Suchergebnisse - convolutional neural network-autoencoder architecture~

Recreating Fingerprint Images by Convolutional Neural Network Autoencoder Architecture von Saponara, Sergio, Elhanashi, Abdussalam, Zheng, Qinghe

“… In this work, a convolutional neural network autoencoder has been used to reconstruct fingerprint images …”
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Plantar Pressure-Based Gait Recognition with and Without Carried Object by Convolutional Neural Network-Autoencoder Architecture von Wu, Chin-Cheng, Tsai, Cheng-Wei, Wu, Fei-En, Chiang, Chi-Hsuan, Chiou, Jin-Chern

“… To improve the disadvantage, we proposed a convolutional neural network autoencoder (CNN-AE) architecture for user classification based on plantar pressure gait recognition …”
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Multivariate time-series cyberattack detection in the distributed secondary control of AC microgrids with convolutional neural network autoencoder ensemble von Roshanzadeh, Behshad, Choi, Jeewon, Bidram, Ali, Martínez-Ramón, Manel

“… An autoencoder is a neural network architecture, where the model is trained to reconstruct its input in an unsupervised manner …”
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A Deep-learning Anomaly-detection Method to Identify Gamma-Ray Bursts in the Ratemeters of the AGILE Anticoincidence System von Parmiggiani, N., Bulgarelli, A., Ursi, A., Macaluso, A., Di Piano, A., Fioretti, V., Aboudan, A., Baroncelli, L., Addis, A., Tavani, M., Pittori, C.

“… The model is implemented with a convolutional neural network autoencoder …”
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Unsupervised electric motor fault detection by using deep autoencoders von Principi, Emanuele, Rossetti, Damiano, Squartini, Stefano, Piazza, Francesco

“… Deep neural networks have been successfully employed for this task, but, up to the authors &#x02BC knowledge, they have never been used in an unsupervised scenario …”
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Detection of Bleeding Events in Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders: Deep Learning Approach von Li, Rumeng, Hu, Baotian, Liu, Feifan, Liu, Weisong, Cunningham, Francesca, McManus, David D, Yu, Hong

“… Bleeding events are common and critical and may cause significant morbidity and mortality. High incidences of bleeding events are associated with …”
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Deep convolutional autoencoders for the time–space reconstruction of liquid rocket engine flames von Zapata Usandivaras, José F., Bauerheim, Michael, Cuenot, Bénédicte, Urbano, Annafederica

“… The integration of high-fidelity numerical simulations into the rocket engine design-cycle promises to cut costs in an ever more competitive launch market …”
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Approximating Human-Level 3D Visual Inferences With Deep Neural Networks von O’Connell, Thomas P., Bonnen, Tyler, Friedman, Yoni, Tewari, Ayush, Sitzmann, Vincent, Tenenbaum, Joshua B., Kanwisher, Nancy

“… Next, we construct a set of candidate 3D-aware DNNs including 3D neural field (Light Field Network), autoencoder, and convolutional architectures …”
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An integrated autoencoder-based hybrid CNN-LSTM model for COVID-19 severity prediction from lung ultrasound von Dastider, Ankan Ghosh, Sadik, Farhan, Fattah, Shaikh Anowarul

“… The proposed convolutional neural network (CNN) architecture implements an autoencoder network and separable convolutional branches fused with a modified …”
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Vehicle Detection From UAV Imagery With Deep Learning: A Review von Bouguettaya, Abdelmalek, Zarzour, Hafed, Kechida, Ahmed, Taberkit, Amine Mohammed

“… Vehicle detection from unmanned aerial vehicle (UAV) imagery is one of the most important tasks in a large number of computer vision-based applications. This …”
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Application of Convolutional Neural Network for Spatiotemporal Bias Correction of Daily Satellite-Based Precipitation von Le, Xuan-Hien, Lee, Giha, Jung, Kwansue, An, Hyun-uk, Lee, Seungsoo, Jung, Younghun

“… This paper presents an efficient approach based on a combination of the convolutional neural network and the autoencoder architecture, called the convolutional autoencoder (ConvAE …”
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A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders von Essien, Aniekan, Giannetti, Cinzia

“… The model comprises a deep convolutional …”
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ASSAF: Advanced and Slim StegAnalysis Detection Framework for JPEG images based on deep convolutional denoising autoencoder and Siamese networks von Cohen, Assaf, Cohen, Aviad, Nissim, Nir

“… Steganography is the art of embedding a confidential message within a host message. Modern steganography is focused on widely used multimedia file formats, …”
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A comparison of deep‐learning‐based inpainting techniques for experimental X‐ray scattering von Chavez, Tanny, Roberts, Eric J., Zwart, Petrus H., Hexemer, Alexander

“… The proposed methods use deep learning neural network architectures, such as convolutional autoencoders, tunable U …”
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NEURAL NETWORK AUTOENCODER MODEL FOR FORMING REDUCED VECTOR CHARACTERISTICS OF ECG SIGNALS von Mnevec, Anton, Ivanushkina, Natalia

“… The paper considers the actual problem of improving neural network models for the classification of cardiovascular pathologies by compressing the information contained in electrocardiographic (ECG) signals …”
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Rice Leaf Disease Classification—A Comparative Approach Using Convolutional Neural Network (CNN), Cascading Autoencoder with Attention Residual U-Net (CAAR-U-Net), and MobileNet-V2 Architectures von Dutta, Monoronjon, Islam Sujan, Md Rashedul, Mojumdar, Mayen Uddin, Chakraborty, Narayan Ranjan, Marouf, Ahmed Al, Rokne, Jon G., Alhajj, Reda

“… ) and the Peak Signal-to-Noise Ratio (PSNR). Following this, this work employed advanced neural network architectures for classification, including Cascading Autoencoder with Attention Residual U-Net (CAAR-U-Net …”
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A systematic review of rolling bearing fault diagnoses based on deep learning and transfer learning: Taxonomy, overview, application, open challenges, weaknesses and recommendations von Hakim, Mohammed, Omran, Abdoulhdi A. Borhana, Ahmed, Ali Najah, Al-Waily, Muhannad, Abdellatif, Abdallah

“… The most widely used DL algorithms for detecting bearing faults include Convolutional Neural Network, Recurrent neural network, Autoencoder, and Generative Adversarial Network …”
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TinyML and edge intelligence applications in cardiovascular disease: A survey von Keivanimehr, Ali Reza, Akbari, Mohammad

“… Tiny machine learning (TinyML) and edge intelligence have emerged as pivotal paradigms for enabling machine learning on resource-constrained devices situated …”
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Overview of Machine Learning: Part 2: Deep Learning for Medical Image Analysis von Le, William Trung, Maleki, Farhad, Romero, Francisco Perdigón, ghani, Reza, Kadoury, Samuel

“… The authors review the main deep learning architectures such as multilayer perceptron, convolutional neural networks, autoencoders, recurrent neural networks, and generative adversarial neural networks …”
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Forecasting Multi-Level Deep Learning Autoencoder Architecture (MDLAA) for Parametric Prediction based on Convolutional Neural Networks von Ayub, Nasir, Sarwar, Nadeem, Ali, Arshad, Khan, Hamayun, Din, Irfanud, Alqahtani, Abdullah M., Abdulnabi, Mohamed Shabbir Hamza, Ali, Aitizaz

“… The proposed Multi-Level Deep Learning Autoencoder Architecture (MDLAA) is used to encode high dimensional input data using CNNs for anomaly detection in High Dimensional Input Datasets (HDDs …”
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