Search Results - "autoencoder model"

Unveiling complex brain dynamics during movie viewing via deep recurrent autoencoder model by Wang, Kexin, Song, Limei, Li, Zhaowei, Wang, Liting, He, Xiaowei, Ren, Yudan, Lv, Jinglei

“…•Leveraging naturalistic fMRI, we characterize the dynamic, complex interactions among large-scale brain networks during the viewing of emotionally charged…”
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VIGA: A variational graph autoencoder model to infer user interest representations for recommendation by Gan, Mingxin, Zhang, Hang

“…Learning representations of both user interests and item characteristics is essentially important for recommendation tasks. Although graph neural network-based…”
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Unveiling complex brain dynamics during movie viewing via deep recursive autoencoder model by Wang, Kexin, Song, Limei, Li, Zhaowei, Wang, Liting, He, Xiaowei, Ren, Yudan, Lv, Jinglei

“…Naturalistic stimuli have become an effective tool to uncover the dynamic functional brain networks triggered by cognitive and emotional real-life experiences…”
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Disorder-specific neurodynamic features in schizophrenia inferred by neurodynamic embedded contrastive variational autoencoder model by Ding, Chaoyue, Sun, Yuqing, Li, Kunchi, Xie, Sangma, Yan, Hao, Li, Peng, Yan, Jun, Chen, Jun, Wang, Huiling, Wang, Huaning, Chen, Yunchun, Yang, Yongfeng, Lv, Luxian, Zhang, Hongxing, Lu, Lin, Zhang, Dai, Chen, Yaojing, Zhang, Zhanjun, Jiang, Tianzi, Liu, Bing

“…) to extract and evaluate macro-scale SCZ-specific features, including subject-level, region-level parameters, and time-varying states…”
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Identifying Individualized Functional Brain Networks: An Unsupervised Deep Temporal Attention Based Autoencoder Model by Wang, Kexin, Ren, Yudan, Liu, Zhengyang, Yin, Song, Ding, Zhenqing, Li, Xiao, He, Xiaowei

“… In this study, we proposed an unsupervised deep temporal attention based autoencoder model (DTA-AE) that aimed to model brain function in a compact, standardized latent space, thus representing complex brain activities via dense embedding vectors…”
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Creating an autoencoder single summary metric to assess gait quality to compare surgical outcomes in children with cerebral palsy: The Shriners Gait Index (SGI) by Wang, Shou-Jen, Tabashum, Thasina, Kruger, Karen M., Krzak, Joseph J., Graf, Adam, Chafetz, Ross S., Linton, Judi, Davids, Jon, Bagley, Anita, Bengani, Kanav, Albert, Mark V.

“… We propose a single summary metric (the Shriners Gait Index (SGI)) to represent the quality of gait using a deep learning autoencoder model, which helps to capture the nonlinear statistical relationships among a number of disparate gait metrics…”
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Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging by Patel, Khush, Xie, Ziqian, Yuan, Hao, Islam, Sheikh Muhammad Saiful, Xie, Yaochen, He, Wei, Zhang, Wanheng, Gottlieb, Assaf, Chen, Han, Giancardo, Luca, Knaack, Alexander, Fletcher, Evan, Fornage, Myriam, Ji, Shuiwang, Zhi, Degui

“… We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants’ T1 or T2-FLAIR (T2…”
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A Knowledge-Based Discovery Approach Couples Artificial Neural Networks With Weight Engineering to Uncover Immune-Related Processes Underpinning Clinical Traits of Breast Cancer by Zhang, Cheng, Correia, Cristina, Weiskittel, Taylor M., Tan, Shyang Hong, Meng-Lin, Kevin, Yu, Grace T., Yao, Jingwen, Yeo, Kok Siong, Zhu, Shizhen, Ung, Choong Yong, Li, Hu

“… The possibility to extract knowledge learned by artificial neural networks (ANNs) from omics data to explain cancer clinical traits is a very attractive subject for novel discovery…”
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Unsupervised abnormality detection through mixed structure regularization (MSR) in deep sparse autoencoders by Freiman, Moti, Manjeshwar, Ravindra, Goshen, Liran

“… Methods We used coronary computed tomography angiography (CCTA) datasets of 90 subjects with expert annotated centerlines…”
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Cross-device federated unsupervised learning for the detection of anomalies in single-lead electrocardiogram signals by Kapsecker, Maximilian, Jonas, Stephan M.

