{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T22:58:57Z","timestamp":1762037937625,"version":"build-2065373602"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2021,4,26]],"date-time":"2021-04-26T00:00:00Z","timestamp":1619395200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,4,26]],"date-time":"2021-04-26T00:00:00Z","timestamp":1619395200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Scientific Research Start Fund for high-level talents","award":["G2020SK18"],"award-info":[{"award-number":["G2020SK18"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s00521-021-05988-7","type":"journal-article","created":{"date-parts":[[2021,4,26]],"date-time":"2021-04-26T20:03:37Z","timestamp":1619467417000},"page":"14667-14680","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Deep learning-enabled block scrambling algorithm for securing telemedicine data of table tennis players"],"prefix":"10.1007","volume":"35","author":[{"given":"Bo","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bojin","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yixuan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,4,26]]},"reference":[{"issue":"10","key":"5988_CR1","doi-asserted-by":"publisher","first-page":"5399","DOI":"10.1007\/s00521-019-04682-z","volume":"32","author":"R Hou","year":"2020","unstructured":"Hou R, Kong Y, Cai B, Liu H (2020) Unstructured big data analysis algorithm and simulation of Internet of Things based on machine learning. Neural Comput Appl 32(10):5399\u20135407","journal-title":"Neural Comput Appl"},{"key":"5988_CR2","doi-asserted-by":"crossref","unstructured":"Mainetti L, Patrono L, Stefanizzi ML (2016) An Internet of sport architecture based on emerging enabling technologies. In: International Multidisciplinary Conference on Computer and Energy Science (SpliTech). IEEE, pp 1\u20136","DOI":"10.1109\/SpliTech.2016.7555928"},{"key":"5988_CR3","doi-asserted-by":"crossref","unstructured":"Liang H (2021) Evaluation of fitness state of sports training based on self-organizing neural network. Neural Comput Appl 1\u201313","DOI":"10.1007\/s00521-020-05551-w"},{"issue":"23","key":"5988_CR4","doi-asserted-by":"publisher","first-page":"17125","DOI":"10.1007\/s00521-020-05203-z","volume":"32","author":"A Kallipolitis","year":"2020","unstructured":"Kallipolitis A, Galliakis M, Menychtas A, Maglogiannis I (2020) Affective analysis of patients in homecare video-assisted telemedicine using computational intelligence. Neural Comput Appl 32(23):17125\u201317136","journal-title":"Neural Comput Appl"},{"key":"5988_CR5","doi-asserted-by":"publisher","first-page":"118839","DOI":"10.1109\/ACCESS.2019.2936945","volume":"7","author":"H Ma","year":"2019","unstructured":"Ma H, Pang X (2019) Research and analysis of sport medical data processing algorithms based on deep learning and Internet of Things. IEEE Access 7:118839\u2013118849","journal-title":"IEEE Access"},{"key":"5988_CR6","unstructured":"Wang H, Dong C, Fu Y (2020) Optimization analysis of sport pattern driven by machine learning and multi-agent. Neural Comput Appl 1\u201311"},{"issue":"4","key":"5988_CR7","doi-asserted-by":"crossref","first-page":"4543","DOI":"10.1109\/TIA.2020.2992575","volume":"56","author":"M Usman","year":"2020","unstructured":"Usman M, Jolfaei A, Jan MA (2020) RaSEC: an intelligent framework for reliable and secure multilevel edge computing in industrial environments. IEEE Trans Ind Appl 56(4):4543\u20134551","journal-title":"IEEE Trans Ind Appl"},{"key":"5988_CR8","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.inffus.2019.07.012","volume":"55","author":"H Qiu","year":"2020","unstructured":"Qiu H, Qiu M, Lu Z (2020) Selective encryption on ECG data in body sensor network based on supervised machine learning. Inform Fus 55:59\u201367","journal-title":"Inform Fus"},{"issue":"5","key":"5988_CR9","doi-asserted-by":"publisher","first-page":"802","DOI":"10.1002\/dta.2380","volume":"10","author":"T