Suchergebnisse - "Stacked autoencoder"

Optimizing sEMG Gesture Recognition with Stacked Autoencoder Neural Network for Bionic Hand von Yadav, Mr. Amol Pandurang, Patil, Dr. Sandip.R.

“… This study presents a novel deep learning approach for surface electromyography (sEMG) gesture recognition using stacked autoencoder neural network …”
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A multi‐feature space constrained stacked autoencoder and its application for uncertain process monitoring von Yang, Jiandong, Wang, Chenhao, Yu, Jianbo, Yan, Xuefeng

“… To mitigate these issues, we propose a novel process monitoring method based on a multi‐feature space constrained stacked autoencoder (MFSCSAE …”
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Denoising magnetic resonance spectroscopy (MRS) data using stacked autoencoder for improving signal‐to‐noise ratio and speed of MRS von Wang, Jing, Ji, Bing, Lei, Yang, Liu, Tian, Mao, Hui, Yang, Xiaofeng

“… Background While magnetic resonance imaging (MRI) provides high resolution anatomical images with sharp soft tissue contrast, magnetic resonance spectroscopy …”
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Multi-DoF continuous estimation for wrist torques using stacked autoencoder von Yu, Yang, Chen, Chen, Sheng, Xinjun, Zhu, Xiangyang

“… In this study, we construct a stacked autoencoder-based deep neural network (SAE-DNN) to continuously estimate multiple degrees-of-freedom (DoFs …”
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A decision support system based on multi-sources information to predict piRNA–disease associations using stacked autoencoder von Zheng, Kai, Liang, Ying, Liu, Yue-Ying, Yasir, Muhammad, Wang, Ping

“… In this study, we proposed a new computational model based on multi-source information and stacked autoencoder, called MSRDA, to predict potential piRNA …”
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Stacked autoencoder with novel integrated activation functions for the diagnosis of autism spectrum disorder von 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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Classification of autistic subjects employing modified volume local binary pattern (MVLBP) and stacked Autoencoder (SAE) on functional magnetic resonance imaging (fMRI) von 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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Comparing Functional and Effective Connectivity Features in Diagnosis of Autism Spectrum Disorder Using Stacked Autoencoder by Resting-State fMRI Data von Navaei, Pegah, Khadem, Ali, Zaferani, Effat Jalaeian

“… ) obtained from resting-state functional Magnetic Resonance Imaging (rs-fMRI) data and stacked autoencoder for diagnosing Autism Spectrum Disorder (ASD …”
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Classification of Silent Speech in English and Bengali Languages Using Stacked Autoencoder von 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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A Home Sleep Apnea State Monitoring System using a Stacked Autoencoder von Takao, Ikuya, Nishio, Keita, Kaburagi, Takashi, Kumagai, Satoshi, Matsumoto, Toshiyuki, Kurihara, Yosuke

“… To reduce the effects from each individual user and their sleeping position, we applied a stacked autoencoder to obtain feature vectors …”
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Dynamic historical information incorporated attention deep learning model for industrial soft sensor modeling von Wang, Yalin, Liu, Diju, Liu, Chenliang, Yuan, Xiaofeng, Wang, Kai, Yang, Chunhua

“… To combat this issue, a novel attention-based dynamic stacked autoencoder networks (AD-SAE) for soft sensor modeling is proposed in this paper …”
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An integrated deep learning model for motor intention recognition of multi-class EEG Signals in upper limb amputees von 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 von 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 von 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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Evaluation of LSTM for predicting grip strength using electromyography: a comparison of setups and methods von Anam, Khairul, Sudrajat, Ahmad, Rizal, Naufal Ainur, Intyanto, Gramandha Wega, Muldayani, Wahyu, Negara, Mohamad Agung Prawira, Sumardi, Bukhori, Saiful, Gitakarma, Made Santo, Castellini, Claudio

“… In particular, we compare long short-term memory (LSTM), together with a stacked autoencoder (LSTM–SAE) and an attention mechanism …”
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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 von 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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The research of sleep staging based on single-lead electrocardiogram and deep neural network von Wei, Ran, Zhang, Xinghua, Wang, Jinhai, Dang, Xin

“… ), rapid-eye-movement (REM) and non-rapid-eye-movement (NREM) sleep stage. We apply the sleep stage stacked autoencoder to constitute a 4-layer DNN …”
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Denoising Magnetic Resonance Spectroscopy (MRS) Data Using Stacked Autoencoder for Improving Signal-to-Noise Ratio and Speed of MRS von Wang, Jing, Ji, Bing, Lei, Yang, Liu, Tian, Mao, Hui, Yang, Xiaofeng

“… We propose to use deep-learning approaches to denoise MRS data without increasing the NSA. The study was conducted using data collected from the brain spectroscopy phantom and human subjects …”
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SAE-based classification of school-aged children with autism spectrum disorders using functional magnetic resonance imaging von 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 and diagnosis of the parkinson disease by stacked autoencoder von Badem, Hasan, Caliskan, Abdullah, Basturk, Alper, Yuksel, Mehmet Emin

“… In this paper we have introduced a new classification method of the parkinson disease which is based on the stacked autoencoder …”
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