| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Guohua Huang, Qingfeng Shen, Guiyang Zhang, Pan Wang, Zu-Guo Y. LSTMCNNsucc: A Bidirectional LSTM and CNN-Based Deep Learning Method for Predicting Lysine Succinylation Sites. BioMed research international. vol 2021. 2021-10-18. PMID:34159204. |
we combined long short-term memory (lstm) and convolutional neural network (cnn) into a deep learning method for predicting succinylation site. |
2021-10-18 |
2023-08-13 |
Not clear |
| Tahjid Ashfaque Mostafa, Sara Soltaninejad, Tara L McIsaac, Irene Chen. A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction. Sensors (Basel, Switzerland). vol 21. issue 19. 2021-10-14. PMID:34640763. |
we compared the performance of several time frequency analysis techniques, including moving windows extracted from the signals, handcrafted features, recurrence plots (rp), short time fourier transform (stft), discreet wavelet transform (dwt) and pseudo wigner ville distribution (pwvd) with deep learning (dl) based long short term memory (lstm) and convolutional neural networks (cnn). |
2021-10-14 |
2023-08-13 |
Not clear |
| Sivamurugan Vellakani, Indumathi Pushba. An enhanced OCT image captioning system to assist ophthalmologists in detecting and classifying eye diseases. Journal of X-ray science and technology. vol 28. issue 5. 2021-10-11. PMID:32597828. |
the methodology used for designing this system involves different deep learning convolutional neural network (cnn) models and long short-term memory networks (lstm). |
2021-10-11 |
2023-08-13 |
human |
| Xiangyu Zhang, Jianqing Li, Zhipeng Cai, Li Zhang, Zhenghua Chen, Chengyu Li. Over-fitting suppression training strategies for deep learning-based atrial fibrillation detection. Medical & biological engineering & computing. vol 59. issue 1. 2021-09-29. PMID:33387183. |
wearable ecg data from 29 patients with arrhythmia were collected for at least 24 h. to verify the effectiveness of the training strategies, a long short-term memory (lstm) and convolution neural network (cnn)-based model was proposed and tested. |
2021-09-29 |
2023-08-13 |
Not clear |
| Panagiotis Marentakis, Pantelis Karaiskos, Vassilis Kouloulias, Nikolaos Kelekis, Stylianos Argentos, Nikolaos Oikonomopoulos, Constantinos Louka. Lung cancer histology classification from CT images based on radiomics and deep learning models. Medical & biological engineering & computing. vol 59. issue 1. 2021-09-29. PMID:33411267. |
we investigated four different families of techniques: (a) radiomics with two classifiers (knn and svm), (b) four state-of-the-art convolutional neural networks (cnns) with transfer learning and fine tuning (alexnet, resnet101, inceptionv3 and inceptionresnetv2), (c) a cnn combined with a long short-term memory (lstm) network to fuse information about the spatial coherency of tumor's ct slices, and (d) combinatorial models (lstm + cnn + radiomics). |
2021-09-29 |
2023-08-13 |
Not clear |
| Sayantan Mitra, Sriparna Saha, Mohammed Hasanuzzama. A Multi-View Deep Neural Network Model for Chemical-Disease Relation Extraction From Imbalanced Datasets. IEEE journal of biomedical and health informatics. vol 24. issue 11. 2021-09-24. PMID:32248129. |
the model is designed as a combination of convolution neural network (cnn) and bidirectional long short term memory (bi-lstm) network along with a multi-layer perceptron (mlp). |
2021-09-24 |
2023-08-13 |
Not clear |
| Xiang Chen, Yu Li, Ruochen Hu, Xu Zhang, Xun Che. Hand Gesture Recognition based on Surface Electromyography using Convolutional Neural Network with Transfer Learning Method. IEEE journal of biomedical and health informatics. vol 25. issue 4. 2021-09-24. PMID:32750962. |
