Výsledky vyhledávání - sparse conventional autoencoder ((((same OR sae) OR sage) OR cae))

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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A Novel Convolutional Autoencoder-Based Clutter Removal Method for Buried Threat Detection in Ground-Penetrating Radar Autor Temlioglu, Eyyup, Erer, Isin

“… A new clutter removal method based on convolutional autoencoders (CAEs) is introduced. The raw GPR image is encoded via successive convolution and pooling layers and then decoded to provide the clutter-free GPR image…”
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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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Enhanced fault detection in digital VLSI circuits using convolutional autoencoders Autor Savalam, Chandrasekhar, Medisetti, Sanjay, Korapati, Prasanti

“… A Convolutional Autoencoder (CAE) is employed to extract spatial and structural features from circuit test patterns, effectively reducing dimensionality while preserving fault-related information…”
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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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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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Deep Learning Augmented Data Assimilation: Reconstructing Missing Information with Convolutional Autoencoders Autor Wang, Yueya, Shi, Xiaoming, Lei, Lili, Fung, Jimmy Chi-Hung

“… By training a convolutional autoencoder (CAE) with a long simulation at a coarse “forecast” resolution (T63), we obtained a deep learning approximation…”
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Deep hyperspectral clustering using attention-enhanced 3D-2D convolutional autoencoder for mineral mapping Autor Peyghambari, Sima, Zhang, Yun

“… However, the most commonly used 3D-convolutional autoencoder (3D-CAE) models have several disadvantages, including intensive computational costs and the potential to lose spatial information…”
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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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Sensor-Driven Surrogate Modeling and Control of Nonlinear Dynamical Systems Using FAE-CAE-LSTM and Deep Reinforcement Learning Autor Kherad, Mahdi, Moayyedi, Mohammad Kazem, Fotouhi-Ghazvini, Faranak, Vahabi, Maryam, Fotouhi, Hossein

“… This paper presents a sensor-driven, non-intrusive reduced-order modeling (NIROM) framework called FAE-CAE-LSTM, which combines convolutional and fully connected autoencoders with a long short-term memory (LSTM) 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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Image fusion based on shift invariant shearlet transform and stacked sparse autoencoder Autor Wang, Peng-Fei, Luo, Xiao-Qing, Li, Xin-Yi, Zhang, Zhan-Cheng

“…Stacked sparse autoencoder is an efficient unsupervised feature extraction method, which has excellent ability in representation of complex data…”
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