Artigos de revistas sobre o tema "Convolutional recurrent neural networks"
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Hindarto, Djarot. "Comparison of RNN Architectures and Non-RNN Architectures in Sentiment Analysis". sinkron 8, n.º 4 (1 de outubro de 2023): 2537–46. http://dx.doi.org/10.33395/sinkron.v8i4.13048.
Texto completo da fonteKassylkassova, Kamila, Zhanna Yessengaliyeva, Gayrat Urazboev e Ayman Kassylkassova. "OPTIMIZATION METHOD FOR INTEGRATION OF CONVOLUTIONAL AND RECURRENT NEURAL NETWORK". Eurasian Journal of Mathematical and Computer Applications 11, n.º 2 (2023): 40–56. http://dx.doi.org/10.32523/2306-6172-2023-11-2-40-56.
Texto completo da fonteLyu, Shengfei, e Jiaqi Liu. "Convolutional Recurrent Neural Networks for Text Classification". Journal of Database Management 32, n.º 4 (outubro de 2021): 65–82. http://dx.doi.org/10.4018/jdm.2021100105.
Texto completo da fonteP., Vijay Babu, e Senthil Kumar R. "Performance Evaluation of Brain Tumor Identification and Examination Using MRI Images with Innovative Convolution Neural Networks and Comparing the Accuracy with RNN Algorithm". ECS Transactions 107, n.º 1 (24 de abril de 2022): 12405–14. http://dx.doi.org/10.1149/10701.12405ecst.
Texto completo da fontePeng, Wenli, Shenglai Zhen, Xin Chen, Qianjing Xiong e Benli Yu. "Study on convolutional recurrent neural networks for speech enhancement in fiber-optic microphones". Journal of Physics: Conference Series 2246, n.º 1 (1 de abril de 2022): 012084. http://dx.doi.org/10.1088/1742-6596/2246/1/012084.
Texto completo da fonteP, Suma, e Senthil Kumar R. "Automatic Classification of Normal and Infected Blood Cells for Leukemia Through Color Based Segmentation Technique Over Innovative CNN Algorithm and Comparing the Error Rate with RNN". ECS Transactions 107, n.º 1 (24 de abril de 2022): 14123–34. http://dx.doi.org/10.1149/10701.14123ecst.
Texto completo da fonteWang, Lin, e Zuqiang Meng. "Multichannel Two-Dimensional Convolutional Neural Network Based on Interactive Features and Group Strategy for Chinese Sentiment Analysis". Sensors 22, n.º 3 (18 de janeiro de 2022): 714. http://dx.doi.org/10.3390/s22030714.
Texto completo da fontePoudel, Sushan, e Dr R. Anuradha. "Speech Command Recognition using Artificial Neural Networks". JOIV : International Journal on Informatics Visualization 4, n.º 2 (26 de maio de 2020): 73. http://dx.doi.org/10.30630/joiv.4.2.358.
Texto completo da fonteWu, Hao, e Saurabh Prasad. "Convolutional Recurrent Neural Networks forHyperspectral Data Classification". Remote Sensing 9, n.º 3 (21 de março de 2017): 298. http://dx.doi.org/10.3390/rs9030298.
Texto completo da fonteLi, Kezhi, John Daniels, Chengyuan Liu, Pau Herrero e Pantelis Georgiou. "Convolutional Recurrent Neural Networks for Glucose Prediction". IEEE Journal of Biomedical and Health Informatics 24, n.º 2 (fevereiro de 2020): 603–13. http://dx.doi.org/10.1109/jbhi.2019.2908488.
Texto completo da fonteZhang, Zao, e Yuan Dong. "Temperature Forecasting via Convolutional Recurrent Neural Networks Based on Time-Series Data". Complexity 2020 (20 de março de 2020): 1–8. http://dx.doi.org/10.1155/2020/3536572.
Texto completo da fonteNguyen, Viet-Hung, Minh-Tuan Nguyen, Jeongsik Choi e Yong-Hwa Kim. "NLOS Identification in WLANs Using Deep LSTM with CNN Features". Sensors 18, n.º 11 (20 de novembro de 2018): 4057. http://dx.doi.org/10.3390/s18114057.
