{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T09:58:57Z","timestamp":1766138337414,"version":"3.37.3"},"reference-count":69,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,5,23]],"date-time":"2023-05-23T00:00:00Z","timestamp":1684800000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,23]],"date-time":"2023-05-23T00:00:00Z","timestamp":1684800000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation","doi-asserted-by":"crossref","award":["62076154","U21A20513"],"award-info":[{"award-number":["62076154","U21A20513"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Anhui Provincial Natural Science Foundation","award":["2008085MF202"],"award-info":[{"award-number":["2008085MF202"]}]},{"name":"the University Natural Sciences Research Project of Anhui Province","award":["KJ2020A0660"],"award-info":[{"award-number":["KJ2020A0660"]}]},{"name":"the Open Project of Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University","award":["MMC202003"],"award-info":[{"award-number":["MMC202003"]}]},{"name":"Scientific Research Projects for Graduate Students in Anhui Universities","award":["YJS20210564"],"award-info":[{"award-number":["YJS20210564"]}]},{"name":"nhui Province Student Innovation Training Project","award":["S202111059266"],"award-info":[{"award-number":["S202111059266"]}]},{"name":"Anhui Province Student Innovation Training Project","award":["S202111059016"],"award-info":[{"award-number":["S202111059016"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s11042-023-15334-9","type":"journal-article","created":{"date-parts":[[2023,5,23]],"date-time":"2023-05-23T08:02:45Z","timestamp":1684828965000},"page":"6273-6295","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Skeleton-based human action recognition by fusing attention based three-stream convolutional neural network and SVM"],"prefix":"10.1007","volume":"83","author":[{"given":"Fang","family":"Ren","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8934-9537","authenticated-orcid":false,"given":"Chao","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anyang","family":"Tong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,23]]},"reference":[{"issue":"4","key":"15334_CR1","doi-asserted-by":"publisher","first-page":"1587","DOI":"10.1007\/s10044-020-00886-5","volume":"23","author":"M Al-Faris","year":"2020","unstructured":"Al-Faris M, Chiverton J P, Yang Y, Ndzi D (2020) Multi-view region-adaptive multi-temporal dmm and rgb action recognition. Pattern Anal Appl 23 (4):1587\u20131602. https:\/\/doi.org\/10.1007\/s10044-020-00886-5","journal-title":"Pattern Anal Appl"},{"issue":"1","key":"15334_CR2","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1080\/21645515.2017.1379639","volume":"14","author":"UA Bhatti","year":"2018","unstructured":"Bhatti U A, Huang M, Wang H, Zhang Y, Mehmood A, Di W (2018) Recommendation system for immunization coverage and monitoring. Human Vacc Immunotherap 14(1):165\u2013171","journal-title":"Human Vacc Immunotherap"},{"issue":"3","key":"15334_CR3","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1080\/17517575.2018.1557256","volume":"13","author":"UA Bhatti","year":"2019","unstructured":"Bhatti U A, Huang M, Wu D, Zhang Y, Mehmood A, Han H (2019) Recommendation system using feature extraction and pattern recognition in clinical care systems. Enterprise Inform Syst 13(3):329\u2013351","journal-title":"Enterprise Inform Syst"},{"key":"15334_CR4","doi-asserted-by":"crossref","unstructured":"Bhatti U A, Ming-Quan Z, Huo Q, Ali S, Hussain A, Yan Y, Yu Z, Yuan L, Nawaz S A (2021) Advanced color edge detection using clifford algebra in satellite images. IEEE Photonics J 13(2)","DOI":"10.1109\/JPHOT.2021.3059703"},{"issue":"6606","key":"15334_CR5","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1126\/science.add9065","volume":"377","author":"UA Bhatti","year":"2022","unstructured":"Bhatti