Výsledky vyhledávání - "LSTM"

Fall Detection With UWB Radars and CNN-LSTM Architecture Autor Maitre, Julien, Bouchard, Kevin, Gaboury, Sebastien

“…Fall detection is a major challenge for researchers. Indeed, a fall can cause injuries such as femoral neck fracture, brain hemorrhage, or skin burns, leading…”
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A Deep Learning-Based Phishing Detection System Using CNN, LSTM, and LSTM-CNN Autor Alshingiti, Zainab, Alaqel, Rabeah, Al-Muhtadi, Jalal, Haq, Qazi Emad Ul, Saleem, Kashif, Faheem, Muhammad Hamza

“… Sensitive information about the users may be accessed and they might be subject to financial harm or identity theft…”
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An improved learning-based LSTM approach for lane change intention prediction subject to imbalanced data Autor Shi, Qian, Zhang, Hui

“…). In this work, we aim to achieve this task by using Long-Short Term Memory (LSTM) network. One critical challenge on this task is that the dataset used for training…”
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Human trajectory prediction and generation using LSTM models and GANs Autor Rossi, Luca, Paolanti, Marina, Pierdicca, Roberto, Frontoni, Emanuele

“…•New deep neural network models are proposed for trajectory prediction.•LSTM and GAN1 models are used for unimodal predictions, GAN3 model for multimodal…”
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Subject-Wise Cognitive Load Detection Using Time–Frequency EEG and Bi-LSTM Autor Yedukondalu, Jammisetty, Sharma, Diksha, Sharma, Lakhan Dev

“…) representations as 2D images. The signal underwent preprocessing, including artifact-free segmentation using filters and subsequent normalization, before being fed into a bidirectional long short-term memory (Bi-LSTM…”
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An Efficient LSTM Network for Emotion Recognition From Multichannel EEG Signals Autor Du, Xiaobing, Ma, Cuixia, Zhang, Guanhua, Li, Jinyao, Lai, Yu-Kun, Zhao, Guozhen, Deng, Xiaoming, Liu, Yong-Jin, Wang, Hongan

“… between EEG signals at different electrodes. Our proposed deep model is called AT tention-based LSTM with D omain D iscriminator (ATDD-LSTM…”
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Normal and pathological gait classification LSTM model Autor Khokhlova, Margarita, Migniot, Cyrille, Morozov, Alexey, Sushkova, Olga, Dipanda, Albert

“…Computer vision-based clinical gait analysis is the subject of permanent research…”
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Parkinson’s disease diagnosis and stage prediction based on gait signal analysis using EMD and CNN–LSTM network Autor B. Vidya, Sasikumar P.

“…Parkinson’s disease (PD) diagnosis is a complex and challenging task which needs the assessment of various motor and non-motor symptoms. As gait impairment is…”
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CNN and LSTM-Based Emotion Charting Using Physiological Signals Autor Dar, Muhammad Najam, Akram, Muhammad Usman, Khawaja, Sajid Gul, Pujari, Amit N.

“…) architecture for the arrangement of 14 channels of EEG, and a combination of Long Short-Term Memory (LSTM…”
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EEG emotion recognition using fusion model of graph convolutional neural networks and LSTM Autor Yin, Yongqiang, Zheng, Xiangwei, Hu, Bin, Zhang, Yuang, Cui, Xinchun

“…) and long-short term memories neural networks (LSTM). In the fusion model, multiple GCNNs are applied…”
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Robust LSTM-Autoencoders for Face De-Occlusion in the Wild Autor Zhao, Fang, Feng, Jiashi, Zhao, Jian, Yang, Wenhan, Yan, Shuicheng

“… Although much research effort has been devoted to developing face de-occlusion methods, most of them can only work well under constrained conditions, such as all of faces are from a pre-defined closed set of subjects…”
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Short-term forecasting of vegetable prices based on LSTM model—Evidence from Beijing’s vegetable data Autor Zhang, Qi, Yang, Weijia, Zhao, Anping, Wang, Xiaodong, Wang, Zengfei, Zhang, Lin

“… Vegetable market pricing is subject to a myriad of complex influences, resulting in nonlinear patterns that conventional time series methodologies often struggle to decode…”
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Sensor-Agnostic, LSTM-Based Human Motion Prediction Using sEMG Data Autor Koo, Bon Ho, Siu, Ho Chit, Petersen, Lonnie G.

“… Two different sEMG sensor platforms were used to collect raw data from subjects conducting exercises, which was used to train a neural network designed to predict the future angular trajectory of the arm…”
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Lower-Limb Joint Torque Prediction Using LSTM Neural Networks and Transfer Learning Autor Zhang, Longbin, Soselia, Davit, Wang, Ruoli, Gutierrez-Farewik, Elena M.

“… In this study, lower limb joint torques during ten daily activities were predicted by long short-term memory (LSTM…”
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Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals Autor Michielli, Nicola, Acharya, U. Rajendra, Molinari, Filippo

“…Automated evaluation of a subject's neurocognitive performance (NCP) is a relevant topic in neurological and clinical studies…”
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Visual prediction method based on time series-driven LSTM model Autor Jumahong, Huxidan, Wang, Yongjie, Aili, Abuduwaili, Wang, Weina

“…Significant progress has been made in time series prediction and image processing problems. However, most of the studies have focused on either the field of…”
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An efficient CNN-LSTM network with spectral normalization and label smoothing technologies for SSVEP frequency recognition Autor Pan, Yudong, Chen, Jianbo, Zhang, Yangsong, Zhang, Yu

“… However, the performance of frequency recognition methods heavily depends on the amount of the calibration data for intra-subject classification…”
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Automatically detecting apnea-hypopnea snoring signal based on VGG19 + LSTM Autor Ding, Li, Peng, Jianxin, Song, Lijuan, Zhang, Xiaowen

“… In this work, a pre-trained VGG19 and the long short-term memory (LSTM) fused model was proposed to classify snoring sounds of simple snorers and OSAHS patients and detect apnea-hypopnea snoring from the whole night recorded sounds of patients…”
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Automated recognition of epilepsy from EEG signals using a combining space–time algorithm of CNN-LSTM Autor Wang, Xiashuang, Wang, Yinglei, Liu, Dunwei, Wang, Ying, Wang, Zhengjun

“… Previous studies primarily focused on binary epilepsy recognition (epilepsy vs. healthy subjects) rather than as ternary status recognition…”
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LSTM Model for Prediction of Heart Failure in Big Data Autor Maragatham, G, Devi, Shobana

“…) like LSTM and connected to longitudinal time stepped EHR. Experience records were contribution to RNN to anticipate the analysis and prescription classes for a resulting visit during heart disappointment (e.g…”
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