Suchergebnisse - "sparse autoencoder network"

EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing von Ozkan, Savas, Kaya, Berk, Akar, Gozde Bozdagi

“… Data acquired from multichannel sensors are a highly valuable asset to interpret the environment for a variety of remote sensing applications. However, low …”
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Softmax regression based deep sparse autoencoder network for facial emotion recognition in human-robot interaction von Chen, Luefeng, Zhou, Mengtian, Su, Wanjuan, Wu, Min, She, Jinhua, Hirota, Kaoru

“… However, DNN suffers from problems of learning efficiency and computational complexity. To address these problems, deep sparse autoencoder network (DSAN …”
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Parameter identification for nonlinear Hammerstein models with stacked sparse autoencoder network von Li, Feng, Song, Liexin, Wang, Tianhu, Liu, Ranran

“… In this paper, a novel parameter identification method is addressed for Hammerstein nonlinear model with stacked sparse autoencoder (SSAE) network. The …”
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Self-Supervised Marine Noise Learning with Sparse Autoencoder Network for Generative Target Magnetic Anomaly Detection von Wang, Shigang, Zhang, Xiangyuan, Zhao, Yifan, Yu, Haozi, Li, Bin

“… As an effective physical field feature to perceive ferromagnetic targets, magnetic anomaly is widely used in covert marine surveillance tasks. However, its …”
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An industrial process fault diagnosis method based on independent slow feature analysis and stacked sparse autoencoder network von Li, Chang, Wen, Chenglin, Zhou, Zhe

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A deep learning based malicious module identification using stacked sparse autoencoder network for VLSI circuit reliability von Priyatharishini, M., Nirmala Devi, M.

“… •A deep learning- based hardware trojan identification method is proposed.•The data imbalance in VLSI circuits is addressed by using deep convolutional …”
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Mineralized-Anomaly Identification Based on Convolutional Sparse Autoencoder Network and Isolated Forest von Yang, Na, Zhang, Zhenkai, Yang, Jianhua, Hong, Zenglin

“… -mineralization samples when they are utilized in mineralized-anomaly identification. This paper utilizes a convolutional sparse autoencoder network for realizing mineralized-anomaly identification …”
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IoT and cloud computing-based automated epileptic seizure detection using optimized Siamese convolutional sparse autoencoder network von Ramkumar, M., Jamaesha, S. Syed, Gowtham, M. S., Kumar, C. Santhosh

“… This research work introduces a Siamese Convolutional Fire Hawk Sparse Autoencoder Network (SCFHSAN ) to detect different states of the epileptic seizures …”
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Compressive Reconstruction Based on Sparse Autoencoder Network Prior for Single-Pixel Imaging von Zeng, Hong, Dong, Jiawei, Li, Qianxi, Chen, Weining, Dong, Sen, Guo, Huinan, Wang, Hao

“… the feature extraction and increase the burden of the system. In this paper, we propose a novel sparse autoencoder network prior for the reconstruction of the single-pixel …”
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Face Recognition Based on Deep Aggregated Sparse Autoencoder Network von Zou, Guofeng, Lin, Dingyi, Fu, Gui-Xia, Shen, Jin, Gao, Mingliang

“… Sparse autoencoder network is sensitive to face noise, and the learning process is easy to ignore the face structure information …”
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Application of Robust Zero-Watermarking Scheme Based on Federated Learning for Securing the Healthcare Data von Han, Baoru, Jhaveri, Rutvij H., Wang, Han, Qiao, Dawei, Du, Jinglong

“… The privacy protection and data security problems existing in the healthcare framework based on the Internet of Medical Things (IoMT) have always attracted …”
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SAE-Net: A Deep Neural Network for SAR Autofocus von Pu, Wei

“… , the sparse autoencoder network (SAE-Net). The proposed SAE-Net is designed to implement SAR imaging and autofocus simultaneously …”
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Deep Learning Based Retrieval of Forest Aboveground Biomass from Combined LiDAR and Landsat 8 Data von Zhang, Linjing, Shao, Zhenfeng, Liu, Jianchen, Cheng, Qimin

“… Estimation of forest aboveground biomass (AGB) is crucial for various technical and scientific applications, ranging from regional carbon and bioenergy …”
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Stacked Sparse Autoencoder Modeling Using the Synergy of Airborne LiDAR and Satellite Optical and SAR Data to Map Forest Above-Ground Biomass von Shao, Zhenfeng, Zhang, Linjing, Wang, Lei

“… Timely, spatially complete, and reliable forest above-ground biomass (AGB) data are a prerequisite to support forest management and policy formulation …”
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EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing von Ozkan, Savas, Kaya, Berk, Akar, Gozde Bozdagi

“… Data acquired from multi-channel sensors is a highly valuable asset to interpret the environment for a variety of remote sensing applications. However, low …”
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Network Intrusion Detection Based on Stacked Sparse Autoencoder and Binary Tree Ensemble Method von Zhang, Baoan, Yu, Yanhua, Li, Jie

“… This paper proposes a Xgboost based on stacked sparse autoencoder network(SSAE-XGB) method to learn latent representation of original data …”
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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

“… Background The quantitative morphological analysis of the trochlear region in the distal femur and the precise staging of the potential dysplastic condition …”
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Hyperspectral characterization and chlorophyll content inversion of reclaimed vegetation in rare earth mines von Li, Hengkai, Zhou, Beibei, Xu, Feng, Wei, Zhian

“… The special physical and chemical properties of the reclaimed land caused by the disturbance of rare earth mining and the environmental stress caused by the …”
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Intrusion Detection Model for Industrial Internet of Things Based on Improved Autoencoder von Zhang, Wumei, Zhang, Yongzhen

“… ) have attracted more and more attention. Aiming to improve the security of industrial IoT, a detection method using stacked sparse autoencoder network model is proposed …”
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Brain tumor classification from MRI scans: a framework of hybrid deep learning model with Bayesian optimization and quantum theory-based marine predator algorithm von Ullah, Muhammad Sami, Khan, Muhammad Attique, Masood, Anum, Mzoughi, Olfa, Saidani, Oumaima, Alturki, Nazik

“… Brain tumor classification is one of the most difficult tasks for clinical diagnosis and treatment in medical image analysis. Any errors that occur throughout …”
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