{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T18:24:07Z","timestamp":1778091847907,"version":"3.51.4"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2021,9,15]],"date-time":"2021-09-15T00:00:00Z","timestamp":1631664000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,9,15]],"date-time":"2021-09-15T00:00:00Z","timestamp":1631664000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"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\/s00521-021-06440-6","type":"journal-article","created":{"date-parts":[[2021,9,15]],"date-time":"2021-09-15T14:03:08Z","timestamp":1631714588000},"page":"14565-14576","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":105,"title":["Internet of things-enabled real-time health monitoring system using deep learning"],"prefix":"10.1007","volume":"35","author":[{"given":"Xingdong","family":"Wu","sequence":"first","affiliation":[]},{"given":"Chao","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Lijun","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Muhammad","family":"Bilal","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,15]]},"reference":[{"key":"6440_CR1","doi-asserted-by":"publisher","unstructured":"Liu X, He P, Chen W, Gao J. (2019) Multi-task deep neural networks for natural language understanding. arXiv doi: https:\/\/doi.org\/10.18653\/v1\/p19-1441","DOI":"10.18653\/v1\/p19-1441"},{"key":"6440_CR2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3078702","author":"J Sun","year":"2021","unstructured":"Sun J, Khan F, Li J, Alshehri MD, Alturki R, Wedyan M (2021) Mutual authentication scheme for ensuring a secure device-to-server communication in the internet of medical things. IEEE Internet Things J. https:\/\/doi.org\/10.1109\/JIOT.2021.3078702","journal-title":"IEEE Internet Things J"},{"issue":"10","key":"6440_CR3","doi-asserted-by":"publisher","first-page":"1657","DOI":"10.1109\/TSMC.2017.2701797","volume":"48","author":"Q Zhang","year":"2017","unstructured":"Zhang Q, Yang LT, Chen Z, Li P (2017) An improved deep computation model based on canonical polyadic decomposition. IEEE Trans Syst Man Cybern Syst 48(10):1657\u20131666","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"issue":"8","key":"6440_CR4","doi-asserted-by":"publisher","first-page":"5829","DOI":"10.1109\/TII.2020.3043802","volume":"17","author":"MA Jan","year":"2021","unstructured":"Jan MA, Khan F, Khan R, Watters P, Alazab M, Rehman AU (2021) A lightweight mutual authentication approach for intelligent wearable devices in health-CPS. IEEE Trans Ind Inf 17(8):5829\u20135839","journal-title":"IEEE Trans Ind Inf"},{"key":"6440_CR5","unstructured":"https:\/\/roboticsbiz.com\/wearables-in-sports-smart-clothing-e-textile-technologies\/ Accessed on May 22, 2021"},{"key":"6440_CR6","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.comcom.2021.04.004","volume":"174","author":"SR Jan","year":"2021","unstructured":"Jan SR, Khan R, Khan F, Jan MA (2021) Marginal and average weight-enabled data aggregation mechanism for the resource-constrained networks. Comput Commun J 174:101\u2013108","journal-title":"Comput Commun J"},{"key":"6440_CR7","doi-asserted-by":"publisher","DOI":"10.1109\/TGCN.2021.3077318","author":"MA Jan","year":"2021","unstructured":"Jan MA, Khan F, Mastorakis S, Adil M, Akbar A, Stergiou N (2021) LightIoT: lightweight and secure communication for energy-efficient IoT in health informatics. IEEE Trans Green Commun Netw. https:\/\/doi.org\/10.1109\/TGCN.2021.3077318","journal-title":"IEEE Trans Green Commun Netw"},{"key":"6440_CR8","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.trc.2018.03.001","volume":"90","author":"Y Wu","year":"2018","unstructured":"Wu Y, Tan H, Qin L, Ran B, Jiang Z (2018) A hybrid deep learning-based traffic flow prediction method and its understanding. Transp Res Part C Emerg Technol 90:166\u2013180. https:\/\/doi.org\/10.1016\/j.trc.2018.03.001","journal-title":"Transp Res Part C Emerg