Yi Zhu, Yun Li, Xiaobing Sun, and Bin Li. "Multi-Feature Extension via Semi-Autoencoder for Personalized Recommendation." Applied Sciences 12, no. 23 (2022): 12408. http://dx.doi.org/10.3390/app122312408.

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Over the past few years, personalized recommendation systems aim to address the problem of information overload to help users achieve useful information and make quick decisions. Recently, due to the benefits of effective representation learning and no labeled data requirements, autoencoder-based models have commonly been used in recommendation systems. Nonetheless, auxiliary information that can effectively enlarge the feature space is always scarce. Moreover, most existing methods ignore the hidden relations between extended features, which significantly affects the recommendation accuracy.

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12

Jang, Hee-Deok, Seokjoon Kwon, Hyunwoo Nam, and Dong Eui Chang. "Semi-Supervised Autoencoder for Chemical Gas Classification with FTIR Spectrum." Sensors 24, no. 11 (2024): 3601. http://dx.doi.org/10.3390/s24113601.

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Chemical warfare agents pose a serious threat due to their extreme toxicity, necessitating swift the identification of chemical gases and individual responses to the identified threats. Fourier transform infrared (FTIR) spectroscopy offers a method for remote material analysis, particularly in detecting colorless and odorless chemical agents. In this paper, we propose a deep neural network utilizing a semi-supervised autoencoder (SSAE) for the classification of chemical gases based on FTIR spectra. In contrast to traditional methods, the SSAE concurrently trains an autoencoder and a classifier

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13

Zhu, Tianyi, Lina Liu, Yibo Sun, et al. "Semi-supervised noise-resilient anomaly detection with feature autoencoder." Knowledge-Based Systems 304 (November 2024): 112445. http://dx.doi.org/10.1016/j.knosys.2024.112445.

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14

Zhou, Cangqi, Hao Ban, Jing Zhang, Qianmu Li, and Yinghua Zhang. "Gaussian Mixture Variational Autoencoder for Semi-Supervised Topic Modeling." IEEE Access 8 (2020): 106843–54. http://dx.doi.org/10.1109/access.2020.3001184.

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15

Gu, Xiang, Qiwei Huang, and Jie Yang. "Overlapping Community Detection in Vehicular Social Networks Based on Graph Attention Autoencoder." Sensors 25, no. 8 (2025): 2601. https://doi.org/10.3390/s25082601.

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Community detection is particularly important in vehicular social networks because it helps identify closely connected groups of vehicles within the network. Community structures with overlapping relationships are identified through network topology and vehicle attribute information, thereby optimizing communication efficiency, supporting resource allocation, and enhancing privacy protection. However, most existing community detection methods focus on non-overlapping communities, usually only considering the topological structure of the network, and often ignoring the attribute information of

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16

Mleih Al-Sbou, Ahed, and Noor Hafhizah Abd Rahim. "An improved hybrid semi-stacked autoencoder for item-features of recommendation system (iHSARS)." Indonesian Journal of Electrical Engineering and Computer Science 30, no. 1 (2023): 481. http://dx.doi.org/10.11591/ijeecs.v30.i1.pp481-490.

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In recent years, information overload has become a phenomenon where it makes people difficult to filter relevant information. To address issues such as high-dimensional data, cold start, and data sparsity, semi-autoencoder is one of the unsupervised deep learning methods used in the recommendation systems. It is particularly useful for reducing data dimensions, capturing latent representations, and flexibly reconstructing various parts of input data. In this article, we propose an improved hybrid semi-stacked autoencoder for item-features of recommendation system (iHSARS) framework. This metho

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17

Jeon, Yongjae, Kyumin Kim, Yelim Lee, Byeong Kwon Kang, and Sang Won Lee. "Development of Fault Diagnosis Model based on Semi-supervised Autoencoder." PHM Society European Conference 8, no. 1 (2024): 7. http://dx.doi.org/10.36001/phme.2024.v8i1.4023.

