| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Hey Wing Liu, Shuo Wang, Shelley Xiuli Ton. DysDiTect: Dyslexia Identification Using CNN-Positional-LSTM-Attention Modeling with Chinese Dictation Task. Brain sciences. vol 14. issue 5. 2024-05-25. PMID:38790423. |
by combining transfer learning of convolutional neural network (cnn) and positional encoding with the temporal-sequential encoding of long short-term memory (lstm) and attention mechanism, we trained and tested the model with handwriting images of 100,000 chinese characters from 1064 children in grades 2-6 (dd = 483; typically developing [td] = 581). |
2024-05-25 |
2024-05-27 |
Not clear |
| Lei Yang, Yibo Jiang, Kang Zeng, Tao Pen. Rolling Bearing Remaining Useful Life Prediction Based on CNN-VAE-MBiLSTM. Sensors (Basel, Switzerland). vol 24. issue 10. 2024-05-25. PMID:38793847. |
hence, a novel rul prediction model called cnn-vae-mbilstm is proposed in this paper by integrating advantages of convolutional neural network (cnn), variational autoencoder (vae), and multiple bi-directional long short-term memory (mbilstm). |
2024-05-25 |
2024-05-27 |
Not clear |
| Jian Shen, Kunlin Li, Huajian Liang, Zeguang Zhao, Yu Ma, Jinwen Wu, Jieshuo Zhang, Yanan Zhang, Bin H. HEMAsNet: A Hemisphere Asymmetry Network Inspired by the Brain for Depression Recognition From Electroencephalogram Signals. IEEE journal of biomedical and health informatics. vol PP. 2024-05-24. PMID:38781058. |
hemasnet employs a combination of multi-scale convolutional neural network (cnn) and long short-term memory (lstm) blocks to extract temporal features from both hemispheres of the brain. |
2024-05-24 |
2024-05-27 |
Not clear |
| Mohamed Talaat, Kian Barari, Xiuhua April Si, Jinxiang X. Schlieren imaging and video classification of alphabet pronunciations: exploiting phonetic flows for speech recognition and speech therapy. Visual computing for industry, biomedicine, and art. vol 7. issue 1. 2024-05-23. PMID:38772963. |
it is hypothesized that speech flows captured by schlieren, when analyzed using a hybrid of convolutional neural network (cnn) and long short-term memory (lstm) network, can recognize alphabet pronunciations, thus facilitating automatic speech recognition and speech disorder therapy. |
2024-05-23 |
2024-05-27 |
human |
| Qingjiang Li, Guilin Zou, Wenlong Zeng, Jie Gao, Feipeng He, Yujun Zhan. ESG guidance and artificial intelligence support for power systems analytics in the energy industry. Scientific reports. vol 14. issue 1. 2024-05-21. PMID:38762582. |
second, to coordinate the operation of various components and guarantee the balance and security of the power system, the cnn-bilstm power load demand forecasting model is built by merging convolutional neural network (cnn) and bidirectional long short-term memory (bilstm). |
2024-05-21 |
2024-05-27 |
Not clear |
| Yi Wang, Shuran Son. Detection of sweet corn seed viability based on hyperspectral imaging combined with firefly algorithm optimized deep learning. Frontiers in plant science. vol 15. 2024-05-17. PMID:38751847. |
finally, the one-dimensional convolutional neural networks (1dcnn), one-dimensional long short-term memory (1dlstm), the cnn combined with the lstm (cnn-lstm), and the proposed firefly algorithm (fa) optimized cnn-lstm (fa-cnn-lstm) are utilized to distinguish spectral images of sweet corn seeds viability grade. |
2024-05-17 |
2024-05-27 |
Not clear |
| Xuhui Feng, Amanjule Muhashi, Yuya Onishi, Ryosuke Ota, Huafeng Li. Transformer-CNN hybrid network for improving PET time of flight prediction. Physics in medicine and biology. 2024-05-15. PMID:38749457. |
the network was trained and tested using the waveform datasets after cropping.
