| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| 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 |
| 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. |
as an alternative to cnn, vision transformers (vit) have strong representation ability with spatial self-attention and channel-level feedforward networks. |
2023-08-30 |
2023-09-07 |
Not clear |
| Lili Guo, Shifei Ding, Longbiao Wang, Jianwu Dan. DSTCNet: Deep Spectro-Temporal-Channel Attention Network for Speech Emotion Recognition. IEEE transactions on neural networks and learning systems. vol PP. 2023-08-25. PMID:37624721. |
among them, the convolutional neural network (cnn) is the most commonly used method to learn emotional representations from spectrograms. |
2023-08-25 |
2023-09-07 |
Not clear |
| Lili Guo, Shifei Ding, Longbiao Wang, Jianwu Dan. DSTCNet: Deep Spectro-Temporal-Channel Attention Network for Speech Emotion Recognition. IEEE transactions on neural networks and learning systems. vol PP. 2023-08-25. PMID:37624721. |
however, cnn does not explicitly model features' associations in the spectral-, temporal-, and channel-wise axes or their relative relevance, which will limit the representation learning. |
2023-08-25 |
2023-09-07 |
Not clear |
| Md Muntasir Zitu, Shijun Zhang, Dwight H Owen, Chienwei Chiang, Lang L. Generalizability of machine learning methods in detecting adverse drug events from clinical narratives in electronic medical records. Frontiers in pharmacology. vol 14. 2023-07-28. PMID:37502211. |
we applied classical machine learning (support vector machine (svm)), deep learning (convolutional neural network (cnn) and bidirectional long short-term memory (bilstm)), and state-of-the-art transformer-based (bidirectional encoder representations from transformers (bert) and clinicalbert) methods trained and tested in the two different corpora and compared performance among them to detect drug-ade relationships. |
2023-07-28 |
2023-08-14 |
Not clear |
| Caterina Magri, Eric Elmoznino, Michael F Bonne. Scene context is predictive of unconstrained object similarity judgments. Cognition. vol 239. 2023-07-23. PMID:37481806. |
in behavioral experiments, we found that contextual prototypes were strongly predictive of human similarity judgments for a large set of objects and rivaled the performance of models based on cnn representations of the objects themselves or word embeddings for their names. |
2023-07-23 |
2023-08-14 |
human |
| George Dimas, Eirini Cholopoulou, Dimitris K Iakovidi. E pluribus unum interpretable convolutional neural networks. Scientific reports. vol 13. issue 1. 2023-07-14. PMID:37452133. |
an epu-cnn model consists of cnn sub-networks, each of which receives a different representation of an input image expressing a perceptual feature, such as color or texture. |
2023-07-14 |
2023-08-14 |
human |
| Ioannis Vernikos, Theodoros Spyropoulos, Evaggelos Spyrou, Phivos Mylona. Human Activity Recognition in the Presence of Occlusion. Sensors (Basel, Switzerland). vol 23. issue 10. 2023-07-11. PMID:37430811. |
the har approach we used is based on a convolutional neural network (cnn) that has been trained using 2d representations of 3d skeletal motion. |
2023-07-11 |
2023-08-14 |
human |
| Ivana Shopovska, Ana Stojkovic, Jan Aelterman, David Van Hamme, Wilfried Philip. High-Dynamic-Range Tone Mapping in Intelligent Automotive Systems. Sensors (Basel, Switzerland). vol 23. issue 12. 2023-07-08. PMID:37420931. |
our proposed approach relies on a lightweight convolutional neural network (cnn) that tone maps hdr video frames into a standard 8-bit representation. |
2023-07-08 |
2023-08-14 |
Not clear |
| Wei Chen, Zixuan Zhou, Junze Bao, Chengniu Wang, Hanqing Chen, Chen Xu, Gangcai Xie, Hongmin Shen, Huiqun W. Classifying Heart-Sound Signals Based on CNN Trained on MelSpectrum and Log-MelSpectrum Features. Bioengineering (Basel, Switzerland). vol 10. issue 6. 2023-06-28. PMID:37370576. |
however, there is no a priori evidence of the best input representation for classifying heart sounds when using cnn models. |
2023-06-28 |
2023-08-14 |
Not clear |
| Yifei Yan, Rongzong Liu, Haobo Chen, Limin Zhang, Qi Zhan. CCT-Unet: A U-shaped Network based on Convolution Coupled Transformer for Segmentation of Peripheral and Transition Zones in Prostate MRI. IEEE journal of biomedical and health informatics. vol PP. 2023-06-27. PMID:37368800. |
although transformer can provide feature representations of the overall appearance and contour representations at long distance, it does not perform well on small-scale datasets of prostate mri due to its insensitivity to local variation such as the heterogeneity of the grayscale intensities in the peripheral zone and transition zone across patients; meanwhile, the convolutional neural network (cnn) could retain these local features well. |
2023-06-27 |
2023-08-14 |
Not clear |