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The maintenance paradigm based on PHM (Prognostics and Health Management) technology, utilizing big data to predict process conditions through manufacturing intelligence, is rising. However, in most industries, there is lack of accurate labeling of sensor data, posing challenges in data utilization due to the significant cost of labeling tasks. Consequently, recent research has focused on semi-supervised learning methodologies, which are applicable in label-absent scenarios. Especially, there is a growing emphasis on semi-supervised autoencoder, which learns both labeled and unlabeled data sim

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Lei, Ke, Zhongsheng Tan, Xiuying Wang, and Zhenliang Zhou. "Semi-Symmetrical, Fully Convolutional Masked Autoencoder for TBM Muck Image Segmentation." Symmetry 16, no. 2 (2024): 222. http://dx.doi.org/10.3390/sym16020222.

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Deep neural networks are effectively utilized for the instance segmentation of muck images from tunnel boring machines (TBMs), providing real-time insights into the surrounding rock condition. However, the high cost of obtaining quality labeled data limits the widespread application of this method. Addressing this challenge, this study presents a semi-symmetrical, fully convolutional masked autoencoder designed for self-supervised pre-training on extensive unlabeled muck image datasets. The model features a four-tier sparse encoder for down-sampling and a two-tier sparse decoder for up-samplin

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Sae-ang, Bee-ing, Wuttipong Kumwilaisak, and Pakorn Kaewtrakulpong. "Semi-Supervised Learning for Defect Segmentation with Autoencoder Auxiliary Module." Sensors 22, no. 8 (2022): 2915. http://dx.doi.org/10.3390/s22082915.

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In general, one may have access to a handful of labeled normal and defect datasets. Most unlabeled datasets contain normal samples because the defect samples occurred rarely. Thus, the majority of approaches for anomaly detection are formed as unsupervised problems. Most of the previous methods have typically chosen an autoencoder to extract the common characteristics of the unlabeled dataset, assumed as normal characteristics, and determine the unsuccessfully reconstructed area as the defect area in an image. However, we could waste the ground truth data if we leave them unused. In addition,

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Kozmin, Artem, Oleg Kalashev, Alexey Chernenko, and Alexey Redyuk. "Semi-Supervised Learned Autoencoder for Classification of Events in Distributed Fibre Acoustic Sensors." Sensors 25, no. 12 (2025): 3730. https://doi.org/10.3390/s25123730.

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The global market for infrastructure security systems based on distributed acoustic sensors is rapidly expanding, driven by the need for timely detection and prevention of potential threats. However, deploying these systems is challenging due to the high costs associated with dataset creation. Additionally, advanced signal processing algorithms are necessary for accurately determining the location and nature of detected events. In this paper, we present an enhanced approach based on semi-supervised learning for developing event classification models tailored for real-time and continuous perime

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Aziz, Fayeem, Aaron S. W. Wong, and Stephan Chalup. "Semi-Supervised Manifold Alignment Using Parallel Deep Autoencoders." Algorithms 12, no. 9 (2019): 186. http://dx.doi.org/10.3390/a12090186.

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The aim of manifold learning is to extract low-dimensional manifolds from high-dimensional data. Manifold alignment is a variant of manifold learning that uses two or more datasets that are assumed to represent different high-dimensional representations of the same underlying manifold. Manifold alignment can be successful in detecting latent manifolds in cases where one version of the data alone is not sufficient to extract and establish a stable low-dimensional representation. The present study proposes a parallel deep autoencoder neural network architecture for manifold alignment and conduct

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22

Rosa, Tiago Gaspar da, Arthur Henrique de Andrade Melani, Fabio Henrique Pereira, Fabio Norikazu Kashiwagi, Gilberto Francisco Martha de Souza, and Gisele Maria De Oliveira Salles. "Semi-Supervised Framework with Autoencoder-Based Neural Networks for Fault Prognosis." Sensors 22, no. 24 (2022): 9738. http://dx.doi.org/10.3390/s22249738.

