| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Anna Elisabeth Schnell, Maarten Leemans, Kasper Vinken, Hans Op de Beec. A computationally informed comparison between the strategies of rodents and humans in visual object recognition. eLife. vol 12. 2023-12-11. PMID:38079481. |
a direct comparison with cnn representations and visual feature analyses revealed that rat performance was best captured by late convolutional layers and partially by visual features such as brightness and pixel-level similarity, while human performance related more to the higher-up fully connected layers. |
2023-12-11 |
2023-12-17 |
human |
| Chen-Chen Fan, Hongjun Yang, Chutian Zhang, Liang Peng, Xiaohu Zhou, Shiqi Liu, Sheng Chen, Zeng-Guang Ho. Graph Reasoning Module for Alzheimer's Disease Diagnosis: A Plug-and-Play Method. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. vol PP. 2023-11-29. PMID:38015665. |
specifically, in grm, an adaptive graph transformer (agt) block is designed to adaptively construct a graph representation based on the feature map given by cnn, a graph convolutional network (gcn) block is adopted to update the graph representation, and a feature map reconstruction (fmr) block is built to convert the learned graph representation to a feature map. |
2023-12-07 |
2023-12-07 |
Not clear |
| Qiushi Wang, Zhicheng Sun, Yueming Zhu, Chunhe Song, Dong L. Intelligent fault diagnosis algorithm of rolling bearing based on optimization algorithm fusion convolutional neural network. Mathematical biosciences and engineering : MBE. vol 20. issue 11. 2023-12-05. PMID:38052632. |
this paper proposes a fault diagnosis method that combines a 1d-cnn with an attention mechanism and hyperparameter optimization to overcome the aforementioned limitations; this method improves the search speed for optimal hyperparameters of cnn models, improves the diagnostic accuracy, and enhances the representation of fault feature information in cnns. |
2023-12-05 |
2023-12-10 |
Not clear |
| Ashfia Jannat Keya, Hasibul Hossain Shajeeb, Md Saifur Rahman, M F Mridh. FakeStack: Hierarchical Tri-BERT-CNN-LSTM stacked model for effective fake news detection. PloS one. vol 18. issue 12. 2023-12-01. PMID:38039283. |
the model combines the power of pre-trained bidirectional encoder representation of transformers (bert) embeddings with a deep convolutional neural network (cnn) having skip convolution block and long short-term memory (lstm). |
2023-12-01 |
2023-12-10 |
Not clear |
| Roberto Garcia-Fernandez, Koldo Portal-Porras, Oscar Irigaray, Zugatz Ansa, Unai Fernandez-Gami. CNN-based flow field prediction for bus aerodynamics analysis. Scientific reports. vol 13. issue 1. 2023-12-01. PMID:38040782. |
to improve the accuracy of the cnn, the field representations obtained are discretized as a function of the expected velocity gradient, so that in the areas where there is a greater variation in velocity, the corresponding neuron is smaller. |
2023-12-01 |
2023-12-10 |
Not clear |
| Chen-Chen Fan, Hongjun Yang, Chutian Zhang, Liang Peng, Xiaohu Zhou, Shiqi Liu, Sheng Chen, Zeng-Guang Ho. Graph Reasoning Module for Alzheimer's Disease Diagnosis: A Plug-and-Play Method. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. vol PP. 2023-11-29. PMID:38015665. |
specifically, in grm, an adaptive graph transformer (agt) block is designed to adaptively construct a graph representation based on the feature map given by cnn, a graph convolutional network (gcn) block is adopted to update the graph representation, and a feature map reconstruction (fmr) block is built to convert the learned graph representation to a feature map. |
2023-11-29 |
2023-12-07 |
Not clear |
| Shotaro Maedera, Tadahaya Mizuno, Hiroyuki Kusuhar. Investigation of latent representation of toxicopathological images extracted by CNN model for understanding compound properties in vivo. Computers in biology and medicine. vol 168. 2023-11-28. PMID:38016375. |
investigation of latent representation of toxicopathological images extracted by cnn model for understanding compound properties in vivo. |
2023-11-28 |
2023-12-07 |
Not clear |
| Shotaro Maedera, Tadahaya Mizuno, Hiroyuki Kusuhar. Investigation of latent representation of toxicopathological images extracted by CNN model for understanding compound properties in vivo. Computers in biology and medicine. vol 168. 2023-11-28. PMID:38016375. |
in this study, we assessed the usefulness of latent representations extracted from toxicopathological images using convolutional neural network (cnn) in estimating compound properties in vivo. |
2023-11-28 |
2023-12-07 |
Not clear |
| Abhishek Verma, Virender Ranga, Dinesh Kumar Vishwakarm. A novel approach for forecasting PM2.5 pollution in Delhi using CATALYST. Environmental monitoring and assessment. vol 195. issue 12. 2023-11-11. PMID:37950817. |
