| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Liangwei Fan, Jianpo Su, Jian Qin, Dewen Hu, Hui She. A Deep Network Model on Dynamic Functional Connectivity With Applications to Gender Classification and Intelligence Prediction. Frontiers in neuroscience. vol 14. 2020-10-06. PMID:33013292. |
here, we proposed the use of an end-to-end deep learning model that combines the convolutional neural network (cnn) and long short-term memory (lstm) network to capture temporal and spatial features of functional connectivity sequences simultaneously. |
2020-10-06 |
2023-08-13 |
human |
| Víctor Suárez-Paniagua, Renzo M Rivera Zavala, Isabel Segura-Bedmar, Paloma Martíne. A two-stage deep learning approach for extracting entities and relationships from medical texts. Journal of biomedical informatics. vol 99. 2020-10-05. PMID:31546016. |
concretely, ner is performed combining a bidirectional long short-term memory (bi-lstm) and a conditional random field (crf), while re applies a convolutional neural network (cnn). |
2020-10-05 |
2023-08-13 |
Not clear |
| Mhaned Oubounyt, Zakaria Louadi, Hilal Tayara, Kil To Chon. DeePromoter: Robust Promoter Predictor Using Deep Learning. Frontiers in genetics. vol 10. 2020-10-01. PMID:31024615. |
deepromoter combines a convolutional neural network (cnn) and a long short-term memory (lstm). |
2020-10-01 |
2023-08-13 |
mouse |
| Yuan Ling, Sadid A Hasan, Oladimeji Farri, Zheng Chen, Rob van Ommering, Charles Yee, Nevenka Dimitrov. A Domain Knowledge-Enhanced LSTM-CRF Model for Disease Named Entity Recognition. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science. vol 2019. 2020-10-01. PMID:31259033. |
in this paper, we propose a domain knowledge-enhanced long short-term memory network-conditional random field (lstm-crf) model for disease named entity recognition, which also augments a character-level convolutional neural network (cnn) and a character-level lstm network for input embedding. |
2020-10-01 |
2023-08-13 |
Not clear |
| Fouzi Harrou, Abdelkader Dairi, Farid Kadri, Ying Su. Forecasting emergency department overcrowding: A deep learning framework. Chaos, solitons, and fractals. vol 139. 2020-10-01. PMID:32982079. |
the vae model was evaluated and compared with seven methods namely recurrent neural network (rnn), long short-term memory (lstm), bidirectional lstm (bilstm), convolutional lstm network (convlstm), restricted boltzmann machine (rbm), gated recurrent units (grus), and convolutional neural network (cnn). |
2020-10-01 |
2023-08-13 |
Not clear |
| Shaodian Zhang, Lin Qiu, Frank Chen, Weinan Zhang, Yong Yu, Noémie Elhada. "We make choices we think are going to save us": Debate and stance identification for online breast cancer CAM discussions. Proceedings of the ... International World-Wide Web Conference. International WWW Conference. vol 2017. 2020-09-30. PMID:28967000. |
we first constructed a supervised classifier based on a long short-term memory neural network (lstm) stacked over a convolutional neural network (cnn) to detect automatically cam-related debates from a popular breast cancer forum. |
2020-09-30 |
2023-08-13 |
Not clear |
| Xiaoqun Yu, Hai Qiu, Shuping Xion. A Novel Hybrid Deep Neural Network to Predict Pre-impact Fall for Older People Based on Wearable Inertial Sensors. Frontiers in bioengineering and biotechnology. vol 8. 2020-09-28. PMID:32117941. |
three deep learning models, convolutional neural network (cnn), long short term memory (lstm), and a novel hybrid model integrating both convolution and long short term memory (convlstm) were proposed and evaluated on a large public dataset of various falls and activities of daily living (adl) acquired with wearable inertial sensors (accelerometer and gyroscope). |