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This paper presents a generic framework for fault prognosis using autoencoder-based deep learning methods. The proposed approach relies upon a semi-supervised extrapolation of autoencoder reconstruction errors, which can deal with the unbalanced proportion between faulty and non-faulty data in an industrial context to improve systems’ safety and reliability. In contrast to supervised methods, the approach requires less manual data labeling and can find previously unknown patterns in data. The technique focuses on detecting and isolating possible measurement divergences and tracking their growt

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23

Harilal, Abhirami, Kyungmin Park, and Manfred Paulini. "Anomaly Detection Based on Machine Learning for the CMS Electromagnetic Calorimeter Online Data Quality Monitoring." EPJ Web of Conferences 320 (2025): 00048. https://doi.org/10.1051/epjconf/202532000048.

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Using a semi-supervised machine learning approach we present a real-time anomaly detection system based on an autoencoder used for online data quality monitoring of the CMS electromagnetic calorimeter operating at the CERN LHC. We introduce a novel method that maximizes the anomaly detection performance making use of the time-dependence of anomalies and the spatial variations in the detector response. The autoencoder-based system efficiently detects anomalies in real time and maintains a very low false discovery rate. We validate the performance of this novel system with anomalies from LHC col

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24

Cui, Jun, Longkun Bai, Guangxu Li, Zhigui Lin, and Penggao Zeng. "Semi-2DCAE: a semi-supervision 2D-CNN AutoEncoder model for feature representation and classification of encrypted traffic." PeerJ Computer Science 9 (November 9, 2023): e1635. http://dx.doi.org/10.7717/peerj-cs.1635.

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Traffic classification is essential in network-related areas such as network management, monitoring, and security. As the proportion of encrypted internet traffic rises, the accuracy of port-based and DPI-based traffic classification methods has declined. The methods based on machine learning and deep learning have effectively improved the accuracy of traffic classification, but they still suffer from inadequate extraction of traffic structure features and poor feature representativeness. This article proposes a model called Semi-supervision 2-Dimensional Convolution AutoEncoder (Semi-2DCAE).

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Yosefpor, Mohammad, Mohammad Reza Mostaan, and Sadegh Raeisi. "Finding semi-optimal measurements for entanglement detection using autoencoder neural networks." Quantum Science and Technology 5, no. 4 (2020): 045006. http://dx.doi.org/10.1088/2058-9565/aba34c.

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Lei, Z., Z. Yi, L. Peng, and S. X. Hui. "Semi-supervised classification of hyperspectral images based on two branch autoencoder." IOP Conference Series: Earth and Environmental Science 502 (June 2, 2020): 012014. http://dx.doi.org/10.1088/1755-1315/502/1/012014.

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27

Fu, Xianghua, Yanzhi Wei, Fan Xu, et al. "Semi-supervised Aspect-level Sentiment Classification Model based on Variational Autoencoder." Knowledge-Based Systems 171 (May 2019): 81–92. http://dx.doi.org/10.1016/j.knosys.2019.02.008.

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28

Csiszárik, Adrián, Melinda F. Kiss, Balázs Maga, Ákos Matszangosz, and Dániel Varga. "Global sinkhorn autoencoder - optimal transport on the latent representation of the full dataset." Annales Universitatis Scientiarum Budapestinensis de Rolando Eötvös Nominatae. Sectio computatorica 57 (2024): 101–15. https://doi.org/10.71352/ac.57.101.

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We propose an Optimal Transport (OT)-based generative model from the Wasserstein Autoencoder (WAE) family of models, with the following innovative property: the optimization of the latent point positions takes place over the full training dataset rather than over a minibatch. Our contributions are the following: 1. We define a new class of global Wasserstein Autoencoder models, and implement an Optimal Transport-based incarnation we call the Global Sinkhorn Autoencoder. 2. We implement several metrics for evaluating such models, both in the unsupervised setting, and in a semi-supervised etting

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29

Wang, Haoran, Zhongze Han, Xiaoshuang Xiong, Xuewei Song, and Chen Shen. "Enhancing Yarn Quality Wavelength Spectrogram Analysis: A Semi-Supervised Anomaly Detection Approach with Convolutional Autoencoder." Machines 12, no. 5 (2024): 309. http://dx.doi.org/10.3390/machines12050309.