U A, Nizamani M M, Huang M (2022) Climate change threatens Pakistan\u2019s snow leopards. Science 377(6606):585\u2013586. https:\/\/doi.org\/10.1126\/science.add9065","journal-title":"Science"},{"key":"15334_CR6","doi-asserted-by":"publisher","first-page":"41019","DOI":"10.1109\/ACCESS.2021.3060744","volume":"9","author":"UA Bhatti","year":"2021","unstructured":"Bhatti U A, Yan Y, Zhou M, Ali S, Hussain A, Huo Q, Yu Z, Yuan L (2021) Time series analysis and forecasting of air pollution particulate matter (pm2.5): an sarima and factor analysis approach. IEEE Access 9:41019\u201341031","journal-title":"IEEE Access"},{"issue":"9","key":"15334_CR7","doi-asserted-by":"publisher","first-page":"13367","DOI":"10.1007\/s11042-020-10257-1","volume":"80","author":"UA Bhatti","year":"2021","unstructured":"Bhatti U A, Yuan L, Yu Z, Li J, Nawaz S A, Mehmood A, Zhang K (2021) New watermarking algorithm utilizing quaternion fourier transform with advanced scrambling and secure encryption. Multimed Tools Applic 80(9):13367\u201313387","journal-title":"Multimed Tools Applic"},{"key":"15334_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2021.3090410","volume":"60","author":"UA Bhatti","year":"2022","unstructured":"Bhatti U A, Yu Z, Chanussot J, Zeeshan Z, Yuan L, Luo W, Nawaz S A, Bhatti M A, Ain Q U, Mehmood A (2022) Local similarity-based spatial-spectral fusion hyperspectral image classification with deep cnn and gabor filtering. IEEE Trans Geosci Remote Sens 60:1\u201315","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"15334_CR9","doi-asserted-by":"crossref","unstructured":"Caetano C, Br\u00e9mond F, Schwartz W R (2019) Skeleton image representation for 3d action recognition based on tree structure and reference joints. In: 2019 32nd SIBGRAPI conference on graphics, patterns and images (SIBGRAPI). IEEE, pp 16\u201323","DOI":"10.1109\/SIBGRAPI.2019.00011"},{"key":"15334_CR10","doi-asserted-by":"crossref","unstructured":"Chen J, Ho C M, Soc I C (2022) Mm-vit: multi-modal video transformer for compressed video action recognition. In: 22nd IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE Winter Conference on Applications of Computer Vision, pp 786\u2013797","DOI":"10.1109\/WACV51458.2022.00086"},{"key":"15334_CR11","doi-asserted-by":"crossref","unstructured":"Dan Y, Jingbing L, Yangxiu F, Wenfeng C, Xiliang X, Bhatti U A, Baoru H (2021) A robust zero-watermarkinging algorithm based on phts-dct for medical images in the encrypted domain. Innovation in Medicine and Healthcare. Proceedings of 9th KES-InMed 2021. Smart Innovation, Systems and Technologies, pp 101\u201313","DOI":"10.1007\/978-981-16-3013-2_9"},{"key":"15334_CR12","doi-asserted-by":"publisher","unstructured":"Dang L M, Min K, Wang H, Piran M J, Lee C H, Moon H (2020) Sensor-based and vision-based human activity recognition: a comprehensive survey. Pattern Recogn, 108. https:\/\/doi.org\/10.1016\/j.patcog.2020.107561","DOI":"10.1016\/j.patcog.2020.107561"},{"key":"15334_CR13","doi-asserted-by":"publisher","first-page":"54078","DOI":"10.1109\/ACCESS.2021.3059650","volume":"9","author":"W Ding","year":"2021","unstructured":"Ding W, Ding C, Li G, Liu K (2021) Skeleton-based square grid for human action recognition with 3d convolutional neural network. IEEE Access 9:54078\u201354089","journal-title":"IEEE Access"},{"key":"15334_CR14","unstructured":"Du Y, Wang W, Wang L (2015) Hierarchical recurrent neural network for skeleton based action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1110\u20131118"},{"key":"15334_CR15","doi-asserted-by":"publisher","unstructured":"Duan H, Zhao Y, Chen K, Lin D, Dai B, Ieee Comp, S O C (2022) Revisiting skeleton-based action recognition. