Výsledky vyhledávání - sparse conventional autoencoder (((same OR sae) OR space))~

EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder Autor Liu, Junxiu, Wu, Guopei, Luo, Yuling, Qiu, Senhui, Yang, Su, Li, Wei, Bi, Yifei

“…), Sparse Autoencoder (SAE), and Deep Neural Network (DNN) together. In the proposed network, the features extracted by the CNN are first sent to SAE for encoding and decoding…”
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A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction Autor Huang, Faming, Zhang, Jing, Zhou, Chuangbing, Wang, Yuhao, Huang, Jinsong, Zhu, Li

“… In this paper, a novel deep learning–based algorithm, the fully connected spare autoencoder (FC-SAE), is proposed for LSP…”
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Robust approach for AMC in frequency selective fading scenarios using unsupervised sparse-autoencoder-based deep neural network Autor Shah, Maqsood Hussain, Dang, Xiaoyu

“…Application of deep learning in the area of automatic modulation classification (AMC) is still evolving. An unsupervised sparse-autoencoder-based deep neural network…”
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A hybrid Intrusion Detection System based on Sparse autoencoder and Deep Neural Network Autor Narayana Rao, K., Venkata Rao, K., P.V.G.D., Prasad Reddy

“… In the first stage, the unsupervised Sparse autoencoder (SAE) with smoothed l1 regularization…”
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An Explainable AI Framework Integrating Variational Sparse Autoencoder and Random Forest for EEG-Based Epilepsy Detection Autor Mishra, Pratiti, Das, Himansu

“…), which combines the strengths of a Sparse Autoencoder (SAE) and a Variational Autoencoder (VAE). The VSAE produces compact, sparse, and informative features for Random Forest (RF…”
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Integrating Enhanced Sparse Autoencoder-Based Artificial Neural Network Technique and Softmax Regression for Medical Diagnosis Autor Ebiaredoh-Mienye, Sarah A., Esenogho, Ebenezer, Swart, Theo G.

“… an enhanced sparse autoencoder (SAE) and Softmax regression, respectively. In the SAE network, sparsity is achieved by penalizing the weights of the network…”
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Enhancing performance of end-to-end communication system using Attention Mechanism-based Sparse Autoencoder over Rayleigh fading channel Autor Sindal, Safalata S., Trivedi, Y.N.

“… To address this issue, we propose a Sparse Autoencoder-based (SAE) model that enforces sparsity and promotes the extraction of robust features…”
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Six Phase Transmission Line Protection Using Bat Algorithm Tuned Stacked Sparse Autoencoder Autor Rao Althi, Tirupathi, Koley, Ebha, Ghosh, Subhojit, Shukla, Sunil Kumar

“… The possibility of larger number of faults in six-phase system complicates the protection task. Furthermore, the harmonics intrusion arising because of nonlinear loading compromises the reliability of the conventional threshold-based protection schemes…”
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K sparse autoencoder-based accelerated reconstruction of magnetic resonance imaging Autor Dhengre, Nikhil, Sinha, Saugata

“… In this work, we propose to implement a K sparse autoencoder model for reconstruction of MR image from undersampled k-space data…”
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Enhancing the reliability of protection scheme for PV integrated microgrid by discriminating between array faults and symmetrical line faults using sparse auto encoder Autor Manohar, Murli, Koley, Ebha, Ghosh, Subhojit

“… In this regard, a protection scheme based on sparse autoencoder (SAE) and deep neural network has been proposed to discriminate between array faults and symmetrical line…”
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Stacked Sparse Autoencoder (SSAE) based framework for nuclei patch classification on breast cancer histopathology Autor Jun Xu, Lei Xiang, Renlong Hang, Jianzhong Wu

“…Softmax, and single layer Sparse Autoencoder (SAE)+Softmax in classifying the nuclei and non-nuclei patches extracted from breast cancer histopathology. The SSAE…”
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Deep learning for pixel-level image fusion: Recent advances and future prospects Autor Liu, Yu, Chen, Xun, Wang, Zengfu, Wang, Z. Jane, Ward, Rabab K., Wang, Xuesong

“…•The difficulties that exist in conventional image fusion research are analyzed.•The advantages of deep learning (DL…”
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Hybrid stacked sparse autoencoder for robust feature extraction and classification in sparse data across multiple domains Autor Abdussamad, Abdulkadir, Said Jadid, Daud, Hanita, Sokkalingam, Rajalingam, Khan, Iliyas Karim

“… These factors inhibit effective feature selection and reduce prediction accuracy. The Stacked Sparse Autoencoder (SSAE…”
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An Enhanced Hierarchical Extreme Learning Machine with Random Sparse Matrix Based Autoencoder Autor Wang, Tianlei, Lai, Xiaoping, Cao, Jiuwen, Vong, Chi-Man, Chen, Badong

“…Recently, by employing the stacked extreme learning machine (ELM) based autoencoders (ELM-AE) and sparse AEs (SAE), multilayer ELM (ML-ELM…”
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Masked autoencoder for highly compressed single-pixel imaging Autor Liu, Haiyan, Chang, Xuyang, Yan, Jun, Guo, Pengyu, Xu, Dong, Bian, Liheng

“… In this way, we can effectively decrease 75% modulation patterns experimentally. To reconstruct the entire image, we designed a highly sparse input and extrapolation network consisting of two modules…”
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Visualization of driving behavior using deep sparse autoencoder Autor Liu, HaiLong, Taniguchi, Tadahiro, Takano, Tosiaki, Tanaka, Yusuke, Takenaka, Kazuhito, Bando, Takashi

“… We used a deep sparse autoencoder to extract the low-dimensional high-level representation from high-dimensional raw driving behavioral data obtained from a control area network…”
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Anomaly-Based Intrusion Detection Model Using Deep Learning for IoT Networks Autor Alsoufi, Muaadh A., Siraj, Maheyzah Md, Ghaleb, Fuad A., Al-Razgan, Muna, Al-Asaly, Mahfoudh Saeed, Alfakih, Taha, Saeed, Faisal

“… Given the unpredictable nature of network technologies and diverse intrusion methods, conventional machine-learning approaches seem to lack efficiency…”
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scZAG: Integrating ZINB-Based Autoencoder with Adaptive Data Augmentation Graph Contrastive Learning for scRNA-seq Clustering Autor Zhang, Tianjiao, Ren, Jixiang, Li, Liangyu, Wu, Zhenao, Zhang, Ziheng, Dong, Guanghui, Wang, Guohua

“… In the high-dimensional gene expression space, cells may form complex topological structures…”
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Deep Learning Augmented Data Assimilation: Reconstructing Missing Information with Convolutional Autoencoders Autor Wang, Yueya, Shi, Xiaoming, Lei, Lili, Fung, Jimmy Chi-Hung

“…). However, the physical principles of radiation dictate that data voids frequently exist in physical space (e.g…”
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A Semi-Supervised Autoencoder With an Auxiliary Task (SAAT) for Power Transformer Fault Diagnosis Using Dissolved Gas Analysis Autor Kim, Sunuwe, Jo, Soo-Ho, Kim, Wongon, Park, Jongmin, Jeong, Jingyo, Han, Yeongmin, Kim, Daeil, Youn, Byeng Dong

“…This paper proposes a semi-supervised autoencoder with an auxiliary task (SAAT) to extract a health feature space for power transformer fault diagnosis using dissolved gas analysis (DGA…”
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