Suchergebnisse - Stacked sparse autoencoder data construction

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  1. 1

    Cost Prediction of Tunnel Construction Based on Interpretative Structural Model and Stacked Sparse Autoencoder von Zhou, Jing-Qun, Liu, Qi-Ming, Ma, Chang-Xi, Li, Dong

    ISSN: 1816-093X, 1816-0948
    Veröffentlicht: Hong Kong International Association of Engineers 01.10.2024
    Veröffentlicht in Engineering letters (01.10.2024)
    “… Recently, the machine learning technique offers an accurate and efficient method for forecasting construction expenses, introducing a novel approach to cost accounting other than conventional calculation techniques …”
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    Journal Article
  2. 2

    Building Extraction Based on an Optimized Stacked Sparse Autoencoder of Structure and Training Samples Using LIDAR DSM and Optical Images von Yan, Yiming, Tan, Zhichao, Su, Nan, Zhao, Chunhui

    ISSN: 1424-8220, 1424-8220
    Veröffentlicht: Switzerland MDPI AG 24.08.2017
    Veröffentlicht in Sensors (Basel, Switzerland) (24.08.2017)
    “… In this paper, a building extraction method is proposed based on a stacked sparse autoencoder with an optimized structure and training samples …”
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    Journal Article
  3. 3

    Stacked sparse autoencoder networks and statistical shape models for automatic staging of distal femur trochlear dysplasia von Cerveri, Pietro, Belfatto, Antonella, Baroni, Guido, Manzotti, Alfonso

    ISSN: 1478-5951, 1478-596X, 1478-596X
    Veröffentlicht: England Wiley Subscription Services, Inc 01.12.2018
    “… ), able to represent the individual morphology of the trochlea by means of a set of parameters and stacked sparse autoencoder (SSPA …”
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    Journal Article
  4. 4

    Research on Deep Adaptive Clustering Method Based on Stacked Sparse Autoencoders for Concrete Truck Mixers Driving Conditions von Huang, Ying, Jiang, Fachao, Xie, Haiming

    ISSN: 2032-6653, 2032-6653
    Veröffentlicht: Basel MDPI AG 15.10.2025
    Veröffentlicht in World electric vehicle journal (15.10.2025)
    “… Then, stacked sparse autoencoders (SSAE) are employed to extract deep features from normalized driving data …”
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  5. 5

    Enhancing breast cancer classification using a deep sparse wavelet autoencoder approach von Alzakari, Sarah A., Hassairi, Salima, Hussan, Amel Ali Al, Ejbali, Ridha

    ISSN: 2045-2322, 2045-2322
    Veröffentlicht: London Nature Publishing Group UK 19.07.2025
    Veröffentlicht in Scientific reports (19.07.2025)
    “… ). The key innovation of our method lies in its construction: DSWAE combines stacked wavelet autoencoders to create a robust model specifically designed for differentiating between distinct categories in 2D breast cancer image datasets …”
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  6. 6

    Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model for Hyperspectral Image Classification von Deng, Cheng, Xue, Yumeng, Liu, Xianglong, Li, Chao, Tao, Dacheng

    ISSN: 0196-2892, 1558-0644
    Veröffentlicht: New York IEEE 01.03.2019
    Veröffentlicht in IEEE transactions on geoscience and remote sensing (01.03.2019)
    “… More specifically, deep joint spectral-spatial feature is first extracted through hierarchical stacked sparse autoencoder (SSAE) networks …”
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  7. 7

    Rock mass type prediction for tunnel boring machine using a novel semi-supervised method von Yu, Honggan, Tao, Jianfeng, Qin, Chengjin, Xiao, Dengyu, Sun, Hao, Liu, Chengliang

    ISSN: 0263-2241, 1873-412X
    Veröffentlicht: London Elsevier Ltd 01.07.2021
    “… •A set of data preprocessing methods is proposed for the cleaning of the big machine data …”
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  8. 8

    scIAE: an integrative autoencoder-based ensemble classification framework for single-cell RNA-seq data von Yin, Qingyang, Wang, Yang, Guan, Jinting, Ji, Guoli

    ISSN: 1467-5463, 1477-4054, 1477-4054
    Veröffentlicht: England Oxford University Press 17.01.2022
    Veröffentlicht in Briefings in bioinformatics (17.01.2022)
    “… Since single-cell gene expression data are high-dimensional and sparse with dropouts, we propose scIAE, an integrative autoencoder-based ensemble classification framework, to firstly perform multiple …”
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  9. 9

