| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Jhabindra Khanal, Hilal Tayara, Quan Zou, Kil To Chon. DeepCap-Kcr: accurate identification and investigation of protein lysine crotonylation sites based on capsule network. Briefings in bioinformatics. 2021-12-09. PMID:34882222. |
in this study, we proposed a deep learning model, deepcap-kcr, a capsule network (capsnet) based on a convolutional neural network (cnn) and long short-term memory (lstm) for robust prediction of kcr sites on histone and nonhistone proteins (mammals). |
2021-12-09 |
2023-08-13 |
human |
| Hamad Naeem, Ali Abdulqader Bin-Sale. A CNN-LSTM network with multi-level feature extraction-based approach for automated detection of coronavirus from CT scan and X-ray images. Applied soft computing. vol 113. 2021-12-07. PMID:34608379. |
finally, long short-term memory (lstm) along the cnn network is used to detect the extracted covid-19 features. |
2021-12-07 |
2023-08-13 |
Not clear |
| Mohamed E M Elhaj-Abdou, Hassan El-Dib, Amr El-Helw, Mohamed El-Habrou. Deep_CNN_LSTM_GO: Protein function prediction from amino-acid sequences. Computational biology and chemistry. vol 95. 2021-12-06. PMID:34601431. |
deep_cnn_lstm_go is an integration between convolutional neural network (cnn) and long short-term memory (lstm) neural network to learn features from amino acid sequences and outputs the three different gene ontology (go). |
2021-12-06 |
2023-08-13 |
Not clear |
| Fangyao Shen, Guojun Dai, Guang Lin, Jianhai Zhang, Wanzeng Kong, Hong Zen. EEG-based emotion recognition using 4D convolutional recurrent neural network. Cognitive neurodynamics. vol 14. issue 6. 2021-12-03. PMID:33101533. |
then, we introduce crnn model, which is combined by convolutional neural network (cnn) and recurrent neural network with long short term memory (lstm) cell. |
2021-12-03 |
2023-08-13 |
Not clear |
| Pristy Paul Thoduparambil, Anna Dominic, Surekha Mariam Varghes. EEG-based deep learning model for the automatic detection of clinical depression. Physical and engineering sciences in medicine. vol 43. issue 4. 2021-11-24. PMID:33090373. |
in this paper, a deep model is designed in which an integration of convolution neural network (cnn) and long short term memory (lstm) is implemented for the detection of depression. |
2021-11-24 |
2023-08-13 |
Not clear |
| Pedro Lara-Benítez, Manuel Carranza-García, José C Riquelm. An Experimental Review on Deep Learning Architectures for Time Series Forecasting. International journal of neural systems. vol 31. issue 3. 2021-11-24. PMID:33588711. |
among all studied models, the results show that long short-term memory (lstm) and convolutional networks (cnn) are the best alternatives, with lstms obtaining the most accurate forecasts. |
2021-11-24 |
2023-08-13 |
Not clear |
| Albert Whata, Charles Chimedz. Deep Learning for SARS COV-2 Genome Sequences. IEEE access : practical innovations, open solutions. vol 9. 2021-11-24. PMID:34812391. |
we propose a deep learning algorithm that uses a convolutional neural network (cnn) as well as a bi-directional long short-term memory (bi-lstm) neural network, for the classification of the severe acute respiratory syndrome coronavirus 2 (sars cov-2) amongst coronaviruses. |
2021-11-24 |
2023-08-13 |
Not clear |
| Hao Lv, Fu-Ying Dao, Zheng-Xing Guan, Hui Yang, Yan-Wen Li, Hao Li. Deep-Kcr: accurate detection of lysine crotonylation sites using deep learning method. Briefings in bioinformatics. vol 22. issue 4. 2021-11-19. PMID:33099604. |
we investigate the performances of convolutional neural network (cnn) and five commonly used classifiers (long short-term memory network, random forest, logitboost, naive bayes and logistic regression) using 10-fold cross-validation and independent set test. |
