| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| 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 |
| Hanyun Li, Wenzao Li, Jiacheng Zhao, Peizhen Yu, Yao Huan. A sentiment analysis approach for travel-related Chinese online review content. PeerJ. Computer science. vol 9. 2023-09-14. PMID:37705661. |
to address the above issues, we introduce a dual-channel algorithm that integrates convolutional neural networks (cnn) and bi-directional long and short-term memory (bilstm) with an attention mechanism (dc-cbla). |
2023-09-14 |
2023-10-07 |
Not clear |
| Walaa Othman, Batol Hamoud, Alexey Kashevnik, Nikolay Shilov, Ammar Al. A Machine Learning-Based Correlation Analysis between Driver Behaviour and Vital Signs: Approach and Case Study. Sensors (Basel, Switzerland). vol 23. issue 17. 2023-09-09. PMID:37687842. |
additionally, new models have been developed using convolutional neural network (cnn) and bidirectional long short-term memory (bilstm) to classify the external events from out-cabin videos, as well as a decision tree classifier to detect the driver's maneuver using accelerometer and gyroscope sensor data. |
2023-09-09 |
2023-10-07 |
Not clear |
| Xiashuang Wang, Yinglei Wang, Dunwei Liu, Ying Wang, Zhengjun Wan. Automated recognition of epilepsy from EEG signals using a combining space-time algorithm of CNN-LSTM. Scientific reports. vol 13. issue 1. 2023-09-08. PMID:37684278. |
in this study, we propose a novel deep learning method that combines a convolution neural network (cnn) with a long short-term memory (lstm) network for multi-class classification including both binary and ternary tasks, using a publicly available benchmark database on epilepsy eegs. |
2023-09-08 |
2023-10-07 |
human |
| Yi-Jun Tang, Ke Yan, Xingyi Zhang, Ye Tian, Bin Li. Protein intrinsically disordered region prediction by combining neural architecture search and multi-objective genetic algorithm. BMC biology. vol 21. issue 1. 2023-09-06. PMID:37674132. |
first, the existing predictors construct network structures based on their own experiences such as convolutional neural network (cnn) which is used to extract the feature of neighboring residues in protein, and long short-term memory (lstm) is used to extract the long-distance dependencies feature of protein residues. |
2023-09-06 |
2023-10-07 |
Not clear |
| Manoj Kumar, Anoop Kumar Patel, Mantosh Biswas, S Shithart. Attention-based bidirectional-long short-term memory for abnormal human activity detection. Scientific reports. vol 13. issue 1. 2023-09-02. PMID:37660111. |
using a convolutional neural network (cnn), a bidirectional long short-term memory (bi-lstm), and an attention mechanism to pay attention to the unique spatiotemporal characteristics of raw video streams, a deep-learning approach has been implemented in the proposed framework to detect anomalous human activity. |
2023-09-02 |
2023-09-07 |
human |
| Mengjuan Xu, Xiang Chen, Antong Sun, Xu Zhang, Xun Che. A Novel Event-Driven Spiking Convolutional Neural Network for Electromyography Pattern Recognition. IEEE transactions on bio-medical engineering. vol 70. issue 9. 2023-08-31. PMID:37030849. |
compared to cnn with the same structure, cnn-long short term memory (cnn-lstm), linear kernel linear discriminant analysis classifier (lda) and spiking multilayer perceptron (spiking mlp), the accuracy of scnn is 50.69%, 33.92%, 32.94% and 9.41% higher in the small sample training experiment, 6.50%, 4.23%, 28.73%, and 2.57% higher in the electrode shifts experiment respectively. |
2023-08-31 |
2023-09-07 |
human |
| Kaidi Liu, Xiaohan Xie, Juanting Yan, Sizong Zhang, Hui Zhan. An adsorption isotherm identification method based on CNN-LSTM neural network. Journal of molecular modeling. vol 29. issue 9. 2023-08-31. PMID:37651008. |
in this paper, we deploy a hybrid of convolutional neural networks (cnn) and long short-term memory (lstm) networks for the identification of adsorption isotherms. |
2023-08-31 |
2023-09-07 |
Not clear |
| Li Shang, Zi Zhang, Fujian Tang, Qi Cao, Hong Pan, Zhibin Li. CNN-LSTM Hybrid Model to Promote Signal Processing of Ultrasonic Guided Lamb Waves for Damage Detection in Metallic Pipelines. Sensors (Basel, Switzerland). vol 23. issue 16. 2023-08-26. PMID:37631596. |
