| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| 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 |
| Anderson P Avila Santos, Breno L S de Almeida, Robson P Bonidia, Peter F Stadler, Polonca Stefanic, Ines Mandic-Mulec, Ulisses Rocha, Danilo S Sanches, André C P L F de Carvalh. BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification. RNA biology. vol 21. issue 1. 2024-03-26. PMID:38528797. |
this study presents biodeepfuse, a hybrid deep learning framework integrating convolutional neural networks (cnn) or bidirectional long short-term memory (bilstm) networks with handcrafted features for enhanced accuracy. |
2024-03-26 |
2024-03-28 |
Not clear |
| Yuanfang Gou, Cheng Guo, Risheng Qi. Ultra short term power load forecasting based on the fusion of Seq2Seq BiLSTM and multi head attention mechanism. PloS one. vol 19. issue 3. 2024-03-22. PMID:38517854. |
convolutional neural networks(cnn) combined with bidirectional long short term memory(bilstm) networks is constructed in the encoder to extract the correlated timing features embedded in external factors affecting power loads. |
2024-03-22 |
2024-03-25 |
Not clear |
| Supriya Mahadevkar, Shruti Patil, Ketan Kotech. Enhancement of handwritten text recognition using AI-based hybrid approach. MethodsX. vol 12. 2024-03-21. PMID:38510932. |
through the integration of convolutional neural networks (cnn) and bidirectional long short-term memory (bilstm) with a connectionist temporal classification (ctc) decoder, the results indicate substantial improvement. |
2024-03-21 |
2024-03-23 |
Not clear |
| Muhammad Usman Tariq, Shuhaida Binti Ismai. Deep learning in public health: Comparative predictive models for COVID-19 case forecasting. PloS one. vol 19. issue 3. 2024-03-14. PMID:38483948. |
in this study, we compared several cutting-edge deep learning models, including long short-term memory (lstm), bidirectional lstm, convolutional neural networks (cnn), hybrid cnn-lstm, multilayer perceptron's, and recurrent neural networks (rnn), to project covid-19 cases in the aforementioned regions. |
2024-03-14 |
2024-03-17 |
Not clear |
| Shengjun Zhao, Tong An, Qi Wang, Fei Qi. Using Machine Learning and Finite Element Analysis to Extract Traction-Separation Relations at Bonding Wire Interfaces of Insulated Gate Bipolar Transistor Modules. Materials (Basel, Switzerland). vol 17. issue 5. 2024-03-13. PMID:38473474. |
a novel machine learning (ml) architecture integrating a convolutional neural network (cnn) and a long short-term memory (lstm) network is used to identify the shape and parameters of the traction separation law (tsl) of the fe-czm model accurately and efficiently. |
2024-03-13 |
2024-03-15 |
Not clear |
| Guanghua Fu, Qingjuan Wei, Yongsheng Yan. Bearing fault diagnosis with parallel CNN and LSTM. Mathematical biosciences and engineering : MBE. vol 21. issue 2. 2024-03-08. PMID:38454688. |
to enhance the quality of feature extraction from bearing vibration signals and the robustness of the model, we construct a fault diagnostic model based on convolutional neural network (cnn) and long short-term memory (lstm) parallel network to extract their temporal and spatial features from two perspectives. |
2024-03-08 |
2024-03-10 |
Not clear |
| Amirhossein Amini, Robab Kalantar. Gold price prediction by a CNN-Bi-LSTM model along with automatic parameter tuning. PloS one. vol 19. issue 3. 2024-03-07. PMID:38452043. |
in this paper, different architectures of deep neural network (dnn) have been proposed based on long short-term memory (lstm) and convolutional-based neural networks (cnn) as a hybrid model, along with automatic parameter tuning to increase the accuracy, coefficient of determination, of the forecasting results. |
2024-03-07 |
2024-03-10 |
Not clear |
| Danyal Khan, Mohammed Alonazi, Maha Abdelhaq, Naif Al Mudawi, Asaad Algarni, Ahmad Jalal, Hui Li. Robust human locomotion and localization activity recognition over multisensory. Frontiers in physiology. vol 15. 2024-03-07. PMID:38449788. |
to achieve accurate activity classification, state-of-the-art deep learning techniques, such as convolutional neural networks (cnn) and long short-term memory (lstm), have been explored. |
2024-03-07 |
2024-03-09 |
human |
| Theofrida Julius Maginga, Emmanuel Masabo, Pierre Bakunzibake, Kwang Soo Kim, Jimmy Nseng. Using wavelet transform and hybrid CNN - LSTM models on VOC & ultrasound IoT sensor data for non-visual maize disease detection. Heliyon. vol 10. issue 4. 2024-02-29. PMID:38420424. |
utilizing convolutional neural networks (cnn) and long short term memory (lstm) models, nonvisual measurements of total volatile organic compounds (vocs) and ultrasound emissions from maize plants were captured and analyzed. |
2024-02-29 |
2024-03-02 |
Not clear |
| Haichen Tian, Weijun Gong, Wei Li, Yurong Qia. PASTFNet: a paralleled attention spatio-temporal fusion network for micro-expression recognition. Medical & biological engineering & computing. 2024-02-27. PMID:38413518. |
inspired by the composite architecture of the convolutional neural network (cnn) and long short-term memory (lstm) for temporal modeling, we propose a novel attention-based multi-scale feature fusion network (amfnet) to encode features of sequential frames, which can learn more expressive facial-detailed features for it implements the integrated use of attention and multi-scale feature fusion, then design an aggregation block to aggregate and acquire temporal features. |
2024-02-27 |
2024-03-01 |
Not clear |