| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Pinpin Qin, Hao Li, Ziming Li, Weilai Guan, Yuxin H. A CNN-LSTM Car-Following Model Considering Generalization Ability. Sensors (Basel, Switzerland). vol 23. issue 2. 2023-01-21. PMID:36679458. |
to explore the potential relationship between the leading vehicle and the following vehicle during car-following, we proposed a novel car-following model combining a convolutional neural network (cnn) with a long short-term memory (lstm) network. |
2023-01-21 |
2023-08-14 |
Not clear |
| Bernhard Hollaus, Bernhard Reiter, Jasper C Volme. Catch Recognition in Automated American Football Training Using Machine Learning. Sensors (Basel, Switzerland). vol 23. issue 2. 2023-01-21. PMID:36679637. |
a convolutional neural network (cnn) is used for feature extraction with downstream long short-term memory (lstm) to classify the video data. |
2023-01-21 |
2023-08-14 |
human |
| Ling-Feng Shi, Zhong-Ye Liu, Ke-Jun Zhou, Yifan Shi, Xiao Jin. Novel Deep Learning Network for Gait Recognition Using Multimodal Inertial Sensors. Sensors (Basel, Switzerland). vol 23. issue 2. 2023-01-21. PMID:36679646. |
some recent studies use a convolutional neural network (cnn) or long short-term memory (lstm) to extract gait features, but the methods based on the cnn and lstm have a high loss rate of time-series and spatial information, respectively. |
2023-01-21 |
2023-08-14 |
Not clear |
| Xingyu Tang, Peijie Zheng, Yuewu Liu, Yuhua Yao, Guohua Huan. LangMoDHS: A deep learning language model for predicting DNase I hypersensitive sites in mouse genome. Mathematical biosciences and engineering : MBE. vol 20. issue 1. 2023-01-18. PMID:36650801. |
the langmodhs mainly comprised the convolutional neural network (cnn), the bi-directional long short-term memory (bi-lstm) and the feed-forward attention. |
2023-01-18 |
2023-08-14 |
mouse |
| Wenbo Yang, Wei Liu, Qun Ga. Prediction of dissolved oxygen concentration in aquaculture based on attention mechanism and combined neural network. Mathematical biosciences and engineering : MBE. vol 20. issue 1. 2023-01-18. PMID:36650799. |
finally, we built an integrated prediction model based on convolutional neural network (cnn), bidirectional long and short-term memory neural network (bilstm) and attention mechanism (am), which is called cnn-bilstm-am model. |
2023-01-18 |
2023-08-14 |
Not clear |
| Qitong Yuan, Keyi Chen, Yimin Yu, Nguyen Quoc Khanh Le, Matthew Chin Heng Chu. Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding. Briefings in bioinformatics. 2023-01-15. PMID:36642410. |
the deep learning module contained two channels: bidirectional long short-term memory (bilstm) and convolutional neural network (cnn). |
2023-01-15 |
2023-08-14 |
Not clear |
| Emad Hmood Salman, Montadar Abas Taher, Yousif I Hammadi, Omar Abdulkareem Mahmood, Ammar Muthanna, Andrey Koucheryav. An Anomaly Intrusion Detection for High-Density Internet of Things Wireless Communication Network Based Deep Learning Algorithms. Sensors (Basel, Switzerland). vol 23. issue 1. 2023-01-08. PMID:36616806. |
in the first model, a custom convolutional neural network (cnn) was constructed and combined with long short term memory (lstm) deep network layers. |
2023-01-08 |
2023-08-14 |
Not clear |
| Yaohua Liu, Jinqiang Cui, Wei Lian. A hybrid learning-based stochastic noise eliminating method with attention-Conv-LSTM network for low-cost MEMS gyroscope. Frontiers in neurorobotics. vol 16. 2023-01-02. PMID:36590082. |
specifically, an attention-based learning architecture of convolutional neural network (cnn) and long short-term memory (lstm) is employed to extract the local features and learn the temporal correlation from the mems imu gyroscope raw signals. |
2023-01-02 |
2023-08-14 |
Not clear |
| Abhinav Narula, Naveen Kumar Vaega. Development of CNN-LSTM combinational architecture for COVID-19 detection. Journal of ambient intelligence and humanized computing. 2023-01-02. PMID:36590235. |
in this paper, a novel combinational architecture is built upon the principles of convolution neural networks (cnn) and long short term memory (lstm) networks to detect covid-19 virus. |
2023-01-02 |
2023-08-14 |
Not clear |
| Yuanlin Wang, Jing Sun, Hongbo Yang, Tao Guo, Jiahua Pan, Weilian Wan. [Heart sound classification based on improved mel frequency cepstrum coefficient and integrated decision network method]. Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi. vol 39. issue 6. 2022-12-27. PMID:36575083. |
three classification networks, convolutional neural network (cnn), long and short-term memory network (lstm), and gated recurrent unit (gru) were combined as integrated decision network. |
2022-12-27 |
2023-08-14 |
Not clear |
| Ayesha Mehboob, Muhammad Usman Akram, Norah Saleh Alghamdi, Anum Abdul Sala. A Deep Learning Based Approach for Grading of Diabetic Retinopathy Using Large Fundus Image Dataset. Diagnostics (Basel, Switzerland). vol 12. issue 12. 2022-12-23. PMID:36553091. |
