Artykuły w czasopismach na temat „Convolutional recurrent neural networks”
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Hindarto, Djarot. "Comparison of RNN Architectures and Non-RNN Architectures in Sentiment Analysis". sinkron 8, nr 4 (1.10.2023): 2537–46. http://dx.doi.org/10.33395/sinkron.v8i4.13048.
Pełny tekst źródłaKassylkassova, Kamila, Zhanna Yessengaliyeva, Gayrat Urazboev i Ayman Kassylkassova. "OPTIMIZATION METHOD FOR INTEGRATION OF CONVOLUTIONAL AND RECURRENT NEURAL NETWORK". Eurasian Journal of Mathematical and Computer Applications 11, nr 2 (2023): 40–56. http://dx.doi.org/10.32523/2306-6172-2023-11-2-40-56.
Pełny tekst źródłaLyu, Shengfei, i Jiaqi Liu. "Convolutional Recurrent Neural Networks for Text Classification". Journal of Database Management 32, nr 4 (październik 2021): 65–82. http://dx.doi.org/10.4018/jdm.2021100105.
Pełny tekst źródłaP., Vijay Babu, i 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, nr 1 (24.04.2022): 12405–14. http://dx.doi.org/10.1149/10701.12405ecst.
Pełny tekst źródłaPeng, Wenli, Shenglai Zhen, Xin Chen, Qianjing Xiong i Benli Yu. "Study on convolutional recurrent neural networks for speech enhancement in fiber-optic microphones". Journal of Physics: Conference Series 2246, nr 1 (1.04.2022): 012084. http://dx.doi.org/10.1088/1742-6596/2246/1/012084.
Pełny tekst źródłaP, Suma, i 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, nr 1 (24.04.2022): 14123–34. http://dx.doi.org/10.1149/10701.14123ecst.
Pełny tekst źródłaWang, Lin, i Zuqiang Meng. "Multichannel Two-Dimensional Convolutional Neural Network Based on Interactive Features and Group Strategy for Chinese Sentiment Analysis". Sensors 22, nr 3 (18.01.2022): 714. http://dx.doi.org/10.3390/s22030714.
Pełny tekst źródłaPoudel, Sushan, i Dr R. Anuradha. "Speech Command Recognition using Artificial Neural Networks". JOIV : International Journal on Informatics Visualization 4, nr 2 (26.05.2020): 73. http://dx.doi.org/10.30630/joiv.4.2.358.
Pełny tekst źródłaWu, Hao, i Saurabh Prasad. "Convolutional Recurrent Neural Networks forHyperspectral Data Classification". Remote Sensing 9, nr 3 (21.03.2017): 298. http://dx.doi.org/10.3390/rs9030298.
Pełny tekst źródłaLi, Kezhi, John Daniels, Chengyuan Liu, Pau Herrero i Pantelis Georgiou. "Convolutional Recurrent Neural Networks for Glucose Prediction". IEEE Journal of Biomedical and Health Informatics 24, nr 2 (luty 2020): 603–13. http://dx.doi.org/10.1109/jbhi.2019.2908488.
Pełny tekst źródłaZhang, Zao, i Yuan Dong. "Temperature Forecasting via Convolutional Recurrent Neural Networks Based on Time-Series Data". Complexity 2020 (20.03.2020): 1–8. http://dx.doi.org/10.1155/2020/3536572.
Pełny tekst źródłaNguyen, Viet-Hung, Minh-Tuan Nguyen, Jeongsik Choi i Yong-Hwa Kim. "NLOS Identification in WLANs Using Deep LSTM with CNN Features". Sensors 18, nr 11 (20.11.2018): 4057. http://dx.doi.org/10.3390/s18114057.
Pełny tekst źródłaShchetinin, E. Yu. "EMOTIONS RECOGNITION IN HUMAN SPEECH USING DEEP NEURAL NETWORKS". Vestnik komp'iuternykh i informatsionnykh tekhnologii, nr 199 (styczeń 2021): 44–51. http://dx.doi.org/10.14489/vkit.2021.01.pp.044-051.
Pełny tekst źródłaHou, 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 (4.09.2021): 1–11. http://dx.doi.org/10.1155/2021/2438656.
Pełny tekst źródłaD, Sreekanth. "Metro Water Fraudulent Prediction in Houses Using Convolutional Neural Network and Recurrent Neural Network". Revista Gestão Inovação e Tecnologias 11, nr 4 (10.07.2021): 1177–87. http://dx.doi.org/10.47059/revistageintec.v11i4.2177.
Pełny tekst źródłaMa, Hao, Chao Chen, Qing Zhu, Haitao Yuan, Liming Chen i Minglei Shu. "An ECG Signal Classification Method Based on Dilated Causal Convolution". Computational and Mathematical Methods in Medicine 2021 (2.02.2021): 1–10. http://dx.doi.org/10.1155/2021/6627939.
Pełny tekst źródłaR, Gayathri, Lydia Beryl D, Gowtham M, Naveen Kumar N i Dr M. S. Anbarasi. "Detection and Classification of Cyberbullying Using CR*". International Journal for Research in Applied Science and Engineering Technology 11, nr 4 (30.04.2023): 24–29. http://dx.doi.org/10.22214/ijraset.2023.49984.
