| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Ping Xiong, Simon Ming-Yuen Lee, Ging Cha. Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review. Frontiers in cardiovascular medicine. vol 9. 2022-04-11. PMID:35402563. |
this review aims to take stock of 59 major dl studies applied to the ecg for mi detection and localization published in recent 5 years, covering convolutional neural network (cnn), long short-term memory (lstm), convolutional recurrent neural network (crnn), gated recurrent unit (gru), residual neural network (resnet), and autoencoder (ae). |
2022-04-11 |
2023-08-13 |
Not clear |
| Anand Shankar, Samarendra Dandapat, Shovan Barm. Seizure Types Classification by Generating Input Images With In-Depth Features From Decomposed EEG Signals For Deep Learning Pipeline. IEEE journal of biomedical and health informatics. vol PP. 2022-03-16. PMID:35294366. |
for classification, a hybrid dl pipeline has been constructed by combining the convolution neural network (cnn) followed by long short-term memory (lstm) for efficient extraction of spatial and time sequence information. |
2022-03-16 |
2023-08-13 |
Not clear |
| Huma Hamid, Noman Naseer, Hammad Nazeer, Muhammad Jawad Khan, Rayyan Azam Khan, Umar Shahbaz Kha. Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks. Sensors (Basel, Switzerland). vol 22. issue 5. 2022-03-10. PMID:35271077. |
dl algorithms, including convolutional neural networks (cnns), long short-term memory (lstm), and bidirectional lstm (bi-lstm) are used to achieve average classification accuracies of 88.50%, 84.24%, and 85.13%, respectively. |
2022-03-10 |
2023-08-13 |
human |
| Zia Ur Rahman, Syed Irfan Ullah, Abdus Salam, Taj Rahman, Inayat Khan, Badam Niaz. Automated Detection of Rehabilitation Exercise by Stroke Patients Using 3-Layer CNN-LSTM Model. Journal of healthcare engineering. vol 2022. 2022-02-14. PMID:35154616. |
due to numerous achievements and increasing popularity of deep learning (dl) techniques, in this research article a dl model that combines convolutional neural network (cnn) and long short-term memory (lstm) is proposed and is named as 3-layer cnn-lstm model. |
2022-02-14 |
2023-08-13 |
human |
| Yoonje Lee, Yu-Seop Kim, Da-In Lee, Seri Jeong, Gu-Hyun Kang, Yong Soo Jang, Wonhee Kim, Hyun Young Choi, Jae Guk Kim, Sang-Hoon Cho. The application of a deep learning system developed to reduce the time for RT-PCR in COVID-19 detection. Scientific reports. vol 12. issue 1. 2022-01-25. PMID:35075153. |
we developed and tested a dl model using the long short-term memory method with a dataset of fluorescence values measured in each cycle of 5810 rt-pcr tests. |
2022-01-25 |
2023-08-13 |
Not clear |
| Mahmoud Ragab, Khalid Eljaaly, Nabil A Alhakamy, Hani A Alhadrami, Adel A Bahaddad, Sayed M Abo-Dahab, Eied M Khali. Deep Ensemble Model for COVID-19 Diagnosis and Classification Using Chest CT Images. Biology. vol 11. issue 1. 2022-01-21. PMID:35053041. |
moreover, a shark optimization algorithm (soa) with an ensemble of dl models, namely recurrent neural networks (rnn), long short-term memory (lstm), and gated recurrent unit (gru), is employed for feature extraction. |
2022-01-21 |
2023-08-13 |
Not clear |
| Abdelgader Alamrouni, Fidan Aslanova, Sagiru Mati, Hamza Sabo Maccido, Afaf A Jibril, A G Usman, S I Abb. Multi-Regional Modeling of Cumulative COVID-19 Cases Integrated with Environmental Forest Knowledge Estimation: A Deep Learning Ensemble Approach. International journal of environmental research and public health. vol 19. issue 2. 2022-01-21. PMID:35055559. |
subsequently, different deep learning (dl) models viz: long short-term memory (lstm), random forest (rf), and ensemble learning (eml) were applied to the second scenario to predict the effect of forest knowledge (fk) during the covid-19 pandemic. |
2022-01-21 |
2023-08-13 |
Not clear |
| Kai-Yu Chen, Li-Wei Chou, Hui-Min Lee, Shuenn-Tsong Young, Cheng-Hung Lin, Yi-Shu Zhou, Shih-Tsang Tang, Ying-Hui La. Human Motion Tracking Using 3D Image Features with a Long Short-Term Memory Mechanism Model-An Example of Forward Reaching. Sensors (Basel, Switzerland). vol 22. issue 1. 2022-01-11. PMID:35009834. |
in this study, we propose a deep learning (dl) human motion tracking technology using 3d image features with a deep bidirectional long short-term memory (dblstm) mechanism model. |
2022-01-11 |
2023-08-13 |
human |
| Binayak Bhandar. Comparative Study of Popular Deep Learning Models for Machining Roughness Classification Using Sound and Force Signals. Micromachines. vol 12. issue 12. 2021-12-24. PMID:34945334. |