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Conference on Computer Vision and Pattern Recognition, pp 2959\u20132968. https:\/\/doi.org\/10.1109\/cvpr52688.2022.00298","DOI":"10.1109\/cvpr52688.2022.00298"},{"key":"15334_CR16","doi-asserted-by":"publisher","first-page":"58256","DOI":"10.1109\/ACCESS.2021.3073107","volume":"9","author":"D Feng","year":"2021","unstructured":"Feng D, Wu Z, Zhang J, Ren T (2021) Multi-scale spatial temporal graph neural network for skeleton-based action recognition. IEEE Access 9:58256\u201358265","journal-title":"IEEE Access"},{"key":"15334_CR17","doi-asserted-by":"publisher","unstructured":"Feng L, Zhao Y, Zhao W, Tang J (2022) A comparative review of graph convolutional networks for human skeleton-based action recognition. Artif Intell Rev, 4275\u20134305. https:\/\/doi.org\/10.1007\/s10462-021-10107-y","DOI":"10.1007\/s10462-021-10107-y"},{"key":"15334_CR18","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.cviu.2017.01.011","volume":"158","author":"F Han","year":"2017","unstructured":"Han F, Reily B, Hoff W, Zhang H (2017) Space-time representation of people based on 3d skeletal data: a review. Comput Vis Image Underst 158:85\u2013105","journal-title":"Comput Vis Image Underst"},{"key":"15334_CR19","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning (ICML). PMLR , pp 448\u2013456"},{"key":"15334_CR20","doi-asserted-by":"crossref","unstructured":"Ke Q, Bennamoun M, An S, Sohel F, Boussaid F (2017) A new representation of skeleton sequences for 3d action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 3288\u20133297","DOI":"10.1109\/CVPR.2017.486"},{"issue":"1","key":"15334_CR21","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1109\/TMRB.2020.3040002","volume":"3","author":"LR Kennedy-Metz","year":"2020","unstructured":"Kennedy-Metz L R, Mascagni P, Torralba A, Dias R D, Perona P, Shah J A, Padoy N, Zenati M A (2020) Computer vision in the operating room: opportunities and caveats. IEEE Trans Med Robot Bion 3(1): 2\u201310","journal-title":"IEEE Trans Med Robot Bion"},{"key":"15334_CR22","doi-asserted-by":"crossref","unstructured":"Koniusz P, Cherian A, Porikli F (2016) Tensor representations via kernel linearization for action recognition from 3d skeletons. In: European Conference on Computer Vision (ECCV). Springer, pp 37\u201353","DOI":"10.1007\/978-3-319-46493-0_3"},{"key":"15334_CR23","unstructured":"Li C, Zhong Q, Xie D, Pu S (2017) Skeleton-based action recognition with convolutional neural networks. In: IEEE International Conference on Multimedia & Expo Workshops (ICMEW). IEEE , pp 597\u2013600"},{"key":"15334_CR24","doi-asserted-by":"crossref","unstructured":"Li C, Zhong Q, Xie D, Pu S (2018) Co-occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation. In: International Joint Conference on Artificial Intelligence (IJCAI)","DOI":"10.24963\/ijcai.2018\/109"},{"key":"15334_CR25","doi-asserted-by":"crossref","unstructured":"Li S, Li W, Cook C, Zhu C, Gao Y (2018) Independently recurrent neural network (indrnn): building a longer and deeper rnn. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 5457\u20135466","DOI":"10.1109\/CVPR.2018.00572"},{"key":"15334_CR26","doi-asserted-by":"crossref","unstructured":"Li M S, Chen S H, Chen X, Zhang Y, Wang Y F, Tian Q, Soc I C (2019) Actional-structural graph convolutional networks for skeleton-based action recognition. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Conference on computer vision and pattern recognition, pp 3590\u20133598","DOI":"10.1109\/CVPR.2019.00371"},{"key":"15334_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13638-022-02106-6","volume":"2022","author":"T Li","year":"2022","unstructured":"Li T, Li J, Liu J, Huang M, Chen Y-W, Bhatti U A (2022) Robust watermarking algorithm for medical images based on log-polar transform. Eurasip J Wireless Commun Network 2022:1. https:\/\/doi.org\/10.1186\/s13638-022-02106-6","journal-title":"Eurasip J Wireless Commun Network"},{"key":"15334_CR28","doi-asserted-by":"publisher","unstructured":"Li