| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Yuhan Zhang, Xiwei Zhang, Zexuan Ji, Sijie Niu, Theodore Leng, Daniel L Rubin, Songtao Yuan, Qiang Che. An integrated time adaptive geographic atrophy prediction model for SD-OCT images. Medical image analysis. vol 68. 2021-06-23. PMID:33260118. |
the proposed model was comprised of bi-directional long short-term memory (bilstm) network-based prediction module and convolutional neural network (cnn)-based refinement module. |
2021-06-23 |
2023-08-13 |
Not clear |
| Ankan Ghosh Dastider, Farhan Sadik, Shaikh Anowarul Fatta. An integrated autoencoder-based hybrid CNN-LSTM model for COVID-19 severity prediction from lung ultrasound. Computers in biology and medicine. vol 132. 2021-06-21. PMID:33684688. |
in-depth result analysis shows a promising improvement in the classification performance by introducing the long short-term memory (lstm) layers after the proposed cnn architecture by an average of 7-12%, which is approximately 17% more than the traditional densenet architecture alone. |
2021-06-21 |
2023-08-13 |
Not clear |
| Wei-Xun Zhang, Xiaoyong Pan, Hong-Bin She. Signal-3L 3.0: Improving Signal Peptide Prediction through Combining Attention Deep Learning with Window-Based Scoring. Journal of chemical information and modeling. vol 60. issue 7. 2021-06-17. PMID:32501689. |
there are three main components in the signal-3l 3.0 prediction engine: (1) a deep bidirectional long short-term memory (bi-lstm) network with a soft self-attention learns abstract features from sequences to determine whether a query protein contains a signal peptide; (2) the statistics propensity window-based cleavage site screening method is applied to generate the set of candidate cleavage sites; (3) the prediction of a conditional random field with a hybrid convolutional neural network (cnn) and bi-lstm is fused with the window-based score for identifying the final unique cleavage site. |
2021-06-17 |
2023-08-13 |
Not clear |
| Amelia A Casciola, Sebastiano K Carlucci, Brianne A Kent, Amanda M Punch, Michael A Muszynski, Daniel Zhou, Alireza Kazemi, Maryam S Mirian, Jason Valerio, Martin J McKeown, Haakon B Nygaar. A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data. Sensors (Basel, Switzerland). vol 21. issue 10. 2021-06-04. PMID:34064694. |
the solution includes a simple band-pass filtering, a data augmentation step, and a model using convolutional (cnn) and long short-term memory (lstm) layers. |
2021-06-04 |
2023-08-13 |
Not clear |
| Hongyu Li, Li Chen, Zaoli Huang, Xiaotong Luo, Huiqin Li, Jian Ren, Yubin Xi. DeepOMe: A Web Server for the Prediction of 2'-O-Me Sites Based on the Hybrid CNN and BLSTM Architecture. Frontiers in cell and developmental biology. vol 9. 2021-06-01. PMID:34055810. |
to address the above issue, we proposed a hybrid deep-learning algorithm named deepome that combined convolutional neural networks (cnn) and bidirectional long short-term memory (blstm) to accurately predict 2'-o-me sites in human transcriptome. |
2021-06-01 |
2023-08-13 |
human |
| Khondoker Nazmoon Nabi, Md Toki Tahmid, Abdur Rafi, Muhammad Ehsanul Kader, Md Asif Haide. Forecasting COVID-19 cases: A comparative analysis between recurrent and convolutional neural networks. Results in physics. vol 24. 2021-05-25. PMID:33898209. |
in light of the above circumstances, probable future outbreak scenarios in brazil, russia, and the united kingdom have been sketched in this study with the help of four deep learning models: long short term memory (lstm), gated recurrent unit (gru), convolutional neural network (cnn) and multivariate convolutional neural network (mcnn). |
2021-05-25 |
2023-08-13 |
Not clear |
| Khondoker Nazmoon Nabi, Md Toki Tahmid, Abdur Rafi, Muhammad Ehsanul Kader, Md Asif Haide. Forecasting COVID-19 cases: A comparative analysis between Recurrent and Convolutional Neural Networks. medRxiv : the preprint server for health sciences. 2021-05-25. PMID:34013282. |
in light of the above circumstances, probable future outbreak scenarios in brazil, russia and the united kingdom have been sketched in this study with the help of four deep learning models: long short term memory (lstm), gated recurrent unit (gru), convolutional neural network (cnn) and multivariate convolutional neural network (mcnn). |
2021-05-25 |
2023-08-13 |
Not clear |
| Mohanad Alkhodari, Luay Fraiwa. Convolutional and recurrent neural networks for the detection of valvular heart diseases in phonocardiogram recordings. Computer methods and programs in biomedicine. vol 200. 2021-05-14. PMID:33494031. |
the neural network architecture was designed to reduce the complexity often found in literature and consisted of a combination of convolutional neural networks (cnn) and recurrent neural networks (rnn) based on bi-directional long short-term memory (bilstm). |
2021-05-14 |
2023-08-13 |
Not clear |
| Jun Meng, Qiang Kang, Zheng Chang, Yushi Lua. PlncRNA-HDeep: plant long noncoding RNA prediction using hybrid deep learning based on two encoding styles. BMC bioinformatics. vol 22. issue Suppl 3. 2021-05-14. PMID:33980138. |
