| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Y Rama Muni Reddy, P Muralidhar, M Sriniva. An Effective Hybrid Deep Learning Model for Single-Channel EEG-Based Subject-Independent Drowsiness Recognition. Brain topography. 2023-11-23. PMID:37995000. |
the proposed model combines the strengths of discrete wavelet long short-term memory (dwlstm) and convolutional neural networks (cnn) models to classify single-channel electroencephalogram (eeg) signals. |
2023-11-23 |
2023-11-29 |
human |
| Jiawei Wu, Peng Ren, Boming Song, Ran Zhang, Chen Zhao, Xiao Zhan. Data glove-based gesture recognition using CNN-BiLSTM model with attention mechanism. PloS one. vol 18. issue 11. 2023-11-17. PMID:37976294. |
in a-cbln, the convolutional neural network (cnn) is employed to capture local features, while the bidirectional long short-term memory (bilstm) is used to extract contextual temporal features of gesture data. |
2023-11-17 |
2023-11-20 |
human |
| Yu Zheng, Huan Yee Koh, Ming Jin, Lianhua Chi, Khoa T Phan, Shirui Pan, Yi-Ping Phoebe Chen, Wei Xian. Correlation-Aware Spatial-Temporal Graph Learning for Multivariate Time-Series Anomaly Detection. IEEE transactions on neural networks and learning systems. vol PP. 2023-11-15. PMID:37962997. |
existing approaches for this problem mostly employ either statistical models which cannot capture the nonlinear relations well or conventional deep learning (dl) models e.g., convolutional neural network (cnn) and long short-term memory (lstm) that do not explicitly learn the pairwise correlations among variables. |
2023-11-15 |
2023-11-20 |
Not clear |
| Shaista Hussain, Jacqueline Chua, Damon Wong, Justin Lo, Aiste Kadziauskiene, Rimvydas Asoklis, George Barbastathis, Leopold Schmetterer, Liu Yon. Predicting glaucoma progression using deep learning framework guided by generative algorithm. Scientific reports. vol 13. issue 1. 2023-11-15. PMID:37968437. |
in this work, we developed a multimodal deep learning model comprising a convolutional neural network (cnn) and a long short-term memory (lstm) network, for glaucoma progression prediction. |
2023-11-15 |
2023-11-20 |
Not clear |
| Nissrin Amrani El Yaakoubi, Caitlin McDonald, Olive Lenno. Prediction of Gait Kinematics and Kinetics: A Systematic Review of EMG and EEG Signal Use and Their Contribution to Prediction Accuracy. Bioengineering (Basel, Switzerland). vol 10. issue 10. 2023-10-30. PMID:37892892. |
long short-term memory (lstm) and convolutional neural network (cnn) tools demonstrated highest accuracies. |
2023-10-30 |
2023-11-08 |
human |
| Xiao-Xia Yin, Sillas Hadjilouca. Digital Filtering Techniques Using Fuzzy-Rules Based Logic Control. Journal of imaging. vol 9. issue 10. 2023-10-27. PMID:37888315. |
we also explain the potential merits of adopting a fuzzy rule based deep learning ensemble classifier which is composed of a convolutional neural network (cnn), a recurrent neural networks (rnn), a long short term memory neural network (lstm) and a gated recurrent unit (gru) approaches, all within a fuzzy min-max (fmm) ensemble. |
2023-10-27 |
2023-11-08 |
Not clear |
| Fikirte Alemayehu, Million Meshesha, Jemal Abat. Amharic political sentiment analysis using deep learning approaches. Scientific reports. vol 13. issue 1. 2023-10-20. PMID:37864050. |
the research employs deep learning techniques, including convolutional neural networks (cnn), bidirectional long short-term memory (bi-lstm), and a hybrid model combining cnn with bi-lstm to analyze and classify sentiments. |
2023-10-20 |
2023-11-08 |
Not clear |
| Xiaochu Wang, Meizhen Wang, Xuejun Liu, Ying Mao, Yang Chen, Songsong Da. Surveillance-image-based outdoor air quality monitoring. Environmental science and ecotechnology. vol 18. 2023-10-16. PMID:37841651. |
our model, which integrates a convolutional neural network (cnn) and long short-term memory (lstm), adeptly captures spatial-temporal image features, enabling air quality estimation at any time of day, including pm |
2023-10-16 |
2023-11-08 |
human |
| Tao Yuhuan Wang, Jiajia Cui, Yao Fa. A wearable-based sports health monitoring system using CNN and LSTM with self-attentions. PloS one. vol 18. issue 10. 2023-10-11. PMID:37819909. |
the proposed model combines a convolutional neural network (cnn), long short-term memory (lstm), and self-attention mechanisms. |
2023-10-11 |
2023-10-15 |
Not clear |
| Xukun Huang, Xiao Chen, Huiwen Tan, Minruihong Wang, Yimin Li, Yuanchen Wei, Jie Zhang, Deyong Chen, Junbo Wang, Yueying Li, Jian Che. Advance of microfluidic flow cytometry enabling high-throughput characterization of single-cell electrical and structural properties. Cytometry. Part A : the journal of the International Society for Analytical Cytology. 2023-10-10. PMID:37814588. |
as a demonstration, two leukemia cell lines (e.g., hl60 vs. jurkat) were analyzed, producing high-classification accuracies of 99.3% based on electrical features extracted from long short-term memory (lstm) of rnn, 96.7% based on structural features extracted from resnet18 of cnn and 100.0% based on combined features enabled by svm. |
2023-10-10 |
2023-10-15 |
Not clear |