Devriendt","year":"2018","unstructured":"Devriendt T, Chokoshvili D, Favaretto M, Borry P (2018) Do athletes have a right to access data in their Athlete Biological Passport? Drug Test Anal 10(5):802\u2013806","journal-title":"Drug Test Anal"},{"key":"5988_CR10","doi-asserted-by":"crossref","unstructured":"Rathore H, Mohamed A, Guizani M, Rathore S (2021) Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach. Neural Comput Appl 1\u201314","DOI":"10.1007\/s00521-021-05704-5"},{"key":"5988_CR11","doi-asserted-by":"crossref","unstructured":"De Leeuw AW, van der Zwaard S, van Baar R, Knobbe A (2021) Personalized machine learning approach to injury monitoring in elite volleyball players. Eur J Sport Sci 1\u201314","DOI":"10.1080\/17461391.2021.1887369"},{"issue":"11","key":"5988_CR12","doi-asserted-by":"publisher","first-page":"1044","DOI":"10.1016\/j.jsams.2020.04.021","volume":"23","author":"JL Oliver","year":"2020","unstructured":"Oliver JL, Ayala F, Croix MBDS, Lloyd RS, Myer GD, Read PJ (2020) Using machine learning to improve our understanding of injury risk and prediction in elite male youth football players. J Sci Med Sport 23(11):1044\u20131048","journal-title":"J Sci Med Sport"},{"key":"5988_CR13","doi-asserted-by":"publisher","first-page":"118969","DOI":"10.1109\/ACCESS.2020.3005189","volume":"8","author":"D Tang","year":"2020","unstructured":"Tang D (2020) Hybridized hierarchical deep convolutional neural network for sports rehabilitation exercises. IEEE Access 8:118969\u2013118977","journal-title":"IEEE Access"},{"issue":"12","key":"5988_CR14","doi-asserted-by":"crossref","first-page":"e3929","DOI":"10.1002\/ett.3929","volume":"31","author":"H Yu","year":"2020","unstructured":"Yu H (2020) Research and optimization of sports injury medical system under the background of Internet of things. Trans Emerg Telecommun Technol 31(12):e3929","journal-title":"Trans Emerg Telecommun Technol"},{"issue":"1","key":"5988_CR15","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.bbe.2019.05.009","volume":"40","author":"M Hatamzadeh","year":"2020","unstructured":"Hatamzadeh M, Hassannejad R, Sharifnezhad A (2020) A new method of diagnosing athlete\u2019s anterior cruciate ligament health status using surface electromyography and deep convolutional neural network. Biocybern Biomed Eng 40(1):65\u201376","journal-title":"Biocybern Biomed Eng"},{"key":"5988_CR16","doi-asserted-by":"crossref","unstructured":"Yuan C, Yang Y, Liu Y (2020) Sports decision-making model based on data mining and neural network. Neural Comput Appl 1\u201314","DOI":"10.1007\/s00521-020-05445-x"},{"key":"5988_CR17","doi-asserted-by":"crossref","unstructured":"Chen H, Liu C (2020) Research on knee injuries in college football training based on artificial neural network. In: IEEE conference on telecommunications, optics and computer science (TOCS). IEEE, pp 35\u201337","DOI":"10.1109\/TOCS50858.2020.9339729"},{"key":"5988_CR18","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.tele.2019.03.005","volume":"41","author":"T Saheb","year":"2019","unstructured":"Saheb T, Izadi L (2019) Paradigm of IoT big data analytics in the healthcare industry: A review of scientific literature and mapping of research trends. Telemat Inform 41:70\u201385","journal-title":"Telemat Inform"},{"key":"5988_CR19","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.jbi.2017.12.011","volume":"78","author":"F Rezaeibagha","year":"2018","unstructured":"Rezaeibagha F, Mu Y (2018) Practical and secure telemedicine systems for user mobility. J Biomed Inform 78:24\u201332","journal-title":"J Biomed Inform"},{"issue":"3","key":"5988_CR20","doi-asserted-by":"publisher","first-page":"52","DOI":"10.4103\/jcls.jcls_74_19","volume":"17","author":"A Aiyegbusi","year":"2020","unstructured":"Aiyegbusi A, Oduntan M (2020) The relationship between grip styles and musculoskeletal injuries in table tennis players in Lagos, Nigeria: a cross-sectional