Search Results - "deep convolutional autoencoder model"

Deep Convolutional Autoencoder Model for Urban Land Use Classification using Mobile Device Data

Authors: Sun, Zhihao Peng, Zhenghiong Yu, Yang et al.

Subject Terms: Urban Land Use Classification, Deep learning, Convolutional Autoencoder Model, Mobile Device Data, Call Detail Records, 11. Sustainability, 15. Life on land

SPAER: Sparse Deep Convolutional Autoencoder Model to Extract Low Dimensional Imaging Biomarkers for Early Detection of Breast Cancer Using Dynamic Thermography

Authors: Bardia Yousefi Hamed Akbari Michelle Hershman et al.

Source: Applied Sciences, Vol 11, Iss 7, p 3248 (2021)

Subject Terms: breast cancer, thermography, sparse deep convolutional autoencoder, matrix factorization, dimensionality reduction, thermomics, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

File Description: electronic resource

Relation: https://www.mdpi.com/2076-3417/11/7/3248; https://doaj.org/toc/2076-3417

Access URL: https://doaj.org/article/3c70757effa743f3b9bef5e4d264eb2f

Deep Convolutional Autoencoder Model for Urban Land Use Classification using Mobile Device Data ; Deep convolutional Autoencoder Model for Urban Land Use Classification using Mobile Device Data

Authors: Sun, Zhihao Peng, Zhenghiong Yu, Yang et al.

Subject Terms: Urban Land Use Classification, Deep learning, Convolutional Autoencoder Model, Mobile Device Data, Call Detail Records, geo, demo

Relation: https://zenodo.org/record/6513201

Availability: https://zenodo.org/record/6513201

SPAER: Sparse Deep Convolutional Autoencoder Model to Extract Low Dimensional Imaging Biomarkers for Early Detection of Breast Cancer Using Dynamic Thermography.

Authors: Yousefi, Bardia Akbari, Hamed Hershman, Michelle et al.

Source: Applied Sciences (2076-3417); Apr2021, Vol. 11 Issue 7, p3248, 15p

Subject Terms: EARLY detection of cancer, THERMOGRAPHY, MATRIX decomposition, DEEP learning, INFRARED imaging, NONNEGATIVE matrices

Deep convolutional autoencoder for urban land use classification using mobile device data.

Authors: Sun, Zhihao Peng, Zhenghong Yu, Yang et al.

Source: International Journal of Geographical Information Science. Nov2022, Vol. 36 Issue 11, p2138-2168. 31p.

Subject Terms: Urban land use, Zoning, Urban planning, Urban policy, Cell phones, Time series analysis

Geographic Terms: Wuhan (China), China

Open Set Bearing Fault Diagnosis with Domain Adaptive Adversarial Network under Varying Conditions.

Authors: Zhang, Bo Li, Feixuan Ma, Ning et al.

Source: Actuators; Apr2024, Vol. 13 Issue 4, p121, 19p

Subject Terms: FAULT diagnosis, MONITORING of machinery, DEEP learning, ROTATING machinery, KURTOSIS

An Aquatic Image Compression Scheme based on Optimized Deep Convolutional Autoencoder.

Authors: Pau, Raj Kumar Vishwakarma, Ankit Chandran, Saravanan

Source: IAENG International Journal of Computer Science. Feb2024, Vol. 51 Issue 2, p83-90. 8p.

Subject Terms: Image compression, Generative adversarial networks, Computer network traffic, Convolutional neural networks, Data compression, Deep learning

Heart Diseases Prediction Using Deep Convolutional Neural Network with Deformable Angular Alignment Module.

Authors: Kumar, Voruganti Naresh Ramatenki, Sateesh Kumar Marri, Sireesha et al.

Source: International Journal of Intelligent Engineering & Systems; 2025, Vol. 18 Issue 11, p551-565, 15p

Subject Terms: HEART diseases, CONVOLUTIONAL neural networks, PREDICTIVE validity, COMPUTER-assisted image analysis (Medicine), FEATURE extraction, DEEP learning, ELECTROCARDIOGRAPHY

Editorial: Monitoring, early warning, and mitigation of natural and engineered slopes – volume IV.

Authors: Qiu, Haijun Nie, Wen Asadi, Afshin

Source: Frontiers in Earth Science; 2025, p1-4, 4p

Subject Terms: LANDSLIDE hazard analysis, PORE water pressure, LANDSLIDES, EARTH pressure, NATURAL disasters, GENERATIVE artificial intelligence

Deep learning based computer aided diagnosis (CAD) tool supported by explainable artificial intelligence for breast cancer exploration.

Authors: Naas, Marwa Mzoughi, Hiba Njeh, Ines et al.