| Mohamed K Elmezughi, Omran Salih, Thomas J Afullo, Kevin J Duff. Path loss modeling based on neural networks and ensemble method for future wireless networks. Heliyon. vol 9. issue 9. 2023-10-09. PMID:37809436. |
this paper presents path loss prediction models based on machine learning algorithms, namely artificial neural network (ann), artificial recurrent neural network (rnn) based on long short-term memory (lstm), shortly known as rnn-lstm, and convolutional neural network (cnn). |
2023-10-09 |
2023-10-15 |
Not clear |
| Fayez Alfayez, Surbhi Bhatia Kha. IoT-blockchain empowered Trinet: optimized fall detection system for elderly safety. Frontiers in bioengineering and biotechnology. vol 11. 2023-10-09. PMID:37811373. |
the proposed approach employs trinet, including long short-term memory, optimized convolutional neural network (cnn), and recurrent neural network for accurate fall detection. |
2023-10-09 |
2023-10-15 |
Not clear |
| Lijia Ma, Wenwei Deng, Yuan Bai, Zhanwei Du, Minfeng Xiao, Lin Wang, Jianqiang Li, Asoke K Nand. Identifying Phage Sequences From Metagenomic Data Using Deep Neural Network With Word Embedding and Attention Mechanism. IEEE/ACM transactions on computational biology and bioinformatics. vol PP. 2023-10-09. PMID:37812548. |
then, we design a deep neural network with a convolutional neural network (cnn) to capture the feature maps in sequences, and with a bi-directional long short-term memory network (bi-lstm) to capture the long-term dependencies between features from both forward and backward directions. |
2023-10-09 |
2023-10-15 |
Not clear |
| Changjiang Li, Quan Zou, Cangzhi Jia, Jia Zhen. AMPpred-MFA: An Interpretable Antimicrobial Peptide Predictor with a Stacking Architecture, Multiple Features, and Multihead Attention. Journal of chemical information and modeling. 2023-10-06. PMID:37799091. |
multiple features and a multihead attention mechanism are utilized on the basis of a bidirectional long short-term memory (lstm) network and a convolutional neural network (cnn). |
2023-10-06 |
2023-10-07 |
Not clear |
| Su Bin Choi, Hyun Sik Shin, Jong-Woong Ki. Convolution Neural Networks for Motion Detection with Electrospun Reversibly-Cross-linkable Polymers and Encapsulated Ag Nanowires. ACS applied materials & interfaces. 2023-10-02. PMID:37782487. |
deep learning algorithms, including a singular 1d convolutional neural network (1d cnn), long short-term memory (lstm) network, and dual-layered combinations of 1d cnn + lstm and lstm + 1d cnn, were deployed for signal classification. |
2023-10-02 |
2023-10-07 |
Not clear |
| Ruici Zhang, Xiang Wen, Huanqiang Cao, Pengfei Cui, Hua Chai, Runbo Hu, Rongjie Y. Critical safety management driver identification based upon temporal variation characteristics of driving behavior. Accident; analysis and prevention. vol 193. 2023-10-02. PMID:37783160. |
deep learning models including convolutional neural network (cnn) and long short-term memory (lstm) were employed to conduct the temporal variation feature mining. |
2023-10-02 |
2023-10-07 |
Not clear |
| Chaozhu Zhang, Hongxing Chu, Mingyuan M. Decoding Algorithm of Motor Imagery Electroencephalogram Signal Based on CLRNet Network Model. Sensors (Basel, Switzerland). vol 23. issue 18. 2023-09-28. PMID:37765751. |
this study uses a combination of a convolutional neural network (cnn) and a long short-term memory (lstm) network to obtain spatial information and temporal correlation from eeg signals. |
2023-09-28 |
2023-10-07 |
Not clear |
| Ali A Rabaan, Muhammed A Bakhrebah, Jawaher Alotaibi, Zuhair S Natto, Rahaf S Alkhaibari, Eman Alawad, Huda M Alshammari, Sara Alwarthan, Mashael Alhajri, Mohammed S Almogbel, Maha H Aljohani, Fadwa S Alofi, Nada Alharbi, Wasl Al-Adsani, Abdulrahman M Alsulaiman, Jehad Aldali, Fatimah Al Ibrahim, Reem S Almaghrabi, Awad Al-Omari, Mohammed Garou. Unleashing the power of artificial intelligence for diagnosing and treating infectious diseases: A comprehensive review. Journal of infection and public health. vol 16. issue 11. 2023-09-28. PMID:37769584. |
artificial neural networks (ann), recurrent neural networks (rnn), support vector machines (svm), multilayer neural networks (mlnn), cnn, long short-term memory (lstm), and random forests (rf) are among the models discussed. |
2023-09-28 |
2023-10-07 |
Not clear |
| Ali Bakhshali, Hossein Najafi, Behnam Behinaein Hamgini, Zhuhong Zhan. Neural network architectures for optical channel nonlinear compensation in digital subcarrier multiplexing systems. Optics express. vol 31. issue 16. 2023-09-15. PMID:37710504. |
we perform nonlinear channel equalization by employing different ann cores including convolutional neural networks (cnn) and long short-term memory (lstm) layers. |
2023-09-15 |
2023-10-07 |
Not clear |
| Kai Liu, Yuanhang Zhang, Huan He, Hui Xiao, Siyuan Wang, Yuteng Zhang, Huiming Li, Xin Qia. Time series prediction of the chemical components of PM Chemosphere. 2023-09-15. PMID:37714468. |
in this study, a convolutional neural network (cnn) and long and short-term memory (lstm) neural networks are integrated to create a cnn-lstm time series prediction model to predict the concentration of pm |
2023-09-15 |
2023-10-07 |
Not clear |