| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Dogan S Polat, Son Nguyen, Paniz Karbasi, Keith Hulsey, Murat Can Cobanoglu, Liqiang Wang, Albert Montillo, Basak E Doga. Machine Learning Prediction of Lymph Node Metastasis in Breast Cancer: Performance of a Multi-institutional MRI-based 4D Convolutional Neural Network. Radiology. Imaging cancer. vol 6. issue 3. 2024-04-12. PMID:38607282. |
a four-dimensional (4d) cnn model integrating temporal information from dynamic image sets was developed. |
2024-04-12 |
2024-04-14 |
human |
| Jingliang Lv, Yu Wang, Haiyue Fu, Yulong Pei, Zhijie Xi. Deep-Learning-Assisted Sensor with Multiple Perception Capabilities for an Intelligent Driver Assistance Monitoring System. ACS applied materials & interfaces. 2024-03-06. PMID:38447140. |
this study develops a multiscale cnn that employs different pooling methods to process kos-ts data and analyze temporal information. |
2024-03-06 |
2024-03-09 |
Not clear |
| Liguo Zhang, Liangyu Zhao, Yongtao Ya. A hybrid neural network-based intelligent body posture estimation system in sports scenes. Mathematical biosciences and engineering : MBE. vol 21. issue 1. 2024-02-02. PMID:38303452. |
specifically, a cnn unit and a long short-term memory (lstm) unit are employed as the backbone network in order to extract key-point information and temporal information from video frames, respectively. |
2024-02-02 |
2024-02-04 |
Not clear |
| Sarita Simaiya, Umesh Kumar Lilhore, Yogesh Kumar Sharma, K B V Brahma Rao, V V R Maheswara Rao, Anupam Baliyan, Anchit Bijalwan, Roobaea Alroobae. A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques. Scientific reports. vol 14. issue 1. 2024-01-16. PMID:38228707. |
the lstm module simulates temporal information that predicts the upcoming vm workload, while a cnn module extracts complicated distinguishing features gathered from vm workload statistics. |
2024-01-16 |
2024-01-19 |
Not clear |
| Takahiro Manabe, F N U Rahul, Yaoyu Fu, Xavier Intes, Steven D Schwaitzberg, Suvranu De, Lora Cavuoto, Anirban Dutt. Distinguishing Laparoscopic Surgery Experts from Novices Using EEG Topographic Features. Brain sciences. vol 13. issue 12. 2023-12-23. PMID:38137154. |
combining spatial and temporal information in the 3d cnn model enhanced classifier accuracy and highlighted the importance of the parietal-temporal-occipital association region in differentiating experts and novices. |
2023-12-23 |
2023-12-25 |
Not clear |
| Enze Shi, Sigang Yu, Yanqing Kang, Jinru Wu, Lin Zhao, Dajiang Zhu, Jinglei Lv, Tianming Liu, Xintao Hu, Shu Zhan. MEET: A Multi-Band EEG Transformer for Brain States Decoding. IEEE transactions on bio-medical engineering. vol PP. 2023-12-07. PMID:38055365. |
compared to the cnn or rnn based models, transformer can better capture the temporal information in eeg signals and focus more on global features of the brain's functional activities. |
2023-12-07 |
2023-12-10 |
Not clear |
| Abdalla Mohammed, Gerald Corz. Spatiotemporal convolutional long short-term memory for regional streamflow predictions. Journal of environmental management. vol 350. 2023-11-28. PMID:38016234. |
the model effectively incorporates both spatial and temporal information, leveraging the cnn to encode spatial patterns and the lstm to learn their temporal relations. |
2023-12-06 |
2023-12-07 |
Not clear |
| Abdalla Mohammed, Gerald Corz. Spatiotemporal convolutional long short-term memory for regional streamflow predictions. Journal of environmental management. vol 350. 2023-11-28. PMID:38016234. |
the model effectively incorporates both spatial and temporal information, leveraging the cnn to encode spatial patterns and the lstm to learn their temporal relations. |
2023-11-28 |
2023-12-07 |
Not clear |
| Pavan C Madhusudana, Neil Birkbeck, Yilin Wang, Balu Adsumilli, Alan C Bovi. CONVIQT: Contrastive Video Quality Estimator. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol PP. 2023-09-08. PMID:37676804. |
distortion type identification and degradation level determination is employed as an auxiliary task to train a deep learning model containing a deep convolutional neural network (cnn) that extracts spatial features, as well as a recurrent unit that captures temporal information. |
2023-09-08 |
2023-10-07 |
Not clear |
| Xuehu Liu, Chenyang Yu, Pingping Zhang, Huchuan L. Deeply Coupled Convolution-Transformer With Spatial-Temporal Complementary Learning for Video-Based Person Re-Identification. IEEE transactions on neural networks and learning systems. vol PP. 2023-05-26. PMID:37235467. |
besides, a gated attention (ga) is used to deliver aggregated temporal information into the cnn and transformer branches for temporal complementary learning. |
2023-05-26 |
2023-08-14 |
Not clear |
