| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Lei Zhan. The Evaluation on the Credit Risk of Enterprises with the CNN-LSTM-ATT Model. Computational intelligence and neuroscience. vol 2022. 2022-10-03. PMID:36188679. |
the convolutional neural network (cnn) and the long short-term memory (lstm) network were used to establish the model, using soft attention, the gradient propagates back to other parts of the model through the attention mechanism module. |
2022-10-03 |
2023-08-14 |
Not clear |
| Hari Mohan Rai, Kalyan Chatterjee, Serhii Dashkevyc. The prediction of cardiac abnormality and enhancement in minority class accuracy from imbalanced ECG signals using modified deep neural network models. Computers in biology and medicine. vol 150. 2022-10-01. PMID:36182760. |
the sequential ensemble technique employs two distinct deep learning models: convolutional neural network (cnn) and a hybrid model, cnn with long short-term memory network (cnn-lstm). |
2022-10-01 |
2023-08-14 |
Not clear |
| Lei-Shan Wang, Zhan-Li Su. iDHS-FFLG: Identifying DNase I Hypersensitive Sites by Feature Fusion and Local-Global Feature Extraction Network. Interdisciplinary sciences, computational life sciences. 2022-09-27. PMID:36166165. |
then, a network consisting of the convolutional neural network (cnn), bidirectional long short-term memory (bilstm) and self-attention mechanism is designed to extract local features and global contextual associations. |
2022-09-27 |
2023-08-14 |
mouse |
| Qi Li, Yunqing Liu, Yujie Shang, Qiong Zhang, Fei Ya. Deep Sparse Autoencoder and Recursive Neural Network for EEG Emotion Recognition. Entropy (Basel, Switzerland). vol 24. issue 9. 2022-09-23. PMID:36141073. |
further, combining a convolutional neural network (cnn) with long short-term memory (lstm) can extract relevant features from task-related features, mine the correlation between the 32 channels of the eeg signal, and integrate contextual information from these frames. |
2022-09-23 |
2023-08-14 |
Not clear |
| Yeong-Hyeon Byeon, Keun-Chang Kwa. Individual Identification by Late Information Fusion of EmgCNN and EmgLSTM from Electromyogram Signals. Sensors (Basel, Switzerland). vol 22. issue 18. 2022-09-23. PMID:36146119. |
thus, we propose an ensemble deep learning model by late information fusion of convolutional neural networks (cnn) and long short-term memory (lstm) from emg signals for robust and discriminative biometrics. |
2022-09-23 |
2023-08-14 |
Not clear |
| Mingcong Shu, Guoliang Chen, Zhenghua Zhan. EL-SLE: Efficient Learning Based Stride-Length Estimation Using a Smartphone. Sensors (Basel, Switzerland). vol 22. issue 18. 2022-09-23. PMID:36146213. |
the model uses adaptive learning to extract different elements for changing and recognition tasks, including long short-term memory (lstm) and convolutional neural network (cnn) modules. |
2022-09-23 |
2023-08-14 |
Not clear |
| Cihun-Siyong Alex Gong, Chih-Hui Simon Su, Yuan-En Liu, De-Yu Guu, Yu-Hua Che. Deep Learning with LPC and Wavelet Algorithms for Driving Fault Diagnosis. Sensors (Basel, Switzerland). vol 22. issue 18. 2022-09-23. PMID:36146421. |
after the original voiceprint fault sounds were filtered and obtained the main fault characteristics, the deep neural network (dnn), convolutional neural network (cnn), and long short-term memory (lstm) architectures are used for identification. |
2022-09-23 |
2023-08-14 |
Not clear |
| Hui Li, Xintang Liu, Dongbao Jia, Yanyan Chen, Pengfei Hou, Haining L. Research on chest radiography recognition model based on deep learning. Mathematical biosciences and engineering : MBE. vol 19. issue 11. 2022-09-20. PMID:36124613. |
the model can generate high-level conclusive impressions and detailed descriptive findings sentence-by-sentence and realize the imitation of the doctoros standard tone by combining a convolutional neural network (cnn) with a long short-term memory (lstm) network through a recurrent structure, and adding a multi-head attention mechanism. |
2022-09-20 |
2023-08-14 |
Not clear |
| Rui Zhang, Xianjing Yao, Lele Ye, Min Che. Students' adaptive deep learning path and teaching strategy of contemporary ceramic art under the background of Internet. Frontiers in psychology. vol 13. 2022-09-19. PMID:36118465. |
among dl-related theories, back propagation neural network (bpnn), convolutional neural network (cnn), long short-term memory (lstm), and gated recurrent unit (gru) are compared to implement a single model and a mixed model. |
2022-09-19 |
2023-08-14 |
Not clear |
| Zhi-Wen Ma, Jian-Ping Zhao, Jing Tian, Chun-Hou Zhen. DeeProPre: A promoter predictor based on deep learning. Computational biology and chemistry. vol 101. 2022-09-18. PMID:36116322. |
in this study, a deep learning algorithm (deepropre) based on bidirectional long short-term memory (bilstm) and convolutional neural network (cnn) was proposed. |
2022-09-18 |
2023-08-14 |
Not clear |