Texto completo da fonteShchetinin, E. Yu. "EMOTIONS RECOGNITION IN HUMAN SPEECH USING DEEP NEURAL NETWORKS". Vestnik komp'iuternykh i informatsionnykh tekhnologii, n.º 199 (janeiro de 2021): 44–51. http://dx.doi.org/10.14489/vkit.2021.01.pp.044-051.
Texto completo da fonteHou, Kai. "Principal Component Analysis and Prediction of Students’ Physical Health Standard Test Results Based on Recurrent Convolution Neural Network". Wireless Communications and Mobile Computing 2021 (4 de setembro de 2021): 1–11. http://dx.doi.org/10.1155/2021/2438656.
Texto completo da fonteD, Sreekanth. "Metro Water Fraudulent Prediction in Houses Using Convolutional Neural Network and Recurrent Neural Network". Revista Gestão Inovação e Tecnologias 11, n.º 4 (10 de julho de 2021): 1177–87. http://dx.doi.org/10.47059/revistageintec.v11i4.2177.
Texto completo da fonteMa, Hao, Chao Chen, Qing Zhu, Haitao Yuan, Liming Chen e Minglei Shu. "An ECG Signal Classification Method Based on Dilated Causal Convolution". Computational and Mathematical Methods in Medicine 2021 (2 de fevereiro de 2021): 1–10. http://dx.doi.org/10.1155/2021/6627939.
Texto completo da fonteR, Gayathri, Lydia Beryl D, Gowtham M, Naveen Kumar N e Dr M. S. Anbarasi. "Detection and Classification of Cyberbullying Using CR*". International Journal for Research in Applied Science and Engineering Technology 11, n.º 4 (30 de abril de 2023): 24–29. http://dx.doi.org/10.22214/ijraset.2023.49984.
Texto completo da fonteGuo, Yanbu, Bingyi Wang, Weihua Li e Bei Yang. "Protein secondary structure prediction improved by recurrent neural networks integrated with two-dimensional convolutional neural networks". Journal of Bioinformatics and Computational Biology 16, n.º 05 (outubro de 2018): 1850021. http://dx.doi.org/10.1142/s021972001850021x.
Texto completo da fontePan, Yumin. "Different Types of Neural Networks and Applications: Evidence from Feedforward, Convolutional and Recurrent Neural Networks". Highlights in Science, Engineering and Technology 85 (13 de março de 2024): 247–55. http://dx.doi.org/10.54097/6rn1wd81.
Texto completo da fonteZ, Farhan, Kavipriya A, Abinaya C e Ezhilarasan M. "Enhanced Image Segmentation Using Convolutional Recurrent Neural Networks". International Innovative Research Journal of Engineering and Technology 5, n.º 3 (30 de março de 2020): 78–83. http://dx.doi.org/10.32595/iirjet.org/v5i3.2020.118.
Texto completo da fonteAlbaqshi, Hussain, e Alaa Sagheer. "Dysarthric Speech Recognition using Convolutional Recurrent Neural Networks". International Journal of Intelligent Engineering and Systems 13, n.º 6 (31 de dezembro de 2020): 384–92. http://dx.doi.org/10.22266/ijies2020.1231.34.
Texto completo da fonteSantacroce, Michael, Daniel Koranek e Rashmi Jha. "Detecting Malicious Assembly using Convolutional, Recurrent Neural Networks". Advances in Science, Technology and Engineering Systems Journal 4, n.º 5 (2019): 46–52. http://dx.doi.org/10.25046/aj040506.
Texto completo da fonteGayathri, P., P. Gowri Priya, L. Sravani, Sandra Johnson e Visanth Sampath. "Convolutional Recurrent Neural Networks Based Speech Emotion Recognition". Journal of Computational and Theoretical Nanoscience 17, n.º 8 (1 de agosto de 2020): 3786–89. http://dx.doi.org/10.1166/jctn.2020.9321.
Texto completo da fonteHu, Wenjin, Jiawei Xiong, Ning Wang, Feng Liu, Yao Kong e Chaozhong Yang. "Integrated Model Text Classification Based on Multineural Networks". Electronics 13, n.º 2 (22 de janeiro de 2024): 453. http://dx.doi.org/10.3390/electronics13020453.