Y, Li J, Shao C, Bhatti U A, Ma J (2022) Robust multi-watermarking algorithm for medical images using patchwork-dct. In: 8th International Conference on Artificial Intelligence and Security (ICAIS). Lecture notes in computer science, vol 13340, pp 386\u2013399, DOI https:\/\/doi.org\/10.1007\/978-3-031-06791-4_31","DOI":"10.1007\/978-3-031-06791-4_31"},{"key":"15334_CR29","doi-asserted-by":"crossref","unstructured":"Liang D, Fan G, Lin G, Chen W, Zhu H (2019) Three-stream convolutional neural network with multi-task and ensemble learning for 3d action recognition. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","DOI":"10.1109\/CVPRW.2019.00123"},{"key":"15334_CR30","doi-asserted-by":"crossref","unstructured":"Lin Z, Zhang W, Deng X, Ma C, Wang H (2020) Image-based pose representation for action recognition and hand gesture recognition, 532\u2013539","DOI":"10.1109\/FG47880.2020.00066"},{"key":"15334_CR31","doi-asserted-by":"crossref","unstructured":"Liu J, Shahroudy A, Xu D, Wang G (2016) Spatio-temporal lstm with trust gates for 3d human action recognition. In: European Conference on Computer Vision (ECCV). Springer, pp 816\u2013833","DOI":"10.1007\/978-3-319-46487-9_50"},{"issue":"1","key":"15334_CR32","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1007\/s11042-017-5532-x","volume":"78","author":"A-A Liu","year":"2019","unstructured":"Liu A-A, Shao Z, Wong Y, Li J, Su Y-T, Kankanhalli M (2019) Lstm-based multi-label video event detection. Multimed Tools Applic 78 (1):677\u2013695. https:\/\/doi.org\/10.1007\/s11042-017-5532-x","journal-title":"Multimed Tools Applic"},{"key":"15334_CR33","doi-asserted-by":"publisher","unstructured":"Liu Z Y, Zhang H W, Chen Z H, Wang Z Y, Ouyang W L, Ieee (2020) Disentangling and unifying graph convolutions for skeleton-based action recognition. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Conference on computer vision and pattern recognition, pp 140\u2013149. https:\/\/doi.org\/10.1109\/cvpr42600.2020.00022","DOI":"10.1109\/cvpr42600.2020.00022"},{"key":"15334_CR34","doi-asserted-by":"crossref","unstructured":"Liu W, Li J, Shao C, Ma J, Huang M, Bhatti U A (2022) Robust zero watermarking algorithm for medical images using local binary pattern and discrete cosine transform. Advances in artificial intelligence and security: 8th international conference on artificial intelligence and security, ICAIS 2022, Proceedings. Communications in computer and information science","DOI":"10.1007\/978-3-031-06764-8_28"},{"key":"15334_CR35","doi-asserted-by":"crossref","unstructured":"Mazzia V, Angarano S, Salvetti F, Angelini F, Chiaberge M (2022) Action transformer: a self-attention model for short-time pose-based human action recognition. Pattern Recogn, 124","DOI":"10.1016\/j.patcog.2021.108487"},{"key":"15334_CR36","doi-asserted-by":"crossref","unstructured":"Nguyen V-T, Nguyen T-N, Le T-L, Pham D-T, Vu H (2021) Adaptive most joint selection and covariance descriptions for a robust skeleton-based human action recognition. Multimed Tools Applic 80(18):27757\u201327783","DOI":"10.1007\/s11042-021-10866-4"},{"key":"15334_CR37","doi-asserted-by":"crossref","unstructured":"Pan H, Chen Y (2019) Multilevel lstm for action recognition based on skeleton sequence. In: 2019 IEEE 21st international conference on high performance computing and communications; IEEE 17th International conference on smart city; IEEE 5th International Conference on Data Science and Systems (HPCC\/SmartCity\/DSS). IEEE, pp 2218\u20132223","DOI":"10.1109\/HPCC\/SmartCity\/DSS.2019.00308"},{"key":"15334_CR38","doi-asserted-by":"publisher","unstructured":"Ruiz A H, Porzi L, Bulo S R, Moreno-Noguer F (2017) 3d cnns on distance matrices for human action recognition, 1087\u20131095. https:\/\/doi.org\/10.1145\/3123266.3123299","DOI":"10.1145\/3123266.3123299"},{"key":"15334_CR39","doi-asserted-by":"crossref","unstructured":"Shahroudy