| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Fath U Min Ullah, Amin Ullah, Khan Muhammad, Ijaz Ul Haq, Sung Wook Bai. Violence Detection Using Spatiotemporal Features with 3D Convolutional Neural Network. Sensors (Basel, Switzerland). vol 19. issue 11. 2020-02-25. PMID:31151184. |
furthermore, we optimized the 3d cnn model using an open visual inference and neural networks optimization toolkit developed by intel, which converts the trained model into intermediate representation and adjusts it for optimal execution at the end platform for the final prediction of violent activity. |
2020-02-25 |
2023-08-13 |
Not clear |
| Sijie Song, Jiaying Liu, Yanghao Li, Zongming Gu. Modality Compensation Network: Cross-Modal Adaptation for Action Recognition. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2020-01-29. PMID:31995485. |
built on deep convolutional neural networks (cnn) and long short term memory (lstm) networks, our model bridges data from source and auxiliary modalities by a modality adaptation block to achieve adaptive representation learning, that the network learns to compensate for the loss of skeletons at test time and even at training time. |
2020-01-29 |
2023-08-13 |
human |
| Jianrui Cai, Zisheng Cao, Lei Zhan. Learning a Single Tucker Decomposition Network for Lossy Image Compression with Multiple Bits-Per-Pixel Rates. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2020-01-15. PMID:31940535. |
by changing the rank of core tensor and its quantization, we can easily adjust the bpp rate of latent image representation within a single cnn. |
2020-01-15 |
2023-08-13 |
Not clear |
| Miroslav Valan, Karoly Makonyi, Atsuto Maki, Dominik Vondráček, Fredrik Ronquis. Automated Taxonomic Identification of Insects with Expert-Level Accuracy Using Effective Feature Transfer from Convolutional Networks. Systematic biology. vol 68. issue 6. 2020-01-09. PMID:30825372. |
specifically, we extract rich representations of intermediate to high-level image features from the cnn architecture vgg16 pretrained on the imagenet data set. |
2020-01-09 |
2023-08-13 |
human |
| Ping Xuan, Lianfeng Zhao, Tiangang Zhang, Yilin Ye, Yan Zhan. Inferring Drug-Related Diseases Based on Convolutional Neural Network and Gated Recurrent Unit. Molecules (Basel, Switzerland). vol 24. issue 15. 2020-01-02. PMID:31349692. |
a novel model based on convolutional neural network (cnn) and gated recurrent unit (gru) is constructed to learn the local and path representations for a drug-disease pair. |
2020-01-02 |
2023-08-13 |
Not clear |
| Senthil Kumar Sundararajan, B Sankaragomathi, D Saravana Priy. Deep Belief CNN Feature Representation Based Content Based Image Retrieval for Medical Images. Journal of medical systems. vol 43. issue 6. 2019-12-13. PMID:31069547. |
deep belief cnn feature representation based content based image retrieval for medical images. |
2019-12-13 |
2023-08-13 |
Not clear |
| Senthil Kumar Sundararajan, B Sankaragomathi, D Saravana Priy. Deep Belief CNN Feature Representation Based Content Based Image Retrieval for Medical Images. Journal of medical systems. vol 43. issue 6. 2019-12-13. PMID:31069547. |
this work proposes an effectual methodology for retrieval of an images utilizing deep belief cnn feature representation. |
2019-12-13 |
2023-08-13 |
Not clear |
| Ming Ma, Mark K Buyyounouski, Varun Vasudevan, Lei Xing, Yong Yan. Dose distribution prediction in isodose feature-preserving voxelization domain using deep convolutional neural network. Medical physics. vol 46. issue 7. 2019-12-12. PMID:31112305. |
to implement a framework for dose prediction using a deep convolutional neural network (cnn) based on the concept of isodose feature-preserving voxelization (ifpv) in simplifying the representation of the dose distribution. |
2019-12-12 |
2023-08-13 |
Not clear |
| Yanbin Wang, Zhu-Hong You, Shan Yang, Xiao Li, Tong-Hai Jiang, Xi Zho. A High Efficient Biological Language Model for Predicting Protein⁻Protein Interactions. Cells. vol 8. issue 2. 2019-11-27. PMID:30717470. |
the model was constructed based on a feature representation method for biological sequences called bio-to-vector (bio2vec) and a convolution neural network (cnn). |
2019-11-27 |
2023-08-13 |
Not clear |
| Sheng Guo, Weilin Huang, Limin Wang, Yu Qia. Locally Supervised Deep Hybrid Model for Scene Recognition. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. vol 26. issue 2. 2019-11-20. PMID:28113936. |
on the other hand, the convolutional features in the middle layers of the cnn also contain meaningful local information, but are not fully explored for image representation. |
2019-11-20 |
2023-08-13 |
Not clear |
| Hakan Bilen, Basura Fernando, Efstratios Gavves, Andrea Vedald. Action Recognition with Dynamic Image Networks. IEEE transactions on pattern analysis and machine intelligence. vol 40. issue 12. 2019-11-20. PMID:29990080. |
