| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| 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 |
| Hang Li, Xiu-Jun Gong, Hua Yu, Chang Zho. Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences. Molecules (Basel, Switzerland). vol 23. issue 8. 2018-11-05. PMID:30071670. |
within the framework, the sequences of two interacting proteins are sequentially fed into the encoding, embedding, convolution neural network (cnn), and long short-term memory (lstm) neural network layers. |
2018-11-05 |
2023-08-13 |
human |
| Bo Sun, Siming Cao, Jun He, Lejun Y. Affect recognition from facial movements and body gestures by hierarchical deep spatio-temporal features and fusion strategy. Neural networks : the official journal of the International Neural Network Society. vol 105. 2018-10-24. PMID:29763743. |
our model learns the spatio-temporal hierarchical features from videos by a proposed deep network, which combines a convolutional neural networks (cnn), bilateral long short-term memory recurrent neural networks (blstm-rnn) with principal component analysis (pca). |
2018-10-24 |
2023-08-13 |
Not clear |
| Ahmed Nait Aicha, Gwenn Englebienne, Kimberley S van Schooten, Mirjam Pijnappels, Ben Krös. Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry. Sensors (Basel, Switzerland). vol 18. issue 5. 2018-09-26. PMID:29786659. |
we compared the performance of three deep learning model architectures (convolutional neural network (cnn), long short-term memory (lstm) and a combination of these two (convlstm)) to each other and to a baseline model with biomechanical features on the same dataset. |
2018-09-26 |
2023-08-13 |
human |
| Yi Zhou, Li Liu, Ling Sha. Vehicle Re-Identification by Deep Hidden Multi-View Inference. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 27. issue 7. 2018-07-30. PMID:29641406. |
our models exploit the great advantages of the cnn and long short-term memory (lstm) to learn transformations across different viewpoints of vehicles. |
2018-07-30 |
2023-08-13 |
Not clear |
| Xiaopu Zhang, Jun Lin, Zubin Chen, Feng Sun, Xi Zhu, Gengfa Fan. An Efficient Neural-Network-Based Microseismic Monitoring Platform for Hydraulic Fracture on an Edge Computing Architecture. Sensors (Basel, Switzerland). vol 18. issue 6. 2018-06-15. PMID:29874808. |
at the data center, a neural network model combined with convolutional neural network (cnn) and long short-term memory (lstm) is designed and this model is trained by using previously obtained data. |
2018-06-15 |
2023-08-13 |
Not clear |
| Byoung Chul K. A Brief Review of Facial Emotion Recognition Based on Visual Information. Sensors (Basel, Switzerland). vol 18. issue 2. 2018-06-05. PMID:29385749. |
this review also focuses on an up-to-date hybrid deep-learning approach combining a convolutional neural network (cnn) for the spatial features of an individual frame and long short-term memory (lstm) for temporal features of consecutive frames. |
2018-06-05 |
2023-08-13 |
Not clear |
| Duona Zhang, Wenrui Ding, Baochang Zhang, Chunyu Xie, Hongguang Li, Chunhui Liu, Jungong Ha. Automatic Modulation Classification Based on Deep Learning for Unmanned Aerial Vehicles. Sensors (Basel, Switzerland). vol 18. issue 3. 2018-03-21. PMID:29558434. |
the contributions include the following: (1) a convolutional neural network (cnn) and long short-term memory (lstm) are combined by two different ways without prior knowledge involved; (2) a large database, including eleven types of single-carrier modulation signals with various noises as well as a fading channel, is collected with various signal-to-noise ratios (snrs) based on a real geographical environment; and (3) experimental results demonstrate that hdmf is very capable of coping with the amc problem, and achieves much better performance when compared with the independent network. |
2018-03-21 |
2023-08-13 |
Not clear |
| Guillermo O Menéndez, Emiliano Cortés, Doris Grumelli, Lucila P Méndez De Leo, Federico J Williams, Nicolás G Tognalli, Alejandro Fainstein, María Elena Vela, Elizabeth A Jares-Erijman, Roberto C Salvarezz. Self-assembly of thiolated cyanine aggregates on Au(111) and Au nanoparticle surfaces. Nanoscale. vol 4. issue 2. 2012-04-16. PMID:22127420. |
in this work the self-assembly of novel thiolated cyanine (cnn) on au(111) and citrate-capped aunps from solutions containing monomers and j-aggregates has been studied by using stm, xps, pm-irras, electrochemical techniques and raman spectroscopy. |
2012-04-16 |
2023-08-12 |
Not clear |