| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Sam Slade, Li Zhang, Haoqian Huang, Houshyar Asadi, Chee Peng Lim, Yonghong Yu, Dezong Zhao, Hanhe Lin, Rong Ga. Neural Inference Search for Multiloss Segmentation Models. IEEE transactions on neural networks and learning systems. vol PP. 2023-06-16. PMID:37327096. |
the first two behaviors are exploratory, leveraging long short-term memory (lstm)-convolutional neural network (cnn)-based velocity predictions, while the third employs n -dimensional matrix rotation for local exploitation. |
2023-06-16 |
2023-08-14 |
Not clear |
| Cristian David David Guerrero Mendez, Cristian Felipe Blanco-Díaz, Andres Felipe Ruiz Olaya, Alberto Lopez-Delis, Sebastian Jaramillo Isaza, Rafhael Milanezi Andrade, Alberto Ferreira de Souza, Denis Delisle-Rodriguez, Anselmo Frizera-Neto, Teodiano Freire Bastos Filh. EEG Motor Imagery classification using Deep Learning approaches in naïve BCI users. Biomedical physics & engineering express. 2023-06-15. PMID:37321179. |
the methods proposed here are based on convolutional neural network (cnn), long short-term memory (lstm)/bidirectional long short-term memory (bilstm), and a combination of cnn and lstm used for upper limb mi signal discrimination on a dataset of 25 naïve bci users. |
2023-06-15 |
2023-08-14 |
human |
| Lingjie Wu, Weiqiang Wang, Chenchi Jian. Deep learning-based prediction for time-dependent chloride penetration in concrete exposed to coastal environment. Heliyon. vol 9. issue 6. 2023-06-14. PMID:37313145. |
the study reveals that bidirectional long short-term memory (bi-lstm) and convolutional neural network (cnn) models exhibit rapid convergence during the training stage, but fail to achieve satisfactory accuracy when predicting chloride profiles. |
2023-06-14 |
2023-08-14 |
Not clear |
| Bishwajit Roy, Lokesh Malviya, Radhikesh Kumar, Sandip Mal, Amrendra Kumar, Tanmay Bhowmik, Jong Wan H. Hybrid Deep Learning Approach for Stress Detection Using Decomposed EEG Signals. Diagnostics (Basel, Switzerland). vol 13. issue 11. 2023-06-11. PMID:37296788. |
the traditional deep learning techniques, namely the convolution neural network (cnn), long short-term memory (lstm), bidirectional long short-term memory (bilstm), gated recurrent unit (gru) and recurrent neural network (rnn) models, have been frequently used for stress detection. |
2023-06-11 |
2023-08-14 |
human |
| Rajendhar Junjuri, Ali Saghi, Lasse Lensu, Erik M Vartiaine. Evaluating different deep learning models for efficient extraction of Raman signals from CARS spectra. Physical chemistry chemical physics : PCCP. 2023-06-08. PMID:37287325. |
in this work, a bidirectional lstm (bi-lstm) neural network is explored for the first time to remove the nrb in the cars spectra automatically, and the results are compared with those of three dl models reported in the literature, namely, convolutional neural network (cnn), long short-term memory (lstm) neural network, and very deep convolutional autoencoders (vector). |
2023-06-08 |
2023-08-14 |
Not clear |
| Adil O Khadidos, Khaled H Alyoubi, Shalini Mahato, Alaa O Khadidos, Sachi Nandan Mohant. Machine Learning based EEG Constructed Depression Detection. Neuroscience letters. 2023-05-31. PMID:37257682. |
the methods used for detection of depression are decision tree, random forest, convolutional neural network (cnn), recurrent neural network (rnn), long short-term memory (lstm), gated recurrent unit (gru), bidirectional long-short term memory (bi-lstm), gradient boosting, extreme gradient boosting (xgboost) along with band power. |
2023-05-31 |
2023-08-14 |
Not clear |
| 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 |