to demonstrate the power of the representation, we introduce a novel four stream cnn architecture which can learn from rgb and optical flow frames as well as from their dynamic image representations. |
2019-11-20 |
2023-08-13 |
Not clear |
| Ruimao Zhang, Liang Lin, Guangrun Wang, Meng Wang, Wangmeng Zu. Hierarchical Scene Parsing by Weakly Supervised Learning with Image Descriptions. IEEE transactions on pattern analysis and machine intelligence. vol 41. issue 3. 2019-11-20. PMID:29993474. |
we propose a deep architecture consisting of two networks: i) a convolutional neural network (cnn) extracting the image representation for pixel-wise object labeling and ii) a recursive neural network (rsnn) discovering the hierarchical object structure and the inter-object relations. |
2019-11-20 |
2023-08-13 |
Not clear |
| Philipp Gysel, Jon Pimentel, Mohammad Motamedi, Soheil Ghias. Ristretto: A Framework for Empirical Study of Resource-Efficient Inference in Convolutional Neural Networks. IEEE transactions on neural networks and learning systems. 2019-11-20. PMID:29993820. |
we present ristretto, a cnn approximation framework that enables empirical investigation of the tradeoff between various number representation and word width choices and the classification accuracy of the model. |
2019-11-20 |
2023-08-13 |
Not clear |
| Chunlei Peng, Nannan Wang, Jie Li, Xinbo Ga. Re-ranking High-Dimensional Deep Local Representation for NIR-VIS Face Recognition. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2019-11-20. PMID:31034414. |
a high-dimensional deep local representation is firstly constructed by extracting and concatenating deep features on local facial patches via a convolutional neural network (cnn). |
2019-11-20 |
2023-08-13 |
human |
| Zhidong Zhao, Yefei Zhang, Yanjun Deng, Xiaohong Zhan. ECG authentication system design incorporating a convolutional neural network and generalized S-Transformation. Computers in biology and medicine. vol 102. 2019-10-28. PMID:30290297. |
finally, the cnn is used for automatic discriminative feature learning and representations, which avoids a tedious feature extraction algorithm. |
2019-10-28 |
2023-08-13 |
human |
| Milad Mostavi, Sirajul Salekin, Yufei Huan. Deep-2'-O-Me: Predicting 2'-O-methylation sites by Convolutional Neural Networks. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. vol 2018. 2019-10-25. PMID:30440889. |
specifically, we adopted dna2vec, a biological sequence embedding method originally inspired by the word2vec model of text analysis, to yield embedded representation of sequences that may or may not contain 2-o-me sites before feeding those features into cnn for classification. |
2019-10-25 |
2023-08-13 |
Not clear |
| Masashi Tsubaki, Kentaro Tomii, Jun Ses. Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences. Bioinformatics (Oxford, England). vol 35. issue 2. 2019-10-24. PMID:29982330. |
in this study, we investigate the use of end-to-end representation learning for compounds and proteins, integrate the representations, and develop a new cpi prediction approach by combining a graph neural network (gnn) for compounds and a convolutional neural network (cnn) for proteins. |
2019-10-24 |
2023-08-13 |
Not clear |
| Hasmila A Omar, Arijit Patra, Joao S Domingos, Paul Leeson, Alison J Noble. Automated Myocardial Wall Motion Classification using Handcrafted Features vs a Deep CNN-based mapping. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. vol 2018. 2019-10-22. PMID:30441060. |
regarding the later classifier, we also investigate the effect of local phase information retrieval through the use of feature asymmetry (fa), and demonstrate that pre-processing videos with fa enables the spatio-temporal cnn to better discover relevant left ventricle endocardial abstractions from low-level features to high-level representations automatically. |
2019-10-22 |
2023-08-13 |
Not clear |
| César J Ortiz-Echeverri, Sebastián Salazar-Colores, Juvenal Rodríguez-Reséndiz, Roberto A Gómez-Loenz. A New Approach for Motor Imagery Classification Based on Sorted Blind Source Separation, Continuous Wavelet Transform, and Convolutional Neural Network. Sensors (Basel, Switzerland). vol 19. issue 20. 2019-10-22. PMID:31635424. |
this work proposes a new method built from the combination of a blind source separation (bss) to obtain estimated independent components, a 2d representation of these component signals using the continuous wavelet transform (cwt), and a classification stage using a convolutional neural network (cnn) approach. |
2019-10-22 |
2023-08-13 |
Not clear |
| Jonathan Rubin, Saman Parvaneh, Asif Rahman, Bryan Conroy, Saeed Babaeizade. Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ECG recordings. Journal of electrocardiology. vol 51. issue 6S. 2019-10-14. PMID:30122456. |
otherwise, it is transformed to a time-frequency representation and classified with the cnn as nsr, af, o, or noise. |
2019-10-14 |
2023-08-13 |
Not clear |