| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Sijie Song, Jiaying Liu, Yanghao Li, Zongming Gu. Modality Compensation Network: Cross-Modal Adaptation for Action Recognition. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2020-01-29. PMID:31995485. |
built on deep convolutional neural networks (cnn) and long short term memory (lstm) networks, our model bridges data from source and auxiliary modalities by a modality adaptation block to achieve adaptive representation learning, that the network learns to compensate for the loss of skeletons at test time and even at training time. |
2020-01-29 |
2023-08-13 |
human |
| Md Shahinur Alam, Ki-Chul Kwon, Md Ashraful Alam, Mohammed Y Abbass, Shariar Md Imtiaz, Nam Ki. Trajectory-Based Air-Writing Recognition Using Deep Neural Network and Depth Sensor. Sensors (Basel, Switzerland). vol 20. issue 2. 2020-01-21. PMID:31936546. |
we employed the long short-term memory (lstm) and a convolutional neural network (cnn) as a recognizer. |
2020-01-21 |
2023-08-13 |
Not clear |
| Xiu-Qin Liu, Bing-Xiu Li, Guan-Rong Zeng, Qiao-Yue Liu, Dong-Mei A. Prediction of Long Non-Coding RNAs Based on Deep Learning. Genes. vol 10. issue 4. 2020-01-09. PMID:30987229. |
we used k-mer embedding vectors obtained through training the glove algorithm as input features and set up the deep learning framework to include a bidirectional long short-term memory model (blstm) layer and a convolutional neural network (cnn) layer with three additional hidden layers. |
2020-01-09 |
2023-08-13 |
Not clear |
| Jehn-Ruey Jiang, Juei-En Lee, Yi-Ming Zen. Time Series Multiple Channel Convolutional Neural Network with Attention-Based Long Short-Term Memory for Predicting Bearing Remaining Useful Life. Sensors (Basel, Switzerland). vol 20. issue 1. 2020-01-02. PMID:31888110. |
the proposed methods divide a time series into multiple channels and take advantage of the convolutional neural network (cnn), the long short-term memory (lstm) network, and the attention-based mechanism for boosting performance. |
2020-01-02 |
2023-08-13 |
Not clear |
| Yuhao Wang, Qibai Chen, Meng Ding, Jiangyun L. High Precision Dimensional Measurement with Convolutional Neural Network and Bi-Directional Long Short-Term Memory (LSTM). Sensors (Basel, Switzerland). vol 19. issue 23. 2019-12-09. PMID:31810201. |
with the recent success of deep learning technology, we propose a sub-pixel edge detection method based on convolution neural network (cnn) and bi-directional long short-term memory (lstm). |
2019-12-09 |
2023-08-13 |
Not clear |
| Mai Xu, Tianyi Li, Zulin Wang, Xin Deng, Ren Yang, Zhenyu Gua. Reducing Complexity of HEVC: A Deep Learning Approach. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2019-11-20. PMID:29994256. |
therefore, this paper proposes a deep learning approach to predict the cu partition for reducing the hevc complexity at both intra-and inter-modes, which is based on convolutional neural network (cnn) and long-and short-term memory (lstm) network. |
2019-11-20 |
2023-08-13 |
Not clear |
| Alessio Petrozziello, Ivan Jordanov, T Aris Papageorghiou, W G Christopher Redman, Antoniya Georgiev. Deep Learning for Continuous Electronic Fetal Monitoring in Labor. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. vol 2018. 2019-10-30. PMID:30441670. |
however, as an example of routinely collected `big' data, efm interpretation should benefit from data-driven computational approaches, such as deep learning, which allow automated evaluation based on large clinical datasets.here we report our investigation of long short term memory (lstm) and convolutional neural networks (cnn) in analyzing efm traces from over 35,000 labors for the prediction of fetal compromise. |
2019-10-30 |
2023-08-13 |
Not clear |
| Shu Lih Oh, Eddie Y K Ng, Ru San Tan, U Rajendra Achary. Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats. Computers in biology and medicine. vol 102. 2019-10-28. PMID:29903630. |
in this paper, we propose an automated system using a combination of convolutional neural network (cnn) and long short-term memory (lstm) for diagnosis of normal sinus rhythm, left bundle branch block (lbbb), right bundle branch block (rbbb), atrial premature beats (apb) and premature ventricular contraction (pvc) on ecg signals. |
2019-10-28 |
2023-08-13 |
Not clear |
| David Ahmedt-Aristizabal, Kien Nguyen, Simon Denman, Sridha Sridharan, Sasha Dionisio, Clinton Fooke. Deep Motion Analysis for Epileptic Seizure Classification. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. vol 2018. 2019-10-25. PMID:30441151. |
we propose a new deep learning approach that leverages recent advances in convolutional neural networks (cnn) and long short-term memory (lstm) networks to automatically extract spatiotemporal features from facial and pose semiology using recorded videos. |
2019-10-25 |
2023-08-13 |
Not clear |
| Jie Sun, Liping Di, Ziheng Sun, Yonglin Shen, Zulong La. County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model. Sensors (Basel, Switzerland). vol 19. issue 20. 2019-10-16. PMID:31600963. |
based on remote sensing data, great progress has been made in this field by using machine learning, especially the deep learning (dl) method, including convolutional neural network (cnn) or long short-term memory (lstm). |
2019-10-16 |
2023-08-13 |
Not clear |
| Ziqian Xie, Odelia Schwartz, Abhishek Prasa. Decoding of finger trajectory from ECoG using deep learning. Journal of neural engineering. vol 15. issue 3. 2019-09-30. PMID:29182152. |