| Anyuan Zhang, Qi Li, Zhenlan Li, Jiming L. Upper Limb Movement Decoding Scheme Based on Surface Electromyography Using Attention-Based Kalman Filter Scheme. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. vol 31. 2023-04-08. PMID:37030694. |
to address these issues, we propose an attention-based kalman filter scheme (akfs), which uses an attention-based cnn model to better extract temporal information and a kf to add prior knowledge of the system. |
2023-04-08 |
2023-08-14 |
human |
| Anyuan Zhang, Qi Li, Zhenlan Li, Jiming L. Upper Limb Movement Decoding Scheme Based on Surface Electromyography Using Attention-Based Kalman Filter Scheme. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. vol 31. 2023-04-08. PMID:37030694. |
our proposed scheme improves the decoding accuracy of the traditional cnn model for a single subject by better capturing the temporal information of the surface electromyography signal and increasing the prior knowledge of the system. |
2023-04-08 |
2023-08-14 |
human |
| Xiaoqing Chen, Hongru Li, Youhe Huang, Weiwei Han, Xia Yu, Pengfei Zhang, Rui Ta. Heart sound classification based on equal scale frequency cepstral coefficients and deep learning. Biomedizinische Technik. Biomedical engineering. 2023-02-13. PMID:36780471. |
we further designed classifiers combining convolutional neural network (cnn), recurrent neural network (rnn) and random forest (rf) layers, which can extract both the spatial and temporal information of the input features. |
2023-02-13 |
2023-08-14 |
human |
| Franziska Hakansson, Dan Børge Jense. Automatic monitoring and detection of tail-biting behavior in groups of pigs using video-based deep learning methods. Frontiers in veterinary science. vol 9. 2023-01-30. PMID:36713870. |
our goal was to develop computer vision-based methods to detect tail biting in pigs using a convolutional neural network (cnn) to extract spatial information, combined with secondary networks accounting for temporal information. |
2023-01-30 |
2023-08-14 |
Not clear |
| Santosh Kumar Yadav, Achleshwar Luthra, Esha Pahwa, Kamlesh Tiwari, Heena Rathore, Hari Mohan Pandey, Peter Corcora. DroneAttention: Sparse weighted temporal attention for drone-camera based activity recognition. Neural networks : the official journal of the International Neural Network Society. vol 159. 2022-12-19. PMID:36535129. |
the swta network can be used as a plug-in module to the existing deep cnn architectures, for optimizing them to learn temporal information by eliminating the need for a separate temporal stream. |
2022-12-19 |
2023-08-14 |
human |
| 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 |
| Imran Ullah Khan, Sitara Afzal, Jong Weon Le. Human Activity Recognition via Hybrid Deep Learning Based Model. Sensors (Basel, Switzerland). vol 22. issue 1. 2022-01-11. PMID:35009865. |
considering these limitations, we develop a hybrid model by incorporating convolutional neural network (cnn) and long short-term memory (lstm) for activity recognition where cnn is used for spatial features extraction and lstm network is utilized for learning temporal information. |
2022-01-11 |
2023-08-13 |
human |
| Weinan Wang, Pedram Mohseni, Kevin L Kilgore, Laleh Najafizade. Cuff-less Blood Pressure Estimation from Photoplethysmography via Visibility Graph and Transfer Learning. IEEE journal of biomedical and health informatics. vol PP. 2021-11-17. PMID:34784289. |
the proposed method estimates bp with low computational budget by 1) creating images from segments of ppg via visibility graph (vg) that preserves the temporal information of the ppg waveform, 2) using pre-trained deep convolutional neural network (cnn) to extract feature vectors from vg images, and 3) solving for the weights and bias between the feature vectors and the reference bps with ridge regression. |
2021-11-17 |
2023-08-13 |
Not clear |
| Jikuo Wang, Xu Qiao, Changchun Liu, Xinpei Wang, YuanYuan Liu, Lianke Yao, Huan Zhan. Automated ECG classification using a non-local convolutional block attention module. Computer methods and programs in biomedicine. vol 203. 2021-05-14. PMID:33735660. |
the spatial and temporal information from ecg signals is fused by combining convolutional neural networks (cnn) with recurrent neural network (rnn). |
2021-05-14 |
2023-08-13 |
Not clear |
| Panayu Keelawat, Nattapong Thammasan, Masayuki Numao, Boonserm Kijsiriku. A Comparative Study of Window Size and Channel Arrangement on EEG-Emotion Recognition Using Deep CNN. Sensors (Basel, Switzerland). vol 21. issue 5. 2021-04-27. PMID:33804366. |
the temporal information in distinct window sizes was found to significantly affect the recognition performance, and cnn was found to be more responsive to changing window sizes than the support vector machine. |
2021-04-27 |
2023-08-13 |
human |