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Abnormal detection plays a pivotal role in the routine maintenance of industrial equipment. Malfunctions or breakdowns in the drafting components of spinning equipment can lead to yarn defects, thereby compromising the overall quality of the production line. Fault diagnosis of spinning equipment entails the examination of component defects through Wavelet Spectrogram Analysis (WSA). Conventional detection techniques heavily rely on manual experience and lack generality. To address this limitation, this current study leverages machine learning technology to formulate a semi-supervised anomaly d

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Ghinea, Liliana Maria, Mihaela Miron, and Marian Barbu. "Semi-Supervised Anomaly Detection of Dissolved Oxygen Sensor in Wastewater Treatment Plants." Sensors 23, no. 19 (2023): 8022. http://dx.doi.org/10.3390/s23198022.

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As the world progresses toward a digitally connected and sustainable future, the integration of semi-supervised anomaly detection in wastewater treatment processes (WWTPs) promises to become an essential tool in preserving water resources and assuring the continuous effectiveness of plants. When these complex and dynamic systems are coupled with limited historical anomaly data or complex anomalies, it is crucial to have powerful tools capable of detecting subtle deviations from normal behavior to enable the early detection of equipment malfunctions. To address this challenge, in this study, we

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31

Costa, Nahuel, Luciano Sanchez, and Ines Couso. "Semi-Supervised Recurrent Variational Autoencoder Approach for Visual Diagnosis of Atrial Fibrillation." IEEE Access 9 (2021): 40227–39. http://dx.doi.org/10.1109/access.2021.3064854.

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32

Hou, Liang, Xiao-yi Luo, Zi-yang Wang, and Jun Liang. "Representation learning via a semi-supervised stacked distance autoencoder for image classification." Frontiers of Information Technology & Electronic Engineering 21, no. 7 (2020): 1005–18. http://dx.doi.org/10.1631/fitee.1900116.

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33

He, Chaobo, Yulong Zheng, Junwei Cheng, Yong Tang, Guohua Chen, and Hai Liu. "Semi-supervised overlapping community detection in attributed graph with graph convolutional autoencoder." Information Sciences 608 (August 2022): 1464–79. http://dx.doi.org/10.1016/j.ins.2022.07.036.

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Zhang, Xiaoxi, Yuan Gao, Xin Wang, Jun Feng, and Yan Shi. "GeoSDVA: A Semi-Supervised Dirichlet Variational Autoencoder Model for Transportation Mode Identification." ISPRS International Journal of Geo-Information 11, no. 5 (2022): 290. http://dx.doi.org/10.3390/ijgi11050290.

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Inferring the transportation modes of travelers is an essential part of intelligent transportation systems. With the development of mobile services, it is easy to effectively obtain massive location readings of travelers with GPS-enabled smart devices, such as smartphones. These readings make understanding human activities very convenient. Therefore, how to automatically infer transportation modes from these massive readings has come into the spotlight. The existing methods for transportation mode identification are usually based on supervised learning. However, the raw GPS readings do not con

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Chen, Jiahong, Jing Wang, Tongxin Shu, and Clarence W. de Silva. "WSN optimization for sampling-based signal estimation using semi-binarized variational autoencoder." Information Sciences 587 (March 2022): 188–205. http://dx.doi.org/10.1016/j.ins.2021.12.022.

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36

Pastor-Serrano, Oscar, Danny Lathouwers, and Zoltán Perkó. "A semi-supervised autoencoder framework for joint generation and classification of breathing." Computer Methods and Programs in Biomedicine 209 (September 2021): 106312. http://dx.doi.org/10.1016/j.cmpb.2021.106312.

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Zhang, Shuyuan, and Tong Qiu. "Semi-supervised LSTM ladder autoencoder for chemical process fault diagnosis and localization." Chemical Engineering Science 251 (April 2022): 117467. http://dx.doi.org/10.1016/j.ces.2022.117467.

