Artigos de revistas sobre o tema "Convolutive Neural Networks"
Crie uma referência precisa em APA, MLA, Chicago, Harvard, e outros estilos
Veja os 50 melhores artigos de revistas para estudos sobre o assunto "Convolutive Neural Networks".
Ao lado de cada fonte na lista de referências, há um botão "Adicionar à bibliografia". Clique e geraremos automaticamente a citação bibliográfica do trabalho escolhido no estilo de citação de que você precisa: APA, MLA, Harvard, Chicago, Vancouver, etc.
Você também pode baixar o texto completo da publicação científica em formato .pdf e ler o resumo do trabalho online se estiver presente nos metadados.
Veja os artigos de revistas das mais diversas áreas científicas e compile uma bibliografia correta.
KIREI, B. S., M. D. TOPA, I. MURESAN, I. HOMANA e N. TOMA. "Blind Source Separation for Convolutive Mixtures with Neural Networks". Advances in Electrical and Computer Engineering 11, n.º 1 (2011): 63–68. http://dx.doi.org/10.4316/aece.2011.01010.
Texto completo da fonteKarhunen, J., A. Cichocki, W. Kasprzak e P. Pajunen. "On Neural Blind Separation with Noise Suppression and Redundancy Reduction". International Journal of Neural Systems 08, n.º 02 (abril de 1997): 219–37. http://dx.doi.org/10.1142/s0129065797000239.
Texto completo da fonteDuan, Yunlong, Ziyu Han e Zhening Tang. "A lightweight plant disease recognition network based on Resnet". Applied and Computational Engineering 5, n.º 1 (14 de junho de 2023): 583–92. http://dx.doi.org/10.54254/2755-2721/5/20230651.
Texto completo da fonteTong, Lian, Lan Yang, Xuan Wang e Li Liu. "Self-aware face emotion accelerated recognition algorithm: a novel neural network acceleration algorithm of emotion recognition for international students". PeerJ Computer Science 9 (26 de setembro de 2023): e1611. http://dx.doi.org/10.7717/peerj-cs.1611.
Texto completo da fonteSineglazov, Victor, e Petro Chynnyk. "Quantum Convolution Neural Network". Electronics and Control Systems 2, n.º 76 (23 de junho de 2023): 40–45. http://dx.doi.org/10.18372/1990-5548.76.17667.
Texto completo da fonteLü Benyuan, 吕本远, 禚真福 Zhuo Zhenfu, 韩永赛 Han Yongsai e 张立朝 Zhang Lichao. "基于Faster区域卷积神经网络的目标检测". Laser & Optoelectronics Progress 58, n.º 22 (2021): 2210017. http://dx.doi.org/10.3788/lop202158.2210017.
Texto completo da fonteAnmin, Kong, e Zhao Bin. "A Parallel Loading Based Accelerator for Convolution Neural Network". International Journal of Machine Learning and Computing 10, n.º 5 (5 de outubro de 2020): 669–74. http://dx.doi.org/10.18178/ijmlc.2020.10.5.989.
Texto completo da fonteSharma, Himanshu, e Rohit Agarwal. "Channel Enhanced Deep Convolution Neural Network based Cancer Classification". Journal of Advanced Research in Dynamical and Control Systems 11, n.º 10-SPECIAL ISSUE (31 de outubro de 2019): 610–17. http://dx.doi.org/10.5373/jardcs/v11sp10/20192849.
Texto completo da fonteAnem, Smt Jayalaxmi, B. Dharani, K. Raveendra, CH Nikhil e K. Akhil. "Leveraging Convolution Neural Network (CNN) for Skin Cancer Identification". International Journal of Research Publication and Reviews 5, n.º 4 (abril de 2024): 2150–55. http://dx.doi.org/10.55248/gengpi.5.0424.0955.
Texto completo da fonteOh, Seokjin, Jiyong An e Kyeong-Sik Min. "Area-Efficient Mapping of Convolutional Neural Networks to Memristor Crossbars Using Sub-Image Partitioning". Micromachines 14, n.º 2 (25 de janeiro de 2023): 309. http://dx.doi.org/10.3390/mi14020309.
Texto completo da fonteReddy*, M. Venkata Krishna, e Pradeep S. "Envision Foundational of Convolution Neural Network". International Journal of Innovative Technology and Exploring Engineering 10, n.º 6 (30 de abril de 2021): 54–60. http://dx.doi.org/10.35940/ijitee.f8804.0410621.