Texto completo da fonteHuang, Feizhen, Jinfang Zeng, Yu Zhang e Wentao Xu. "Convolutional recurrent neural networks with multi-sized convolution filters for sound-event recognition". Modern Physics Letters B 34, n.º 23 (25 de abril de 2020): 2050235. http://dx.doi.org/10.1142/s0217984920502358.
Texto completo da fonteKim, Deageon. "Research On Text Classification Based On Deep Neural Network". International Journal of Communication Networks and Information Security (IJCNIS) 14, n.º 1s (31 de dezembro de 2022): 100–113. http://dx.doi.org/10.17762/ijcnis.v14i1s.5618.
Texto completo da fonteKhan, Muhammad Ashfaq. "HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System". Processes 9, n.º 5 (10 de maio de 2021): 834. http://dx.doi.org/10.3390/pr9050834.
Texto completo da fonteSolovyeva, Elena, e Ali Abdullah. "Binary and Multiclass Text Classification by Means of Separable Convolutional Neural Network". Inventions 6, n.º 4 (19 de outubro de 2021): 70. http://dx.doi.org/10.3390/inventions6040070.
Texto completo da fonteRymarczyk, T., D. Wójcik, Ł. Maciura, W. Rosa e M. Bartosik. "Body surface potential mapping time series recognition using convolutional and recurrent neural networks". Journal of Physics: Conference Series 2408, n.º 1 (1 de dezembro de 2022): 012001. http://dx.doi.org/10.1088/1742-6596/2408/1/012001.
Texto completo da fonteWan, Renzhuo, Shuping Mei, Jun Wang, Min Liu e Fan Yang. "Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting". Electronics 8, n.º 8 (7 de agosto de 2019): 876. http://dx.doi.org/10.3390/electronics8080876.
Texto completo da fonteCasabianca, Pietro, e Yu Zhang. "Acoustic-Based UAV Detection Using Late Fusion of Deep Neural Networks". Drones 5, n.º 3 (26 de junho de 2021): 54. http://dx.doi.org/10.3390/drones5030054.
Texto completo da fonteXu, Zhijing, Yuhao Huo, Kun Liu e Sidong Liu. "Detection of ship targets in photoelectric images based on an improved recurrent attention convolutional neural network". International Journal of Distributed Sensor Networks 16, n.º 3 (março de 2020): 155014772091295. http://dx.doi.org/10.1177/1550147720912959.
Texto completo da fonteLiu, Xuanxin, Fu Xu, Yu Sun, Haiyan Zhang e Zhibo Chen. "Convolutional Recurrent Neural Networks for Observation-Centered Plant Identification". Journal of Electrical and Computer Engineering 2018 (2018): 1–7. http://dx.doi.org/10.1155/2018/9373210.
Texto completo da fonteKwak, Jin-Yeol, e Yong-Joo Chung. "Sound Event Detection Using Derivative Features in Deep Neural Networks". Applied Sciences 10, n.º 14 (17 de julho de 2020): 4911. http://dx.doi.org/10.3390/app10144911.
Texto completo da fonteWang, Weiping, Feng Zhang, Xi Luo e Shigeng Zhang. "PDRCNN: Precise Phishing Detection with Recurrent Convolutional Neural Networks". Security and Communication Networks 2019 (29 de outubro de 2019): 1–15. http://dx.doi.org/10.1155/2019/2595794.
Texto completo da fonteChen, Jingwen, Yingwei Pan, Yehao Li, Ting Yao, Hongyang Chao e Tao Mei. "Temporal Deformable Convolutional Encoder-Decoder Networks for Video Captioning". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julho de 2019): 8167–74. http://dx.doi.org/10.1609/aaai.v33i01.33018167.
Texto completo da fonteLiang, Kaiwei, Na Qin, Deqing Huang e Yuanzhe Fu. "Convolutional Recurrent Neural Network for Fault Diagnosis of High-Speed Train Bogie". Complexity 2018 (23 de outubro de 2018): 1–13. http://dx.doi.org/10.1155/2018/4501952.