Source: Applied Intelligence; May2025, Vol. 55 Issue 7, p1-20, 20p

A fault diagnosis method for rolling bearings in open-set domain adaptation with adversarial learning.

Authors: Lei, Tongfei Pan, Feng Hu, Jiabei et al.

Source: Scientific Reports; 3/28/2025, Vol. 15 Issue 1, p1-19, 19p

Integrating multi-source monitoring data and deep convolutional autoencoder technology for slope failure pattern recognition.

Authors: Han, Nana Miao, Wending Li, Mingzhi et al.

Source: Frontiers in Earth Science; 2025, p1-11, 11p

Subject Terms: PATTERN recognition systems, SLOPES (Soil mechanics), AUTOENCODERS, INDUSTRIAL safety, FAILURE mode & effects analysis, LANDSLIDES

Unsupervised Learning for Machinery Adaptive Fault Detection Using Wide-Deep Convolutional Autoencoder with Kernelized Attention Mechanism.

Authors: Yan, Hao Si, Xiangfeng Liang, Jianqiang et al.

Source: Sensors (14248220); Dec2024, Vol. 24 Issue 24, p8053, 19p

Subject Terms: FAULT diagnosis, AUTOENCODERS, INDUSTRIAL applications, SCREWS, MACHINERY, DEEP learning

Fast and Scalable Distributed Deep Convolutional Autoencoder for fMRI Big Data Analytics

Authors: Makkie, Milad Huang, Heng Zhao, Yu et al.

Index Terms: Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Quantitative Biology - Neurons and Cognition, Statistics - Machine Learning, text

URL: http://arxiv.org/abs/1710.08961

Deep learning-assisted medical image compression challenges and opportunities: systematic review.

Authors: Bourai, Nour El Houda Merouani, Hayet Farida Djebbar, Akila

Source: Neural Computing & Applications; Jun2024, Vol. 36 Issue 17, p10067-10108, 42p

Subject Terms: IMAGE compression, COMPUTER-assisted image analysis (Medicine), DIAGNOSTIC imaging, DEEP learning, ARTIFICIAL intelligence, DATA transmission systems

On the Fine-Grained Crowd Analysis via Passive WiFi Sensing

Authors: Lifei Hao Baoqi Huang Bing Jia et al.

Source: IEEE Transactions on Mobile Computing. 23:6697-6711

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Machine Learning Techniques Using Deep Instinctive Encoder-Based Feature Extraction for Optimized Breast Cancer Detection.

Authors: Priyadarshni, Vaishnawi Sharma, Sanjay Kumar Rahmani, Mohammad Khalid Imam et al.

Source: Computers, Materials & Continua; 2024, Vol. 78 Issue 2, p2242-2468, 28p

Subject Terms: MACHINE learning, EARLY detection of cancer, FEATURE extraction, ARTIFICIAL neural networks, BREAST cancer, DEEP learning

Open Set Bearing Fault Diagnosis with Domain Adaptive Adversarial Network under Varying Conditions

Authors: Bo Zhang Feixuan Li Ning Ma et al.

Source: Actuators, Vol 13, Iss 4, p 121 (2024)

Subject Terms: open set fault diagnosis, adversarial domain networks, rotating machines, multiple classifiers, Materials of engineering and construction. Mechanics of materials, TA401-492, Production of electric energy or power. Powerplants. Central stations, TK1001-1841

Relation: https://www.mdpi.com/2076-0825/13/4/121; https://doaj.org/toc/2076-0825; https://doaj.org/article/a1604e439e36465aaf598b6d5c999e90

Availability: https://doi.org/10.3390/act13040121
https://doaj.org/article/a1604e439e36465aaf598b6d5c999e90

Interpretable unsupervised learning enables accurate clustering with high-throughput imaging flow cytometry.

Authors: Zhang, Zunming Chen, Xinyu Tang, Rui et al.

Source: Scientific Reports; 11/23/2023, Vol. 13 Issue 1, p1-11, 11p

Subject Terms: FLOW cytometry, LEUKOCYTES, CELL analysis, THREE-dimensional imaging, CELL culture, IMAGE representation

Deep Learning for Medical Image-Based Cancer Diagnosis.

Authors: Jiang, Xiaoyan Hu, Zuojin Wang, Shuihua et al.

Source: Cancers; Jul2023, Vol. 15 Issue 14, p3608, 72p

Subject Terms: TUMOR diagnosis, DEEP learning, DIGITAL image processing, X-rays, MAGNETIC resonance imaging, POSITRON emission tomography computed tomography, DIAGNOSTIC imaging, COMPUTER-aided diagnosis, COMPUTED tomography, ARTIFICIAL neural networks, PREDICTION models