A, Liu J, Ng T-T, Wang G (2016) Ntu rgb+d: a large scale dataset for 3d human activity analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1010\u20131019","DOI":"10.1109\/CVPR.2016.115"},{"key":"15334_CR40","doi-asserted-by":"publisher","unstructured":"Shao Z, Han J, Marnerides D, Debattista K (2022) Region-object relation-aware dense captioning via transformer. IEEE Transactions on Neural Networks and Learning Systems. https:\/\/doi.org\/10.1109\/tnnls.2022.3152990","DOI":"10.1109\/tnnls.2022.3152990"},{"key":"15334_CR41","doi-asserted-by":"publisher","unstructured":"Shao Z, Han J, Debattista K, Pang Y (2023) Textual context-aware dense captioning with diverse words, 1\u201315. https:\/\/doi.org\/10.1109\/TMM.2023.3241517","DOI":"10.1109\/TMM.2023.3241517"},{"key":"15334_CR42","doi-asserted-by":"publisher","first-page":"103386","DOI":"10.1016\/j.jvcir.2021.103386","volume":"82","author":"X Shen","year":"2022","unstructured":"Shen X, Ding Y (2022) Human skeleton representation for 3d action recognition based on complex network coding and lstm. J Vis Commun Image Represent 82:103386. https:\/\/doi.org\/10.1016\/j.jvcir.2021.103386","journal-title":"J Vis Commun Image Represent"},{"key":"15334_CR43","doi-asserted-by":"publisher","first-page":"103386","DOI":"10.1016\/j.jvcir.2021.103386","volume":"82","author":"X Shen","year":"2022","unstructured":"Shen X, Ding Y (2022) Human skeleton representation for 3d action recognition based on complex network coding and lstm. J Vis Commun Image Represent 82:103386. https:\/\/doi.org\/10.1016\/j.jvcir.2021.103386","journal-title":"J Vis Commun Image Represent"},{"key":"15334_CR44","doi-asserted-by":"publisher","unstructured":"Shi L, Zhang Y, Cheng J, Lu H (2019) Skeleton-based action recognition with directed graph neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 7904\u20137913. https:\/\/doi.org\/10.1109\/CVPR.2019.00810","DOI":"10.1109\/CVPR.2019.00810"},{"key":"15334_CR45","doi-asserted-by":"publisher","unstructured":"Shi L, Zhang Y, Cheng J, Lu H (2019) Two-stream adaptive graph convolutional networks for skeleton-based action recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 12018\u201312027. https:\/\/doi.org\/10.1109\/CVPR.2019.01230","DOI":"10.1109\/CVPR.2019.01230"},{"key":"15334_CR46","doi-asserted-by":"publisher","first-page":"9532","DOI":"10.1109\/tip.2020.3028207","volume":"29","author":"L Shi","year":"2020","unstructured":"Shi L, Zhang Y F, Cheng J, Lu H Q (2020) Skeleton-based action recognition with multi-stream adaptive graph convolutional networks. IEEE Trans Image Process 29:9532\u20139545. https:\/\/doi.org\/10.1109\/tip.2020.3028207","journal-title":"IEEE Trans Image Process"},{"key":"15334_CR47","doi-asserted-by":"crossref","unstructured":"Si C, Jing Y, Wang W, Wang L, Tan T (2018) Skeleton-based action recognition with spatial reasoning and temporal stack learning. In: Proceedings of the European conference on computer vision (ECCV), pp 103\u2013118","DOI":"10.1007\/978-3-030-01246-5_7"},{"issue":"7","key":"15334_CR48","doi-asserted-by":"publisher","first-page":"1359","DOI":"10.1007\/s13042-019-01044-y","volume":"11","author":"M Singla","year":"2020","unstructured":"Singla M, Ghosh D, Shukla KK (2020) A survey of robust optimization based machine learning with special reference to support vector machines. Int J Mach Learn Cybern 11(7):1359\u20131385","journal-title":"Int J Mach Learn Cybern"},{"key":"15334_CR49","doi-asserted-by":"publisher","first-page":"52532","DOI":"10.1109\/ACCESS.2019.2911705","volume":"7","author":"B Su","year":"2019","unstructured":"Su B, Wu H, Sheng M, Shen C (2019) Accurate hierarchical human actions recognition from kinect skeleton data. IEEE Access 7:52532\u201352541","journal-title":"IEEE