المؤلفون: Yang Ding, Hao Yan, Jingyuan He, Juanjuan Yin, A. Ruhan

المصدر: Complex & Intelligent Systems, Vol 11, Iss 1, Pp 1-23 (2025)

مصطلحات موضوعية: Hyperspectral anomaly detection (HAD), Graph neural networks (GNN), Graph convolutional networks(GCN), Right-shift phenomenon, Beta wavelet, Electronic computers. Computer science, QA75.5-76.95, Information technology, T58.5-58.64

وصف الملف: electronic resource

Relation: https://doaj.org/toc/2199-4536; https://doaj.org/toc/2198-6053

URL الوصول: https://doaj.org/article/e272f40b51dd42588801553a4eea569a

المؤلفون: Avirmed Enkhbat, Timothy K. Shih, Munkhjargal Gochoo, Pimpa Cheewaprakobkit, Wisnu Aditya, Thai Duy Quy, Hsinchih Lin, Yu-Ting Lin

المصدر: IEEE Access, Vol 12, Pp 125319-125331 (2024)

مصطلحات موضوعية: Action recognition, graph convolutional networks (GCN), temporal convolutional networks (TCN), erhu performance evaluation, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

وصف الملف: electronic resource

Relation: https://ieeexplore.ieee.org/document/10663716/; https://doaj.org/toc/2169-3536

URL الوصول: https://doaj.org/article/86cef1fd18f24cc1907168e725ce81ee

المؤلفون: Murat Koca, Isa Avci

المصدر: IEEE Access, Vol 12, Pp 143343-143351 (2024)

مصطلحات موضوعية: Ad-hoc network, graph convolutional networks (GCN), industrial control system (ICS), Internet of Things (IoT), intrusion detection system (IDS), security vulnerabilities, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

وصف الملف: electronic resource

Relation: https://ieeexplore.ieee.org/document/10689427/; https://doaj.org/toc/2169-3536

URL الوصول: https://doaj.org/article/e62b3e5841eb45dbac9a10a913aa8fbc

المؤلفون: Tun Li, Yanbing Liu, Qilie Liu, Wei Xu, Yunpeng Xiao, Hong Liu

المصدر: Digital Communications and Networks, Vol 9, Iss 5, Pp 1090-1100 (2023)

مصطلحات موضوعية: Malware, Representation learning, Graph convolutional networks(GCN), Tensor decomposition, Propagation prediction, Information technology, T58.5-58.64

وصف الملف: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2352864822001067; https://doaj.org/toc/2352-8648

URL الوصول: https://doaj.org/article/fbdfb5169bb744a2852929e94e0a08ad

المؤلفون: Lijuan Shen, Liping Jiang

المصدر: PeerJ Computer Science, Vol 10, p e1858 (2024)

مصطلحات موضوعية: Recommendation system, User-book rating prediction, Graph convolutional networks (GCN), Neural matrix factorization (NMF), Deep learning techniques, Electronic computers. Computer science, QA75.5-76.95

وصف الملف: electronic resource

Relation: https://peerj.com/articles/cs-1858.pdf; https://peerj.com/articles/cs-1858/; https://doaj.org/toc/2376-5992

URL الوصول: https://doaj.org/article/d2eb2a08b6274716b3dc05dc4df7c41b

المؤلفون: Yuan Zhang, Tao Han, Bing Wei, Kuangrong Hao, Lei Gao

المصدر: Complex & Intelligent Systems, Vol 9, Iss 6, Pp 6359-6371 (2023)

مصطلحات موضوعية: Graph convolutional networks (GCN), Semi-supervised, Few labeled data, Textile defect recognition, Electronic computers. Computer science, QA75.5-76.95, Information technology, T58.5-58.64

وصف الملف: electronic resource

Relation: https://doaj.org/toc/2199-4536; https://doaj.org/toc/2198-6053

URL الوصول: https://doaj.org/article/611f295e60914ecba4ee18c20d01cd07

المؤلفون: Jinhui Li, Jianying Huang, Hoon Kang

المصدر: IEEE Access, Vol 11, Pp 146090-146102 (2023)

