nach oben

Neural Computing and Applications

Erschienen in:

26.05.2022 | Review

Applications of graph convolutional networks in computer vision

verfasst von: Pingping Cao, Zeqi Zhu, Ziyuan Wang, Yanping Zhu, Qiang Niu

Erschienen in: Neural Computing and Applications | Ausgabe 16/2022

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Graph Convolutional Network (GCN) which models the potential relationship between non-Euclidean spatial data has attracted researchers’ attention in deep learning in recent years. It has been widely used in different computer vision tasks by modeling the latent space, topology, semantics, and other information in Euclidean spatial data and has achieved significant success. To better understand the work principles and future GCN applications in the computer vision field, this study reviewed the basic principles of GCN, summarized the difficulties and solutions using GCN in different visual tasks, and introduced in detail the methods for constructing graphs from the Euclidean spatial data in different visual tasks. At the same time, the review divided the application of GCN in basic visual tasks into image recognition, object detection, semantic segmentation, instance segmentation and object tracking. The role and performance of GCN in basic visual tasks were summarized and compared in detail for different tasks. This review emphasizes that the application of GCN in computer vision faces three challenges: computational complexity, the paradigm of constructing graphs from the Euclidean spatial data, and the interpretability of the model. Finally, this review proposes two future trends of GCN in the vision field, namely model lightweight and fusing GCN with other models to improve the performance of the visual model and meet the higher requirements of vision tasks.

Vorheriger Artikel Attention mechanism in neural networks: where it comes and where it goes

Nächster Artikel RETRACTED ARTICLE: A fuzzy-based adaptive multi-input–output scheme in lieu of diabetic and hypertension management for post-operative patients: an human–machine interface approach with its continuum

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324CrossRef

Zhou FY, Jin LP, Dong J (2017) Review of convolutional neural network. Chin J Comput

Zhang S, Tong H, Xu J, Maciejewski R (2019) Graph convolutional networks: a comprehensive review. Comput Social Netw 6(1):1–23CrossRef

Gong Y, Jia Y, Leung T, Toshev A, Ioffe S (2013) Deep convolutional ranking for multilabel image annotation. arXiv preprint arXiv:1312.4894

Wang J, Yang Y, Mao J, Huang Z, Huang C, Xu W (2016) Cnn-rnn: a unified framework for multi-label image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2285–2294

Zhu F, Li H, Ouyang W, Yu N, Wang X (2017) Learning spatial regularization with image-level supervisions for multi-label image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5513–5522

Wang Z, Chen T, Li G, Xu R, Lin L (2017) Multi-label image recognition by recurrently discovering attentional regions. In: Proceedings of the IEEE international conference on computer vision, pp 464–472

Li Q, Qiao M, Bian W, Tao D (2016) Conditional graphical lasso for multi-label image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2977–2986

Li X, Zhao F, Guo Y (2014) Multi-label image classification with a probabilistic label enhancement model. In: UAI 1:1–10

10.

Lee C-W, Fang W, Yeh C-K, Wang Y-CF (2018) Multi-label zero-shot learning with structured knowledge graphs. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1576–1585

11.

Chen Z-M, Wei X-S, Wang P, Guo Y (2019) Multi-label image recognition with graph convolutional networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 5177–5186

12.

Bruna J, Zaremba W, Szlam A, LeCun Y (2013) Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203

13.

Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907

14.

Zhang M, Chen Y (2018) Link prediction based on graph neural networks. Adv Neural Inf Process Syst 31:5165–5175

15.

Simonovsky M, Komodakis N (2017) Dynamic edge-conditioned filters in convolutional neural networks on graphs. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3693–3702

16.

Xu K, Hu W, Leskovec J, Jegelka S (2018) How powerful are graph neural networks? arXiv preprint arXiv:1810.00826

17.

Ying R, You J, Morris C, Ren X, Hamilton WL, Leskovec J (2018) Hierarchical graph representation learning with differentiable pooling. arXiv preprint arXiv:1806.08804

18.

