| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Hossein Abbasimehr, Reza Pak. Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization. Chaos, solitons, and fractals. vol 142. 2021-01-20. PMID:33281305. |
in this study, we propose three hybrid approaches for forecasting covid-19 time series methods based on combining three deep learning models such as multi-head attention, long short-term memory (lstm), and convolutional neural network (cnn) with the bayesian optimization algorithm. |
2021-01-20 |
2023-08-13 |
Not clear |
| Zhao Dong, Jing Men, Zhiwen Yang, Jason Jerwick, Airong Li, Rudolph E Tanzi, Chao Zho. FlyNet 2.0: drosophila heart 3D (2D + time) segmentation in optical coherence microscopy images using a convolutional long short-term memory neural network. Biomedical optics express. vol 11. issue 3. 2021-01-12. PMID:32206429. |
a custom convolutional neural network (cnn) integrated with convolutional long short-term memory (lstm) achieves accurate 3d (2d + time) segmentation in cross-sectional videos of the drosophila heart acquired by an optical coherence microscopy (ocm) system. |
2021-01-12 |
2023-08-13 |
drosophila_melanogaster |
| Yaqing Zhang, Jinling Chen, Jen Hong Tan, Yuxuan Chen, Yunyi Chen, Dihan Li, Lei Yang, Jian Su, Xin Huang, Wenliang Ch. An Investigation of Deep Learning Models for EEG-Based Emotion Recognition. Frontiers in neuroscience. vol 14. 2021-01-12. PMID:33424547. |
here, we investigated the application of several deep learning models to the research field of eeg-based emotion recognition, including deep neural networks (dnn), convolutional neural networks (cnn), long short-term memory (lstm), and a hybrid model of cnn and lstm (cnn-lstm). |
2021-01-12 |
2023-08-13 |
human |
| Hong Yang, Shanshan Gong, Yaqing Liu, Zhengkui Lin, Yi Q. A Multi-task Learning Model for Daily Activity Forecast in Smart Home. Sensors (Basel, Switzerland). vol 20. issue 7. 2021-01-04. PMID:32235653. |
secondly, a parallel multi-task learning model which combines a convolutional neural network (cnn) with bidirectional long short-term memory (bi-lstm) units are developed as the forecast model. |
2021-01-04 |
2023-08-13 |
Not clear |
| Xiaoyang Liu, Zhigang Zeng, Donald C Wunsch I. Memristor-based LSTM network with in situ training and its applications. Neural networks : the official journal of the International Neural Network Society. vol 131. 2020-12-31. PMID:32841836. |
artificial neural networks (anns), such as the convolutional neural network (cnn) and long short-term memory (lstm), have high complexity and contain large numbers of parameters. |
2020-12-31 |
2023-08-13 |
Not clear |
| Nan Wang, Fan Yang, Ridong Zhang, Furong Ga. Intelligent Fault Diagnosis for Chemical Processes Using Deep Learning Multimodel Fusion. IEEE transactions on cybernetics. vol PP. 2020-12-30. PMID:33378269. |
different from previous deep learning diagnosis methods, this method uses long short-term memory (lstm) and convolutional neural network (cnn) to extract features separately. |
2020-12-30 |
2023-08-13 |
Not clear |
| Tak Sung Heo, Yu Seop Kim, Jeong Myeong Choi, Yeong Seok Jeong, Soo Young Seo, Jun Ho Lee, Jin Pyeong Jeon, Chulho Ki. Prediction of Stroke Outcome Using Natural Language Processing-Based Machine Learning of Radiology Report of Brain MRI. Journal of personalized medicine. vol 10. issue 4. 2020-12-29. PMID:33339385. |
in addition to conventional ml algorithms, dl algorithms such as the convolutional neural network (cnn), long short-term memory, and multilayer perceptron were used to predict poor outcomes using 5-fold cross-validation and grid search techniques. |
2020-12-29 |
2023-08-13 |
human |
| Fangfang Chen, Meng Xiao, Cheng Chen, Chen Chen, Ziwei Yan, Huijie Han, Shuailei Zhang, Feilong Yue, Rui Gao, Xiaoyi L. Discrimination of alcohol dependence based on the convolutional neural network. PloS one. vol 15. issue 10. 2020-12-15. PMID:33108388. |
in this paper, a total of 20 sites of single nucleotide polymorphisms (snps) on the serotonin 3 receptor a gene (htr3a) and b gene (htr3b) are used for feature fusion with age, education and marital status information, and the grid search-support vector machine (gs-svm), the convolutional neural network (cnn) and the convolutional neural network combined with long and short-term memory (cnn-lstm) are used to classify and discriminate between alcohol-dependent patients (ad) and the non-alcohol-dependent control group. |
2020-12-15 |
2023-08-13 |
Not clear |
| Zhanghui Liu, Yudong Zhang, Yuzhong Chen, Xinwen Fan, Chen Don. Detection of Algorithmically Generated Domain Names Using the Recurrent Convolutional Neural Network with Spatial Pyramid Pooling. Entropy (Basel, Switzerland). vol 22. issue 9. 2020-12-10. PMID:33286827. |
the recurrent convolutional neural network combines a convolutional neural network (cnn) and a bi-directional long short-term memory network (bi-lstm) to extract both the semantic and contextual information from domain names. |
2020-12-10 |
2023-08-13 |
Not clear |
| Ranjana Koshy, Ausif Mahmoo. Enhanced Deep Learning Architectures for Face Liveness Detection for Static and Video Sequences. Entropy (Basel, Switzerland). vol 22. issue 10. 2020-12-10. PMID:33286954. |
