| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Tianzhe Bao, Chao Wang, Pengfei Yang, Sheng Quan Xie, Zhi-Qiang Zhang, Ping Zho. LSTM-AE for Domain Shift Quantification in Cross-day Upper-limb Motion Estimation Using Surface Electromyography. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. vol PP. 2023-05-30. PMID:37252871. |
herein, a prevalent hybrid framework that combines a convolutional neural network (cnn) and a long short-term memory network (lstm), i.e. |
2023-05-30 |
2023-08-14 |
Not clear |
| Ehzaz Mustafa, Ehtisham Khan Jadoon, Sardar Khaliq-Uz-Zaman, Mohammad Ali Humayun, Mohammed Mara. An Ensembled Framework for Human Breast Cancer Survivability Prediction Using Deep Learning. Diagnostics (Basel, Switzerland). vol 13. issue 10. 2023-05-27. PMID:37238173. |
specifically, we design a convolutional neural network (cnn) for clinical modalities, a deep neural network (dnn) for copy number variations (cnv), and a long short-term memory (lstm) architecture for gene expression modalities to effectively handle multi-dimensional data. |
2023-05-27 |
2023-08-14 |
human |
| Xin Wang, Zhenwei Zhou, Shilie He, Junbin Liu, Wei Cu. Performance Degradation Modeling and Its Prediction Algorithm of an IGBT Gate Oxide Layer Based on a CNN-LSTM Network. Micromachines. vol 14. issue 5. 2023-05-27. PMID:37241583. |
the degradation prediction model of the igbt gate oxide layer is constructed by the convolutional neural network and long short-term memory (cnn-lstm) network, which show the highest fitting accuracy compared with long short-term memory (lstm), convolutional neural network (cnn), support vector regression (svr), gaussian process regression (gpr), and cnn-lstm models in our experiment. |
2023-05-27 |
2023-08-14 |
Not clear |
| Saddam Hussain Khan, Javed Iqbal, Syed Agha Hassnain, Muhammad Owais, Samih M Mostafa, Myriam Hadjouni, Amena Mahmou. COVID-19 detection and analysis from lung CT images using novel channel boosted CNNs. Expert systems with applications. vol 229. 2023-05-23. PMID:37220492. |
in the first phase, a novel sb-stm-brnet cnn is developed, incorporating a new channel squeezed and boosted (sb) and dilated convolutional-based split-transform-merge (stm) block to detect covid-19 infected lung ct images. |
2023-05-23 |
2023-08-14 |
human |
| Arash Gharehbaghi, Elaheh Partovi, Ankica Babi. Parralel Recurrent Convolutional Neural Network for Abnormal Heart Sound Classification. Studies in health technology and informatics. vol 302. 2023-05-19. PMID:37203741. |
the performance of the pcnn is evaluated and compared to the one obtained from a serial form of the convolutional neural network (scnn) as well as two other baseline studies: a long- and short-term memory (lstm) neural network and a conventional cnn (ccnn). |
2023-05-19 |
2023-08-14 |
Not clear |
| Jinhua Zhang, Zhengyang Zhao, Jie Yan, Peng Chen. Ultra-Short-Term Wind Power Forecasting Based on CGAN-CNN-LSTM Model Supported by Lidar. Sensors (Basel, Switzerland). vol 23. issue 9. 2023-05-14. PMID:37177571. |
then, the convolutional neural network (cnn) is used to extract the eigenvalues of the data, combined with the long short-term memory network (lstm) to jointly construct a feature extraction module, and add an attention mechanism after the lstm to assign weights to features, accelerate model convergence, and construct an ultra-short-term wind power forecasting model combined with the cgan-cnn-lstm. |
2023-05-14 |
2023-08-14 |
Not clear |
| Niannian Liu, Zequn Zhang, Yanan Wu, Yinglong Wang, Ying Lian. CRBSP:Prediction of CircRNA-RBP binding sites based on multimodal intermediate fusion. IEEE/ACM transactions on computational biology and bioinformatics. vol PP. 2023-05-03. PMID:37130249. |