compared to the constant fraction discriminator (cfd), cnn, cnn with attention, long short-term memory (lstm) and transformer, our network achieved an average ctr of 189 ps, reducing it by 82 ps (more than 30%), 13 ps (6.4%), 12 ps (6.0%), 16 ps (7.8%) and 9 ps (4.6%), respectively. |
2024-05-15 |
2024-05-27 |
Not clear |
| Shuang Zhou, Meiling Du, XiaoYu Liu, Hongyan She. Algorithm for community security risk assessment and influencing factors analysis by back propagation neural network. Heliyon. vol 10. issue 9. 2024-05-10. PMID:38720748. |
these traditional models include convolutional neural network (cnn), long short-term memory network (lstm), bidirectional encoder representations from transformers (bert), generative pre-trained transformer (gpt), and extreme gradient boosting (xgboost). |
2024-05-10 |
2024-05-27 |
Not clear |
| Xiaodong Wang, Xinchao Shi, Junbo Chen, Xu Guo, Donghai L. Exploring optimal pathways for enterprise procurement management systems based on fast neural modeling and semantic segmentation. Heliyon. vol 10. issue 7. 2024-05-01. PMID:38689967. |
this framework commences with the application of a cnn and long short-term memory (lstm) network for in-depth feature analysis and initial identification of historical procurement data, subsequently leveraging reinforcement learning methodologies to enhance the model's autonomy and intelligence for the purpose of optimization. |
2024-05-01 |
2024-05-03 |
Not clear |
| Oussama Jlassi, Philippe C Dixo. The effect of time normalization and biomechanical signal processing techniques of ground reaction force curves on deep-learning model performance. Journal of biomechanics. vol 168. 2024-04-27. PMID:38677026. |
using a public dataset (gutenburg gait database, a ground reaction force database of level overground walking at self-selected walking speed involving 350 healthy individuals), we trained convolutional neural network (cnn) and long short-term memory (lstm) models to predict binary sex (male, female) using three-dimensional ground-reaction forces to which we applied different processing approaches: zero padding, interpolation to 100% of signal, filtering, and scaling (min-max, body mass). |
2024-04-27 |
2024-04-30 |
Not clear |
| Martin Vibæk, Abdolrahman Peimankar, Uffe Kock Wiil, Daniel Arvidsson, Jan Christian Brøn. Energy Expenditure Prediction from Accelerometry Data Using Long Short-Term Memory Recurrent Neural Networks. Sensors (Basel, Switzerland). vol 24. issue 8. 2024-04-27. PMID:38676136. |
the energy expenditure was modelled using multiple linear regression (mlr), stacked long short-term memory (lstm) networks, and combined convolutional neural networks (cnn) and lstm. |
2024-04-27 |
2024-04-29 |
human |
| Xiuli Du, Xinyue Wang, Luyao Zhu, Xiaohui Ding, Yana Lv, Shaoming Qiu, Qingli Li. Electroencephalographic Signal Data Augmentation Based on Improved Generative Adversarial Network. Brain sciences. vol 14. issue 4. 2024-04-27. PMID:38672017. |
the generator consists of a long short-term memory (lstm) network and the discriminator consists of a convolutional neural network (cnn) which uses the gradient penalty-based wasserstein distance as the loss function in model training. |
2024-04-27 |
2024-04-29 |
Not clear |
| Shoaib Sattar, Rafia Mumtaz, Mamoon Qadir, Sadaf Mumtaz, Muhammad Ajmal Khan, Timo De Waele, Eli De Poorter, Ingrid Moerman, Adnan Shahi. Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets. Sensors (Basel, Switzerland). vol 24. issue 8. 2024-04-27. PMID:38676101. |
multiple dl models, including a convolutional neural network (cnn), a long short-term memory (lstm) network, and a self-supervised learning (ssl)-based model using autoencoders are explored and compared in this study. |