Pełny tekst źródłaGuo, Yanbu, Bingyi Wang, Weihua Li i 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, nr 05 (październik 2018): 1850021. http://dx.doi.org/10.1142/s021972001850021x.
Pełny tekst źródłaPan, Yumin. "Different Types of Neural Networks and Applications: Evidence from Feedforward, Convolutional and Recurrent Neural Networks". Highlights in Science, Engineering and Technology 85 (13.03.2024): 247–55. http://dx.doi.org/10.54097/6rn1wd81.
Pełny tekst źródłaZ, Farhan, Kavipriya A, Abinaya C i Ezhilarasan M. "Enhanced Image Segmentation Using Convolutional Recurrent Neural Networks". International Innovative Research Journal of Engineering and Technology 5, nr 3 (30.03.2020): 78–83. http://dx.doi.org/10.32595/iirjet.org/v5i3.2020.118.
Pełny tekst źródłaAlbaqshi, Hussain, i Alaa Sagheer. "Dysarthric Speech Recognition using Convolutional Recurrent Neural Networks". International Journal of Intelligent Engineering and Systems 13, nr 6 (31.12.2020): 384–92. http://dx.doi.org/10.22266/ijies2020.1231.34.
Pełny tekst źródłaSantacroce, Michael, Daniel Koranek i Rashmi Jha. "Detecting Malicious Assembly using Convolutional, Recurrent Neural Networks". Advances in Science, Technology and Engineering Systems Journal 4, nr 5 (2019): 46–52. http://dx.doi.org/10.25046/aj040506.
Pełny tekst źródłaGayathri, P., P. Gowri Priya, L. Sravani, Sandra Johnson i Visanth Sampath. "Convolutional Recurrent Neural Networks Based Speech Emotion Recognition". Journal of Computational and Theoretical Nanoscience 17, nr 8 (1.08.2020): 3786–89. http://dx.doi.org/10.1166/jctn.2020.9321.
Pełny tekst źródłaHu, Wenjin, Jiawei Xiong, Ning Wang, Feng Liu, Yao Kong i Chaozhong Yang. "Integrated Model Text Classification Based on Multineural Networks". Electronics 13, nr 2 (22.01.2024): 453. http://dx.doi.org/10.3390/electronics13020453.
Pełny tekst źródłaHuang, Feizhen, Jinfang Zeng, Yu Zhang i Wentao Xu. "Convolutional recurrent neural networks with multi-sized convolution filters for sound-event recognition". Modern Physics Letters B 34, nr 23 (25.04.2020): 2050235. http://dx.doi.org/10.1142/s0217984920502358.
Pełny tekst źródłaKim, Deageon. "Research On Text Classification Based On Deep Neural Network". International Journal of Communication Networks and Information Security (IJCNIS) 14, nr 1s (31.12.2022): 100–113. http://dx.doi.org/10.17762/ijcnis.v14i1s.5618.
Pełny tekst źródłaKhan, Muhammad Ashfaq. "HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System". Processes 9, nr 5 (10.05.2021): 834. http://dx.doi.org/10.3390/pr9050834.
Pełny tekst źródłaSolovyeva, Elena, i Ali Abdullah. "Binary and Multiclass Text Classification by Means of Separable Convolutional Neural Network". Inventions 6, nr 4 (19.10.2021): 70. http://dx.doi.org/10.3390/inventions6040070.
Pełny tekst źródłaRymarczyk, T., D. Wójcik, Ł. Maciura, W. Rosa i M. Bartosik. "Body surface potential mapping time series recognition using convolutional and recurrent neural networks". Journal of Physics: Conference Series 2408, nr 1 (1.12.2022): 012001. http://dx.doi.org/10.1088/1742-6596/2408/1/012001.
Pełny tekst źródłaWan, Renzhuo, Shuping Mei, Jun Wang, Min Liu i Fan Yang. "Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting". Electronics 8, nr 8 (7.08.2019): 876. http://dx.doi.org/10.3390/electronics8080876.
Pełny tekst źródłaCasabianca, Pietro, i Yu Zhang. "Acoustic-Based UAV Detection Using Late Fusion of Deep Neural Networks". Drones 5, nr 3 (26.06.2021): 54. http://dx.doi.org/10.3390/drones5030054.
Pełny tekst źródłaXu, Zhijing, Yuhao Huo, Kun Liu i 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, nr 3 (marzec 2020): 155014772091295. http://dx.doi.org/10.1177/1550147720912959.
Pełny tekst źródłaLiu, Xuanxin, Fu Xu, Yu Sun, Haiyan Zhang i 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.
Pełny tekst źródłaKwak, Jin-Yeol, i Yong-Joo Chung. "Sound Event Detection Using Derivative Features in Deep Neural Networks". Applied Sciences 10, nr 14 (17.07.2020): 4911. http://dx.doi.org/10.3390/app10144911.