then, two types of target networks, in the forms of cnn-only and cnn+lstm (long short-term memory) respectively, are designed with the same cnn architecture as the feature extraction network. |
2021-09-24 |
2023-08-13 |
Not clear |
| Jingshu Bi, Yuanjie Zheng, Chongjing Wang, Yanhui Din. An Attention based Bidirectional LSTM Method to Predict the Binding of TCR and Epitope. IEEE/ACM transactions on computational biology and bioinformatics. vol PP. 2021-09-24. PMID:34559661. |
in this paper, we introduce a hybrid model composed of bidirectional long short-term memory networks (bilstm), attention and convolutional neural networks (cnn) that can identied the binding of tcrs to epitopes. |
2021-09-24 |
2023-08-13 |
Not clear |
| Soheila Gheisari, Sahar Shariflou, Jack Phu, Paul J Kennedy, Ashish Agar, Michael Kalloniatis, S Mojtaba Golza. A combined convolutional and recurrent neural network for enhanced glaucoma detection. Scientific reports. vol 11. issue 1. 2021-09-23. PMID:33479405. |
a total of 1810 fundus images and 295 fundus videos were used to train a cnn and a combined cnn and long short-term memory rnn. |
2021-09-23 |
2023-08-13 |
Not clear |
| Ruojun Li, Ganesh Prasanna Balakrishnan, Jiaming Nie, Y U Li, Emmanuel Agu, Kristin Grimone, Debra Herman, Ana M Abrantes, Michael D Stei. Estimation of Blood Alcohol Concentration From Smartphone Gait Data Using Neural Networks. IEEE access : practical innovations, open solutions. vol 9. 2021-09-21. PMID:34527505. |
using data gathered from a large controlled alcohol study, we perform regression analysis using a bi-directional long short term memory (bi-lstm) and convolutional neural network (cnn) architectures to predict a person's blood alcohol concentration (bac) from their smartphone's accelerometer and gyroscope data. |
2021-09-21 |
2023-08-13 |
Not clear |
| Ramish Jamil, Imran Ashraf, Furqan Rustam, Eysha Saad, Arif Mehmood, Gyu Sang Cho. Detecting sarcasm in multi-domain datasets using convolutional neural networks and long short term memory network model. PeerJ. Computer science. vol 7. 2021-09-21. PMID:34541306. |
this study proposes a hybrid approach where the convolutional neural networks (cnn) are used for feature extraction while the long short-term memory (lstm) is trained and tested on those features. |
2021-09-21 |
2023-08-13 |
Not clear |
| Vidya K Sudarshan, Mikkel Brabrand, Troels Martin Range, Uffe Kock Wii. Performance evaluation of Emergency Department patient arrivals forecasting models by including meteorological and calendar information: A comparative study. Computers in biology and medicine. vol 135. 2021-09-13. PMID:34166880. |
therefore, in this paper, machine learning (ml)-based random forest (rf) regressor, and deep neural network (dnn)-based long short-term memory (lstm) and convolutional neural network (cnn) methods, which have not been explored to the same extent as the other time series techniques, are implemented by incorporating meteorological and calendar parameters for the development of forecasting models. |
2021-09-13 |
2023-08-13 |
Not clear |
| Hong He, Xinyue Liu, Yong Ha. A progressive deep wavelet cascade classification model for epilepsy detection. Artificial intelligence in medicine. vol 118. 2021-09-08. PMID:34412840. |
convolutional neural network (cnn), long short-term memory (lstm), multi-grained cascade forest (gcforest) and wavelet cascade model (wcm). |
2021-09-08 |
2023-08-13 |
human |
| A W Olthof, P M A van Ooijen, L J Cornelisse. Deep Learning-Based Natural Language Processing in Radiology: The Impact of Report Complexity, Disease Prevalence, Dataset Size, and Algorithm Type on Model Performance. Journal of medical systems. vol 45. issue 10. 2021-09-07. PMID:34480231. |
