Zeitschriftenartikel zum Thema „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 (01.10.2023): 2537–46. http://dx.doi.org/10.33395/sinkron.v8i4.13048.
Der volle Inhalt der QuelleKassylkassova, Kamila, Zhanna Yessengaliyeva, Gayrat Urazboev und 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.
Der volle Inhalt der QuelleLyu, Shengfei, und Jiaqi Liu. „Convolutional Recurrent Neural Networks for Text Classification“. Journal of Database Management 32, Nr. 4 (Oktober 2021): 65–82. http://dx.doi.org/10.4018/jdm.2021100105.
Der volle Inhalt der QuelleP., Vijay Babu, und 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.
Der volle Inhalt der QuellePeng, Wenli, Shenglai Zhen, Xin Chen, Qianjing Xiong und Benli Yu. „Study on convolutional recurrent neural networks for speech enhancement in fiber-optic microphones“. Journal of Physics: Conference Series 2246, Nr. 1 (01.04.2022): 012084. http://dx.doi.org/10.1088/1742-6596/2246/1/012084.
Der volle Inhalt der QuelleP, Suma, und 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.
Der volle Inhalt der QuelleWang, Lin, und 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.
Der volle Inhalt der QuellePoudel, Sushan, und 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.
Der volle Inhalt der QuelleWu, Hao, und 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.
Der volle Inhalt der QuelleLi, Kezhi, John Daniels, Chengyuan Liu, Pau Herrero und Pantelis Georgiou. „Convolutional Recurrent Neural Networks for Glucose Prediction“. IEEE Journal of Biomedical and Health Informatics 24, Nr. 2 (Februar 2020): 603–13. http://dx.doi.org/10.1109/jbhi.2019.2908488.
Der volle Inhalt der QuelleZhang, Zao, und 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.
Der volle Inhalt der QuelleNguyen, Viet-Hung, Minh-Tuan Nguyen, Jeongsik Choi und 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.
Der volle Inhalt der QuelleShchetinin, E. Yu. „EMOTIONS RECOGNITION IN HUMAN SPEECH USING DEEP NEURAL NETWORKS“. Vestnik komp'iuternykh i informatsionnykh tekhnologii, Nr. 199 (Januar 2021): 44–51. http://dx.doi.org/10.14489/vkit.2021.01.pp.044-051.
Der volle Inhalt der QuelleHou, 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 (04.09.2021): 1–11. http://dx.doi.org/10.1155/2021/2438656.
Der volle Inhalt der QuelleD, 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.
Der volle Inhalt der QuelleMa, Hao, Chao Chen, Qing Zhu, Haitao Yuan, Liming Chen und Minglei Shu. „An ECG Signal Classification Method Based on Dilated Causal Convolution“. Computational and Mathematical Methods in Medicine 2021 (02.02.2021): 1–10. http://dx.doi.org/10.1155/2021/6627939.
Der volle Inhalt der QuelleR, Gayathri, Lydia Beryl D, Gowtham M, Naveen Kumar N und 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.
Der volle Inhalt der QuelleGuo, Yanbu, Bingyi Wang, Weihua Li und 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 (Oktober 2018): 1850021. http://dx.doi.org/10.1142/s021972001850021x.
Der volle Inhalt der QuellePan, 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.
Der volle Inhalt der QuelleZ, Farhan, Kavipriya A, Abinaya C und 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.
Der volle Inhalt der QuelleAlbaqshi, Hussain, und 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.
Der volle Inhalt der QuelleSantacroce, Michael, Daniel Koranek und 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.
Der volle Inhalt der QuelleGayathri, P., P. Gowri Priya, L. Sravani, Sandra Johnson und Visanth Sampath. „Convolutional Recurrent Neural Networks Based Speech Emotion Recognition“. Journal of Computational and Theoretical Nanoscience 17, Nr. 8 (01.08.2020): 3786–89. http://dx.doi.org/10.1166/jctn.2020.9321.
Der volle Inhalt der QuelleHu, Wenjin, Jiawei Xiong, Ning Wang, Feng Liu, Yao Kong und 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.
Der volle Inhalt der QuelleHuang, Feizhen, Jinfang Zeng, Yu Zhang und 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.
Der volle Inhalt der QuelleKim, 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.
Der volle Inhalt der QuelleKhan, 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.
Der volle Inhalt der QuelleSolovyeva, Elena, und 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.