cnn (convolution neural networks) was used to extract global information and bilstm (bidirectional long- and short-term memory network) encoder and lstm (long- and short-term memory network) decoder for local sequence information. |
2023-05-03 |
2023-08-14 |
Not clear |
| Siyuan Wang, Ying Ren, Bisheng Xia, Kai Liu, Huiming L. Prediction of atmospheric pollutants in urban environment based on coupled deep learning model and sensitivity analysis. Chemosphere. 2023-05-03. PMID:37137395. |
this study develops a model that combines an attention mechanism, convolutional neural network (cnn), and long short-term memory (lstm) unit to predict the o |
2023-05-03 |
2023-08-14 |
Not clear |
| Zhixin Zhou, Zhidong Zhao, Xianfei Zhang, Xiaohong Zhang, Pengfei Jiao, Xuanyu Y. Identifying fetal status with fetal heart rate: Deep learning approach based on long convolution. Computers in biology and medicine. vol 159. 2023-04-27. PMID:37105114. |
most of existing fhr classification methods are based on combing different deep learning models, such as cnn (convolutional neural network), lstm (long short-term memory) and transformer. |
2023-04-27 |
2023-08-14 |
Not clear |
| Meng Wang, Zongwei Yang, Caiwang Tai, Fan Zhang, Qiaofeng Zhang, Kejun Shen, Chengbin Gu. Prediction of road dust concentration in open-pit coal mines based on multivariate mixed model. PloS one. vol 18. issue 4. 2023-04-26. PMID:37099504. |
create a cnn-bilstm-attention multivariate hybrid model consisting of a convolutional neural network (cnn), a bidirectional long short-term memory neural network (bilstm), and an attention mechanism, prediction of pm2.5 concentration in the next 24h. |
2023-04-26 |
2023-08-14 |
Not clear |
| Muhammad Usman Hadi, Rizwan Qureshi, Ayesha Ahmed, Nadeem Iftikha. A lightweight CORONA-NET for COVID-19 detection in X-ray images. Expert systems with applications. vol 225. 2023-04-17. PMID:37063778. |
this study aims to provide a lightweight deep learning method that incorporates a convolutional neural network (cnn), discrete wavelet transform (dwt), and a long short-term memory (lstm), called corona-net for diagnosing covid-19 from chest x-ray images. |
2023-04-17 |
2023-08-14 |
Not clear |
| Ramez Abdalla, Waleed Al-Hakimi, Nelson Perozo, Philip Jaege. Real-Time Liquid Rate and Water Cut Prediction From the Electrical Submersible Pump Sensors Data Using Machine-Learning Algorithms. ACS omega. vol 8. issue 14. 2023-04-17. PMID:37065027. |
this study introduces a robust workflow utilizing symbolic regression, extreme gradient boosted trees, and a deep learning model that includes a pipeline of convolutional neural network (cnn) layers and long short-term memory algorithm (lstm) layers to predict liquid rate and water cut in real time based on pump sensors' data. |
2023-04-17 |
2023-08-14 |
Not clear |
| Naya Nagy, Malak Aljabri, Afrah Shaahid, Amnah Albin Ahmed, Fatima Alnasser, Linda Almakramy, Manar Alhadab, Shahad Alfaddag. Phishing URLs Detection Using Sequential and Parallel ML Techniques: Comparative Analysis. Sensors (Basel, Switzerland). vol 23. issue 7. 2023-04-13. PMID:37050527. |
four models, namely, random forest (rf), naïve bayes (nb), convolutional neural network (cnn), and long short-term memory (lstm) were deployed to carry out the experiments. |
2023-04-13 |
2023-08-14 |
Not clear |
| Kuan Li, Bin Ao, Xin Wu, Qing Wen, Ejaz Ul Haq, Jianping Yi. Parkinson's disease detection and classification using EEG based on deep CNN-LSTM model. Biotechnology & genetic engineering reviews. 2023-04-11. PMID:37039259. |
