| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Mohammadreza Samadi, Saeedeh Momtaz. Fake news detection: deep semantic representation with enhanced feature engineering. International journal of data science and analytics. 2023-06-26. PMID:37362632. |
to substantiate the effectiveness of feature engineering besides semantic features, we proposed a deep neural architecture in which three parallel convolutional neural network (cnn) layers extract semantic features from contextual representation vectors. |
2023-06-26 |
2023-08-14 |
Not clear |
| Siqi Liu, Jiangshu Wei, Gang Liu, Bei Zho. Image classification model based on large kernel attention mechanism and relative position self-attention mechanism. PeerJ. Computer science. vol 9. 2023-06-22. PMID:37346614. |
although cnn is adept at capturing the local feature details, it cannot easily obtain the global representation of features. |
2023-06-22 |
2023-08-14 |
Not clear |
| Siqi Liu, Jiangshu Wei, Gang Liu, Bei Zho. Image classification model based on large kernel attention mechanism and relative position self-attention mechanism. PeerJ. Computer science. vol 9. 2023-06-22. PMID:37346614. |
this model optimizes its shortcomings in the difficulty of capturing the global representation of features by introducing large kernel attention (lka) in cnn while using the transformer blocks with relative position self-attention variant to alleviate the problem of detail deterioration in local features of the transformer. |
2023-06-22 |
2023-08-14 |
Not clear |
| Shoaib Ahmed, Dost Muhammad Khan, Saima Sadiq, Muhammad Umer, Faisal Shahzad, Khalid Mahmood, Heba Mohsen, Imran Ashra. Temporal analysis and opinion dynamics of COVID-19 vaccination tweets using diverse feature engineering techniques. PeerJ. Computer science. vol 9. 2023-06-22. PMID:37346678. |
the influence of term frequency-inverse document frequency, bag of words (bow), word2vec, and combination of tf-idf and bow are explored with classifiers including random forest, gradient boosting machine, extra tree classifier (etc), logistic regression, naïve bayes, stochastic gradient descent, multilayer perceptron, convolutional neural network (cnn), bidirectional encoder representations from transformers (bert), long short-term memory (lstm), and recurrent neural network (rnn). |
2023-06-22 |
2023-08-14 |
Not clear |
| Mohsen Sadat Shahabi, Ahmad Shalbaf, Reza Rostami, Reza Kazem. A convolutional recurrent neural network with attention for response prediction to repetitive transcranial magnetic stimulation in major depressive disorder. Scientific reports. vol 13. issue 1. 2023-06-22. PMID:37349335. |
pre-treatment electro-encephalogram (eeg) signal of public tdbrain dataset and 46 proprietary mdd subjects were utilized to create time-frequency representations using continuous wavelet transform (cwt) to be fed into the two powerful pre-trained convolutional neural networks (cnn) named vgg16 and efficientnetb0. |
2023-06-22 |
2023-08-14 |
human |
| Abdelghani Dahou, Mohamed Abd Elaziz, Samia Allaoua Chelloug, Mohammed A Awadallah, Mohammed Azmi Al-Betar, Mohammed A A Al-Qaness, Agostino Forestier. Intrusion Detection System for IoT Based on Deep Learning and Modified Reptile Search Algorithm. Computational intelligence and neuroscience. vol 2022. 2023-06-19. PMID:37332528. |
a simple yet effective convolutional neural network (cnn) is implemented as the core feature extractor of the framework to learn better and more relevant representations of the input data in a lower-dimensional space. |
2023-06-19 |
2023-08-14 |
Not clear |
| Shangwang Liu, Tongbo Cai, Xiufang Tang, Changgeng Wan. MRL-Net: Multi-scale Representation Learning Network for COVID-19 Lung CT Image Segmentation. IEEE journal of biomedical and health informatics. vol PP. 2023-06-14. PMID:37314916. |
to tackle this issue, we propose a multi-scale representation learning network (mrl-net) that integrates cnn with transformer via two bridge unit: dual multi-interaction attention (dma) and dual boundary attention (dba). |
2023-06-14 |
2023-08-14 |
Not clear |
| Shangwang Liu, Tongbo Cai, Xiufang Tang, Changgeng Wan. MRL-Net: Multi-scale Representation Learning Network for COVID-19 Lung CT Image Segmentation. IEEE journal of biomedical and health informatics. vol PP. 2023-06-14. PMID:37314916. |
secondly, for enhanced feature representation, dma is proposed to fuse the local detailed feature of cnn and the global context information of transformer. |
2023-06-14 |
2023-08-14 |
Not clear |
| Mohammad Hassan Tayarani Najara. A genetic programming-based convolutional deep learning algorithm for identifying COVID-19 cases via X-ray images. Artificial intelligence in medicine. vol 142. 2023-06-14. PMID:37316095. |
a graph representation for cnn architecture is proposed and evolutionary operators including crossover and mutation are specifically designed for the proposed representation. |
2023-06-14 |
2023-08-14 |
Not clear |
| Qiang Zhang, Yaming Zheng, Qiangqiang Yuan, Meiping Song, Haoyang Yu, Yi Xia. Hyperspectral Image Denoising: From Model-Driven, Data-Driven, to Model-Data-Driven. IEEE transactions on neural networks and learning systems. vol PP. 2023-06-06. PMID:37279128. |