here, we used deep neural networks consisting of convolutional neural networks (cnn) and a special kind of recurrent neural network (rnn) called long short term memory (lstm) to address these needs. |
2019-09-30 |
2023-08-13 |
Not clear |
| Pengfei Zhang, Cuiling Lan, Junliang Xing, Wenjun Zeng, Jianru Xue, Nanning Zhen. View Adaptive Neural Networks for High Performance Skeleton-Based Human Action Recognition. IEEE transactions on pattern analysis and machine intelligence. vol 41. issue 8. 2019-08-22. PMID:30714909. |
instead of re-positioning the skeletons using a fixed human-defined prior criterion, we design two view adaptive neural networks, i.e., va-rnn and va-cnn, which are respectively built based on the recurrent neural network (rnn) with the long short-term memory (lstm) and the convolutional neural network (cnn). |
2019-08-22 |
2023-08-13 |
human |
| b' H\\xc3\\xbcseyin Kutlu, Engin Avc\\xc4\\xb. A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks. Sensors (Basel, Switzerland). vol 19. issue 9. 2019-08-20. PMID:31035406.' |
in this paper, a new liver and brain tumor classification method is proposed by using the power of convolutional neural network (cnn) in feature extraction, the power of discrete wavelet transform (dwt) in signal processing, and the power of long short-term memory (lstm) in signal classification. |
2019-08-20 |
2023-08-13 |
Not clear |
| Κostas Μ Tsiouris, Vasileios C Pezoulas, Michalis Zervakis, Spiros Konitsiotis, Dimitrios D Koutsouris, Dimitrios I Fotiadi. A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals. Computers in biology and medicine. vol 99. 2019-07-10. PMID:29807250. |
in this work, long short-term memory (lstm) networks are introduced in epileptic seizure prediction using eeg signals, expanding the use of deep learning algorithms with convolutional neural networks (cnn). |
2019-07-10 |
2023-08-13 |
Not clear |
| Lingfeng Xu, Xiang Chen, Shuai Cao, Xu Zhang, Xun Che. Feasibility Study of Advanced Neural Networks Applied to sEMG-Based Force Estimation. Sensors (Basel, Switzerland). vol 18. issue 10. 2019-04-09. PMID:30257489. |
to find out the feasibility of different neural networks in semg-based force estimation, in this paper, three types of networks, namely convolutional neural network (cnn), long short-term memory (lstm) network and their combination (c-lstm) were applied to predict muscle force generated in static isometric elbow flexion across three different circumstances (multi-subject, subject-dependent and subject-independent). |
2019-04-09 |
2023-08-13 |
Not clear |
| Congcong Wen, Shufu Liu, Xiaojing Yao, Ling Peng, Xiang Li, Yuan Hu, Tianhe Ch. A novel spatiotemporal convolutional long short-term neural network for air pollution prediction. The Science of the total environment. vol 654. 2019-03-07. PMID:30841384. |
high-level spatiotemporal features were extracted through the combination of the convolutional neural network (cnn) and long short-term memory neural network (lstm-nn), and meteorological data and aerosol data were also integrated, in order to improve model prediction performance. |
2019-03-07 |
2023-08-13 |
Not clear |
| Jiewei Jiang, Xiyang Liu, Lin Liu, Shuai Wang, Erping Long, Haoqing Yang, Fuqiang Yuan, Deying Yu, Kai Zhang, Liming Wang, Zhenzhen Liu, Dongni Wang, Changzun Xi, Zhuoling Lin, Xiaohang Wu, Jiangtao Cui, Mingmin Zhu, Haotian Li. Predicting the progression of ophthalmic disease based on slit-lamp images using a deep temporal sequence network. PloS one. vol 13. issue 7. 2019-01-22. PMID:30063738. |
in this study, we present an end-to-end temporal sequence network (tempseq-net) to automatically predict the progression of ophthalmic disease, which includes employing convolutional neural network (cnn) to extract high-level features from consecutive slit-lamp images and applying long short term memory (lstm) method to mine the temporal relationship of features. |
2019-01-22 |
2023-08-13 |
Not clear |
| Haixia Long, Bo Liao, Xingyu Xu, Jialiang Yan. A Hybrid Deep Learning Model for Predicting Protein Hydroxylation Sites. International journal of molecular sciences. vol 19. issue 9. 2019-01-21. PMID:30231550. |
in this paper, we proposed a novel approach for predicting hydroxylation using a hybrid deep learning model integrating the convolutional neural network (cnn) and long short-term memory network (lstm). |
2019-01-21 |
2023-08-13 |
human |
| Jen Hong Tan, Yuki Hagiwara, Winnie Pang, Ivy Lim, Shu Lih Oh, Muhammad Adam, Ru San Tan, Ming Chen, U Rajendra Achary. Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals. Computers in biology and medicine. vol 94. 2019-01-17. PMID:29358103. |
this paper proposes the implementation of long short-term memory (lstm) network with convolutional neural network (cnn) to automatically diagnose cad ecg signals accurately. |
2019-01-17 |
2023-08-13 |
Not clear |
| Zied Tayeb, Juri Fedjaev, Nejla Ghaboosi, Christoph Richter, Lukas Everding, Xingwei Qu, Yingyu Wu, Gordon Cheng, Jörg Conrad. Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals. Sensors (Basel, Switzerland). vol 19. issue 1. 2019-01-17. PMID:30626132. |
we developed three deep learning models: (1) a long short-term memory (lstm); (2) a spectrogram-based convolutional neural network model (cnn); and (3) a recurrent convolutional neural network (rcnn), for decoding motor imagery movements directly from raw eeg signals without (any manual) feature engineering. |
2019-01-17 |
2023-08-13 |
human |