we also develop a novel deep architecture for face liveness detection on video frames that uses the diffusion of images followed by a deep convolutional neural network (cnn) and a long short-term memory (lstm) to classify the video sequence as real or fake. |
2020-12-10 |
2023-08-13 |
Not clear |
| Patrick Thiam, Hans A Kestler, Friedhelm Schwenke. Two-Stream Attention Network for Pain Recognition from Video Sequences. Sensors (Basel, Switzerland). vol 20. issue 3. 2020-12-08. PMID:32033240. |
each input stream is fed into a specific attention network consisting of a convolutional neural network (cnn) coupled to a bidirectional long short-term memory (bilstm) recurrent neural network (rnn). |
2020-12-08 |
2023-08-13 |
Not clear |
| Yi Xia, ZhiMing Yao, Qiang Ye, Nan Chen. A Dual-Modal Attention-Enhanced Deep Learning Network for Quantification of Parkinson's Disease Characteristics. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. vol 28. issue 1. 2020-11-16. PMID:31603824. |
the proposed system is a dual-modal deep-learning-based model, where left and right gait is modeled separately by a convolutional neural network (cnn) followed by an attention-enhanced long short-term memory (lstm) network. |
2020-11-16 |
2023-08-13 |
Not clear |
| Yang Li, Huahu Xu, Minjie Bian, Junsheng Xia. Attention Based CNN-ConvLSTM for Pedestrian Attribute Recognition. Sensors (Basel, Switzerland). vol 20. issue 3. 2020-11-10. PMID:32028568. |
an attention-based neural network consisting of convolutional neural networks (cnn), channel attention (catt) and convolutional long short-term memory (convlstm) is proposed (cnn-catt-convlstm). |
2020-11-10 |
2023-08-13 |
Not clear |
| Yongbo Liang, Shimin Yin, Qunfeng Tang, Zhenyu Zheng, Mohamed Elgendi, Zhencheng Che. Deep Learning Algorithm Classifies Heartbeat Events Based on Electrocardiogram Signals. Frontiers in physiology. vol 11. 2020-10-30. PMID:33117191. |
this new approach aims to improve accuracy and reduce training time by combining the convolutional neural network (cnn) with the bidirectional long short-term memory (bilstm). |
2020-10-30 |
2023-08-13 |
human |
| P Nejedly, V Kremen, V Sladky, J Cimbalnik, P Klimes, F Plesinger, I Viscor, M Pail, J Halamek, B H Brinkmann, M Brazdil, P Jurak, G Worrel. Exploiting Graphoelements and Convolutional Neural Networks with Long Short Term Memory for Classification of the Human Electroencephalogram. Scientific reports. vol 9. issue 1. 2020-10-27. PMID:31388101. |
the goal is accomplished using cnn with long short-term memory (lstm). |
2020-10-27 |
2023-08-13 |
human |
| Han Yu, Akane San. Passive Sensor Data Based Future Mood, Health, and Stress Prediction: User Adaptation Using Deep Learning. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. vol 2020. 2020-10-27. PMID:33019313. |
we compared deep long short-term memory (lstm) network and the combination of convolutional neural network (cnn) and the lstm model. |
2020-10-27 |
2023-08-13 |
human |
| Md Nasim Khan, Mohamed M Ahme. Trajectory-level fog detection based on in-vehicle video camera with TensorFlow deep learning utilizing SHRP2 naturalistic driving data. Accident; analysis and prevention. vol 142. 2020-10-21. PMID:32408146. |
the study used the shrp2 naturalistic driving study (nds) video data and utilized several promising deep learning techniques, including deep neural network (dnn), recurrent neural network (rnn), long short-term memory (lstm), and convolutional neural network (cnn). |
2020-10-21 |
2023-08-13 |
Not clear |
| Nada M Elshennawy, Dina M Ibrahi. Deep-Pneumonia Framework Using Deep Learning Models Based on Chest X-Ray Images. Diagnostics (Basel, Switzerland). vol 10. issue 9. 2020-10-20. PMID:32872384. |
in this paper, four different models are developed by changing the used deep learning method; two pre-trained models, resnet152v2 and mobilenetv2, a convolutional neural network (cnn), and a long short-term memory (lstm). |
2020-10-20 |
2023-08-13 |
Not clear |
| Fei Zhu, Fei Ye, Yuchen Fu, Quan Liu, Bairong She. Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network. Scientific reports. vol 9. issue 1. 2020-10-15. PMID:31043666. |
to address this problem, we propose a generative adversarial network (gan), which is composed of a bidirectional long short-term memory(lstm) and convolutional neural network(cnn), referred as bilstm-cnn,to generate synthetic ecg data that agree with existing clinical data so that the features of patients with heart disease can be retained. |
2020-10-15 |
2023-08-13 |
human |
| Tong Gao, Wei Sheng, Mingliang Zhou, Bin Fang, Futing Luo, Jiajun L. Method for Fault Diagnosis of Temperature-Related MEMS Inertial Sensors by Combining Hilbert-Huang Transform and Deep Learning. Sensors (Basel, Switzerland). vol 20. issue 19. 2020-10-08. PMID:33019773. |
in this paper, we propose a novel method for fault diagnosis in micro-electromechanical system (mems) inertial sensors using a bidirectional long short-term memory (blstm)-based hilbert-huang transform (hht) and a convolutional neural network (cnn). |
2020-10-08 |
2023-08-13 |
Not clear |