Suchergebnisse - sparse convolutional autoencoder (sae)

EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder von 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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Out-of-Roundness Wheel Damage Identification in Railway Vehicles Using AutoEncoder Models von Melo, Renato, Finotti, Rafaelle, Guedes, António, Gonçalves, Vítor, Meixedo, Andreia, Ribeiro, Diogo, Barbosa, Flávio, Cury, Alexandre

“… ), Sparse AutoEncoder (SAE), and Convolutional AutoEncoder (CAE)—to detect and quantify structural anomalies in railway vehicle wheels, such as polygonization …”
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Diabetes detection using deep learning techniques with oversampling and feature augmentation von García-Ordás, María Teresa, Benavides, Carmen, Benítez-Andrades, José Alberto, Alaiz-Moretón, Héctor, García-Rodríguez, Isaías

“… •Sparse auto encoder was trained for feature augmentation.•Results obtained demonstrate the powerful of the model using convolutional layers•A 92.31 …”
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Deep learning for pixel-level image fusion: Recent advances and future prospects von 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) techniques for image fusion are …”
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A 3D convolutional neural network based near-field acoustical holography method with sparse sampling rate on measuring surface von Wang, Jiaxuan, Zhang, Zhifu, Huang, Yizhe, Li, Zhuang, Huang, Qibai

“… Based on 3D convolutional neural network (3D-CNN) and stacked autoencoder (SAE), a method called CSA-NAH is proposed to reduce the wraparound error under sparse measuring …”
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Alz-SAENet: A Deep Sparse Autoencoder based Model for Alzheimer’s Classification von Reddy, G Nagarjuna, Reddy, K Nagi

“… ) and deep sparse autoencoder (SAE). Optimal features derived from the bottleneck layer of the hyper-tuned SAE network are subsequently …”
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Leveraging explainable artificial intelligence with ensemble of deep learning model for dementia prediction to enhance clinical decision support systems von Medani, Mohamed, Elhessewi, Ghada Moh. Samir, Alqahtani, Mohammed, Asklany, Somia A., Alamro, Sulaiman, Albalawneh, Da’ad, Alshammeri, Menwa, Assiri, Mohammed

“… The prevalence of dementia is growing worldwide due to the fast ageing of the population. Dementia is an intricate illness that is frequently produced by a …”
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Flight Test Sensor Fault Diagnosis Based on Data-Fusion and Machine Learning Method von Wang, Hongxin, Xu, Degang, Wen, Xin, Song, Jinsheng, Li, Linwen

“… However, it is challenging to achieve accurate FDC only based on single senor readings. In this paper, a fused FDC model among multiple different sensors is stabled by a hybrid deep learning architecture combining a sparse autoencoder (SAE …”
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Localization of False Data Injection Attacks in Smart Grids: A Deep Learning Framework Based on SAE-CNN von Jiang, Wenlong, Gao, Wengen, Zhu, Guangyao, Sun, Wanjun

“… To address this challenge, this paper introduces SAE-CNN, a hybrid method combining Sparse Autoencoder (SAE …”
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Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification von Jozdani, Shahab Eddin, Johnson, Brian Alan, Chen, Dongmei

“… With the advent of high-spatial resolution (HSR) satellite imagery, urban land use/land cover (LULC) mapping has become one of the most popular applications in …”
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Industrial Internet of Things Cyber Threats Detection Through Deep Feature Learning and Stacked Sparse Autoencoder Based Classification von Vijay Anand, R., Magesh, G., Alagiri, I., Brahmam, Madala Guru, Senthil Kumar, C., Kesavan, M., Abdullah, Azween Bin

“… ABSTRACT In recent times, the industrial system has integrated with industrial Internet of Things (IoT) applications to enable the ease of production process …”
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Confirmnet: Convolutional Firmnet and Application to Image Denoising and Inpainting von Pokala, Praveen Kumar, Kumar Uttam, Prakash, Seelamantula, Chandra Sekhar

“… ). As an application, we develop the ConFirmNet based sparse autoencoder (ConFirmNet-SAE) for learning an application-specific convolutional dictionary, the applications being image denoising and inpainting …”
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Patch-based Sparse and Convolutional Autoencoders for Anomaly Detection in Hyperspectral Images von Rezvanian, Amir Reza, Imani, Maryam, Ghassemian, Hassan

“… The proposed networks are deep fully-connected sparse autoencoders (SAE) and deep one-dimensional convolutional autoencoders (CAE …”
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Anomaly-Based Intrusion Detection Model Using Deep Learning for IoT Networks von Alsoufi, Muaadh A., Siraj, Maheyzah Md, Ghaleb, Fuad A., Al-Razgan, Muna, Al-Asaly, Mahfoudh Saeed, Alfakih, Taha, Saeed, Faisal

“… The rapid growth of Internet of Things (IoT) devices has brought numerous benefits to the interconnected world. However, the ubiquitous nature of IoT networks …”
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Static video summarization using multi-CNN with sparse autoencoder and random forest classifier von Nair, Madhu S., Mohan, Jesna

“… ). The features are extracted using four pre-trained models of CNN. These vectors are fed to Sparse Autoencoder, which outputs a combined representation of the input feature vectors …”
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SV-SAE: Layer-Wise Pruning for Autoencoder Based on Link Contributions von Rheey, Joohong, Park, Hyunggon

“… The resulting pruned model is referred to as a Shapley Value-based Sparse AutoEncoder (SV-SAE). Using cooperative game theory, the proposed algorithm models the autoencoder …”
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Transfer learning based on improved stacked autoencoder for bearing fault diagnosis von Luo, Shuyang, Huang, Xufeng, Wang, Yanzhi, Luo, Rongmin, Zhou, Qi

“… Stacked autoencoder (SAE) has been widely employed in deep transfer learning research since it is a semi-supervised algorithm …”
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Fault Classification of Axial and Radial Roller Bearings Using Transfer Learning through a Pretrained Convolutional Neural Network von Hemmer, Martin, Van Khang, Huynh, Robbersmyr, Kjell, Waag, Tor, Meyer, Thomas

“… ), support vector machine (SVM), and sparse autoencoder-based SVM (SAE-SVM). Within this framework, three fault classifiers based on CNN, SVM, and SAE-SVM utilizing transfer …”
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A Novel Harris Hawk Algorithm (HHA) to Optimize Deep Autoencoder (DAE) Method of Deep Learning for Cancer Diagnosis von Kumar, N. Jagadish, Yuvaraj, G., Hameed, A. Thariq, Noor, A. Mohamed Sheik, Johindhan, E., Vasanth, P. Rudresh

“… A deep autoencoder architecture …”
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Microscopic segmentation and classification of COVID‐19 infection with ensemble convolutional neural network von Amin, Javeria, Anjum, Muhammad Almas, Sharif, Muhammad, Rehman, Amjad, Saba, Tanzila, Zahra, Rida

“… In Phase III, segmented images are passed to the stack sparse autoencoder (SSAE …”
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