مصطلحات موضوعية: Graph convolutional networks (GCN), spatial temporal graph convolutional networks (STGCN), 3D datasets, human pose prediction, directional & symbolic method, human joints key points, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

وصف الملف: electronic resource

Relation: https://ieeexplore.ieee.org/document/10353963/; https://doaj.org/toc/2169-3536

URL الوصول: https://doaj.org/article/c8ec688904a546f288bc617d072bd566

المؤلفون: Zhengjing Ma, Gang Mei, Salvatore Cuomo, Francesco Piccialli

المصدر: Journal of King Saud University: Computer and Information Sciences, Vol 34, Iss 6, Pp 3433-3447 (2022)

مصطلحات موضوعية: Heterogeneous data sources, Data fusion, Radial basis functions (RBF), Deep learning, Graph convolutional networks (GCN), Electronic computers. Computer science, QA75.5-76.95

وصف الملف: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S1319157822001203; https://doaj.org/toc/1319-1578

URL الوصول: https://doaj.org/article/4dc8fdaeb2654cc2bb7474af1dae04ff

المؤلفون: XIAO Zeguan, CHEN Qingliang

المصدر: Jisuanji kexue yu tansuo, Vol 16, Iss 2, Pp 395-402 (2022)

مصطلحات موضوعية: aspect, sentiment analysis, bidirectional encoder representations from transformers (bert), dependency tree, graph convolutional networks (gcn), Electronic computers. Computer science, QA75.5-76.95

وصف الملف: electronic resource

Relation: http://fcst.ceaj.org/fileup/1673-9418/PDF/2009003.pdf; https://doaj.org/toc/1673-9418

URL الوصول: https://doaj.org/article/8354c6c567024bd9820d18c96e35ea09

المؤلفون: Haoming Wan, Panpan Tang, Bangsen Tian, Hongbo Yu, Caifeng Jin, Bo Zhao, Hui Wang

المصدر: Applied Sciences; Volume 13; Issue 4; Pages: 2610

مصطلحات موضوعية: water extraction, object-based image analysis (OBIA), superpixel, synthetic aperture radar (SAR), graph convolutional networks (GCN), threshold segmentation

جغرافية الموضوع: agris

وصف الملف: application/pdf

Relation: https://dx.doi.org/10.3390/app13042610

الاتاحة: https://doi.org/10.3390/app13042610

المؤلفون: Ruiheng Li, Yi Di, Hao Tian, Lu Gan

المصدر: Electronics, Vol 12, Iss 4138, p 4138 (2023)

مصطلحات موضوعية: transient electromagnetic inversion, strata electrical structure imaging, non-Euclidean features, long-dependency feature, graph convolutional networks (GCN), long short-term memory (LSTM), Electronics, TK7800-8360

Relation: https://www.mdpi.com/2079-9292/12/19/4138; https://doaj.org/toc/2079-9292; https://doaj.org/article/68c445eb2fd74a4b9163e6571be3c023

الاتاحة: https://doi.org/10.3390/electronics12194138
https://doaj.org/article/68c445eb2fd74a4b9163e6571be3c023

المؤلفون: Fangming Guo, Zhongwei Li, Ziqi Xin, Xue Zhu, Leiquan Wang, Jie Zhang

المصدر: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 8160-8170 (2021)

مصطلحات موضوعية: Graph convolutional networks(GCN), hyperspectral image (HSI) classification, spectral-spatial fusion, spectral-spatial graph, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809

وصف الملف: electronic resource

Relation: https://ieeexplore.ieee.org/document/9511023/; https://doaj.org/toc/2151-1535

URL الوصول: https://doaj.org/article/9389d0cb5f8f41feb429b17b0f75a6e9

المؤلفون: Bing Du, Xiaomu Cheng, Yiping Duan, Huansheng Ning

المصدر: Brain Sciences; Volume 12; Issue 2; Pages: 228

مصطلحات موضوعية: brain decoding, variational autoencoder (VAE), generative adversarial network (GAN), graph convolutional networks (GCN), functional magnetic resonance imaging (fMRI), brain–computer interface (BCI)

