| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Muhammad Usman Tariq, Shuhaida Binti Ismail, Muhammad Babar, Ashir Ahma. Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting. PloS one. vol 18. issue 7. 2023-07-20. PMID:37471397. |
we evaluate the performance of long short-term memory (lstm), bi-directional lstm, convolutional neural networks (cnn), cnn-lstm, multilayer perceptron, gated recurrent unit (gru), and recurrent neural networks (rnn). |
2023-07-20 |
2023-08-14 |
Not clear |
| Kun Liu, Yong Liu, Shuo Ji, Chi Gao, Shizhong Zhang, Jun F. A Novel Gait Phase Recognition Method Based on DPF-LSTM-CNN Using Wearable Inertial Sensors. Sensors (Basel, Switzerland). vol 23. issue 13. 2023-07-14. PMID:37447755. |
the advantages of long short-term memory (lstm) and convolutional neural network (cnn) are combined (lstm-cnn) in this paper, then a gait phase recognition method based on lstm-cnn neural network model is proposed. |
2023-07-14 |
2023-08-14 |
Not clear |
| Wei Zhong, Chen Qian, Wanjun Liu, Liping Zhu, Runze L. Feature Screening for Interval-Valued Response with Application to Study Association between Posted Salary and Required Skills. Journal of the American Statistical Association. vol 118. issue 542. 2023-07-14. PMID:37448462. |
we find that the skill words like optimization, long short-term memory (lstm), convolutional neural networks (cnn), collaborative filtering, are positively correlated with the salary while the words like excel, office, data collection, may negatively contribute to the salary. |
2023-07-14 |
2023-08-14 |
Not clear |
| Jing Chen, Jiping Wang, Qun Yuan, Zhao Yan. CNN-LSTM Model for Recognizing Video-Recorded Actions Performed in a Traditional Chinese Exercise. IEEE journal of translational engineering in health and medicine. vol 11. 2023-07-12. PMID:37435544. |
we first developed a combined convolutional neural network (cnn) and long short-term memory (lstm) model for recognizing the sequence of actions captured in video frames, and applied it to recognize the actions of baduanjin. |
2023-07-12 |
2023-08-14 |
human |
| Gi-Taek An, Jung-Min Park, Kyung-Soon Le. Contrastive Learning-Based Anomaly Detection for Actual Corporate Environments. Sensors (Basel, Switzerland). vol 23. issue 10. 2023-07-11. PMID:37430676. |
to evaluate the effectiveness of the proposed method, we compared it with traditional deep learning models, such as the convolutional neural network (cnn) and long short-term memory (lstm). |
2023-07-11 |
2023-08-14 |
Not clear |
| Yufeng Zheng, Erik Blasc. Facial Micro-Expression Recognition Enhanced by Score Fusion and a Hybrid Model from Convolutional LSTM and Vision Transformer. Sensors (Basel, Switzerland). vol 23. issue 12. 2023-07-08. PMID:37420815. |
then, a hybrid nn model is created by combining a convolutional neural network (cnn), a recurrent neural network (rnn, e.g., long short-term memory (lstm)), and a vision transformer. |
2023-07-08 |
2023-08-14 |
human |
| Sameer Sayyad, Satish Kumar, Arunkumar Bongale, Ketan Kotecha, Ajith Abraha. Remaining Useful-Life Prediction of the Milling Cutting Tool Using Time-Frequency-Based Features and Deep Learning Models. Sensors (Basel, Switzerland). vol 23. issue 12. 2023-07-08. PMID:37420825. |
in this work, the authors considers the time-frequency domain (tfd) features such as short-time fourier-transform (stft) and different wavelet transforms (wt) along with deep learning (dl) models such as long short-term memory (lstm), different variants of lstn, convolutional neural network (cnn), and hybrid models that are a combination of ccn with lstm variants for rul estimation. |
2023-07-08 |
2023-08-14 |
Not clear |
| Arash Gharehbaghi, Elaheh Partovi, Ankica Babi. Recurrent vs Non-Recurrent Convolutional Neural Networks for Heart Sound Classification. Studies in health technology and informatics. vol 305. 2023-06-30. PMID:37387059. |