the dl architectures considered in this study include multi-layer perceptron (mlp), convolution neural network (cnn), long short-term memory (lstm), and transformer. |
2021-12-24 |
2023-08-13 |
Not clear |
| Arash Heidari, Nima Jafari Navimipour, Mehmet Unal, Shiva Touma. The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions. Computers in biology and medicine. vol 141. 2021-12-20. PMID:34929464. |
dl techniques used in covid-19 have also been categorized into seven main distinct categories as long short term memory networks (lstm), self-organizing maps (soms), conventional neural networks (cnns), generative adversarial networks (gans), recurrent neural networks (rnns), autoencoders, and hybrid approaches. |
2021-12-20 |
2023-08-13 |
human |
| Ruopeng Xie, Jiahui Li, Jiawei Wang, Wei Dai, André Leier, Tatiana T Marquez-Lago, Tatsuya Akutsu, Trevor Lithgow, Jiangning Song, Yanju Zhan. DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy. Briefings in bioinformatics. vol 22. issue 3. 2021-11-22. PMID:32599617. |
specifically, four classical algorithms, including random forest, support vector machines, extreme gradient boosting and multilayer perceptron, and three dl algorithms, including convolutional neural networks, long short-term memory networks and deep neural networks are employed to train 62 baseline models using these features. |
2021-11-22 |
2023-08-13 |
Not clear |
| 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 |
| Madini O Alassafi, Mutasem Jarrah, Reem Alotaib. Time series predicting of COVID-19 based on deep learning. Neurocomputing. vol 468. 2021-11-01. PMID:34690432. |
in this study, we also proposed a dl approach that includes recurrent neural network (rnn) and long short-term memory (lstm) networks for predicting the probable numbers of covid-19 cases. |
2021-11-01 |
2023-08-13 |
Not clear |
| Kwanho Jeong, Ather Abbas, Jingyeong Shin, Moon Son, Young Mo Kim, Kyung Hwa Ch. Prediction of biogas production in anaerobic co-digestion of organic wastes using deep learning models. Water research. vol 205. 2021-10-26. PMID:34600230. |
we propose a hybrid dl architecture, i.e., da-lstm-vsn, wherein a dual-stage-attention (da)-based long short-term memory (lstm) network is integrated with variable selection networks (vsns). |
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 |
| 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 |
| Yangde Gao, Cheol Hong Kim, Jong-Myon Ki. A Novel Hybrid Deep Learning Method for Fault Diagnosis of Rotating Machinery Based on Extended WDCNN and Long Short-Term Memory. Sensors (Basel, Switzerland). vol 21. issue 19. 2021-10-14. PMID:34640934. |
to enhance the self-learning capacity and improve the intelligent diagnosis accuracy of dl for rotating machinery, a novel hybrid deep learning method (nhdlm) based on extended deep convolutional neural networks with wide first-layer kernels (ewdcnn) and long short-term memory (lstm) is proposed for complex environments. |
2021-10-14 |
2023-08-13 |
Not clear |
| Raja Sher Afgun Usmani, Thulasyammal Ramiah Pillai, Ibrahim Abaker Targio Hashem, Mohsen Marjani, Rafiza Shaharudin, Mohd Talib Lati. Air pollution and cardiorespiratory hospitalization, predictive modeling, and analysis using artificial intelligence techniques. Environmental science and pollution research international. vol 28. issue 40. 2021-10-12. PMID:34075501. |
we propose the enhanced long short-term memory (elstm) model and provide a comparison with other ai techniques, i.e., lstm, dl, and vector autoregressive (var). |
2021-10-12 |
2023-08-13 |
human |
| Luca Pedrelli, Xavier Hinau. Hierarchical-Task Reservoir for Online Semantic Analysis From Continuous Speech. IEEE transactions on neural networks and learning systems. vol PP. 2021-09-28. PMID:34570710. |
overall, the htr outperformed the other state-of-the-art reservoir-based approaches and it resulted in extremely efficient with respect to typical recurrent neural networks (rnns) in deep learning (dl) [e.g., long short term memory (lstms)]. |
2021-09-28 |
2023-08-13 |
Not clear |
| Marvi Waheed, Hammad Afzal, Khawir Mehmoo. NT-FDS-A Noise Tolerant Fall Detection System Using Deep Learning on Wearable Devices. Sensors (Basel, Switzerland). vol 21. issue 6. 2021-04-27. PMID:33809080. |
the work focuses on deep learning (dl) particularly recurrent neural networks (rnns) with an underlying bidirectional long short-term memory (bilstm) stack to implement fds based on wearable sensors. |
2021-04-27 |
2023-08-13 |
Not clear |