Search Results - "stacked autoencoder (SAE)"

Classification of autistic subjects employing modified volume local binary pattern (MVLBP) and stacked Autoencoder (SAE) on functional magnetic resonance imaging (fMRI) by M., Kaviya Elakkiya, Dejey

“… The object of the proposed research is to automatically classify autistic subjects utilizing fMRI with high degree of accuracy…”
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An integrated deep learning model for motor intention recognition of multi-class EEG Signals in upper limb amputees by Idowu, Oluwagbenga Paul, Ilesanmi, Ademola Enitan, Li, Xiangxin, Samuel, Oluwarotimi Williams, Fang, Peng, Li, Guanglin

“…•Proposed an integrated deep learning model for recognition of multiple classes of motor intention tasks from raw EEG signals acquired from trans-humeral…”
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Improving Performance of Devanagari Script Input-Based P300 Speller Using Deep Learning by Kshirsagar, G. B., Londhe, N. D.

“… For this, two proven deep learning algorithms, stacked autoencoder (SAE) and deep…”
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Learning deep multi-manifold structure feature representation for quality prediction with an industrial application by Liu, Chenliang, Wang, Kai, Wang, Yalin, Yuan, Xiaofeng

“… subject to different distributions, which means there exist different manifold structures under broad operations…”
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The research of sleep staging based on single-lead electrocardiogram and deep neural network by Wei, Ran, Zhang, Xinghua, Wang, Jinhai, Dang, Xin

“…The polysomnogram (PSG) analysis is considered the golden standard for sleep staging under the clinical environment. The electroencephalogram (EEG) signal is…”
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EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation by Jirayucharoensak, Suwicha, Israsena, P., Pan-ngum, Setha

“… The DLN is implemented with a stacked autoencoder (SAE) using hierarchical feature learning approach…”
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Stacked autoencoder with novel integrated activation functions for the diagnosis of autism spectrum disorder by M, Kaviya Elakkiya, Dejey

“…) of autism screening. In the proposed work, two novel integrated activation functions such as Li-ReLU and S-RReLU are developed to aid in the classification of autistic subjects and typical controls (TC…”
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Automatic tissue characterization of air trapping in chest radiographs using deep neural networks by Mansoor, Awais, Perez, Geovanny, Nino, Gustavo, Linguraru, Marius George

“… In this work, we present a stacked autoencoder (SAE) deep learning architecture for automated…”
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SAE-based classification of school-aged children with autism spectrum disorders using functional magnetic resonance imaging by Xiao, Zhiyong, Wang, Canhua, Jia, Nan, Wu, Jianhua

“… subject’s dataset was decomposed into 30 independent components (IC). Secondly, some key ICs were selected and inputted into a stacked autoencoder (SAE…”
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Classification of Silent Speech in English and Bengali Languages Using Stacked Autoencoder by Ghosh, Rajdeep, Sinha, Nidul, Phadikar, Souvik

“…The purpose of a brain–computer interface (BCI) is to enhance or support the normal functions of disabled people, and as such, BCIs have been utilized for a…”
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Learning Deep Multimanifold Structure Feature Representation for Quality Prediction With an Industrial Application by Liu, Chenliang, Wang, Kai, Wang, Yalin, Yuan, Xiaofeng

“… subject to different distributions, which means there exist different manifold structures under broad operations…”
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Electroencephalograph-Based Hand Movement Pattern Recognition for Prosthetic Robot Control Using a Combination of Long Short-Term Memory and Stacked Autoencoder Methods by Hana Sasono, Muchamad Arif, Akbar, Afgan Satrio, Fatoni, Moch. Rijal, Nanda Imron, Arizal Mujibtamala, Anam, Khairul

“…) and Stacked Autoencoder (SAE) architecture based on EEG signals. Offline tests were conducted by adjusting various parameters on LSTM and SAE, achieving an average accuracy of 99.89…”
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Attention-stacking adaptive fuzzy neural networks for autism diagnosis by Zhang, Shuzhan, Xiao, Lei, Huang, Hui, Hu, Zhongyi

“…) fuzzy system, a Stacked Autoencoder (SAE), and a Transformer. This integration aims to reduce the impact of uncertainty factors, enhance the model's interpretability…”
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Human action recognition with deep learning and structural optimization using a hybrid heuristic algorithm by Ozcan, Tayyip, Basturk, Alper

“… Employed classification methods and data types are of crucial importance on HAR performance. In this paper, sensor data based activity recognition is performed using stacked autoencoders (SAE…”
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Motor Imagery EEG Signal Classification Using Deep Learning for Brain Computer Interfaces by Rezaaei Tabar, Yousef

“… Convolutional Neural Networks (CNN) and Stacked Autoencoders (SAE) networks were investigated for MI EEG classification…”
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Stacked Autoencoders for the P300 Component Detection by Vařeka, Lukáš, Mautner, Pavel

“…Novel neural network training methods (commonly referred to as deep learning) have emerged in recent years. Using a combination of unsupervised pre-training…”
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A multimodal computer‐aided diagnostic system for precise identification of renal allograft rejection: Preliminary results by Shehata, Mohamed, Shalaby, Ahmed, Switala, Andrew E., El‐Baz, Maryam, Ghazal, Mohammed, Fraiwan, Luay, Khalil, Ashraf, El‐Ghar, Mohamed Abou, Badawy, Mohamed, Bakr, Ashraf M., Dwyer, Amy, Elmaghraby, Adel, Giridharan, Guruprasad, Keynton, Robert, El‐Baz, Ayman

“…Purpose Early assessment of renal allograft function post‐transplantation is crucial to minimize and control allograft rejection. Biopsy — the gold standard —…”
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