Texto completo da fonteWang Xuanqi, 王选齐, 杨锋 Yang Feng, 曹斌 Cao Bin, 刘静 Liu Jing, 魏德健 Wei Dejian e 曹慧 Cao Hui. "卷积神经网络在甲状腺结节诊断中的应用". Laser & Optoelectronics Progress 59, n.º 8 (2022): 0800002. http://dx.doi.org/10.3788/lop202259.0800002.
Texto completo da fonteWang, Lei. "Application Research of Deep Convolutional Neural Network in Computer Vision". Journal of Networking and Telecommunications 2, n.º 2 (6 de agosto de 2020): 23. http://dx.doi.org/10.18282/jnt.v2i2.886.
Texto completo da fonteHaffner, Oto, Erik Kučera, Peter Drahoš e Ján Cigánek. "Using Entropy for Welds Segmentation and Evaluation". Entropy 21, n.º 12 (28 de novembro de 2019): 1168. http://dx.doi.org/10.3390/e21121168.
Texto completo da fonteYang Guowei, 杨国威, 周楠 Zhou Nan, 杨敏 Yang Min, 张永帅 Zhang Yongshuai e 王以忠 Wang Yizhong. "融合卷积神经网络和相关滤波的焊缝自动跟踪". Chinese Journal of Lasers 48, n.º 22 (2021): 2202011. http://dx.doi.org/10.3788/cjl202148.2202011.
Texto completo da fonteXing Yongxin, 邢永鑫, 吴碧巧 Wu Biqiao, 吴松平 Wu Songping e 王天一 Wang Tianyi. "基于卷积神经网络和迁移学习的奶牛个体识别". Laser & Optoelectronics Progress 58, n.º 16 (2021): 1628002. http://dx.doi.org/10.3788/lop202158.1628002.
Texto completo da fonteChen Wenhao, 陈文豪, 何敬 He Jing e 刘刚 Liu Gang. "引入注意力机制的卷积神经网络高光谱图像分类". Laser & Optoelectronics Progress 59, n.º 18 (2022): 1811001. http://dx.doi.org/10.3788/lop202259.1811001.
Texto completo da fonteLi Zhuorong, 李卓容, 唐云祁 Tang Yunqi e 蔡能斌 Cai Nengbin. "基于卷积神经网络的现场勘查照片分类方法". Laser & Optoelectronics Progress 60, n.º 4 (2023): 0410007. http://dx.doi.org/10.3788/lop212827.
Texto completo da fonteXianhao Shen, Xianhao Shen, Changhong Zhu Xianhao Shen, Yihao Zang Changhong Zhu e Shaohua Niu Yihao Zang. "A Method for Detecting Abnormal Data of Network Nodes Based on Convolutional Neural Network". 電腦學刊 33, n.º 3 (junho de 2022): 049–58. http://dx.doi.org/10.53106/199115992022063303004.
Texto completo da fonteYan, Ming, e Zhe He. "Dance Action Recognition Model Using Deep Learning Network in Streaming Media Environment". Journal of Environmental and Public Health 2022 (12 de setembro de 2022): 1–10. http://dx.doi.org/10.1155/2022/8955326.
Texto completo da fonteBelorutsky, R. Yu, e S. V. Zhitnik. "SPEECH RECOGNITION BASED ON CONVOLUTION NEURAL NETWORKS". Issues of radio electronics, n.º 4 (10 de maio de 2019): 47–52. http://dx.doi.org/10.21778/2218-5453-2019-4-47-52.
Texto completo da fonteКonarev, D., e А. Gulamov. "ACCURACY IMPROVING OF PRE-TRAINED NEURAL NETWORKS BY FINE TUNING". EurasianUnionScientists 5, n.º 1(82) (15 de fevereiro de 2021): 26–28. http://dx.doi.org/10.31618/esu.2413-9335.2021.5.82.1231.
Texto completo da fonteGeum, Young Hee, Arjun Kumar Rathie e Hwajoon Kim. "Matrix Expression of Convolution and Its Generalized Continuous Form". Symmetry 12, n.º 11 (29 de outubro de 2020): 1791. http://dx.doi.org/10.3390/sym12111791.