Pełny tekst źródłaWang, Weiping, Feng Zhang, Xi Luo i Shigeng Zhang. "PDRCNN: Precise Phishing Detection with Recurrent Convolutional Neural Networks". Security and Communication Networks 2019 (29.10.2019): 1–15. http://dx.doi.org/10.1155/2019/2595794.
Pełny tekst źródłaChen, Jingwen, Yingwei Pan, Yehao Li, Ting Yao, Hongyang Chao i Tao Mei. "Temporal Deformable Convolutional Encoder-Decoder Networks for Video Captioning". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17.07.2019): 8167–74. http://dx.doi.org/10.1609/aaai.v33i01.33018167.
Pełny tekst źródłaLiang, Kaiwei, Na Qin, Deqing Huang i Yuanzhe Fu. "Convolutional Recurrent Neural Network for Fault Diagnosis of High-Speed Train Bogie". Complexity 2018 (23.10.2018): 1–13. http://dx.doi.org/10.1155/2018/4501952.
Pełny tekst źródłaWang, Guanchao. "Analysis of sentiment analysis model based on deep learning". Applied and Computational Engineering 5, nr 1 (14.06.2023): 750–56. http://dx.doi.org/10.54254/2755-2721/5/20230694.
Pełny tekst źródłaYüksel, Kıvanç, i Władysław Skarbek. "Convolutional and Recurrent Neural Networks for Face Image Analysis". Foundations of Computing and Decision Sciences 44, nr 3 (1.09.2019): 331–47. http://dx.doi.org/10.2478/fcds-2019-0017.
Pełny tekst źródłaLiu, Nan. "Study on the Application of Improved Audio Recognition Technology Based on Deep Learning in Vocal Music Teaching". Mathematical Problems in Engineering 2022 (18.08.2022): 1–12. http://dx.doi.org/10.1155/2022/1002105.
Pełny tekst źródłaLe, Viet-Tuan, Kiet Tran-Trung i Vinh Truong Hoang. "A Comprehensive Review of Recent Deep Learning Techniques for Human Activity Recognition". Computational Intelligence and Neuroscience 2022 (20.04.2022): 1–17. http://dx.doi.org/10.1155/2022/8323962.
Pełny tekst źródłaCheng, Yepeng, Zuren Liu i Yasuhiko Morimoto. "Attention-Based SeriesNet: An Attention-Based Hybrid Neural Network Model for Conditional Time Series Forecasting". Information 11, nr 6 (5.06.2020): 305. http://dx.doi.org/10.3390/info11060305.
Pełny tekst źródłaFantaye, Tessfu Geteye, Junqing Yu i Tulu Tilahun Hailu. "Advanced Convolutional Neural Network-Based Hybrid Acoustic Models for Low-Resource Speech Recognition". Computers 9, nr 2 (2.05.2020): 36. http://dx.doi.org/10.3390/computers9020036.
Pełny tekst źródłaZhao, Ping, Zhijie Fan*, Zhiwei Cao i Xin Li. "Intrusion Detection Model Using Temporal Convolutional Network Blend Into Attention Mechanism". International Journal of Information Security and Privacy 16, nr 1 (styczeń 2022): 1–20. http://dx.doi.org/10.4018/ijisp.290832.
Pełny tekst źródłaFabien-Ouellet, Gabriel, i Rahul Sarkar. "Seismic velocity estimation: A deep recurrent neural-network approach". GEOPHYSICS 85, nr 1 (19.12.2019): U21—U29. http://dx.doi.org/10.1190/geo2018-0786.1.
Pełny tekst źródłaLi, Haoliang, Shiqi Wang i AlexC Kot. "Image Recapture Detection with Convolutional and Recurrent Neural Networks". Electronic Imaging 2017, nr 7 (29.01.2017): 87–91. http://dx.doi.org/10.2352/issn.2470-1173.2017.7.mwsf-329.
Pełny tekst źródłaShang, Jin, i Mingxuan Sun. "Geometric Hawkes Processes with Graph Convolutional Recurrent Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17.07.2019): 4878–85. http://dx.doi.org/10.1609/aaai.v33i01.33014878.
Pełny tekst źródłaQin, Chen, Jo Schlemper, Jose Caballero, Anthony N. Price, Joseph V. Hajnal i Daniel Rueckert. "Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction". IEEE Transactions on Medical Imaging 38, nr 1 (styczeń 2019): 280–90. http://dx.doi.org/10.1109/tmi.2018.2863670.
Pełny tekst źródłaZuo, Zhen, Bing Shuai, Gang Wang, Xiao Liu, Xingxing Wang, Bing Wang i Yushi Chen. "Learning Contextual Dependence With Convolutional Hierarchical Recurrent Neural Networks". IEEE Transactions on Image Processing 25, nr 7 (lipiec 2016): 2983–96. http://dx.doi.org/10.1109/tip.2016.2548241.
Pełny tekst źródłaCakir, Emre, Giambattista Parascandolo, Toni Heittola, Heikki Huttunen i Tuomas Virtanen. "Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection". IEEE/ACM Transactions on Audio, Speech, and Language Processing 25, nr 6 (czerwiec 2017): 1291–303. http://dx.doi.org/10.1109/taslp.2017.2690575.
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