2020-09-28 |
2023-08-13 |
Not clear |
| Yongjie Ping, Chao Chen, Lu Wu, Yinglong Wang, Minglei Sh. Automatic Detection of Atrial Fibrillation Based on CNN-LSTM and Shortcut Connection. Healthcare (Basel, Switzerland). vol 8. issue 2. 2020-09-28. PMID:32443926. |
in this paper, a combination of an 8-layer convolutional neural network (cnn) with a shortcut connection and 1-layer long short-term memory (lstm), named 8csl, was proposed for the electrocardiogram (ecg) classification task. |
2020-09-28 |
2023-08-13 |
Not clear |
| Md Zabirul Islam, Md Milon Islam, Amanullah Asra. A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. Informatics in medicine unlocked. vol 20. 2020-09-28. PMID:32835084. |
this paper aims to introduce a deep learning technique based on the combination of a convolutional neural network (cnn) and long short-term memory (lstm) to diagnose covid-19 automatically from x-ray images. |
2020-09-28 |
2023-08-13 |
Not clear |
| Zhen Wang, Xiaoyan Du, Yang Yang, Guoqing Zhan. Study on miR-384-5p activates TGF-β signaling pathway to promote neuronal damage in abutment nucleus of rats based on deep learning. Artificial intelligence in medicine. vol 101. 2020-09-21. PMID:31813493. |
the deep learning techniques, like convolution neural networks (cnn), long short-term memory (lstm), autoencoders, deep generative models and deep belief networks have already been applied to efficiently analyse possible large collections of data. |
2020-09-21 |
2023-08-13 |
rat |
| Juan Zhao, QiPing Feng, Patrick Wu, Roxana A Lupu, Russell A Wilke, Quinn S Wells, Joshua C Denny, Wei-Qi We. Learning from Longitudinal Data in Electronic Health Record and Genetic Data to Improve Cardiovascular Event Prediction. Scientific reports. vol 9. issue 1. 2020-08-13. PMID:30679510. |
we applied logistic regression, random forests, gradient boosting trees, convolutional neural networks (cnn) and recurrent neural networks with long short-term memory (lstm) units. |
2020-08-13 |
2023-08-13 |
Not clear |
| Ahmed Sedik, Abdullah M Iliyasu, Basma Abd El-Rahiem, Mohammed E Abdel Samea, Asmaa Abdel-Raheem, Mohamed Hammad, Jialiang Peng, Fathi E Abd El-Samie, Ahmed A Abd El-Lati. Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections. Viruses. vol 12. issue 7. 2020-08-05. PMID:32708803. |
faced with a calamity on one side and absence of reliable data on the other, this study presents two data-augmentation models to enhance learnability of the convolutional neural network (cnn) and the convolutional long short-term memory (convlstm)-based deep learning models (dadlms) and, by doing so, boost the accuracy of covid-19 detection. |
2020-08-05 |
2023-08-13 |
Not clear |
| b' K\\xc4\\xb1ymet Kaya, \\xc5\\x9eule G\\xc3\\xbcnd\\xc3\\xbcz \\xc3\\x96\\xc4\\x9f\\xc3\\xbcd\\xc3\\xbcc\\xc3\\xb. Deep Flexible Sequential (DFS) Model for Air Pollution Forecasting. Scientific reports. vol 10. issue 1. 2020-08-03. PMID:32098977.' |
dfs model is a hybrid & flexible deep model including long short term memory (lstm) and convolutional neural network (cnn). |
2020-08-03 |
2023-08-13 |
Not clear |
| Halil Kilicoglu, Zeshan Peng, Shabnam Tafreshi, Tung Tran, Graciela Rosemblat, Jodi Schneide. Confirm or refute?: A comparative study on citation sentiment classification in clinical research publications. Journal of biomedical informatics. vol 91. 2020-06-19. PMID:30753947. |