| Deju Shen, Yuqin Deng, Chunyan Lin, Jianshu Li, Xuehua Lin, Chaoning Zo. Clinical Characteristics and Gene Mutation Analysis of Poststroke Epilepsy. Contrast media & molecular imaging. vol 2022. 2022-09-15. PMID:36105439. |
this study suggested a model categorizing poststroke patients based on eeg signals that utilized wavelet, long short-term memory (lstm), and convolutional neural networks (cnn). |
2022-09-15 |
2023-08-14 |
Not clear |
| Anum Naseem, Raja Habib, Tabbasum Naz, Muhammad Atif, Muhammad Arif, Samia Allaoua Chellou. Novel Internet of Things based approach toward diabetes prediction using deep learning models. Frontiers in public health. vol 10. 2022-09-12. PMID:36091536. |
in order to detect the presence of the fatal disease, six different machine learning techniques are used i.e., support vector machine (svm), logistic regression, artificial neural network (ann), convolutional neural network (cnn), recurrent neural network (rnn), and long short-term memory (lstm). |
2022-09-12 |
2023-08-14 |
Not clear |
| Milagros Jaén-Vargas, Karla Miriam Reyes Leiva, Francisco Fernandes, Sérgio Barroso Gonçalves, Miguel Tavares Silva, Daniel Simões Lopes, José Javier Serrano Olmed. Effects of sliding window variation in the performance of acceleration-based human activity recognition using deep learning models. PeerJ. Computer science. vol 8. 2022-09-12. PMID:36091986. |
the objective of this research was to analyze the performance of four dl models: a simple deep neural network (dnn); a convolutional neural network (cnn); a long short-term memory network (lstm); and a hybrid model (cnn-lstm), when variating the sliding window size using fixed overlapped windows to identify an optimal window size for har. |
2022-09-12 |
2023-08-14 |
human |
| Pratyush Nandi, Madhav Ra. A Novel CNN-LSTM Model Based Non-Invasive Cuff-Less Blood Pressure Estimation System. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. vol 2022. 2022-09-10. PMID:36086017. |
a convolutional neural network (cnn) followed by a long short-term memory layer (lstm) was designed and applied to ecg and ppg signals to present accurate systolic blood pressure (sbp), and diastolic blood pressure (dbp). |
2022-09-10 |
2023-08-14 |
human |
| Ru Jia, Young-Chae Song, Dong-Mei Piao, Keugtae Kim, Chae-Young Lee, Jungsu Par. Exploration of deep learning models for real-time monitoring of state and performance of anaerobic digestion with online sensors. Bioresource technology. 2022-09-10. PMID:36087652. |
the model performance was compared for several deep learning models of convolutional neural network (cnn), long short-term memory (lstm), dense layer, and their combinations. |
2022-09-10 |
2023-08-14 |
Not clear |
| Muhammad Haseeb Arshad, Muhammad Bilal, Abdullah Gan. Human Activity Recognition: Review, Taxonomy and Open Challenges. Sensors (Basel, Switzerland). vol 22. issue 17. 2022-09-09. PMID:36080922. |
convolutional neural network (cnn), long short-term memory (lstm), and support vector machine (svm) are the most prominent techniques in the literature reviewed that are being utilized for the task of har. |
2022-09-09 |
2023-08-14 |
human |
| Jianbo Zheng, Jian Liao, Zongbin Che. End-to-End Continuous/Discontinuous Feature Fusion Method with Attention for Rolling Bearing Fault Diagnosis. Sensors (Basel, Switzerland). vol 22. issue 17. 2022-09-09. PMID:36080947. |
this method comprises long short-term memory (lstm), convolutional neural networks (cnn) and attention mechanism, which automatically extracts the continuous and discontinuous features from vibration signals for fault diagnosis. |
2022-09-09 |
2023-08-14 |
Not clear |
| Abdulnaser A Hagar, Bharti W Gawal. Apache Spark and Deep Learning Models for High-Performance Network Intrusion Detection Using CSE-CIC-IDS2018. Computational intelligence and neuroscience. vol 2022. 2022-09-05. PMID:36059395. |
this work proposes three models, two deep learning convolutional neural networks (cnn), long short-term memory (lstm), and apache spark, to improve the detection of all types of attacks. |
2022-09-05 |
2023-08-14 |
Not clear |
| Zair Bouzidi, Mourad Amad, Abdelmalek Boudrie. Enhancing Warning, Situational Awareness, Assessment and Education in Managing Emergency: Case Study of COVID-19. SN computer science. vol 3. issue 6. 2022-08-29. PMID:36035507. |
this paper proposes a hybrid of deep convolutional neural network (cnn)-long short-term memory (lstm)-based model to efficiently retrieve crisis information. |
2022-08-29 |
2023-08-14 |
Not clear |
| Nada M Elshennawy, Dina M Ibrahim, Amany M Sarhan, Mohamed Araf. Deep-Risk: Deep Learning-Based Mortality Risk Predictive Models for COVID-19. Diagnostics (Basel, Switzerland). vol 12. issue 8. 2022-08-26. PMID:36010198. |
the second predictive model, which we refer to as cv-lstm + cnn, is developed by combining the long short-term memory (lstm) approach with a cnn model. |
2022-08-26 |
2023-08-14 |
Not clear |