study. J Clin Sci 17(3):52\u201361","journal-title":"J Clin Sci"},{"key":"5988_CR21","unstructured":"Rahardja U, Hardini M, Al Nasir AL, Aini Q (2020) Taekwondo sports test and training data management using blockchain. In: 5th International conference on informatics and computing (ICIC). IEEE, pp 1\u20136"},{"key":"5988_CR22","doi-asserted-by":"publisher","first-page":"106951","DOI":"10.1109\/ACCESS.2019.2931917","volume":"7","author":"J Liu","year":"2019","unstructured":"Liu J, Tang H, Sun R, Du X, Guizani M (2019) Lightweight and Privacy-Preserving Medical Services Access for Healthcare Cloud. IEEE Access 7:106951\u2013106961","journal-title":"IEEE Access"},{"key":"5988_CR23","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.patcog.2019.05.030","volume":"94","author":"X Yu","year":"2019","unstructured":"Yu X, Jiang F, Du J, Gong D (2019) A cross-domain collaborative filtering algorithm with expanding user and item features via the latent factor space of auxiliary domains. Pattern Recogn 94:96\u2013109","journal-title":"Pattern Recogn"},{"key":"5988_CR24","doi-asserted-by":"crossref","unstructured":"Ning X, Gong K, Li W, Zhang L (2020) JWSAA: joint weak saliency and attention aware for person re-identification. Neurocomputing","DOI":"10.1016\/j.neucom.2020.05.106"},{"key":"5988_CR25","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.knosys.2017.11.010","volume":"141","author":"X Yu","year":"2018","unstructured":"Yu X, Chu Y, Jiang F, Guo Y, Gong D (2018) SVMs classification based two-side cross domain collaborative filtering by inferring intrinsic user and item features. Knowl-Based Syst 141:80\u201391","journal-title":"Knowl-Based Syst"},{"key":"5988_CR26","doi-asserted-by":"crossref","unstructured":"Cai W, Liu B, Wei Z, Li M, Kan J (2021) TARDB-Net: triple-attention guided residual dense and BiLSTM networks for hyperspectral image classification. Multimed Tools Appl 1\u201322","DOI":"10.1007\/s11042-020-10188-x"},{"key":"5988_CR27","doi-asserted-by":"publisher","first-page":"71353","DOI":"10.1109\/ACCESS.2020.2986267","volume":"8","author":"Z Wang","year":"2020","unstructured":"Wang Z, Zou C, Cai W (2020) Small sample classification of hyperspectral remote sensing images based on sequential joint deeping learning model. IEEE Access 8:71353\u201371363","journal-title":"IEEE Access"},{"issue":"5","key":"5988_CR28","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1007\/s11704-014-3161-3","volume":"8","author":"X Yu","year":"2014","unstructured":"Yu X, Yang J, Xie Z (2014) Training SVMs on a bound vectors set based on Fisher projection. Front Comput Sci 8(5):793\u2013806","journal-title":"Front Comput Sci"},{"issue":"8","key":"5988_CR29","doi-asserted-by":"publisher","first-page":"3091","DOI":"10.3390\/su12083091","volume":"12","author":"L Huang","year":"2020","unstructured":"Huang L, Xie G, Blenkinsopp J, Huang R, Bin H (2020) Crowdsourcing for sustainable urban logistics: Exploring the factors influencing crowd Workers\u2019 participative behavior. Sustainability 12(8):3091","journal-title":"Sustainability"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-05988-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-05988-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-05988-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,2]],"date-time":"2023-11-02T19:44:08Z","timestamp":1698954248000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-05988-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,26]]},"references-count":29,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["5988"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-05988-7","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2021,4,26]]},"assertion":[{"value":"14 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 April 2021","order":3,"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 conflict of interest for publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}