long short-term memory (lstm) and convolutional neural network (cnn) can automatically extract and learn the abstract information from the encoded rna sequences to avoid complex feature engineering. |
2021-05-14 |
2023-08-13 |
Not clear |
| Ishaani Priyadarshini, Chase Cotto. A novel LSTM-CNN-grid search-based deep neural network for sentiment analysis. The Journal of supercomputing. 2021-05-13. PMID:33967391. |
we propose a novel long short-term memory (lstm)-convolutional neural networks (cnn)-grid search-based deep neural network model for sentiment analysis. |
2021-05-13 |
2023-08-13 |
Not clear |
| Kanghae Choi, Hokyoung Ryu, Jieun Ki. Deep Residual Networks for User Authentication via Hand-Object Manipulations. Sensors (Basel, Switzerland). vol 21. issue 9. 2021-05-03. PMID:33922833. |
we employed three convolutional neural network (cnn)-based deep residual networks (resnets) with multiple depths (i.e., 50, 101, and 152 layers) and two recurrent neural network (rnn)-based long short-term memory (lstms): simple and bidirectional. |
2021-05-03 |
2023-08-13 |
Not clear |
| Waseem Ullah, Amin Ullah, Tanveer Hussain, Zulfiqar Ahmad Khan, Sung Wook Bai. An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos. Sensors (Basel, Switzerland). vol 21. issue 8. 2021-05-03. PMID:33923712. |
we extract spatial cnn features from a series of video frames and feed them to the proposed residual attention-based long short-term memory (lstm) network, which can precisely recognize anomalous activity in surveillance videos. |
2021-05-03 |
2023-08-13 |
Not clear |
| Maike Stoeve, Dominik Schuldhaus, Axel Gamp, Constantin Zwick, Bjoern M Eskofie. From the Laboratory to the Field: IMU-Based Shot and Pass Detection in Football Training and Game Scenarios Using Deep Learning. Sensors (Basel, Switzerland). vol 21. issue 9. 2021-05-03. PMID:33924985. |
in addition, three different types of neural networks, namely a convolutional neural net (cnn), a long short term memory net (lstm) and a convolutional lstm (convlstm) are compared. |
2021-05-03 |
2023-08-13 |
human |
| Ohoud Nafea, Wadood Abdul, Ghulam Muhammad, Mansour Alsulaima. Sensor-Based Human Activity Recognition with Spatio-Temporal Deep Learning. Sensors (Basel, Switzerland). vol 21. issue 6. 2021-04-27. PMID:33803891. |
this study introduces a new methodology using convolution neural networks (cnn) with varying kernel dimensions along with bi-directional long short-term memory (bilstm) to capture features at various resolutions. |
2021-04-27 |
2023-08-13 |
human |
| Abdolkarim Saeedi, Maryam Saeedi, Arash Maghsoudi, Ahmad Shalba. Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach. Cognitive neurodynamics. vol 15. issue 2. 2021-04-16. PMID:33854642. |
the third method is based on long short-term memory (lstm) model, while the fourth and fifth algorithms utilized a combination of cnn with lstm model namely, 1dcnn-lstm and 2dcnn-lstm. |
2021-04-16 |
2023-08-13 |
human |
| Jiyi Jang, Ather Abbas, Minjeong Kim, Jingyeong Shin, Young Mo Kim, Kyung Hwa Ch. Prediction of antibiotic-resistance genes occurrence at a recreational beach with deep learning models. Water research. vol 196. 2021-04-13. PMID:33744657. |
accordingly, in this study, we predicted args occurrence that are primarily found on the coast after rainfall using a conventional long short-term memory (lstm), lstm-convolutional neural network (cnn) hybrid model, and input attention (ia)-lstm. |
2021-04-13 |
2023-08-13 |
Not clear |
| Kyutae Kim, Jongpil Jeon. Real-Time Monitoring for Hydraulic States Based on Convolutional Bidirectional LSTM with Attention Mechanism. Sensors (Basel, Switzerland). vol 20. issue 24. 2021-04-09. PMID:33322319. |
in addition, we propose real-time monitoring based on a deep-learning model that uses convergence of a convolutional neural network (cnn), a bidirectional long short-term memory network (bilstm), and an attention mechanism. |
2021-04-09 |
2023-08-13 |
Not clear |
| Kyutae Kim, Jongpil Jeon. Real-Time Monitoring for Hydraulic States Based on Convolutional Bidirectional LSTM with Attention Mechanism. Sensors (Basel, Switzerland). vol 20. issue 24. 2021-04-09. PMID:33322319. |
experimental results show that the proposed model works better than other deep-learning models, such as cnn or long short-term memory (lstm). |
2021-04-09 |
2023-08-13 |
Not clear |
| Leyuan Liu, Yibin Hou, Jian He, Jonathan Lungu, Ruihai Don. An Energy-Efficient Fall Detection Method Based on FD-DNN for Elderly People. Sensors (Basel, Switzerland). vol 20. issue 15. 2021-04-07. PMID:32731465. |
fd-dnn, which combines the convolutional neural networks (cnn) with long short-term memory (lstm) algorithms, was tested on both with online and offline datasets. |
2021-04-07 |
2023-08-13 |
human |
| Yashi Nan, Nigel H Lovell, Stephen J Redmond, Kejia Wang, Kim Delbaere, Kimberley S van Schoote. Deep Learning for Activity Recognition in Older People Using a Pocket-Worn Smartphone. Sensors (Basel, Switzerland). vol 20. issue 24. 2021-04-06. PMID:33334028. |
deep learning algorithms, including convolutional neural network (cnn) and long short-term memory (lstm), were evaluated in this study. |
2021-04-06 |
2023-08-13 |
Not clear |