Der volle Inhalt der QuelleRymarczyk, T., D. Wójcik, Ł. Maciura, W. Rosa und M. Bartosik. „Body surface potential mapping time series recognition using convolutional and recurrent neural networks“. Journal of Physics: Conference Series 2408, Nr. 1 (01.12.2022): 012001. http://dx.doi.org/10.1088/1742-6596/2408/1/012001.
Der volle Inhalt der QuelleWan, Renzhuo, Shuping Mei, Jun Wang, Min Liu und Fan Yang. „Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting“. Electronics 8, Nr. 8 (07.08.2019): 876. http://dx.doi.org/10.3390/electronics8080876.
Der volle Inhalt der QuelleCasabianca, Pietro, und 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.
Der volle Inhalt der QuelleXu, Zhijing, Yuhao Huo, Kun Liu und 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 (März 2020): 155014772091295. http://dx.doi.org/10.1177/1550147720912959.
Der volle Inhalt der QuelleLiu, Xuanxin, Fu Xu, Yu Sun, Haiyan Zhang und 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.
Der volle Inhalt der QuelleKwak, Jin-Yeol, und 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.
Der volle Inhalt der QuelleWang, Weiping, Feng Zhang, Xi Luo und 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.
Der volle Inhalt der QuelleChen, Jingwen, Yingwei Pan, Yehao Li, Ting Yao, Hongyang Chao und 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.
Der volle Inhalt der QuelleLiang, Kaiwei, Na Qin, Deqing Huang und 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.
Der volle Inhalt der QuelleWang, 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.
Der volle Inhalt der QuelleYüksel, Kıvanç, und Władysław Skarbek. „Convolutional and Recurrent Neural Networks for Face Image Analysis“. Foundations of Computing and Decision Sciences 44, Nr. 3 (01.09.2019): 331–47. http://dx.doi.org/10.2478/fcds-2019-0017.
Der volle Inhalt der QuelleLiu, 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.
Der volle Inhalt der QuelleLe, Viet-Tuan, Kiet Tran-Trung und 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.
Der volle Inhalt der QuelleCheng, Yepeng, Zuren Liu und Yasuhiko Morimoto. „Attention-Based SeriesNet: An Attention-Based Hybrid Neural Network Model for Conditional Time Series Forecasting“. Information 11, Nr. 6 (05.06.2020): 305. http://dx.doi.org/10.3390/info11060305.
Der volle Inhalt der QuelleFantaye, Tessfu Geteye, Junqing Yu und Tulu Tilahun Hailu. „Advanced Convolutional Neural Network-Based Hybrid Acoustic Models for Low-Resource Speech Recognition“. Computers 9, Nr. 2 (02.05.2020): 36. http://dx.doi.org/10.3390/computers9020036.
Der volle Inhalt der QuelleZhao, Ping, Zhijie Fan*, Zhiwei Cao und Xin Li. „Intrusion Detection Model Using Temporal Convolutional Network Blend Into Attention Mechanism“. International Journal of Information Security and Privacy 16, Nr. 1 (Januar 2022): 1–20. http://dx.doi.org/10.4018/ijisp.290832.
Der volle Inhalt der QuelleFabien-Ouellet, Gabriel, und 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.
Der volle Inhalt der QuelleLi, Haoliang, Shiqi Wang und 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.
Der volle Inhalt der QuelleShang, Jin, und 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.
Der volle Inhalt der QuelleQin, Chen, Jo Schlemper, Jose Caballero, Anthony N. Price, Joseph V. Hajnal und Daniel Rueckert. „Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction“. IEEE Transactions on Medical Imaging 38, Nr. 1 (Januar 2019): 280–90. http://dx.doi.org/10.1109/tmi.2018.2863670.
Der volle Inhalt der QuelleZuo, Zhen, Bing Shuai, Gang Wang, Xiao Liu, Xingxing Wang, Bing Wang und Yushi Chen. „Learning Contextual Dependence With Convolutional Hierarchical Recurrent Neural Networks“. IEEE Transactions on Image Processing 25, Nr. 7 (Juli 2016): 2983–96. http://dx.doi.org/10.1109/tip.2016.2548241.
Der volle Inhalt der QuelleCakir, Emre, Giambattista Parascandolo, Toni Heittola, Heikki Huttunen und Tuomas Virtanen. „Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection“. IEEE/ACM Transactions on Audio, Speech, and Language Processing 25, Nr. 6 (Juni 2017): 1291–303. http://dx.doi.org/10.1109/taslp.2017.2690575.
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