the study considers various combinations of parallel and cascaded integration of cnn with gated recurrent network (grn) as well as long- short term memory (lstm) and explores the accuracy and sensitivity of each integration independently, using the physionet dataset of heart sound recordings. |
2023-06-30 |
2023-08-14 |
Not clear |
| Yingchao Xue, Hui Jian. Monitoring of Chlorpyrifos Residues in Corn Oil Based on Raman Spectral Deep-Learning Model. Foods (Basel, Switzerland). vol 12. issue 12. 2023-06-28. PMID:37372614. |
this study presents a novel method for the quantitative detection of residual chlorpyrifos in corn oil through raman spectroscopy using a combined long short-term memory network (lstm) and convolutional neural network (cnn) architecture. |
2023-06-28 |
2023-08-14 |
Not clear |
| Antong Sun, Xiang Chen, Mengjuan Xu, Xu Zhang, Xun Che. Feasibility study on the application of a spiking neural network in myoelectric control systems. Frontiers in neuroscience. vol 17. 2023-06-28. PMID:37378016. |
compared with a convolutional neural network (cnn), long short-term memory network (lstm) and linear discriminant analysis (lda), snn can effectively reduce the number of repetitions in the training set, and its power consumption was reduced by 1-2 orders of magnitude. |
2023-06-28 |
2023-08-14 |
Not clear |
| Pengfei Xie, Jujuan Zhuang, Geng Tian, Jialiang Yan. Emvirus: An embedding-based neural framework for human-virus protein-protein interactions prediction. Biosafety and health. vol 5. issue 3. 2023-06-27. PMID:37362223. |
in this work, we present an embedding-based neural framework with convolutional neural network (cnn) and bi-directional long short-term memory unit (bi-lstm) architecture, named emvirus, to predict human-virus ppis (including human-sars-cov-2 ppis). |
2023-06-27 |
2023-08-14 |
human |
| M Chithambarathanu, M K Jeyakuma. Survey on crop pest detection using deep learning and machine learning approaches. Multimedia tools and applications. 2023-06-26. PMID:37362671. |
a clear overview of recent research in the area of crop pests and pathogens identification using techniques in machine learning techniques like random forest (rf), support vector machine (svm), and decision tree (dt), naive bayes (nb), and also some deep learning methods like convolutional neural network (cnn), long short-term memory (lstm), deep convolutional neural network (dcnn), deep belief network (dbn) was presented. |
2023-06-26 |
2023-08-14 |
human |
| Amin Zabardast, Elif Güney Tamer, Yeşim Aydın Son, Arif Yılma. An automated framework for evaluation of deep learning models for splice site predictions. Scientific reports. vol 13. issue 1. 2023-06-23. PMID:37353532. |
convolutional neural network (cnn), long short-term memory (lstm) and its bidirectional version (blstm), gated recurrent unit (gru), and its bidirectional version (bgru) are promising models. |
2023-06-23 |
2023-08-14 |
human |
| Shoaib Ahmed, Dost Muhammad Khan, Saima Sadiq, Muhammad Umer, Faisal Shahzad, Khalid Mahmood, Heba Mohsen, Imran Ashra. Temporal analysis and opinion dynamics of COVID-19 vaccination tweets using diverse feature engineering techniques. PeerJ. Computer science. vol 9. 2023-06-22. PMID:37346678. |
the influence of term frequency-inverse document frequency, bag of words (bow), word2vec, and combination of tf-idf and bow are explored with classifiers including random forest, gradient boosting machine, extra tree classifier (etc), logistic regression, naïve bayes, stochastic gradient descent, multilayer perceptron, convolutional neural network (cnn), bidirectional encoder representations from transformers (bert), long short-term memory (lstm), and recurrent neural network (rnn). |
2023-06-22 |
2023-08-14 |
Not clear |
| 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 |