Texto completo da fonteWang, Guanchao. "Analysis of sentiment analysis model based on deep learning". Applied and Computational Engineering 5, n.º 1 (14 de junho de 2023): 750–56. http://dx.doi.org/10.54254/2755-2721/5/20230694.
Texto completo da fonteYüksel, Kıvanç, e Władysław Skarbek. "Convolutional and Recurrent Neural Networks for Face Image Analysis". Foundations of Computing and Decision Sciences 44, n.º 3 (1 de setembro de 2019): 331–47. http://dx.doi.org/10.2478/fcds-2019-0017.
Texto completo da fonteLiu, Nan. "Study on the Application of Improved Audio Recognition Technology Based on Deep Learning in Vocal Music Teaching". Mathematical Problems in Engineering 2022 (18 de agosto de 2022): 1–12. http://dx.doi.org/10.1155/2022/1002105.
Texto completo da fonteLe, Viet-Tuan, Kiet Tran-Trung e Vinh Truong Hoang. "A Comprehensive Review of Recent Deep Learning Techniques for Human Activity Recognition". Computational Intelligence and Neuroscience 2022 (20 de abril de 2022): 1–17. http://dx.doi.org/10.1155/2022/8323962.
Texto completo da fonteCheng, Yepeng, Zuren Liu e Yasuhiko Morimoto. "Attention-Based SeriesNet: An Attention-Based Hybrid Neural Network Model for Conditional Time Series Forecasting". Information 11, n.º 6 (5 de junho de 2020): 305. http://dx.doi.org/10.3390/info11060305.
Texto completo da fonteFantaye, Tessfu Geteye, Junqing Yu e Tulu Tilahun Hailu. "Advanced Convolutional Neural Network-Based Hybrid Acoustic Models for Low-Resource Speech Recognition". Computers 9, n.º 2 (2 de maio de 2020): 36. http://dx.doi.org/10.3390/computers9020036.
Texto completo da fonteZhao, Ping, Zhijie Fan*, Zhiwei Cao e Xin Li. "Intrusion Detection Model Using Temporal Convolutional Network Blend Into Attention Mechanism". International Journal of Information Security and Privacy 16, n.º 1 (janeiro de 2022): 1–20. http://dx.doi.org/10.4018/ijisp.290832.
Texto completo da fonteFabien-Ouellet, Gabriel, e Rahul Sarkar. "Seismic velocity estimation: A deep recurrent neural-network approach". GEOPHYSICS 85, n.º 1 (19 de dezembro de 2019): U21—U29. http://dx.doi.org/10.1190/geo2018-0786.1.
Texto completo da fonteLi, Haoliang, Shiqi Wang e AlexC Kot. "Image Recapture Detection with Convolutional and Recurrent Neural Networks". Electronic Imaging 2017, n.º 7 (29 de janeiro de 2017): 87–91. http://dx.doi.org/10.2352/issn.2470-1173.2017.7.mwsf-329.
Texto completo da fonteShang, Jin, e Mingxuan Sun. "Geometric Hawkes Processes with Graph Convolutional Recurrent Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julho de 2019): 4878–85. http://dx.doi.org/10.1609/aaai.v33i01.33014878.
Texto completo da fonteQin, Chen, Jo Schlemper, Jose Caballero, Anthony N. Price, Joseph V. Hajnal e Daniel Rueckert. "Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction". IEEE Transactions on Medical Imaging 38, n.º 1 (janeiro de 2019): 280–90. http://dx.doi.org/10.1109/tmi.2018.2863670.
Texto completo da fonteZuo, Zhen, Bing Shuai, Gang Wang, Xiao Liu, Xingxing Wang, Bing Wang e Yushi Chen. "Learning Contextual Dependence With Convolutional Hierarchical Recurrent Neural Networks". IEEE Transactions on Image Processing 25, n.º 7 (julho de 2016): 2983–96. http://dx.doi.org/10.1109/tip.2016.2548241.
Texto completo da fonteCakir, Emre, Giambattista Parascandolo, Toni Heittola, Heikki Huttunen e Tuomas Virtanen. "Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection". IEEE/ACM Transactions on Audio, Speech, and Language Processing 25, n.º 6 (junho de 2017): 1291–303. http://dx.doi.org/10.1109/taslp.2017.2690575.
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