Zhang M, Cui Z, Neumann M, Chen Y (2018) An end-to-end deep learning architecture for graph classification. In: Thirty-Second AAAI conference on artificial intelligence

19.

Hamilton WL, Ying R, Leskovec J (2017) Inductive representation learning on large graphs. In: Proceedings of the 31st international conference on neural information processing systems, pp 1025–1035

20.

Pei H, Wei B, Chang KC-C, Lei Y, Yang B (2020) Geom-gcn: Geometric graph convolutional networks. arXiv preprint arXiv:2002.05287

21.

Veličković P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. arXiv preprint arXiv:1710.10903

22.

Xu B, Shen H, Cao Q, Qiu Y, Cheng X (2019) Graph wavelet neural network. arXiv preprint arXiv:1904.07785

23.

Wang H, Zhang F, Zhang M, Leskovec J, Zhao M, Li W, Wang Z (2019) Knowledge-aware graph neural networks with label smoothness regularization for recommender systems. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining, pp 968–977

24.

Wang H, Zhang F, Wang J, Zhao M, Li W, Xie X, Guo M (2019) Exploring high-order user preference on the knowledge graph for recommender systems. ACM Trans Inf Syst (TOIS) 37(3):1–26CrossRef

25.

Fan W, Ma Y, Li Q, He Y, Zhao E, Tang J, Yin D (2019) Graph neural networks for social recommendation. In: The World Wide Web conference, pp. 417–426

26.

Ying R, He R, Chen K, Eksombatchai P, Hamilton WL, Leskovec J (2018) Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining, pp 974–983

27.

Cui Z, Henrickson K, Ke R, Wang Y (2019) Traffic graph convolutional recurrent neural network: a deep learning framework for network-scale traffic learning and forecasting. IEEE Trans Intell Transp Syst 21(11):4883–4894CrossRef

28.

Zhang J, Shi X, Xie J, Ma H, King I, Yeung D-Y (2018) Gaan: Gated attention networks for learning on large and spatiotemporal graphs. arXiv preprint arXiv:1803.07294

29.

Yu B, Yin H, Zhu Z (2017) Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875

30.

Duvenaud D, Maclaurin D, Aguilera-Iparraguirre J, Gómez-Bombarelli R, Hirzel T, Aspuru-Guzik A, Adams RP (2015)Convolutional networks on graphs for learning molecular fingerprints. arXiv preprint arXiv:1509.09292

31.

Kearnes S, McCloskey K, Berndl M, Pande V, Riley P (2016) Molecular graph convolutions: moving beyond fingerprints. J Comput Aided Mol Des 30(8):595–608CrossRef

32.

Zitnik M, Agrawal M, Leskovec J (2018) Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics 34(13):457–466CrossRef

33.

Fout AM (2017) Protein interface prediction using graph convolutional networks. Ph.D. thesis, Colorado State University

34.

De Cao N, Kipf T (2018) Molgan: an implicit generative model for small molecular graphs. arXiv preprint arXiv:1805.11973

35.

You J, Liu B, Ying R, Pande V, Leskovec J (2018) Graph convolutional policy network for goal-directed molecular graph generation. arXiv preprint arXiv:1806.02473

36.

Liu X, Luo Z, Huang H (2018) Jointly multiple events extraction via attention-based graph information aggregation. arXiv preprint arXiv:1809.09078

37.

Nguyen TH, Grishman R (2018) Graph convolutional networks with argument-aware pooling for event detection. In: Thirty-second AAAI conference on artificial intelligence

38.

Beck D, Haffari G, Cohn T (2018) Graph-to-sequence learning using gated graph neural networks. arXiv preprint arXiv:1806.09835

39.

Peng H, Li J, He Y, Liu Y, Bao M, Wang L, Song Y, Yang Q (2018) Large-scale hierarchical text classification with recursively regularized deep graph-cnn. In: Proceedings of the 2018 world wide web conference, pp 1063–1072

40.

Yao L, Mao C, Luo Y (2019) Graph convolutional networks for text classification. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 7370–7377

41.

