| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Puneet Kumar, Balasubramanian Rama. A BERT based dual-channel explainable text emotion recognition system. Neural networks : the official journal of the International Neural Network Society. vol 150. 2022-03-31. PMID:35358887. |
then the embedding vectors are fed as the inputs to the dual-channel network containing two network channels made up of convolutional neural network (cnn) and bidirectional long short term memory (bilstm) network. |
2022-03-31 |
2023-08-13 |
Not clear |
| Hemanth Kari, Surya Manikhanta Sowri Bandi, Aditya Kumar, Venkata Rajesh Yell. DeePromClass: Delineator for Eukaryotic Core Promoters employing Deep Neural Networks. IEEE/ACM transactions on computational biology and bioinformatics. vol PP. 2022-03-30. PMID:35353704. |
to this end, we implemented convolutional neural network (cnn) and long short-term memory (lstm) recurrent neural network architecture for five model systems with [-100 to +50] segments relative to the transcription start site being the core promoter. |
2022-03-30 |
2023-08-13 |
mouse |
| Madhurananda Pahar, Igor Miranda, Andreas Diacon, Thomas Niesle. Automatic Non-Invasive Cough Detection based on Accelerometer and Audio Signals. Journal of signal processing systems. 2022-03-28. PMID:35341095. |
logistic regression (lr), support vector machine (svm) and multilayer perceptron (mlp) classifiers provide a baseline and are compared with three deep architectures, convolutional neural network (cnn), long short-term memory (lstm) network, and residual-based architecture (resnet50) using a leave-one-out cross-validation scheme. |
2022-03-28 |
2023-08-13 |
Not clear |
| Ke-Wei Chen, Laura Bear, Che-Wei Li. Solving Inverse Electrocardiographic Mapping Using Machine Learning and Deep Learning Frameworks. Sensors (Basel, Switzerland). vol 22. issue 6. 2022-03-26. PMID:35336502. |
the fully connected neural network (fcn), long short-term memory (lstm), convolutional neural network (cnn) methods were used for constructing the model. |
2022-03-26 |
2023-08-13 |
Not clear |
| Harnain Kour, Manoj K Gupt. An hybrid deep learning approach for depression prediction from user tweets using feature-rich CNN and bi-directional LSTM. Multimedia tools and applications. 2022-03-23. PMID:35317471. |
the proposed model, however, is a hybrid of two deep learning architectures, convolutional neural network (cnn) and bi-directional long short-term memory (bilstm) that after optimization obtains an accuracy of 94.28% on benchmark depression dataset containing tweets. |
2022-03-23 |
2023-08-13 |
Not clear |
| Kamel K Mohammed, Aboul Ella Hassanien, Heba M Afif. Classification of Ear Imagery Database using Bayesian Optimization based on CNN-LSTM Architecture. Journal of digital imaging. 2022-03-17. PMID:35296939. |
this paper presented an ear diagnosis approach based on a convolutional neural network (cnn) as feature extraction and long short-term memory (lstm) as a classifier algorithm. |
2022-03-17 |
2023-08-13 |
Not clear |
| Anand Shankar, Samarendra Dandapat, Shovan Barm. Seizure Types Classification by Generating Input Images With In-Depth Features From Decomposed EEG Signals For Deep Learning Pipeline. IEEE journal of biomedical and health informatics. vol PP. 2022-03-16. PMID:35294366. |
for classification, a hybrid dl pipeline has been constructed by combining the convolution neural network (cnn) followed by long short-term memory (lstm) for efficient extraction of spatial and time sequence information. |
2022-03-16 |
2023-08-13 |
Not clear |
| Hongye Cao, Ling Han, Liangzhi L. A deep learning method for cyanobacterial harmful algae blooms prediction in Taihu Lake, China. Harmful algae. vol 113. 2022-03-15. PMID:35287935. |
finally, the features of cyanohabs area and meteorological data were extracted by convolutional neural networks (cnn) model and used as the input of long short term memory network (lstm). |
2022-03-15 |
2023-08-13 |
Not clear |
| Muhammad Aftab, Rashid Amin, Deepika Koundal, Hamza Aldabbas, Bader Alouffi, Zeshan Iqba. Classification of COVID-19 and Influenza Patients Using Deep Learning. Contrast media & molecular imaging. vol 2022. 2022-03-14. PMID:35280712. |
our proposed long short-term memory (lstm) technique outperformed the cnn model in the evaluation phase on chest x-ray images, achieving 98% accuracy. |
2022-03-14 |
2023-08-13 |
Not clear |
| Kenshi Saho, Sora Hayashi, Mutsuki Tsuyama, Lin Meng, Masao Masug. Machine Learning-Based Classification of Human Behaviors and Falls in Restroom via Dual Doppler Radar Measurements. Sensors (Basel, Switzerland). vol 22. issue 5. 2022-03-10. PMID:35270868. |
machine learning methods, including the convolutional neural network (cnn), long short-term memory, support vector machine, and random forest methods, are applied to the doppler radar data to verify the model's efficiency and features. |
2022-03-10 |
2023-08-13 |
human |
| Muhammad Shadab Alam Hashmi, Muhammad Ibrahim, Imran Sarwar Bajwa, Hafeez-Ur-Rehman Siddiqui, Furqan Rustam, Ernesto Lee, Imran Ashra. Railway Track Inspection Using Deep Learning Based on Audio to Spectrogram Conversion: An on-the-Fly Approach. Sensors (Basel, Switzerland). vol 22. issue 5. 2022-03-10. PMID:35271130. |
two convolutional neural networks (cnn) models, convolutional 1d and convolutional 2d, and one recurrent neural network (rnn) model, a long short-term memory (lstm) model, are used in this regard. |