later, we comprehensively review existing hsi denoising methods, from model-driven strategy (nonlocal mean, total variation, sparse representation, low-rank matrix approximation, and low-rank tensor factorization), data-driven strategy 2-d convolutional neural network (cnn), 3-d cnn, hybrid, and unsupervised networks, to model-data-driven strategy. |
2023-06-06 |
2023-08-14 |
Not clear |
| Md Rajib Hossain, Mohammed Moshiul Hoque, Nazmul Siddique, Iqbal H Sarke. CovTiNet: Covid text identification network using attention-based positional embedding feature fusion. Neural computing & applications. vol 35. issue 18. 2023-05-22. PMID:37213320. |
the covtinet incorporates an attention-based position embedding feature fusion for text-to-feature representation and attention-based cnn for covid text identification. |
2023-05-22 |
2023-08-14 |
Not clear |
| Chunyan Zeng, Shixiong Feng, Dongliang Zhu, Zhifeng Wan. Source Acquisition Device Identification from Recorded Audio Based on Spatiotemporal Representation Learning with Multi-Attention Mechanisms. Entropy (Basel, Switzerland). vol 25. issue 4. 2023-05-16. PMID:37190414. |
the spatial probability distribution features of audio signals are employed as inputs to the branch of the cnn for spatial representation learning, and the temporal spectral features of audio signals are fed into the branch of the rd-tcn network for temporal representation learning. |
2023-05-16 |
2023-08-14 |
Not clear |
| Bangze Zhang, Xiaoyan Wang, Liangui Liu, Denghui Zhang, Xiaojie Huang, Ming Xia, Weiwei Jiang, Xiangsheng Huan. CeLNet: a correlation-enhanced lightweight network for medical image segmentation. Physics in medicine and biology. 2023-05-12. PMID:37172613. |
convolutional neural networks(cnn) have been widely adopted for medical image segmentation with their outstanding feature representation capabilities. |
2023-05-12 |
2023-08-14 |
Not clear |
| R Karanjit, R Pally, S Samad. FloodIMG: Flood image DataBase system. Data in brief. vol 48. 2023-05-11. PMID:37168598. |
a breakthrough in building models for image processing came with the discovery that a convolutional neural network (cnn) can progressively extract higher-level representations of the image content. |
2023-05-11 |
2023-08-14 |
Not clear |
| Surabhi Adhikari, Surendrabikram Thapa, Usman Naseem, Hai Ya Lu, Gnana Bharathy, Mukesh Prasa. Explainable hybrid word representations for sentiment analysis of financial news. Neural networks : the official journal of the International Neural Network Society. vol 164. 2023-05-06. PMID:37148607. |
we then fed our proposed word representation to a convolutional neural network (cnn) with attention to capture the sentiment. |
2023-05-06 |
2023-08-14 |
Not clear |
| Yanwu Yang, Chenfei Ye, Ting M. A deep connectome learning network using graph convolution for connectome-disease association study. Neural networks : the official journal of the International Neural Network Society. vol 164. 2023-05-06. PMID:37148611. |
in recent years, deep learning methods including convolutional neural network (cnn) and graph neural network (gnn), have shifted the development of connectome-wide association studies (cwas) and made breakthroughs for connectome representation learning by leveraging deep embedded features. |
2023-05-06 |
2023-08-14 |
Not clear |
| Zhiyang Lu, Jian Wang, Zheng Li, Shihui Ying, Jun Wang, Jun Shi, Dinggang She. Two-Stage Self-Supervised Cycle-Consistency Transformer Network for Reducing Slice Gap in MR Images. IEEE journal of biomedical and health informatics. vol PP. 2023-05-01. PMID:37126622. |
a hybrid transformer and cnn structure is utilized to build an interpolation model, which explores both local and global slice representations. |
2023-05-01 |
2023-08-14 |
Not clear |
| Shuo Cheng, Zheng Wang, Bo Yang, Kimihiko Nakan. Convolutional Neural Network-Based Lane-Change Strategy via Motion Image Representation for Automated and Connected Vehicles. IEEE transactions on neural networks and learning systems. vol PP. 2023-04-18. PMID:37071514. |
motivated by human beings' underlying driving paradigm and the convolutional neural network's (cnn) dramatic capability of extracting features and learning strategies, this article proposes a cnn-based lane-change decision-making method via the dynamic motion image representation. |
2023-04-18 |
2023-08-14 |
human |
| Daniel Nolte, Omid Bazgir, Souparno Ghosh, Ranadip Pa. Federated learning framework integrating REFINED CNN and Deep Regression Forests. Bioinformatics advances. vol 3. issue 1. 2023-04-10. PMID:37033467. |
additionally, we demonstrate that the recently conceptualized representation of features as images with neighborhood dependencies cnn framework can be combined with the proposed federated deep regression forests to provide improved performance as compared to existing approaches. |
2023-04-10 |
2023-08-14 |
Not clear |
| Prashant Pandey, Mustafa Chasmai, Tanuj Sur, Brejesh Lal. Robust Prototypical Few-Shot Organ Segmentation with Regularized Neural-ODEs. IEEE transactions on medical imaging. vol PP. 2023-04-08. PMID:37030728. |
r-pnode constrains support and query features from the same classes to lie closer in the representation space thereby improving the performance over the existing convolutional neural network (cnn) based fss methods. |
2023-04-08 |
2023-08-14 |
Not clear |