to derive attributes of the pm2.5 timeline of data, a pre-existing cnn model is utilized to transform the data into visual representations, which are analyzed subsequently. |
2023-11-11 |
2023-11-20 |
Not clear |
| Dong Chen, Xingjia Pan, Fan Tang, Weiming Dong, Changsheng X. SPA IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol PP. 2023-10-18. PMID:37847621. |
spa by exploring the localizable representations in deep cnn, weakly supervised object localization (wsol) methods could determine the position of the object in each image just trained by the classification task. |
2023-10-18 |
2023-11-08 |
Not clear |
| Roohum Jegan, R Jayagowr. Voice pathology detection using optimized convolutional neural networks and explainable artificial intelligence-based analysis. Computer methods in biomechanics and biomedical engineering. 2023-10-18. PMID:37850553. |
firstly, the input voice samples are first transformed into mel-spectrogram time-frequency visual representations and fed for training the cnn model. |
2023-10-18 |
2023-11-08 |
bee |
| Roohum Jegan, R Jayagowr. Voice pathology detection using optimized convolutional neural networks and explainable artificial intelligence-based analysis. Computer methods in biomechanics and biomedical engineering. 2023-10-18. PMID:37850553. |
experimental results emphasize that proposed abc optimized cnn model shows improved accuracy performance by 1.02% compared to conventional cnn network illustrating data-independent discriminative representation ability. |
2023-10-18 |
2023-11-08 |
bee |
| Xinhang Song, Chenlong Liu, Haitao Zeng, Yaohui Zhu, Gongwei Chen, Xiaorong Qin, Shuqiang Jian. Composite Object Relation Modeling for Few-shot Scene Recognition. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol PP. 2023-10-10. PMID:37812539. |
those works usually use a convolutional neural network (cnn) to learn the global image representations from training tasks, which are then adapted to novel tasks. |
2023-10-10 |
2023-10-15 |
Not clear |
| Md Haidar Sharif, Lei Jiao, Christian W Omli. CNN-ViT Supported Weakly-Supervised Video Segment Level Anomaly Detection. Sensors (Basel, Switzerland). vol 23. issue 18. 2023-09-28. PMID:37765792. |
in this paper, we first address taking advantage of two pretrained feature extractors for cnn (e.g., c3d and i3d) and vit (e.g., clip), for effectively extracting discerning representations. |
2023-09-28 |
2023-10-07 |
Not clear |
| Azeddine Mjahad, Mohamed Saban, Hossein Azarmdel, Alfredo Rosado-Muño. Efficient Extraction of Deep Image Features Using a Convolutional Neural Network (CNN) for Detecting Ventricular Fibrillation and Tachycardia. Journal of imaging. vol 9. issue 9. 2023-09-27. PMID:37754954. |
the results show that using tf representations as a form of image, combined in this case with a cnn classifier, raises the classification performance above the results in previous works. |
2023-09-27 |
2023-10-07 |
Not clear |
| Ethan Fast, Manjima Dhar, Binbin Che. TAPIR: a T-cell receptor language model for predicting rare and novel targets. bioRxiv : the preprint server for biology. 2023-09-25. PMID:37745475. |
tapir employs deep convolutional neural network (cnn) encoders to process tcr and target sequences across flexible representations (e.g., beta-chain only, unknown mhc allele, etc.) |
2023-09-25 |
2023-10-07 |
human |
| Hanyun Li, Wenzao Li, Jiacheng Zhao, Peizhen Yu, Yao Huan. A sentiment analysis approach for travel-related Chinese online review content. PeerJ. Computer science. vol 9. 2023-09-14. PMID:37705661. |
a hybrid feature network combining cnn and bilstm can improve the model's representation ability. |
2023-09-14 |
2023-10-07 |
Not clear |
| Kaicong Sun, Qian Wang, Dinggang She. Joint Cross-Attention Network with Deep Modality Prior for Fast MRI Reconstruction. IEEE transactions on medical imaging. vol PP. 2023-09-11. PMID:37695966. |
to enhance the representation ability of the proposed model, we deploy vision transformer (vit) and cnn in the image and k-space domains, respectively. |
2023-09-11 |
2023-10-07 |
Not clear |
| Amr Farahat, Felix Effenberger, Martin Vinc. A novel feature-scrambling approach reveals the capacity of convolutional neural networks to learn spatial relations. Neural networks : the official journal of the International Neural Network Society. vol 167. 2023-09-06. PMID:37673027. |
these results provide novel insights into the nature of cnn representations and the extent to which they rely on the spatial arrangement of features for object classification. |
2023-09-06 |
2023-10-07 |
human |
| Zhong-Yu Li, Shanghua Gao, Ming-Ming Chen. SERE: Exploring Feature Self-Relation for Self-Supervised Transformer. IEEE transactions on pattern analysis and machine intelligence. vol PP. 2023-08-30. PMID:37647184. |
learning representations with self-supervision for convolutional networks (cnn) has been validated to be effective for vision tasks. |
2023-08-30 |
2023-09-07 |
Not clear |