machine learning approaches in recent years, including convolutional neural networks (cnn) and long short-term memory (lstm), have exhibited their advantages to overcome these challenges for the signal processing and data classification of complex systems, thus showing great potential for damage detection in critical oil/gas pipeline structures. |
2023-08-26 |
2023-09-07 |
Not clear |
| Jimin Jun, Hong Kook Ki. Informer-Based Temperature Prediction Using Observed and Numerical Weather Prediction Data. Sensors (Basel, Switzerland). vol 23. issue 16. 2023-08-26. PMID:37631584. |
recently, deep-learning-based temperature prediction models have been proposed, demonstrating successful performances, such as conventional neural network (cnn)-based models, bi-directional long short-term memory (blstm)-based models, and a combination of both neural networks, cnn-blstm. |
2023-08-26 |
2023-09-07 |
Not clear |
| Khawla Seddiki, Fŕed Eric Precioso, Melissa Sanabria, Michel Salzet, Isabelle Fournier, Arnaud Droi. Early Diagnosis: End-to-End CNN-LSTM Models for Mass Spectrometry Data Classification. Analytical chemistry. 2023-08-25. PMID:37624777. |
it is a combination of a convolutional neural network (cnn) and a long short-term memory (lstm) network. |
2023-08-25 |
2023-09-07 |
Not clear |
| Lantian Yao, Yuntian Zhang, Wenshuo Li, Chia-Ru Chung, Jiahui Guan, Wenyang Zhang, Ying-Chih Chiang, Tzong-Yi Le. DeepAFP: an effective computational framework for identifying antifungal peptides based on deep learning. Protein science : a publication of the Protein Society. 2023-08-18. PMID:37595093. |
deepafp fully leverages and mines composition information, evolutionary information, and physicochemical properties of peptides by employing combined kernels from multiple branches of convolutional neural network (cnn) with bi-directional long short-term memory (bilstm) layers. |
2023-08-18 |
2023-10-07 |
human |
| Wuyi Yang, Wenlei Chang, Zhongchang Song, Fuqiang Niu, Xianyan Wang, Yu Zhan. Denoising odontocete echolocation clicks using a hybrid model with convolutional neural network and long short-term memory network. The Journal of the Acoustical Society of America. vol 154. issue 2. 2023-08-15. PMID:37581404. |
in this study, a hybrid model based on the convolutional neural network (cnn) and long short-term memory (lstm) network-called a hybrid cnn-lstm model-was proposed to denoise echolocation clicks. |
2023-08-15 |
2023-09-07 |
Not clear |
| Iva Matetić, Ivan Štajduhar, Igor Wolf, Sandi Ljubi. Improving the Efficiency of Fan Coil Units in Hotel Buildings through Deep-Learning-Based Fault Detection. Sensors (Basel, Switzerland). vol 23. issue 15. 2023-08-12. PMID:37571501. |
we tested three contemporary dl modeling approaches: convolutional neural network (cnn), long short-term memory network (lstm), and a combination of cnn and gated recurrent unit (gru). |
2023-08-12 |
2023-08-16 |
Not clear |
| Chaojun Wen, Xiaoqing Lin, Yuxuan Ying, Yunfeng Ma, Hong Yu, Xiaodong Li, Jianhua Ya. Dioxin emission prediction from a full-scale municipal solid waste incinerator: Deep learning model in time-series input. Waste management (New York, N.Y.). vol 170. 2023-08-10. PMID:37562201. |
then, the dioxin emission prediction performance of the machine learning and deep learning models, including long short-term memory (lstm) and convolutional neural networks (cnn), with normal input and time-series input are compared. |
2023-08-10 |
2023-08-16 |
Not clear |
| Mohsen Sadat Shahabi, Ahmad Shalbaf, Reza Rostam. Prediction of response to repetitive transcranial magnetic stimulation for major depressive disorder using hybrid Convolutional recurrent neural networks and raw Electroencephalogram Signal. Cognitive neurodynamics. vol 17. issue 4. 2023-07-31. PMID:37522037. |
in this work, we proposed a hybrid model created by pre-trained convolutional neural networks (cnn) models and bidirectional long short-term memory (blstm) cells to predict response to rtms treatment from raw eeg signal. |
2023-07-31 |
2023-08-14 |
Not clear |
| Rudresh Deepak Shirwaikar, Iram Sarwari, Mehwish Najam, Shama H . Has Machine Learning Enhanced the Diagnosis of Autism Spectrum Disorder? Critical reviews in biomedical engineering. vol 51. issue 1. 2023-07-31. PMID:37522537. |
diagnosis of autism has recently made substantial use of long short term memory (lstm), convolutional neural network (cnn) and its variants, the random forest (rf) and naive bayes (nb) machine learning techniques. |
2023-07-31 |
2023-08-14 |
Not clear |