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Wu, Xinya, Yan Zhang, Changming Cheng, and Zhike Peng. "A hybrid classification autoencoder for semi-supervised fault diagnosis in rotating machinery." Mechanical Systems and Signal Processing 149 (February 2021): 107327. http://dx.doi.org/10.1016/j.ymssp.2020.107327.

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Liu, Jie, Kechen Song, Mingzheng Feng, Yunhui Yan, Zhibiao Tu, and Liu Zhu. "Semi-supervised anomaly detection with dual prototypes autoencoder for industrial surface inspection." Optics and Lasers in Engineering 136 (January 2021): 106324. http://dx.doi.org/10.1016/j.optlaseng.2020.106324.

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Soumaya, Zaghbani, Boujneh Nouredine, and Salim Bouhlel Med. "Semi-supervised auto-encoder for facial attributes recognition." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 4 (2020): 2169–76. https://doi.org/10.12928/TELKOMNIKA.v18i4.14836.

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The particularity of our faces encourages many researchers to exploit their features in different domains such as user identification, behaviour analysis, computer technology, security, and psychology. In this paper, we present a method for facial attributes analysis. The work addressed to analyse facial images and extract features in the purpose to recognize demographic attributes: age, gender, and ethnicity (AGE). In this work, we exploited the robustness of deep learning (DL) using an updating version of autoencoders called the deep sparse autoencoder (DSAE). In this work we used a new arch

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Esmaeili, Fatemeh, Erica Cassie, Hong Phan T. Nguyen, Natalie O. V. Plank, Charles P. Unsworth, and Alan Wang. "Anomaly Detection for Sensor Signals Utilizing Deep Learning Autoencoder-Based Neural Networks." Bioengineering 10, no. 4 (2023): 405. http://dx.doi.org/10.3390/bioengineering10040405.

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Anomaly detection is a significant task in sensors’ signal processing since interpreting an abnormal signal can lead to making a high-risk decision in terms of sensors’ applications. Deep learning algorithms are effective tools for anomaly detection due to their capability to address imbalanced datasets. In this study, we took a semi-supervised learning approach, utilizing normal data for training the deep learning neural networks, in order to address the diverse and unknown features of anomalies. We developed autoencoder-based prediction models to automatically detect anomalous data recorded

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Zeng, Mengjie, Shunming Li, Ranran Li, et al. "A Hierarchical Sparse Discriminant Autoencoder for Bearing Fault Diagnosis." Applied Sciences 12, no. 2 (2022): 818. http://dx.doi.org/10.3390/app12020818.

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Although some traditional autoencoders and their extensions have been widely used in the research of intelligent fault diagnosis of rotating parts, their feature extraction capabilities are limited without label information. In response to this problem, this research proposes a hierarchical sparse discriminant autoencoder (HSDAE) method for fault diagnosis of rotating components, which is a new semi-supervised autoencoder structure. By considering the sparsity of autoencoders, a hierarchical sparsity strategy was proposed to improve the stacked sparsity autoencoders, and the particle swarm opt

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Aouedi, Ons, Kandaraj Piamrat, and Dhruvjyoti Bagadthey. "Handling partially labeled network data: A semi-supervised approach using stacked sparse autoencoder." Computer Networks 207 (April 2022): 108742. http://dx.doi.org/10.1016/j.comnet.2021.108742.

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Gogna, Anupriya, Angshul Majumdar, and Rabab Ward. "Semi-supervised Stacked Label Consistent Autoencoder for Reconstruction and Analysis of Biomedical Signals." IEEE Transactions on Biomedical Engineering 64, no. 9 (2017): 2196–205. http://dx.doi.org/10.1109/tbme.2016.2631620.

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Wang, Jian, Jiale Zhao, and Ting Cheng. "SGAAE-AC: A Semi-Supervised Graph Attention Autoencoder for Electroencephalography (EEG) Age Clustering." Applied Sciences 14, no. 13 (2024): 5392. http://dx.doi.org/10.3390/app14135392.