وصف الملف: application/pdf

Relation: https://dx.doi.org/10.3390/brainsci12020228

الاتاحة: https://doi.org/10.3390/brainsci12020228

المؤلفون: Jungi Kim, Haneol Seo, Muhammad Tahir Naseem, Chan-Su Lee

المصدر: Sensors; Volume 22; Issue 13; Pages: 4863

مصطلحات موضوعية: graph convolutional networks (GCN), gait classification, spatiotemporal graph convolutional networks (ST-GCN), multiple-input branches (MIB), global average pooling (GAP), temporal convolutional network (TCN)

وصف الملف: application/pdf

Relation: Physical Sensors; https://dx.doi.org/10.3390/s22134863

الاتاحة: https://doi.org/10.3390/s22134863

المؤلفون: Rui Wang, Chenyang Huang, Xiangyang Wang

المصدر: IEEE Access, Vol 8, Pp 38472-38480 (2020)

مصطلحات موضوعية: Human pose estimation, graph convolutional networks (GCN), global relation reasoning, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

وصف الملف: electronic resource

Relation: https://ieeexplore.ieee.org/document/8990101/; https://doaj.org/toc/2169-3536

URL الوصول: https://doaj.org/article/95d4a17a82c54f4584aec8e014e21f8c

المؤلفون: Floriane Montanari (10714152), Lara Kuhnke (7111903), Antonius ter Laak (7112047), Djork-Arné Clevert (5565588)

مصطلحات موضوعية: Cheminformatics, Computational Chemistry, Artificial Intelligence and Image Processing, Cheminformatics and Quantitative Structure-Activity Relationships, ADMET, Multitask Deep Learning, Solubility, Graph convolutional networks (GCN)

Relation: https://figshare.com/articles/journal_contribution/Modeling_physico-chemical_ADMET_endpoints_with_multitask_graph_convolutional_networks/14499807

الاتاحة: https://doi.org/10.6084/m9.figshare.14499807.v1

المؤلفون: Mendoza Chirinos, Juliana

المساهمون: Pérez Bernal, Juan Fernando, Medina Sánchez, Pablo Alexander, Bravo Vega, Carlos Andrés

مصطلحات موضوعية: Dengue, Epidemiology, Machine learning, Spatio-temporal prediction, LSTM Neural networks, Graph Convolutional Networks (GCN), Public health, Climatic factors, Socioeconomic factors, Ingeniería