    An automatic and integrated self-diagnosing system for the silting disease of drainage pipelines based on SSAE-TSNE and MS-LSTM von Di, Danyang, Wang, Dianchang, Fang, Hongyuan, He, Qiang, Zhou, Lifen, Chen, Xianming, Sun, Bin, Zhang, Jinping

    ISSN: 0886-7798, 1878-4364
    Veröffentlicht: Elsevier Ltd 01.06.2023
    Veröffentlicht in Tunnelling and underground space technology (01.06.2023)
    “… •Siltation diagnosing systems of drainage pipes cannot achieve full coverage.•Generative adversarial network solves the small data sample problem …”
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  10. 10

    A novel modulation classification method in cognitive radios using higher-order cumulants and denoising stacked sparse autoencoder von Xu Zhu, Fujii, Takeo

    Veröffentlicht: Asia Pacific Signal and Information Processing Association 01.12.2016
    “… We use Stacked Denoising Sparse Autoencoder as a classifier for single-carrier modulation classification …”
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  11. 11

    A Distributed Anomaly Detection Method of Operation Energy Consumption Using Smart Meter Data von Ye Yuan, Kebin Jia

    Veröffentlicht: IEEE 01.09.2015
    “… An IOT-based distributed structure is implemented to execute data interaction. Stacked sparse autoencoder is used to extract the high-level representation from massive monitoring data acquired automatically from actual smart meter network …”
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    Tagungsbericht
  12. 12

    Health Status Assessment of Diesel Engine Valve Clearance Based on BFA-BOA-VMD Adaptive Noise Reduction and Multi-Channel Information Fusion von Liu, Yangshuo, Kang, Jianshe, Wen, Liang, Bai, Yunjie, Guo, Chiming

    ISSN: 1424-8220, 1424-8220
    Veröffentlicht: Basel MDPI AG 24.10.2022
    Veröffentlicht in Sensors (Basel, Switzerland) (24.10.2022)
    “… Regarding the problem of the valve gap health status being difficult to assess due to the complex composition of the condition monitoring signal during the …”
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  13. 13

    Deep Learning Role in Early Diagnosis of Prostate Cancer von Reda, Islam, Khalil, Ashraf, Elmogy, Mohammed, Abou El-Fetouh, Ahmed, Shalaby, Ahmed, Abou El-Ghar, Mohamed, Elmaghraby, Adel, Ghazal, Mohammed, El-Baz, Ayman

    ISSN: 1533-0346, 1533-0338, 1533-0338
    Veröffentlicht: Los Angeles, CA SAGE Publications 01.01.2018
    Veröffentlicht in Technology in cancer research & treatment (01.01.2018)
    “… of those apparent diffusion coefficients using a generalized Gaussian Markov random field model. Then, construction of the cumulative …”
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  14. 14

    Improving the second-tier classification of methylmalonic acidemia patients using a machine learning ensemble method von Zhu, Zhi-Xing, Genchev, Georgi Z., Wang, Yan-Min, Ji, Wei, Ren, Yong-Yong, Tian, Guo-Li, Sriswasdi, Sira, Lu, Hui

    ISSN: 1708-8569, 1867-0687, 1867-0687
    Veröffentlicht: Singapore Springer Nature Singapore 01.10.2024
    Veröffentlicht in World journal of pediatrics : WJP (01.10.2024)
    “… We utilized mass spectrometry-based features consisting of 11 amino acids and 31 carnitines derived from dried blood samples of neonatal patients, followed by additional ratio feature construction …”
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  15. 15

    A Deep Transfer NOx Emission Inversion Model of Diesel Vehicles with Multisource External Influence von Xu, Zhenyi, Wang, Ruibin, Kang, Yu, Zhang, Yujun, Xia, Xiushan, Wang, Renjun

    ISSN: 0197-6729, 2042-3195
    Veröffentlicht: London Hindawi 30.12.2021
    Veröffentlicht in Journal of advanced transportation (30.12.2021)
    “… By installing on-board diagnostics (OBD) on tested vehicles, the after-treatment exhaust emissions can be monitored in real time to construct driving cycle-based emission models, which can provide data support for the construction …”
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  16. 16

    Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification von Deng, Cheng, Xue, Yumeng, Liu, Xianglong, Li, Chao, Tao, Dacheng

    ISSN: 2331-8422
    Veröffentlicht: Ithaca Cornell University Library, arXiv.org 04.04.2019
    Veröffentlicht in arXiv.org (04.04.2019)
    “… More specifically, deep joint spectral-spatial feature is first extracted through hierarchical stacked sparse autoencoder (SSAE) networks …”
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