Song L, Wang Z, Yu M, Zhang Y, Florian R, Gildea D (2018) Exploring graph-structured passage representation for multi-hop reading comprehension with graph neural networks. arXiv preprint arXiv:1809.02040

42.

Gupta A, Narayan S, Khan S, Khan FS, Shao L, van de Weijer J (2021) Generative multi-label zero-shot learning. arXiv preprint arXiv:2101.11606

43.

Cao P, Chen P, Niu Q (2021) Multi-label image recognition with two-stream dynamic graph convolution networks. Image Vis Comput 113:104238CrossRef

44.

Wang Y, Sun Y, Liu Z, Sarma SE, Bronstein MM, Solomon JM (2019) Dynamic graph cnn for learning on point clouds. Acm Trans Graph (tog) 38(5):1–12CrossRef

45.

Michieli U, Borsato E, Rossi L, Zanuttigh P (2020) Gmnet: Graph matching network for large scale part semantic segmentation in the wild. In: European conference on computer vision, pp 397–414. Springer

46.

Hu R, Rohrbach A, Darrell T, Saenko K (2019) Language-conditioned graph networks for relational reasoning. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 10294–10303

47.

Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M (2020) Graph neural networks: a review of methods and applications. AI Open 1:57–81CrossRef

48.

Zhang Z, Cui P, Zhu W (2020) Deep learning on graphs: a survey. IEEE Trans Knowl Data Eng

49.

Wu Z, Pan S, Chen F, Long G, Zhang C, Philip SY (2020) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32(1):4–24MathSciNetCrossRef

50.

Chami I, Abu-El-Haija S, Perozzi B, Ré C, Murphy K (2020) Machine learning on graphs: a model and comprehensive taxonomy

51.

Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. Adv Neural Inf Process Syst 29:3844–3852

52.

Sen P, Namata G, Bilgic M, Getoor L, Galligher B, Eliassi-Rad T (2008) Collective classification in network data 29:93–93

53.

Wang Y, He D, Li F, Long X, Zhou Z, Ma J, Wen S (2020) Multi-label classification with label graph superimposing. Proc AAAI Conf Artif Intell 34:12265–12272

54.

Ye J, He J, Peng X, Wu W, Qiao Y (2020) Attention-driven dynamic graph convolutional network for multi-label image recognition. In: European conference on computer vision, pp 649–665. Springer

55.

Akata Z, Reed S, Walter D, Lee H, Schiele B (2015) Evaluation of output embeddings for fine-grained image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2927–2936

56.

Long Y, Liu L, Shao L, Shen F, Ding G, Han J (2017) From zero-shot learning to conventional supervised classification: Unseen visual data synthesis. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1627–1636

57.

Romera-Paredes B, Torr P (2015) An embarrassingly simple approach to zero-shot learning. In: International conference on machine learning, pp 2152–2161. PMLR

58.

Frome A, Corrado G, Shlens J, Bengio S, Dean J, Ranzato M, Mikolov T (2013) Devise: a deep visual-semantic embedding model

59.

Li Y, Wang D, Hu H, Lin Y, Zhuang Y (2017) Zero-shot recognition using dual visual-semantic mapping paths. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3279–3287

60.

Kampffmeyer M, Chen Y, Liang X, Wang H, Zhang Y, Xing EP (2019) Rethinking knowledge graph propagation for zero-shot learning. In: Proceedings of the IEEE/cvf conference on computer vision and pattern recognition, pp 11487–11496

61.

Rong Y, Huang W, Xu T, Huang J (2019) Dropedge: towards deep graph convolutional networks on node classification. arXiv preprint arXiv:1907.10903

62.

Chen M, Wei Z, Huang Z, Ding B, Li Y (2020) Simple and deep graph convolutional networks. In: International conference on machine learning, pp 1725–1735. PMLR

63.

Li G, Muller M, Thabet A, Ghanem B (2019) Deepgcns: Can gcns go as deep as cnns? In: Proceedings of the IEEE/CVF international conference on computer vision, pp 9267–9276

64.

Caramalau R, Bhattarai B, Kim T-K (2021) Sequential graph convolutional network for active learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 9583–9592

65.