Access"},{"key":"15334_CR50","doi-asserted-by":"publisher","unstructured":"Tang Y, Tian Y, Lu J, Li P, Zhou J (2018) Deep progressive reinforcement learning for skeleton-based action recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 5323\u20135332. https:\/\/doi.org\/10.1109\/CVPR.2018.00558","DOI":"10.1109\/CVPR.2018.00558"},{"key":"15334_CR51","doi-asserted-by":"publisher","unstructured":"Tong A, Tang C, Wang W (2022) Semi-supervised action recognition from temporal augmentation using curriculum learning. IEEE Trans Circuits Syst Video Technol, 1\u20131. https:\/\/doi.org\/10.1109\/TCSVT.2022.3210271","DOI":"10.1109\/TCSVT.2022.3210271"},{"key":"15334_CR52","doi-asserted-by":"crossref","unstructured":"Vemulapalli R, Chellapa R (2016) Rolling rotations for recognizing human actions from 3d skeletal data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp 4471\u20134479","DOI":"10.1109\/CVPR.2016.484"},{"key":"15334_CR53","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1109\/TIP.2019.2925285","volume":"29","author":"L Wang","year":"2020","unstructured":"Wang L, Huynh D Q, Koniusz P (2020) A comparative review of recent kinect-based action recognition algorithms. IEEE Trans Image Process 29:15\u201328. https:\/\/doi.org\/10.1109\/TIP.2019.2925285","journal-title":"IEEE Trans Image Process"},{"key":"15334_CR54","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon I S (2018) Cbam: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"issue":"3","key":"15334_CR55","doi-asserted-by":"publisher","first-page":"1250","DOI":"10.1109\/TCSVT.2021.3077512","volume":"32","author":"H Wu","year":"2022","unstructured":"Wu H, Ma X, Li Y (2022) Spatiotemporal multimodal learning with 3d cnns for video action recognition. IEEE Trans Circ Syst Video Technol 32 (3):1250\u20131261. https:\/\/doi.org\/10.1109\/TCSVT.2021.3077512","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"15334_CR56","doi-asserted-by":"crossref","unstructured":"Xia L, Chen C-C, Aggarwal J K (2012) View invariant human action recognition using histograms of 3d joints. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, pp 20\u201327","DOI":"10.1109\/CVPRW.2012.6239233"},{"key":"15334_CR57","doi-asserted-by":"publisher","unstructured":"Xiliang X, Jingbing L, Dan Y, Yangxiu F, Wenfeng C, Bhatti U A, Baoru H (2021) Robust zero watermarking algorithm for encrypted medical images based on dwt-gabor. Innovation in Medicine and Healthcare. Proceedings of 9th KES-InMed 2021. Smart Innovation, Systems and Technologies. https:\/\/doi.org\/10.1007\/978-981-16-3013-2_7","DOI":"10.1007\/978-981-16-3013-2_7"},{"key":"15334_CR58","doi-asserted-by":"crossref","unstructured":"Xu W, Wu M, Zhu J, Zhao M (2021) Multi-scale skeleton adaptive weighted gcn for skeleton-based human action recognition in iot. Appl Soft Comput, 104","DOI":"10.1016\/j.asoc.2021.107236"},{"key":"15334_CR59","doi-asserted-by":"crossref","unstructured":"Yan S, Xiong Y, Lin D (2018) Spatial temporal graph convolutional networks for skeleton-based action recognition. In: AAAI Conference on Artificial Intelligence, pp 7444\u20137452","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"15334_CR60","doi-asserted-by":"publisher","unstructured":"Yangxiu F, Jing L, Jingbing L, Dan Y, Wenfeng C, Xiliang X, Baoru H, Bhatti U A (2021) A novel robust watermarking algorithm for encrypted medical image based on Bandelet-DCT. https:\/\/doi.org\/10.1007\/978-981-16-3013-2_6","DOI":"10.1007\/978-981-16-3013-2_6"},{"key":"15334_CR61","doi-asserted-by":"crossref","unstructured":"Yu L, Tian L, Du Q, Bhutto J A (2022) Multi-stream adaptive 3d attention graph convolution network for skeleton-based action recognition. Appl Intell","DOI":"10.1007\/s10489-022-04179-8"},{"key":"15334_CR62","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.neucom.2022.09.071","volume":"512","author":"R Yue","year":"2022","unstructured":"Yue