Texto completo da fonteTian, Feng, Shiao Zhang, Miao Cao e Xiaojun Huang. "Research on accelerated coding absorber design with deep learning". Physica Scripta 98, n.º 9 (24 de agosto de 2023): 096003. http://dx.doi.org/10.1088/1402-4896/acf00a.
Texto completo da fonteGafarov, Fail, Andrey Berdnikov e Pavel Ustin. "Online social network user performance prediction by graph neural networks". International Journal of Advances in Intelligent Informatics 8, n.º 3 (30 de novembro de 2022): 285. http://dx.doi.org/10.26555/ijain.v8i3.859.
Texto completo da fonteChimakurthi, Venkata Naga Satya Surendra. "Application of Convolution Neural Network for Digital Image Processing". Engineering International 8, n.º 2 (31 de dezembro de 2020): 149—xxx. http://dx.doi.org/10.18034/ei.v8i2.592.
Texto completo da fonteAkbar, Mutaqin. "Traffic sign recognition using convolutional neural networks". Jurnal Teknologi dan Sistem Komputer 9, n.º 2 (5 de março de 2021): 120–25. http://dx.doi.org/10.14710/jtsiskom.2021.13959.
Texto completo da fonteSyamala Rao, P., Dr G.P.SaradhiVarma e Rajasekhar Mutukuri. "Effective and High Computing Algorithms for Convolution Neural Networks". International Journal of Engineering & Technology 7, n.º 3.31 (24 de agosto de 2018): 66. http://dx.doi.org/10.14419/ijet.v7i3.31.18203.
Texto completo da fonteGuo Congzhou, 郭从洲, 李可 Li Ke, 朱奕坤 Zhu Yikun, 童晓冲 Tong Xiaochong e 王习文 Wang Xiwen. "文本图像倾斜角度检测的深度卷积神经网络方法". Laser & Optoelectronics Progress 58, n.º 14 (2021): 1410007. http://dx.doi.org/10.3788/lop202158.1410007.
Texto completo da fonteBunrit, Supaporn, Thuttaphol Inkian, Nittaya Kerdprasop e Kittisak Kerdprasop. "Text-Independent Speaker Identification Using Deep Learning Model of Convolution Neural Network". International Journal of Machine Learning and Computing 9, n.º 2 (abril de 2019): 143–48. http://dx.doi.org/10.18178/ijmlc.2019.9.2.778.
Texto completo da fonteShi, Lin, e Lei Zheng. "An IGWOCNN Deep Method for Medical Education Quality Estimating". Mathematical Problems in Engineering 2022 (9 de agosto de 2022): 1–5. http://dx.doi.org/10.1155/2022/9037726.
Texto completo da fonteSrinivas, K., B. Kavitha Rani, M. Varaprasad Rao, G. Madhukar e B. Venkata Ramana. "Convolution Neural Networks for Binary Classification". Journal of Computational and Theoretical Nanoscience 16, n.º 11 (1 de novembro de 2019): 4877–82. http://dx.doi.org/10.1166/jctn.2019.8399.
Texto completo da fonteBass, L. P., Yu A. Plastinin e I. Yu Skryabysheva. "The machine training in problems of satellite images’s processing". Metrologiya, n.º 4 (2020): 15–37. http://dx.doi.org/10.32446/0132-4713.2020-4-15-37.
Texto completo da fonteWu, Chenxi, Rong Jiang, Xin Wu, Chao Zhong e Caixia Huang. "A Time–Frequency Residual Convolution Neural Network for the Fault Diagnosis of Rolling Bearings". Processes 12, n.º 1 (25 de dezembro de 2023): 54. http://dx.doi.org/10.3390/pr12010054.
Texto completo da fonteLiao, Shengbin, Xiaofeng Wang e ZongKai Yang. "A heterogeneous two-stream network for human action recognition". AI Communications 36, n.º 3 (21 de agosto de 2023): 219–33. http://dx.doi.org/10.3233/aic-220188.
Texto completo da fonteMohinabonu, Agzamova. "ENHANCING FACIAL RECOGNITION THROUGH CONTRASTIVE CONVOLUTION: A COMPREHENSIVE METHODOLOGY". American Journal of Engineering and Technology 5, n.º 11 (1 de novembro de 2023): 105–14. http://dx.doi.org/10.37547/tajet/volume05issue11-15.