Chai Y, Sun P, Ngiam J, Wang W, Caine B, Vasudevan V, Zhang X, Anguelov D (2021) To the point: Efficient 3d object detection in the range image with graph convolution kernels. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 16000–16009

66.

Li H, Miao S, Feng R (2020) Dg-fpn: Learning dynamic feature fusion based on graph convolution network for object detection. In: 2020 IEEE international conference on multimedia and expo (ICME), pp 1–6. IEEE

67.

Xu M, Fu P, Liu B, Li J (2021) Multi-stream attention-aware graph convolution network for video salient object detection. IEEE Trans Image Process 30:4183–4197CrossRef

68.

Zhang W, Lin Z, Cheng J, Ma C, Deng X, Wang H (2020) Sta-gcn: two-stream graph convolutional network with spatial-temporal attention for hand gesture recognition. Vis Comput 36(10):2433–2444CrossRef

69.

Liu Y, Wang R, Shan S, Chen X (2018) Structure inference net: Object detection using scene-level context and instance-level relationships. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6985–6994

70.

Du X, Shi X, Huang R (2019) Repgn: Object detection with relational proposal graph network. arXiv preprint arXiv:1904.08959

71.

Landrieu L, Simonovsky M (2018) Large-scale point cloud semantic segmentation with superpoint graphs. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4558–4567

72.

Qi X, Liao R, Jia J, Fidler S, Urtasun R (2017) 3d graph neural networks for rgbd semantic segmentation. In: Proceedings of the IEEE international conference on computer vision, pp 5199–5208

73.

Te G, Hu W, Zheng A, Guo Z (2018) Rgcnn: Regularized graph cnn for point cloud segmentation. In: Proceedings of the 26th ACM international conference on multimedia, pp 746–754

74.

Yang L, Zhuang J, Fu H, Zhou K, Zheng Y (2020) Sketchgcn: Semantic sketch segmentation with graph convolutional networks. arXiv e-prints, 2003

75.

Lu Y, Chen Y, Zhao D, Chen J (2019) Graph-fcn for image semantic segmentation. In: International symposium on neural networks, pp 97–105. Springer

76.

Ke L, Tai Y-W, Tang C-K (2021) Deep occlusion-aware instance segmentation with overlapping bilayers. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 4019–4028

77.

Ling H, Gao J, Kar A, Chen W, Fidler S (2019) Fast interactive object annotation with curve-gcn. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 5257–5266

78.

Dong Z, Zhang R, Shao X (2019) Automatic annotation and segmentation of object instances with deep active curve network, vol 7, pp 147501–147512. IEEE

79.

Kim N, Kang B, Cho Y (2021) Split gcn: Effective interactive annotation for segmentation of disconnected instance

80.

Wang N, Zhang Y, Li Z, Fu Y, Liu W, Jiang Y-G (2018) Pixel2mesh: Generating 3d mesh models from single rgb images. In: Proceedings of the European conference on computer vision (ECCV), pp 52–67

81.

Guo D, Shao Y, Cui Y, Wang Z, Zhang L, Shen C (2021) Graph attention tracking. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 9543–9552

82.

Li J, Gao X, Jiang T (2020) Graph networks for multiple object tracking. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp 719–728

83.

Brasó G, Leal-Taixé L (2020) Learning a neural solver for multiple object tracking. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 6247–6257

84.

Jiang X, Li P, Li Y, Zhen X (2019) Graph neural based end-to-end data association framework for online multiple-object tracking. arXiv preprint arXiv:1907.05315

85.

Shi Y, Zheng Y, Guo K, Zhu L, Qu Y (2018) Intrinsic or extrinsic evaluation: an overview of word embedding evaluation. In: 2018 IEEE international conference on data mining workshops (ICDMW), pp 1255–1262. IEEE

86.

Jaderberg M, Simonyan K, Zisserman A et al (2015) Spatial transformer networks. Adv Neural Inf Process Syst 28:2017–2025

87.