the deep cnn network is utilized to acquire the structural features of ecg signals and extract meaningful information from them, after which the signals are sent via a long short-term memory network to extract the features' context dependency. |
2023-04-11 |
2023-08-14 |
Not clear |
| Ijaz Ahmad, Xin Wang, Danish Javeed, Prabhat Kumar, Oluwarotimi Williams Samuel, Shixiong Che. A Hybrid Deep Learning Approach for Epileptic Seizure Detection in EEG signals. IEEE journal of biomedical and health informatics. vol PP. 2023-04-10. PMID:37037252. |
second, we integrate a 1d convolutional neural network (cnn) with a bidirectional long short-term memory (bilstm) network based on truncated backpropagation through time (tbptt) to efficiently extract spatial and temporal sequence information while reducing computational complexity. |
2023-04-10 |
2023-08-14 |
Not clear |
| Anyuan Zhang, Qi Li, Zhenlan Li, Jiming L. Upper Limb Movement Decoding Scheme Based on Surface Electromyography Using Attention-Based Kalman Filter Scheme. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. vol 31. 2023-04-08. PMID:37030694. |
the proposed attention-based cnn model outperformed the vanilla cnn model and a hybrid cnn-long short-term memory (lstm) model in intra-session and intra-session long-time use. |
2023-04-08 |
2023-08-14 |
human |
| Zhengsen Pan, Shusen Zhou, Tong Liu, Chanjuan Liu, Mujun Zang, Qingjun Wan. WVDL: weighted voting deep learning model for predicting RNA-protein binding sites. IEEE/ACM transactions on computational biology and bioinformatics. vol PP. 2023-04-07. PMID:37028092. |
thus, in our study, we propose a weighted voting deep learning model (wvdl), which uses weighted voting method to combine convolutional neural network (cnn), long short term memory network (lstm) and residual network (resnet). |
2023-04-07 |
2023-08-14 |
Not clear |
| Tengfei Gao, Dan Chen, Yunbo Tang, Zhekai Ming, Xiaoli L. EEG Reconstruction with a Dual-scale CNN-LSTM Model for Deep Artifact Removal. IEEE journal of biomedical and health informatics. vol PP. 2023-04-04. PMID:37015612. |
this study proposes an approach (namely duocl) to deep artifact removal with a dual-scale cnn (convolutional neural network)-lstm (long short-term memory) model, operating on the raw eeg in three phases: 1) morphological feature extraction, a dual-branch cnn utilizes convolution kernels of two different scales to learn morphological features (individual sample); 2) feature reinforcement, the dual-scale features are then reinforced with temporal dependencies (inter-sample) captured by lstm; and 3) eeg reconstruction, the resulting feature vectors are finally aggregated to reconstruct the artifact-free eeg via a terminal fully connected layer. |
2023-04-04 |
2023-08-14 |
Not clear |
| Farhan Fuad Abir, Muhammad E H Chowdhury, Malisha Islam Tapotee, Adam Mushtak, Amith Khandakar, Sakib Mahmud, Md Anwarul Hasa. PCovNet+: A CNN-VAE anomaly detection framework with LSTM embeddings for smartwatch-based COVID-19 detection. Engineering applications of artificial intelligence. vol 122. 2023-04-03. PMID:37006447. |
a convolutional neural network (cnn)-based variational autoencoder (vae) architecture was used as the primary model along with a long short-term memory (lstm) network to create latent space embeddings for the vae. |
2023-04-03 |
2023-08-14 |
human |
| Matteo Stefanini, Marta Lovino, Rita Cucchiara, Elisa Ficarr. Predicting gene and protein expression levels from DNA and protein sequences with Perceiver. Computer methods and programs in biomedicine. vol 234. 2023-04-02. PMID:37004267. |
the state-of-the-art models (e.g., xpresso and basenjii) predict mrna levels exploiting convolutional (cnn) or long short term memory (lstm) networks. |
2023-04-02 |
2023-08-14 |
Not clear |