Search Results - sparse conventional autoencoder (((same OR sae) OR space))

EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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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