وصف الملف: 20 páginas; application/pdf

Relation: D. Martínez-Bello, A. López-Quílez and A. T. Prieto, ‘Spatiotemporal modeling of relative risk of dengue disease in colombia,’ Stochastic Environmental Research and Risk Assessment, vol. 32, pp. 1587–1601, 6 Jun. 2018, issn: 14363259. doi:10.1007/s00477-017-1461-5.; I. N. de Salud, Boletín epidemiológico semanal semana epidemiológica 12, 2024. [Online]. Available: https://www.ins.gov.co/buscador-eventos/BoletinEpidemiologico/2024_Bolet%C3%ADn_epidemiologico_semana_12.pdf.; K. Roster and F. A. Rodrigues, ‘Neural networks for dengue prediction: A systematic review,’ arXiv preprint arXiv:2106.12905, 2021.; S. Deng, S. Wang, H. Rangwala, L. Wang and Y. Ning, ‘Cola-gnn: Cross-location attention based graph neural networks for long-term ili prediction,’ Association for Computing Machinery, Oct. 2020, pp. 245– 254, isbn: 9781450368599. doi:10.1145/3340531.3411975.; V. B. Nguyen, T. S. Hy, L. Tran-Thanh and N. Nghiem, ‘Predicting covid-19 pandemic by spatio-temporal graph neural networks: A new zealand’s study,’ May 2023. [Online]. Available: http://arxiv.org/abs/2305.07731.; Z. Liu, G. Wan, B. A. Prakash, M. S. Y. Lau and W. Jin, ‘A review of graph neural networks in epidemic modeling,’ Mar. 2024. [Online]. Available: http://arxiv.org/abs/2403.19852.; S. Job, X. Tao, T. Cai et al., ‘Exploring causal learning through graph neural networks: An in-depth review,’ Nov. 2023. [Online]. Available: http://arxiv.org/abs/2311.14994.; J. C. Walrand and H. S. V. Pravin, Optimal causal coding-decoding poblems, 1983.; M. de Salud y Protección Social-Federación Médica Colombiana, Dengue - memorias, 2012.; J. C. Castrillón, J. C. Castaño and S. Urcuqui, ‘Dengue en colombia: Diez años de evolución,’ Revista Chilena de Infectologia, vol. 32, pp. 142–149, 2 2014. doi: http://dx.doi.org/10 .4067/S0716-10182015000300002. [Online]. Available: www.sochinf.cl.; [11] K.-T. Kuo, D. Moukheiber, S. C. Ordonez et al., ‘Denguenet: Dengue prediction using spatiotemporal satellite imagery for resource-limited countries,’ Jan. 2024. [Online]. Available: http://arxiv.org/abs/2401.11114.; D. A. Martínez-Bello, A. López-Quílez and A. T. Prieto, ‘Spatio-temporal modeling of zika and dengue infections within colombia,’ International Journal of Environmental Research and Public Health, vol. 15, 7 Jul. 2018, issn: 16604601. doi:10.3390/ijerph15071376.; L. Wang, A. Adiga, J. Chen, A. Sadilek, S. Venkatramanan and M. Marathe, Causalgnn: Causal-based graph neural networks for spatio-temporal epidemic forecasting, 2022. [Online]. Available: www.aaai.org.; C. Fritz, E. Dorigatti and D. Rügamer, ‘Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly covid-19 cases in germany,’ Scientific Reports, vol. 12, 1 Dec. 2022, issn: 20452322. doi:10.1038/s41598-022-07757-5.; IDEAM - Instituto de Hidrología, Meteorología y Estudios Ambientales, DHIME - Sistema de Información Hidrológica, Accessed: 2024-04-15, 2024. [Online]. Available: http://dhime.ideam.gov.co/atencionciudadano/.; INS - Instituto Nacional de Salud, Portal SIVIGILA, Accessed: 2024-04-15, 2024. [Online]. Available: https://portalsivigila.ins.gov.co/.; DANE - Departamento Administrativo Nacional de Estadística, Estadísticas por Tema, Accessed: 2024-04-15, 2024. [Online]. Available: https://www.dane.gov.co/index.php/estadisticas-por-tema.; I. N. d. S. Ministerio de Salud y Protección Social, Dengue en Colombia: epidemiología de la reemergencia a la hiperendemia. Bogotá, Colombia: Ministerio de Salud y Protección Social, 2017. [Online]. Available: https://www.minsalud.gov.co/sites/rid/Lists/BibliotecaDigital/RIDE/INEC/INV/Dengue%20en%20Colombia.pdf.; Departamento Administrativo Nacional de Estadística (DANE). ‘Medida de pobreza multidimensional de fuente censal.’ Accessed: 2024-04-15. (2024), [Online]. Available: https://www.dane.gov.co/index.php/estadisticas- por- tema/pobreza-y-condiciones-de-vida/pobreza-y- desigualdad/medida- de-pobreza-multidimensional-de-fuente-censal.; Y. Hu, A. Huber, J. Anumula and S.-C. Liu, Overcoming the vanishing gradient problem in plain recurrent networks, 2019. arXiv: 1801.06105 [cs.NE]. [Online]. Available: https://arxiv.org/abs/1801.06105.; V. Urošević and S. Dimitrijević, ‘Optimum input sequence size for a sliding window-based lstm neural network used in short-term electrical load forecasting,’ in 2021 29th Telecommunications Forum (TELFOR), 2021, pp. 1–4. doi:10.1109/TELFOR52709.2021.9653206.; M. Shi, Y. Tang, X. Zhu, Y. Zhuang, M. Lin and J. Liu, ‘Feature-attention graph convolutional networks for noise resilient learning,’ IEEE Transactions on Cybernetics, vol. 52, no. 8, pp. 7719–7731, 2022. doi:10.1109/TCYB.2022.3143798.; A. Hart, J. Smith and C. Lee, ‘Improving graph networks through selection-based convolution,’ in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 1234–1243. [Online]. Available: https://openaccess.thecvf.com/content/ WACV2024/papers/Hart_Improving_Graph_Networks_Through_Selection-Based_Convolution_WACV_2024_paper.pdf.; X. Gong and W. Cheng, ‘Exploiting edge features for graph neural networks,’ in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 5674–5683. [Online]. Available: https://openaccess.thecvf.com/content_CVPR_2019/papers/Gong_Exploiting_Edge_Features_for_Graph_Neural_Networks_CVPR_2019_paper.pdf.; J. Lu, Y. Tian, S. Wang, M. Sheng and X. Zheng, Pearnet: A pearson correlation-based graph attention network for sleep stage recognition, 2022. arXiv: 2209.13645 [eess.SP]. [Online]. Available: https://arxiv.org/abs/2209.13645.; S. Huang, Y. Zhang, G. Peng et al., ‘Mf-gcn-lstm: A cloud-edge distributed framework for key positions prediction in grid projects,’ Journal of Cloud Computing, vol. 11, no. 1, p. 55, 2022. doi:10.1186/s13677-022-00310-9. [Online]. Available: https://doi.org/10.1186/s13677-022-00310-9.; X. Ren and S. Yuan, ‘Gcn-lstm combined model for urban link mean speed prediction in the regional traffic network,’ in 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), 2022, pp. 1–7. doi:10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927766.; https://hdl.handle.net/1992/75226; instname:Universidad de los Andes; reponame:Repositorio Institucional Séneca; repourl:https://repositorio.uniandes.edu.co/