Yang G, Ramanan D (2019) Volumetric correspondence networks for optical flow. Adv Neural Inf Process Syst 32:794–805

88.

Kang K, Ouyang W, Li H, Wang X (2016) Object detection from video tubelets with convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 817–825

89.

Kang K, Li H, Yan J, Zeng X, Yang B, Xiao T, Zhang C, Wang Z, Wang R, Wang X et al (2017) T-cnn: Tubelets with convolutional neural networks for object detection from videos. IEEE Trans Circuits Syst Video Technol 28(10):2896–2907CrossRef

90.

Li Y, Huang C, Loy CC, Tang X (2016) Human attribute recognition by deep hierarchical contexts. In: European conference on computer vision, pp 684–700. Springer

91.

Ge Z, Mahapatra D, Sedai S, Garnavi R, Chakravorty R (2018) Chest x-rays classification: a multi-label and fine-grained problem. arXiv preprint arXiv:1807.07247

92.

Jain H, Prabhu Y, Varma M (2016) Extreme multi-label loss functions for recommendation, tagging, ranking & other missing label applications. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 935–944

93.

Yang X, Li Y, Luo J (2015) Pinterest board recommendation for twitter users. In: Proceedings of the 23rd ACM international conference on multimedia, pp 963–966

94.

He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

95.

Liu L, Guo S, Huang W, Scott MR (2019) Decoupling category-wise independence and relevance with self-attention for multi-label image classification. In: ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1682–1686. IEEE

96.

Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580–587

97.

Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst 28:91–99

98.

Lin T-Y, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117–2125

99.

Ma L, Kan X, Xiao Q, Liu W, Sun P (2017) Yes-net: an effective detector based on global information. arXiv preprint arXiv:1706.09180

100.

Lin T-Y, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988

101.

O Pinheiro PO, Collobert R, Dollár P (2015) Learning to segment object candidates. Adv Neural Inf Process Syst 28

102.

Pinheiro PO, Lin T-Y, Collobert R, Dollár P (2016) Learning to refine object segments. In: European conference on computer vision, pp 75–91. Springer

103.

Castrejon L, Kundu K, Urtasun R, Fidler S (2017) Annotating object instances with a polygon-rnn. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5230–5238

104.

Chen J, Sheng H, Zhang Y, Xiong Z (2017) Enhancing detection model for multiple hypothesis tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 18–27

105.

Sheng H, Zhang Y, Chen J, Xiong Z, Zhang J (2018) Heterogeneous association graph fusion for target association in multiple object tracking. IEEE Trans Circuits Syst Video Technol 29(11):3269–3280CrossRef

106.

Sanchez-Matilla R, Poiesi F, Cavallaro A (2016) Online multi-target tracking with strong and weak detections. In: european conference on computer vision, pp 84–99. Springer

107.

Fu Z, Angelini F, Chambers J, Naqvi SM (2019) Multi-level cooperative fusion of gm-phd filters for online multiple human tracking. IEEE Trans Multimedia 21(9):2277–2291CrossRef

108.

Kim C, Li F, Rehg JM (2018) Multi-object tracking with neural gating using bilinear lstm. In: Proceedings of the European conference on computer vision (ECCV), pp 200–215

Titel: Applications of graph convolutional networks in computer vision
verfasst von: Pingping Cao
Zeqi Zhu
Ziyuan Wang
Yanping Zhu
Qiang Niu
Publikationsdatum: 26.05.2022
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 16/2022
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-022-07368-1

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 16/2022

CGAN-based synthetic multivariate time-series generation: a solution to data scarcity in solar flare forecasting

Optimized feature selection method using particle swarm intelligence with ensemble learning for cancer classification based on microarray datasets

A fuzzy-based adaptive multi-input–output scheme in lieu of diabetic and hypertension management for post-operative patients: an human–machine interface approach with its continuum

Deep learning for high-impedance fault detection and classification: transformer-CNN

Stochastic stability for delayed semi-Markovian genetic regulatory networks with partly unknown transition rates by employing new integral inequalities

A convolutional neural network based approach to financial time series prediction

Premium Partner