using a corpus of 285 discussion sections from as many publications (a total of 4,182 citations), we developed a rule-based method as well as supervised machine learning models based on support vector machines (svm) and two variants of deep neural networks; namely, convolutional neural network (cnn) and bidirectional long short-term memory (bilstm). |
2020-06-19 |
2023-08-13 |
Not clear |
| Fatemeh Taheri Dezaki, Zhibin Liao, Christina Luong, Hany Girgis, Neeraj Dhungel, Amir H Abdi, Delaram Behnami, Ken Gin, Robert Rohling, Purang Abolmaesumi, Teresa Tsan. Cardiac Phase Detection in Echocardiograms With Densely Gated Recurrent Neural Networks and Global Extrema Loss. IEEE transactions on medical imaging. vol 38. issue 8. 2020-05-15. PMID:30582532. |
we explore two cnn architectures: densenet and resnet, and four rnn architectures: long short-term memory, bi-directional lstm, gated recurrent unit (gru), and bi-gru, and compare the performance of these models. |
2020-05-15 |
2023-08-13 |
Not clear |
| Ruojun Li, Ganesh Prasanna Balakrishnan, Jiaming Nie, Yu Li, Emmanuel Agu, Michael Stein, Ana Abrantes, Debra Herman, Kristin Grimon. On Smartphone Sensability of Bi-Phasic User Intoxication Levels from Diverse Walk Types in Standardized Field Sobriety Tests. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. vol 2019. 2020-05-11. PMID:31946584. |
in this paper, we compare how accurately long short term memory (lstm), convolution neural network (cnn), random forest, gradient boosted machines (gbm) and neural network classifiers are able to detect intoxication levels of drunk subjects who performed normal, walk-and-turn and standing on one foot sfst walks. |
2020-05-11 |
2023-08-13 |
human |
| Robert D Chambers, Nathanael C Yode. FilterNet: A Many-to-Many Deep Learning Architecture for Time Series Classification. Sensors (Basel, Switzerland). vol 20. issue 9. 2020-05-04. PMID:32354082. |
it adapts popular convolutional neural network (cnn) and long short-term memory (lstm) motifs which have excelled in activity recognition benchmarks, implementing them in a many-to-many architecture to markedly improve frame-by-frame accuracy, event segmentation accuracy, model size, and computational efficiency. |
2020-05-04 |
2023-08-13 |
Not clear |
| Mona Kirstin Fehling, Fabian Grosch, Maria Elke Schuster, Bernhard Schick, Jörg Lohschelle. Fully automatic segmentation of glottis and vocal folds in endoscopic laryngeal high-speed videos using a deep Convolutional LSTM Network. PloS one. vol 15. issue 2. 2020-04-22. PMID:32040514. |
the segmentation quality of the best performing convolutional neural network (cnn) model, which uses long short-term memory (lstm) cells to take also the temporal context into account, was intensely investigated on 15 test video sequences comprising 100 consecutive images each. |
2020-04-22 |
2023-08-13 |
human |
| Xiangyu Zhou, Zhengjiang Liu, Fengwu Wang, Yajuan Xie, Xuexi Zhan. Using Deep Learning to Forecast Maritime Vessel Flows. Sensors (Basel, Switzerland). vol 20. issue 6. 2020-04-09. PMID:32235812. |
in this paper, we propose three deep learning-based solutions to forecast the inflow and outflow of vessels within a given region, including a convolutional neural network (cnn), a long short-term memory (lstm) network, and the integration of a bidirectional lstm network with a cnn (bdlstm-cnn). |
2020-04-09 |
2023-08-13 |
Not clear |
| Fatemeh Koochaki, Laleh Najafizade. Eye Gaze-based Early Intent Prediction Utilizing CNN-LSTM. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. vol 2019. 2020-03-12. PMID:31946133. |
by employing a combination of convolution neuronal network (cnn) and long short term memory (lstm), early prediction of the user's intention is enabled. |
2020-03-12 |
2023-08-13 |
human |