third framework used the long short term memory module in cnn to emphasize the network in remembering information over a long time span. |
2022-12-23 |
2023-08-14 |
Not clear |
| Ahmed Almulihi, Hager Saleh, Ali Mohamed Hussien, Sherif Mostafa, Shaker El-Sappagh, Khaled Alnowaiser, Abdelmgeid A Ali, Moatamad Refaat Hassa. Ensemble Learning Based on Hybrid Deep Learning Model for Heart Disease Early Prediction. Diagnostics (Basel, Switzerland). vol 12. issue 12. 2022-12-23. PMID:36553222. |
the first hybrid model is convolutional neural network (cnn)-long short-term memory (lstm) (cnn-lstm), which integrates cnn and lstm. |
2022-12-23 |
2023-08-14 |
Not clear |
| Sabbir Ahmed, Sameera Mubarak, Jia Tina Du, Santoso Wibow. Forecasting the Status of Municipal Waste in Smart Bins Using Deep Learning. International journal of environmental research and public health. vol 19. issue 24. 2022-12-23. PMID:36554676. |
thus, this study proposes forecasting models comprising of 1d cnn (convolutional neural networks) long short-term memory (lstm), gated recurrent units (gru) and bidirectional long short-term memory (bi-lstm) for time series prediction of public bins. |
2022-12-23 |
2023-08-14 |
Not clear |
| Swati B Bhonde, Sharmila K Wagh, Jayashree R Prasa. Identification of cancer types from gene expressions using learning techniques. Computer methods in biomechanics and biomedical engineering. 2022-12-23. PMID:36562388. |
finally, for classification blend of convolutional neural network (cnn) and bi-directional long short term memory (bi-lstm) is used to predict the target type of cancer. |
2022-12-23 |
2023-08-14 |
Not clear |
| Jingjing Meng, Jianguo He, Min Huang, Yang Li, Baoyu Zhu, Xinxin Kong, Zhe Han, Xin Li, Yang Li. Predictive correction method based on deep learning for a phase compensation system with frozen flow turbulence. Optics letters. vol 47. issue 24. 2022-12-20. PMID:36538452. |
we propose a deep learning method that includes convolution neural network (cnn) and convolutional long short-term memory (convlstm) models to realize atmospheric turbulence compensation and correction of distorted beams. |
2022-12-20 |
2023-08-14 |
Not clear |
| Huong Thi Thu Vu, Hoang-Long Cao, Dianbiao Dong, Tom Verstraten, Joost Geeroms, Bram Vanderborgh. Comparison of machine learning and deep learning-based methods for locomotion mode recognition using a single inertial measurement unit. Frontiers in neurorobotics. vol 16. 2022-12-16. PMID:36524219. |
therefore, this study evaluated three deep learning-based models for locomotion mode recognition, namely recurrent neural network (rnn), long short-term memory (lstm) neural network, and convolutional neural network (cnn), and compared the recognition performance of deep learning models to the machine learning model with random forest classifier (rfc). |
2022-12-16 |
2023-08-14 |
human |
| b' Krist\\xc3\\xadna Machov\\xc3\\xa1, Mari\\xc3\\xa1n Mach, Michal Porezan\\xc3\\xb. Deep Learning in the Detection of Disinformation about COVID-19 in Online Space. Sensors (Basel, Switzerland). vol 22. issue 23. 2022-12-11. PMID:36502024.' |
we have trained three detection models based on three architectures using cnn (convolutional neural networks), lstm (long short-term memory), and their combination. |
2022-12-11 |
2023-08-14 |
Not clear |
| Ankita Singh, Shayok Chakraborty, Zhe He, Shubo Tian, Shenghao Zhang, Mia Liza A Lustria, Neil Charness, Nelson A Roque, Erin R Harrell, Walter R Boo. Deep learning-based predictions of older adults' adherence to cognitive training to support training efficacy. Frontiers in psychology. vol 13. 2022-12-05. PMID:36467206. |
experimental evaluations corroborated the promise and potential of deep learning models for adherence prediction, which furnished highest mean f-scores of 75.5, 75.5, and 74.6% for the convolution neural network (cnn), long short-term memory (lstm) network, and cnn-lstm models respectively. |
2022-12-05 |
2023-08-14 |
human |
| Wael A Altabey, Mohammad Noori, Zhishen Wu, Mohamed A Al-Moghazy, Sallam A Kourite. Studying Acoustic Behavior of BFRP Laminated Composite in Dual-Chamber Muffler Application Using Deep Learning Algorithm. Materials (Basel, Switzerland). vol 15. issue 22. 2022-11-26. PMID:36431563. |
the first dnn is called a recurrent neural network with long short-term memory blocks (rnn-lstm), where the other is called a convolutional neural network (cnn). |
2022-11-26 |
2023-08-14 |
Not clear |
| Hisham ElMoaqet, Mohammad Eid, Mutaz Ryalat, Thomas Penze. A Deep Transfer Learning Framework for Sleep Stage Classification with Single-Channel EEG Signals. Sensors (Basel, Switzerland). vol 22. issue 22. 2022-11-26. PMID:36433422. |
generating tf images from continuous wavelet transform along with a deep transfer architecture composed of a pre-trained googlenet cnn followed by a bidirectional long short-term memory (bilstm) network showed the best scoring performance among all tested scenarios. |
2022-11-26 |
2023-08-14 |
human |