2021-11-19 |
2023-08-13 |
human |
| Fatma Murat, Ferhat Sadak, Ozal Yildirim, Muhammed Talo, Ender Murat, Murat Karabatak, Yakup Demir, Ru-San Tan, U Rajendra Achary. Review of Deep Learning-Based Atrial Fibrillation Detection Studies. International journal of environmental research and public health. vol 18. issue 21. 2021-11-19. PMID:34769819. |
dl models based on deep neural network, convolutional neural network (cnn), recurrent neural network, long short-term memory, and hybrid structures were discussed. |
2021-11-19 |
2023-08-13 |
Not clear |
| Bader Alouffi, Abdullah Alharbi, Radhya Sahal, Hager Sale. An Optimized Hybrid Deep Learning Model to Detect COVID-19 Misleading Information. Computational intelligence and neuroscience. vol 2021. 2021-11-19. PMID:34790233. |
accordingly, in this work, we have proposed a hybrid deep learning model that uses convolutional neural network (cnn) and long short-term memory (lstm) to detect covid-19 fake news. |
2021-11-19 |
2023-08-13 |
Not clear |
| Yang Li, Chengbo Yi. Application of Dual-Channel Convolutional Neural Network Algorithm in Semantic Feature Analysis of English Text Big Data. Computational intelligence and neuroscience. vol 2021. 2021-11-17. PMID:34782834. |
following the analysis of the effect of cnn, artificial neural network (ann), and recurrent neural network (rnn) on english text data analysis, the more effective long short-term memory (lstm) and the gated recurrent unit (gru) neural network (nn) are introduced, and each network is combined with the dual-channel cnn, respectively, and comprehensively analyzed under comparative experiments. |
2021-11-17 |
2023-08-13 |
Not clear |
| Yumeng Liu, Xiaolong Wang, Bin Li. RFPR-IDP: reduce the false positive rates for intrinsically disordered protein and region prediction by incorporating both fully ordered proteins and disordered proteins. Briefings in bioinformatics. vol 22. issue 2. 2021-11-12. PMID:32112084. |
in this regard, we propose a new method called rfpr-idp trained with both fully ordered proteins and disordered proteins, which is constructed based on the combination of convolution neural network (cnn) and bidirectional long short-term memory (bilstm). |
2021-11-12 |
2023-08-13 |
Not clear |
| Jing Li, Dongliang Chen, Ning Yu, Ziping Zhao, Zhihan L. Emotion Recognition of Chinese Paintings at the Thirteenth National Exhibition of Fines Arts in China Based on Advanced Affective Computing. Frontiers in psychology. vol 12. 2021-11-10. PMID:34744913. |
the proposed algorithm is compared with long short-term memory (lstm), cnn, recurrent neural network (rnn), alexnet, and deep neural network (dnn) algorithms from the training set and test set, respectively, the emotion recognition accuracy of the proposed algorithm reaches 92.23 and 97.11% in the training set and test set, respectively, the training time is stable at about 54.97 s, and the test time is stable at about 23.74 s. in addition, the analysis of the acceleration efficiency of each algorithm shows that the improved alexnet algorithm is suitable for processing a large amount of brain image data, and the acceleration ratio is also higher than other algorithms. |
2021-11-10 |
2023-08-13 |
Not clear |
| Saranlita Chotirat, Phayung Meesa. Part-of-Speech tagging enhancement to natural language processing for Thai wh-question classification with deep learning. Heliyon. vol 7. issue 10. 2021-11-08. PMID:34746470. |
the deep learning techniques were bidirectional long short-term memory (bilstm), convolutional neural networks (cnn), and hybrid model, which combined cnn and bilstm model. |
2021-11-08 |
2023-08-13 |
Not clear |