الاتاحة: https://hdl.handle.net/1992/75226

المؤلفون: Jolérus, Henrik

مصطلحات موضوعية: Artificial intelligence, Machine learning, Deep learning, Time series regression, Recurrent neural networks (RNN), Long short-term memory (LSTM), Graph neural networks, Graph convolutional networks (GCN), Intelligent transportation systems (ITP), Short term traffic prediction (STTP), Traffic forecasting, Crowd insights, Artificiell intelligens, Maskininlärning, Djupinlärning, Regression av tidsserier, Smarta transportsystem, Trafikprognos, Folkströmsdata, Computer Sciences, Datavetenskap (datalogi)

وصف الملف: application/pdf

Relation: TRITA-EECS-EX; 2024:48

الاتاحة: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-345856

المؤلفون: Feng, Ao, Gao, Zhengjie, Song, Xinyu, Ke, Ke, Xu, Tianhao, Zhang, Xuelei

المصدر: All Faculty Scholarship for the College of the Sciences

مصطلحات موضوعية: Deep learning, sentiment analysis, graph convolutional networks (GCN), Computer Sciences

وصف الملف: application/pdf

Relation: https://digitalcommons.cwu.edu/cotsfac/382; https://digitalcommons.cwu.edu/cgi/viewcontent.cgi?article=1382&context=cotsfac

الاتاحة: https://digitalcommons.cwu.edu/cotsfac/382
https://digitalcommons.cwu.edu/cgi/viewcontent.cgi?article=1382&context=cotsfac

المؤلفون: Pandey, Rajendra Kumar, Rathore, Yogesh Kumar

المصدر: Med Biol Eng Comput ; ISSN:1741-0444

مصطلحات موضوعية: 3D heart reconstruction, Cardiac imaging, Cardiac segmentation, Deep learning, GANs, Graph convolutional networks (GCN), Statistical shape modeling (SSM)

Relation: https://doi.org/10.1007/s11517-024-03273-y; https://pubmed.ncbi.nlm.nih.gov/39753994

الاتاحة: https://doi.org/10.1007/s11517-024-03273-y
https://pubmed.ncbi.nlm.nih.gov/39753994