2024-04-27 |
2024-04-29 |
human |
| Huiqiang Su, Shaojuan Ma, Xinyi X. The multi-strategy hybrid forecasting base on SSA-VMD-WST for complex system. PloS one. vol 19. issue 4. 2024-04-18. PMID:38635832. |
thirdly, on the basis of the above data noise reduction and reconstruction, our proposal combines convolutional neural network (cnn) and bidirectional short-term memory (bilstm) model, which is used to analyze the evolution trend of real time sequence data. |
2024-04-18 |
2024-04-21 |
Not clear |
| Paramasivam A, Ferlin Deva Shahila D, Jenath M, Sivakumaran T S, Sakthivel Sankaran, Pavan Sai Kiran Reddy Pittu, Vijayalakshmi . Development of artificial intelligence edge computing based wearable device for fall detection and prevention of elderly people. Heliyon. vol 10. issue 8. 2024-04-17. PMID:38628753. |
further, the various deep learning algorithms such as convolutional neural network (cnn), recurrent neural network (rnn), long short-term memory (lstm), gated recurrent unit (gru) are utilized for activity recognition of elderly. |
2024-04-17 |
2024-04-19 |
Not clear |
| Omar Ibrahim Aboulol. Improving traffic accident severity prediction using MobileNet transfer learning model and SHAP XAI technique. PloS one. vol 19. issue 4. 2024-04-09. PMID:38593130. |
to predict the severity of injuries in accidents, multilayer perceptron (mlp), convolutional neural network (cnn), long short-term memory (lstm), residual networks (resnet), efficientnetb4, inceptionv3, extreme inception (xception), and mobilenet are employed. |
2024-04-09 |
2024-04-12 |
Not clear |
| Jinsong Ke, Jianmei Zhao, Hongfei Li, Lei Yuan, Guanghui Dong, Guohua Wan. Prediction of protein N-terminal acetylation modification sites based on CNN-BiLSTM-attention model. Computers in biology and medicine. vol 174. 2024-04-08. PMID:38588617. |
in this study, deepcba, a model based on the hybrid framework of convolutional neural network (cnn), bidirectional long short-term memory network (bilstm), and attention mechanism deep learning, was constructed to detect the n-terminal acetylation sites. |
2024-04-08 |
2024-04-11 |
Not clear |
| Samgyu Yang, Mohamed Abdel-Aty, Zubayer Islam, Dongdong Wan. Real-time crash prediction on express managed lanes of Interstate highway with anomaly detection learning. Accident; analysis and prevention. vol 201. 2024-04-06. PMID:38581772. |
the most performance gain is attained through the combination of convolutional neural network (cnn) and long short-term memory (lstm) in an ensemble, resulting in a 0.78 auc, 0.79 sensitivity, and a 0.22 false alarm rate. |
2024-04-06 |
2024-04-10 |
Not clear |
| Sadia Din, Marwa Qaraqe, Omar Mourad, Khalid Qaraqe, Erchin Serpedi. ECG-based cardiac arrhythmias detection through ensemble learning and fusion of deep spatial-temporal and long-range dependency features. Artificial intelligence in medicine. vol 150. 2024-03-29. PMID:38553158. |
different deep-learning techniques to detect heart arrhythmias such as convolutional neural network (cnn), long short-term memory (lstm), transformer, and hybrid cnn-lstm were proposed. |
2024-03-29 |
2024-04-01 |
Not clear |
| Sania Gul, Muhammad Salman Khan, Ata Ur-Rehma. DEW: A wavelet approach of rare sound event detection. PloS one. vol 19. issue 3. 2024-03-28. PMID:38547253. |
it requires only a single epoch of training, which is 5, 10, 200, and 600 times lesser than the models based on cnns and deep neural networks (dnns), cnn with long short-term memory (lstm) network, convolutional recurrent neural network (crnn), and cnn respectively. |
2024-03-28 |
2024-03-31 |
Not clear |