| Neeraj, Vatsal Singhal, Jimson Mathew, Ranjan Kumar Beher. Detection of alcoholism using EEG signals and a CNN-LSTM-ATTN network. Computers in biology and medicine. vol 138. 2021-11-04. PMID:34656864. |
this paper proposes a deep learning architecture that uses a combination of fast fourier transform (fft), a convolution neural network (cnn), long short-term memory (lstm), and a recently proposed attention mechanism for extracting spatio-temporal features from multi-channel eeg signals. |
2021-11-04 |
2023-08-13 |
human |
| Anca Loredana Udriștoiu, Irina Mihaela Cazacu, Lucian Gheorghe Gruionu, Gabriel Gruionu, Andreea Valentina Iacob, Daniela Elena Burtea, Bogdan Silviu Ungureanu, Mădălin Ionuț Costache, Alina Constantin, Carmen Florina Popescu, Ștefan Udriștoiu, Adrian Săftoi. Real-time computer-aided diagnosis of focal pancreatic masses from endoscopic ultrasound imaging based on a hybrid convolutional and long short-term memory neural network model. PloS one. vol 16. issue 6. 2021-10-27. PMID:34181680. |
the mla is based on a deep learning method which combines convolutional (cnn) and long short-term memory (lstm) neural networks. |
2021-10-27 |
2023-08-13 |
Not clear |
| Borja Saez-Mingorance, Javier Mendez-Gomez, Gianfranco Mauro, Encarnacion Castillo-Morales, Manuel Pegalajar-Cuellar, Diego P Morales-Santo. Air-Writing Character Recognition with Ultrasonic Transceivers. Sensors (Basel, Switzerland). vol 21. issue 20. 2021-10-27. PMID:34695913. |
after acquiring the track, different deep learning algorithms, such as long short-term memory (lstm), convolutional neural networks (cnn), convolutional autoencoder (convautoencoder), and convolutional lstm have been evaluated for character recognition. |
2021-10-27 |
2023-08-13 |
Not clear |
| Xiangyu Qian, Ye Qiu, Qingzu He, Yuer Lu, Hai Lin, Fei Xu, Fangfang Zhu, Zhilong Liu, Xiang Li, Yuping Cao, Jianwei Shua. A Review of Methods for Sleep Arousal Detection Using Polysomnographic Signals. Brain sciences. vol 11. issue 10. 2021-10-26. PMID:34679339. |
for deep learning methods, different models are discussed by now, including convolution neural network (cnn), recurrent neural network (rnn), long-term and short-term memory neural network (lstm), residual neural network (resnet), and the combinations of these neural networks. |
2021-10-26 |
2023-08-13 |
human |
| Suyanto Suyanto, Ade Romadhony, Febryanti Sthevanie, Rezza Nafi Ismai. Augmented words to improve a deep learning-based Indonesian syllabification. Heliyon. vol 7. issue 10. 2021-10-26. PMID:34693050. |
in this paper, two procedures: massive data augmentation and validation, are proposed to improve a deep learning-based syllabification, using a combination of bidirectional long short-term memory (bilstm), convolutional neural networks (cnn), and conditional random fields (crf) for a low-resource indonesian language. |
2021-10-26 |
2023-08-13 |
Not clear |
| Nouf Rahimi, Fathy Eassa, Lamiaa Elrefae. One- and Two-Phase Software Requirement Classification Using Ensemble Deep Learning. Entropy (Basel, Switzerland). vol 23. issue 10. 2021-10-25. PMID:34681988. |
in this research, three ensemble approaches were applied: accuracy as a weight ensemble, mean ensemble, and accuracy per class as a weight ensemble with a combination of four different dl models-long short-term memory (lstm), bidirectional long short-term memory (bilstm), a gated recurrent unit (gru), and a convolutional neural network (cnn)-in order to classify the software requirement (sr) specification, the binary classification of srs into functional requirement (frs) or non-functional requirements (nfrs), and the multi-label classification of both frs and nfrs into further experimental classes. |
2021-10-25 |
2023-08-13 |
Not clear |