Výsledky vyhledávání - "deep convolutional autoencoder model"

SPAER: Sparse Deep Convolutional Autoencoder Model to Extract Low Dimensional Imaging Biomarkers for Early Detection of Breast Cancer Using Dynamic Thermography Autor Yousefi, Bardia, Akbari, Hamed, Hershman, Michelle, Kawakita, Satoru, Fernandes, Henrique C., Ibarra-Castanedo, Clemente, Ahadian, Samad, Maldague, Xavier P. V.

“…) prior to mammography. In this study, we propose a sparse deep convolutional autoencoder model named SPAER to extract low-dimensional deep thermomics to aid breast cancer diagnosis…”
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Deep convolutional autoencoder for urban land use classification using mobile device data Autor Sun, Zhihao, Peng, Zhenghong, Yu, Yang, Jiao, Hongzan

“…Mobile phone data can provide insightful location-based information on the interactions between individuals and the urban environment, e.g. urban land-use…”
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Open Set Bearing Fault Diagnosis with Domain Adaptive Adversarial Network under Varying Conditions Autor Zhang, Bo, Li, Feixuan, Ma, Ning, Ji, Wen, Ng, See-Kiong

“…Bearing fault diagnosis is a pivotal aspect of monitoring rotating machinery. Recently, numerous deep learning models have been developed for intelligent…”
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An Aquatic Image Compression Scheme based on Optimized Deep Convolutional Autoencoder Autor Paul, Raj Kumar, Vishwakarma, Ankit, Chandran, Saravanan

“…: important information must be recovered in the reconstructed image. An optimized deep convolutional autoencoder model has been implemented using the deep learning approach for solving the challenges…”
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Fast and Scalable Distributed Deep Convolutional Autoencoder for fMRI Big Data Analytics Autor Makkie, Milad, Huang, Heng, Zhao, Yu, Vasilakos, Athanasios V, Liu, Tianming

“…In recent years, analyzing task-based fMRI (tfMRI) data has become an essential tool for understanding brain function and networks. However, due to the sheer…”
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On the Fine-Grained Crowd Analysis via Passive WiFi Sensing Autor Hao, Lifei, Huang, Baoqi, Jia, Bing, Mao, Guoqiang

“… with: to relieve the influences of the randomness and sparsity induced by passive WiFi sensing, an attention-based deep convolutional autoencoder model is designed to recover accurate crowd density maps…”
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