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 fonteHu, Kejian, e Xiaoguang Wu. "A Bridge Structure 3D Representation for Deep Neural Network and Its Application in Frequency Estimation". Advances in Civil Engineering 2022 (22 de março de 2022): 1–13. http://dx.doi.org/10.1155/2022/1999013.
Texto completo da fonteSapunov, V. V., S. A. Botman, G. V. Kamyshov e N. N. Shusharina. "Application of Convolution with Periodic Boundary Condition for Processing Data from Cylindrical Electrode Arrays". INFORMACIONNYE TEHNOLOGII 27, n.º 3 (15 de março de 2021): 125–31. http://dx.doi.org/10.17587/it.27.125-131.
Texto completo da fonteVazquez, Napoli R., Dan P. Fernandes e Daniel H. Chen. "Control Valve Stiction: Experimentation, Modeling, Model Validation and Detection with Convolution Neural Network". International Journal of Chemical Engineering and Applications 10, n.º 6 (dezembro de 2019): 195–99. http://dx.doi.org/10.18178/ijcea.2019.10.6.768.
Texto completo da fonteXu Mingzhu, 许明珠, 徐浩 Xu Hao, 孔鹏 Kong Peng e 吴艳兰 Wu Yanlan. "结合植被指数和卷积神经网络的遥感植被分类方法". Laser & Optoelectronics Progress 59, n.º 24 (2022): 2428005. http://dx.doi.org/10.3788/lop202259.2428005.
Texto completo da fonteRustam, Rustam, Rita Noveriza, Siti Khotijah, Syamsul Rizal, Melati Melati, Nor Kumalasari Caecar Pratiwi, Muhammad Hablul Barri e Koredianto Usman. "Convolution Neural Network Approach for Early Identification of Patchouli Leaf Disease in IndonesiaConvolution Neural Network Approach for Early Identification of Patchouli Leaf Disease in Indonesia". Journal of Image and Graphics 12, n.º 2 (2024): 137–44. http://dx.doi.org/10.18178/joig.12.2.137-144.
Texto completo da fonteYang, Liming, Yihang Yang, Jinghui Yang, Ningyuan Zhao, Ling Wu, Liguo Wang e Tianrui Wang. "FusionNet: A Convolution–Transformer Fusion Network for Hyperspectral Image Classification". Remote Sensing 14, n.º 16 (19 de agosto de 2022): 4066. http://dx.doi.org/10.3390/rs14164066.
Texto completo da fonteYan, Peizhi, e Yi Feng. "Using Convolution and Deep Learning in Gomoku Game Artificial Intelligence". Parallel Processing Letters 28, n.º 03 (setembro de 2018): 1850011. http://dx.doi.org/10.1142/s0129626418500111.
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 fonteWang, Wei, Yanjie Zhu, Zhuoxu Cui e Dong Liang. "Is Each Layer Non-trivial in CNN? (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 35, n.º 18 (18 de maio de 2021): 15915–16. http://dx.doi.org/10.1609/aaai.v35i18.17954.
Texto completo da fonteArsirii, Olena О., e Denys V. Petrosiuk. "An adaptive convolutional neural network model for human facial expression recognition". Herald of Advanced Information Technology 6, n.º 2 (3 de julho de 2023): 128–38. http://dx.doi.org/10.15276/hait.06.2023.8.
Texto completo da fonteGu, Yafeng, e Li Deng. "STAGCN: Spatial–Temporal Attention Graph Convolution Network for Traffic Forecasting". Mathematics 10, n.º 9 (8 de maio de 2022): 1599. http://dx.doi.org/10.3390/math10091599.
Texto completo da fonteGao, Yuan, Laurence T. Yang, Dehua Zheng, Jing Yang e Yaliang Zhao. "Quantized Tensor Neural Network". ACM/IMS Transactions on Data Science 2, n.º 4 (30 de novembro de 2021): 1–18. http://dx.doi.org/10.1145/3491255.
Texto completo da fonteLi, Yong, Luping Wang e Fen Liu. "Multi-Branch Attention-Based Grouped Convolution Network for Human Activity Recognition Using Inertial Sensors". Electronics 11, n.º 16 (12 de agosto de 2022): 2526. http://dx.doi.org/10.3390/electronics11162526.
Texto completo da fonte