“…Background: Federated unsupervised learning offers a promising approach to leveraging decentralized data stored on consumer devices, addressing concerns about…”
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A Single-Trial P300 Detector Based on Symbolized EEG and Autoencoded-(1D)CNN to Improve ITR Performance in BCIs by De Venuto, Daniela, Mezzina, Giovanni

“…In this paper, we propose a breakthrough single-trial P300 detector that maximizes the information translate rate (ITR) of the brain–computer interface (BCI),…”
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A secure marine mammal audio retrieval algorithm based on deep biohashing by Zhang, Yuan, Li, Deshun, Gao, Shulin, Zhu, Rongxin, Huang, Xiangdang, Yang, Qiuling

“…) autoencoder model to extract the deep features of dynamic Gammatone Cepstral Coefficients (GTCC), which improves robustness and discrimination of retrieval algorithm…”
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Estimation of daily bicycle traffic volumes using sparse data by El Esawey, Mohamed, Mosa, Ahmed Ibrahem, Nasr, Khaled

“… Automatic counters (e.g., loop detectors) used to collect such continuous data are subject to periodic malfunctions, leading to sporadic data gaps…”
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Exploring an Efficient Remote Biomedical Signal Monitoring Framework for Personal Health in the COVID-19 Pandemic by Tang, Zhongyun, Hu, Haiyang, Xu, Chonghuan, Zhao, Kaidi

“…Nowadays people are mostly focused on their work while ignoring their health which in turn is creating a drastic effect on their health in the long run. Remote…”
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Contrastive Clustering-Based Patient Normalization to Improve Automated In Vivo Oral Cancer Diagnosis from Multispectral Autofluorescence Lifetime Images by Caughlin, Kayla, Duran-Sierra, Elvis, Cheng, Shuna, Cuenca, Rodrigo, Ahmed, Beena, Ji, Jim, Martinez, Mathias, Al-Khalil, Moustafa, Al-Enazi, Hussain, Jo, Javier A., Busso, Carlos

“…Background: Multispectral autofluorescence lifetime imaging systems have recently been developed to quickly and non-invasively assess tissue properties for…”
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An Autoencoder and Machine Learning Model to Predict Suicidal Ideation with Brain Structural Imaging by Weng, Jun-Cheng, Lin, Tung-Yeh, Tsai, Yuan-Hsiung, Cheok, Man, Chang, Yi-Peng, Chen, Vincent

“…) were separately trained in different machine learning models. A convolutional neural network (CNN)-based autoencoder model…”
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Deep Gaussian Mixture-Hidden Markov Model for Classification of EEG Signals by Wang, Min, Abdelfattah, Sherif, Moustafa, Nour, Hu, Jiankun

“… It consists of two components. The first component uses an autoregressive-deep variational autoencoder model for automatic feature extraction, and the second component uses a Gaussian mixture-hidden Markov model for EEG…”
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Neural models for detection and classification of brain states and transitions by Marin-Llobet, Arnau, Manasanch, Arnau, Dalla Porta, Leonardo, Torao-Angosto, Melody, Sanchez-Vives, Maria V.

“…Exploring natural or pharmacologically induced brain dynamics, such as sleep, wakefulness, or anesthesia, provides rich functional models for studying brain…”
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Generalizing a Small Facial Image Dataset Using Facial Generative Adversarial Networks for Stroke's Facial Weakness Screening by Phienphanich, Phongphan, Lerthirunvibul, Nichapa, Charnnarong, Ekabhat, Munthuli, Adirek, Tantibundhit, Charturong, Suwanwela, Nijasri C.

“… Our "real facial image dataset" comprises of face images of normal subjects and stroke patients…”
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Self-Updatable Database System Based on Human Motion Assessment Framework by Lee, Kyoungoh, Park, Yeseung, Huh, Jungwoo, Kang, Jiwoo, Lee, Sanghoon

“… Observing and detecting human motion in intelligent surveillance camera systems is essential for understanding the intentions of target subjects…”
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