in this study, we investigate these factors on the performance of four types [a fully connected neural network (dense), a long short-term memory recurrent neural network (lstm), a convolutional neural network (cnn), and a bidirectional encoder representations from transformers (bert)] of deep learning-based nlp. two datasets consisting of radiologist-annotated reports of both trauma radiographs (n = 2469) and chest radiographs and computer tomography (ct) studies (n = 2255) were split into training sets (80%) and testing sets (20%). |
2021-09-07 |
2023-08-13 |
Not clear |
| Madhurananda Pahar, Marisa Klopper, Robin Warren, Thomas Niesle. COVID-19 cough classification using machine learning and global smartphone recordings. Computers in biology and medicine. vol 135. 2021-09-06. PMID:34182331. |
a leave-p-out cross-validation scheme was used to train and evaluate seven machine learning classifiers: logistic regression (lr), k-nearest neighbour (knn), support vector machine (svm), multilayer perceptron (mlp), convolutional neural network (cnn), long short-term memory (lstm) and a residual-based neural network architecture (resnet50). |
2021-09-06 |
2023-08-13 |
human |
| Shixiang Yu, Xin Li, Weilai Lu, Hanfei Li, Yu Vincent Fu, Fanghua Li. Analysis of Raman Spectra by Using Deep Learning Methods in the Identification of Marine Pathogens. Analytical chemistry. vol 93. issue 32. 2021-08-31. PMID:34339167. |
here, we have proposed two new methods that involve raman spectroscopy combined with a long short-term memory (lstm) neural network and compared them with a method using a normal convolutional neural network (cnn). |
2021-08-31 |
2023-08-13 |
Not clear |
| Debadyuti Mukherjee, Koustav Dhar, Friedhelm Schwenker, Ram Sarka. Ensemble of Deep Learning Models for Sleep Apnea Detection: An Experimental Study. Sensors (Basel, Switzerland). vol 21. issue 16. 2021-08-31. PMID:34450866. |
our work mainly consists of an experimental study of different ensemble techniques applied on three deep learning models-two convolutional neural network (cnn) based models, and a combination of cnn and long short-term memory (lstm) models, which were previously proposed in the osa detection domain. |
2021-08-31 |
2023-08-13 |
Not clear |
| Bruno Barros, Paulo Lacerda, Célio Albuquerque, Aura Conc. Pulmonary COVID-19: Learning Spatiotemporal Features Combining CNN and LSTM Networks for Lung Ultrasound Video Classification. Sensors (Basel, Switzerland). vol 21. issue 16. 2021-08-31. PMID:34450928. |
a convolutional neural network (cnn) performed the extraction of spatial features, and the temporal dependence was learned using a long short-term memory (lstm). |
2021-08-31 |
2023-08-13 |
Not clear |
| Jun Zhang, Qingcai Chen, Bin Li. DeepDRBP-2L: A New Genome Annotation Predictor for Identifying DNA-Binding Proteins and RNA-Binding Proteins Using Convolutional Neural Network and Long Short-Term Memory. IEEE/ACM transactions on computational biology and bioinformatics. vol 18. issue 4. 2021-08-30. PMID:31722485. |
in this study, a two-level predictor named deepdrbp-2l was proposed by combining convolutional neural network (cnn) and the long short-term memory (lstm). |
2021-08-30 |
2023-08-13 |
Not clear |
| Wenjia Chen, Jinlin L. Forecasting Teleconsultation Demand Using an Ensemble CNN Attention-Based BILSTM Model with Additional Variables. Healthcare (Basel, Switzerland). vol 9. issue 8. 2021-08-30. PMID:34442130. |
the proposed ensemble cnn attention-based bilstm model (eca-bilstm) combines shallow convolutional neural networks (cnns), attention mechanisms, and bidirectional long short-term memory (bilstm). |
2021-08-30 |
2023-08-13 |
Not clear |