R, Tian Z, Du S (2022) Action recognition based on rgb and skeleton data sets: a survey. Neurocomputing 512:287\u2013306. https:\/\/doi.org\/10.1016\/j.neucom.2022.09.071","journal-title":"Neurocomputing"},{"issue":"4","key":"15334_CR63","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.3233\/IDA-205388","volume":"25","author":"Z Zeeshan","year":"2021","unstructured":"Zeeshan Z, ul Ain Q, Bhatti U A, Memon W H, Ali S, Nawaz S A, Nizamani M M, Mehmood A, Bhatti M A, Shoukat M U (2021) Feature-based multi-criteria recommendation system using a weighted approach with ranking correlation. Intell Data Anal 25(4):1013\u20131029","journal-title":"Intell Data Anal"},{"key":"15334_CR64","doi-asserted-by":"publisher","unstructured":"Zeng C, Liu J, Li J, Cheng J, Zhou J, Nawaz S A, Xiao X, Bhatti U A (2022) Multi-watermarking algorithm for medical image based on kaze-dct. J Ambient Intell Humaniz Comput, https:\/\/doi.org\/10.1007\/s12652-021-03539-5","DOI":"10.1007\/s12652-021-03539-5"},{"issue":"9","key":"15334_CR65","doi-asserted-by":"publisher","first-page":"2330","DOI":"10.1109\/TMM.2018.2802648","volume":"20","author":"S Zhang","year":"2018","unstructured":"Zhang S, Yang Y, Xiao J, Liu X, Yang Y, Xie D, Zhuang Y (2018) Fusing geometric features for skeleton-based action recognition using multilayer lstm networks. IEEE Trans Multimed 20(9):2330\u20132343. https:\/\/doi.org\/10.1109\/TMM.2018.2802648","journal-title":"IEEE Trans Multimed"},{"issue":"5","key":"15334_CR66","doi-asserted-by":"publisher","first-page":"3443","DOI":"10.1109\/JIOT.2021.3099164","volume":"9","author":"J Zhang","year":"2021","unstructured":"Zhang J, Lou Y, Wang J, Wu K, Lu K, Jia X (2021) Evaluating adversarial attacks on driving safety in vision-based autonomous vehicles. IEEE Internet Things J 9(5):3443\u20133456","journal-title":"IEEE Internet Things J"},{"key":"15334_CR67","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1016\/j.neucom.2019.05.058","volume":"358","author":"Z Zheng","year":"2019","unstructured":"Zheng Z, An G, Wu D, Ruan Q (2019) Spatial-temporal pyramid based convolutional neural network for action recognition. Neurocomputing 358:446\u2013455. https:\/\/doi.org\/10.1016\/j.neucom.2019.05.058","journal-title":"Neurocomputing"},{"key":"15334_CR68","doi-asserted-by":"crossref","unstructured":"Zhu W, Lan C, Xing J, Zeng W, Li Y, Shen L, Xie X (2016) Co-occurrence feature learning for skeleton based action recognition using regularized deep lstm networks. In: Proceedings of the AAAI conference on artificial intelligence, vol 30","DOI":"10.1609\/aaai.v30i1.10451"},{"key":"15334_CR69","doi-asserted-by":"publisher","first-page":"107096","DOI":"10.1016\/j.cmpb.2022.107096","volume":"226","author":"Q Zhuang","year":"2022","unstructured":"Zhuang Q, Gan S, Zhang L (2022) Human-computer interaction based health diagnostics using resnet34 for tongue image classification. Comput Methods Programs Biomed 226:107096. https:\/\/doi.org\/10.1016\/j.cmpb.2022.107096","journal-title":"Comput Methods Programs Biomed"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15334-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-15334-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15334-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,6]],"date-time":"2024-01-06T05:18:48Z","timestamp":1704518328000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-15334-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,23]]},"references-count":69,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["15334"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-15334-9","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2023,5,23]]},"assertion":[{"value":"10 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 April 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 May 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}