2022-03-10 |
2023-08-13 |
Not clear |
| Geng Du-Yan, Wang Jia-Xing, Wang Yan, Liu Xuan-Y. Convolutional neural network is a good technique for sleep staging based on HRV: a comparative analysis. Neuroscience letters. 2022-03-01. PMID:35227774. |
based on two independent public datasets, we construct three end-to-end automatic sleep staging models: fully connected neural networks (fcn), convolutional neural networks (cnn) and long short-term memory networks (lstm). |
2022-03-01 |
2023-08-13 |
human |
| Huaqing Peng, Heng Li, Yu Zhang, Siyuan Wang, Kai Gu, Mifeng Re. Multi-Sensor Vibration Signal Based Three-Stage Fault Prediction for Rotating Mechanical Equipment. Entropy (Basel, Switzerland). vol 24. issue 2. 2022-02-25. PMID:35205459. |
firstly, based on the vibration signals from multiple sensors, a convolutional neural network (cnn) and long short-term memory (lstm) network are combined to extract the spatiotemporal features of the degradation period and fault type by means of the cross-entropy loss function. |
2022-02-25 |
2023-08-13 |
Not clear |
| Zhongzhong Guo, Shangqi Yu, Jiazhi Fu, Kai Ma, Rui Zhan. Screening and functional prediction of differentially expressed genes in walnut endocarp during hardening period based on deep neural network under agricultural internet of things. PloS one. vol 17. issue 2. 2022-02-24. PMID:35202404. |
then, the convolutional neural network (cnn) and long and short-term memory (lstm) network model are adopted to construct an expression gene screening and function prediction model. |
2022-02-24 |
2023-08-13 |
Not clear |
| Xin Wan, Xiaoyong Li, Xinzhi Wang, Xiaohui Yi, Yinzhong Zhao, Xinzhong He, Renren Wu, Mingzhi Huan. Water quality prediction model using Gaussian process regression based on deep learning for carbon neutrality in papermaking wastewater treatment system. Environmental research. 2022-02-21. PMID:35189104. |
a new water quality prediction cswlstm-gpr model, which fused the spatial feature of convolutional neural network (cnn), the temporal feature of sharing-weight long short-term memory (swlstm) and the probabilistic reliability of gaussian process regression (gpr), was applied for monitoring papermaking wastewater treatment system with high-precision point prediction and interval prediction. |
2022-02-21 |
2023-08-13 |
Not clear |
| Shai Kadish, David Schmid, Jarryd Son, Edward Boj. Computer Vision-Based Classification of Flow Regime and Vapor Quality in Vertical Two-Phase Flow. Sensors (Basel, Switzerland). vol 22. issue 3. 2022-02-15. PMID:35161751. |
the approach makes use of computer vision techniques and deep learning to train a convolutional neural network (cnn), which is used for individual frame classification and image feature extraction, and a deep long short-term memory (lstm) network, used to capture temporal information present in a sequence of image feature sets and to make a final vapor quality or flow regime classification. |
2022-02-15 |
2023-08-13 |
Not clear |
| Tahani Alqurash. Stance Analysis of Distance Education in the Kingdom of Saudi Arabia during the COVID-19 Pandemic Using Arabic Twitter Data. Sensors (Basel, Switzerland). vol 22. issue 3. 2022-02-15. PMID:35161752. |
several classical machine-learning models and deep-learning models, including ensemble random forest (rf), support vector machine (svm), adaptive boosting (adaboost), multinomial naïve bayes (mnb), convolutional neural network (cnn), and long short-term memory (lstm), were tested on this data, and the best-performing models were selected to analyze the public stance towards distance education. |
2022-02-15 |
2023-08-13 |
Not clear |
| Georgios Petmezas, Grigorios-Aris Cheimariotis, Leandros Stefanopoulos, Bruno Rocha, Rui Pedro Paiva, Aggelos K Katsaggelos, Nicos Maglavera. Automated Lung Sound Classification Using a Hybrid CNN-LSTM Network and Focal Loss Function. Sensors (Basel, Switzerland). vol 22. issue 3. 2022-02-15. PMID:35161977. |
features initially extracted from short-time fourier transform (stft) spectrograms via a convolutional neural network (cnn) are given as input to a long short-term memory (lstm) network that memorizes the temporal dependencies between data and classifies four types of lung sounds, including normal, crackles, wheezes, and both crackles and wheezes. |
2022-02-15 |
2023-08-13 |
Not clear |
| Zia Ur Rahman, Syed Irfan Ullah, Abdus Salam, Taj Rahman, Inayat Khan, Badam Niaz. Automated Detection of Rehabilitation Exercise by Stroke Patients Using 3-Layer CNN-LSTM Model. Journal of healthcare engineering. vol 2022. 2022-02-14. PMID:35154616. |
due to numerous achievements and increasing popularity of deep learning (dl) techniques, in this research article a dl model that combines convolutional neural network (cnn) and long short-term memory (lstm) is proposed and is named as 3-layer cnn-lstm model. |
2022-02-14 |
2023-08-13 |
human |
| Xin Jing, Jungang Luo, Shangyao Zhang, Na We. Runoff forecasting model based on variational mode decomposition and artificial neural networks. Mathematical biosciences and engineering : MBE. vol 19. issue 2. 2022-02-09. PMID:35135221. |
a neoteric hybrid runoff forecasting model based on variational mode decomposition (vmd), convolution neural networks (cnn), and long short-term memory (lstm) called vmd-cnn-lstm, is proposed to improve the runoff forecasting performance further. |
2022-02-09 |
2023-08-13 |
Not clear |