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The structural and cognitive functions of the brain undergo significant changes throughout an individual’s lifetime. The analysis of EEG background waves based on age groups will help reveal the correlation between human cognitive development ability and their age, and provide a new perspective for a deeper understanding of neurodegenerative diseases. Unfortunately, the available literature shows that, in recent years, the analysis of EEG signal background waves at different age groups has been extremely rare. To address the vacuum of this research, this paper introduces an innovative semi-sup

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Hua, Chengcheng, Hong Wang, Hong Wang, Shaowen Lu, Chong Liu, and Syed Madiha Khalid. "A Novel Method of Building Functional Brain Network Using Deep Learning Algorithm with Application in Proficiency Detection." International Journal of Neural Systems 29, no. 01 (2019): 1850015. http://dx.doi.org/10.1142/s0129065718500156.

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Functional brain network (FBN) has become very popular to analyze the interaction between cortical regions in the last decade. But researchers always spend a long time to search the best way to compute FBN for their specific studies. The purpose of this study is to detect the proficiency of operators during their mineral grinding process controlling based on FBN. To save the search time, a novel semi-data-driven method of computing functional brain connection based on stacked autoencoder (BCSAE) is proposed in this paper. This method uses stacked autoencoder (SAE) to encode the multi-channel E

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Liang, Jun, Daoguang Liu, Yinxiao Zhan, and Jiayu Fan. "Nonlinear Dynamic Process Monitoring Based on Discriminative Denoising Autoencoder and Canonical Variate Analysis." Actuators 13, no. 11 (2024): 440. http://dx.doi.org/10.3390/act13110440.

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Modern industrial processes are characterized by increasing complexity, often exhibiting pronounced dynamic behaviors and significant nonlinearity. Addressing these dynamic and nonlinear characteristics is essential for effective process monitoring. However, many existing methods for process monitoring and fault diagnosis are insufficient in handling these challenges. In this article, we present a novel process monitoring approach, CVA-DisDAE, which integrates an improved Denoising Autoencoder (DAE) with Canonical Variate Analysis (CVA) to address the challenges posed by dynamic behaviors and

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Xue, Hansheng, Jiajie Peng, and Xuequn Shang. "Towards Gene Function Prediction via Multi-Networks Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 10069–70. http://dx.doi.org/10.1609/aaai.v33i01.330110069.

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Multi-networks integration methods have achieved prominent performance on many network-based tasks, but these approaches often incur information loss problem. In this paper, we propose a novel multi-networks representation learning method based on semi-supervised autoencoder, termed as DeepMNE, which captures complex topological structures of each network and takes the correlation among multinetworks into account. The experimental results on two realworld datasets indicate that DeepMNE outperforms the existing state-of-the-art algorithms.

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Ooi, Sai Kit, Dave Tanny, Junghui Chen, and Kai Wang. "Developing semi-supervised variational autoencoder-generative adversarial network models to enhance quality prediction performance." Chemometrics and Intelligent Laboratory Systems 217 (October 2021): 104385. http://dx.doi.org/10.1016/j.chemolab.2021.104385.

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Tong, Li, Hang Wu, and May D. Wang. "CAESNet: Convolutional AutoEncoder based Semi-supervised Network for improving multiclass classification of endomicroscopic images." Journal of the American Medical Informatics Association 26, no. 11 (2019): 1286–96. http://dx.doi.org/10.1093/jamia/ocz089.

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Abstract Objective This article presents a novel method of semisupervised learning using convolutional autoencoders for optical endomicroscopic images. Optical endomicroscopy (OE) is a newly emerged biomedical imaging modality that can support real-time clinical decisions for the grade of dysplasia. To enable real-time decision making, computer-aided diagnosis (CAD) is essential for its high speed and objectivity. However, traditional supervised CAD requires a large amount of training data. Compared with the limited number of labeled images, we can collect a larger number of unlabeled images.

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