Technol"},{"key":"6440_CR9","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2021.3069629","author":"Yu Xu","year":"2021","unstructured":"Xu Yu, Zhan D, Liu L, Lv H, Lingwei Xu, Junwei Du (2021) A privacy-preserving cross-domain healthcare wearables recommendation algorithm based on domain-dependent and domain-independent feature fusion. IEEE J Biomed Health Inform. https:\/\/doi.org\/10.1109\/JBHI.2021.3069629","journal-title":"IEEE J Biomed Health Inform"},{"key":"6440_CR10","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.matdes.2018.11.060","volume":"162","author":"S Feng","year":"2019","unstructured":"Feng S, Zhou H, Dong H (2019) Using deep neural network with a small dataset to predict material defects. Mater Des 162:300\u2013310. https:\/\/doi.org\/10.1016\/j.matdes.2018.11.060","journal-title":"Mater Des"},{"key":"6440_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.14569\/IJACSA.2018.090501","volume":"9","author":"JH Miao","year":"2018","unstructured":"Miao JH, Miao KH (2018) Cardiotocographic diagnosis of fetal health based on multiclass morphologic pattern predictions using deep learning classification. Int J Adv Comput Sci Appl 9:1\u201311. https:\/\/doi.org\/10.14569\/IJACSA.2018.090501","journal-title":"Int J Adv Comput Sci Appl"},{"key":"6440_CR12","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/JBHI.2016.2636665","volume":"21","author":"D Ravi","year":"2017","unstructured":"Ravi D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B et al (2017) Deep learning for health informatics. IEEE J Biomed Heal Inf 21:4\u201321. https:\/\/doi.org\/10.1109\/JBHI.2016.2636665","journal-title":"IEEE J Biomed Heal Inf"},{"key":"6440_CR13","doi-asserted-by":"crossref","first-page":"3157","DOI":"10.1080\/03610928808829796","volume":"17","author":"L Smith","year":"1988","unstructured":"Smith L (1988) A tutorial on principal components analysis. Commun Stat - Theory Methods 17:3157\u20133175","journal-title":"Commun Stat - Theory Methods"},{"key":"6440_CR14","doi-asserted-by":"publisher","first-page":"114785","DOI":"10.1016\/j.eswa.2021.114785","volume":"174","author":"H Sindi","year":"2021","unstructured":"Sindi H, Nour M, Rawa M, \u00d6zt\u00fcrk \u015e, Polat K (2021) A novel hybrid deep learning approach including combination of 1D power signals and 2D signal images for power quality disturbance classification. Expert Syst Appl 174:114785","journal-title":"Expert Syst Appl"},{"key":"6440_CR15","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1016\/j.neunet.2020.03.017","volume":"126","author":"H Shahamat","year":"2020","unstructured":"Shahamat H, Abadeh MS (2020) Brain MRI analysis using a deep learning based evolutionary approach. Neural Netw 126:218\u2013234","journal-title":"Neural Netw"},{"key":"6440_CR16","doi-asserted-by":"publisher","first-page":"21","DOI":"10.2197\/ipsjtbio.12.21","volume":"12","author":"C Fang","year":"2019","unstructured":"Fang C, Moriwaki Y, Li C, Shimizu K (2019) Prediction of antifungal peptides by deep learning with character embedding. IPSJ Trans Bioinf 12:21\u201329","journal-title":"IPSJ Trans Bioinf"},{"key":"6440_CR17","doi-asserted-by":"publisher","first-page":"369","DOI":"10.2174\/138920010791514261","volume":"11","author":"K-C Chou","year":"2010","unstructured":"Chou K-C (2010) Graphic rule for drug metabolism systems. Curr Drug Metab 11:369\u2013378. https:\/\/doi.org\/10.2174\/138920010791514261","journal-title":"Curr Drug Metab"},{"key":"6440_CR18","doi-asserted-by":"publisher","first-page":"2417","DOI":"10.1109\/TKDE.2017.2740926","volume":"29","author":"TT Wong","year":"2017","unstructured":"Wong TT, Yang NY (2017) Dependency analysis of accuracy estimates in k-fold cross-validation. IEEE Trans Knowl Data Eng 29:2417\u20132427. https:\/\/doi.org\/10.1109\/TKDE.2017.2740926","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"6440_CR19","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.jtbi.2011.10.004","volume":"293","author":"G Liu","year":"2012","unstructured":"Liu G, Liu J, Cui X, Cai L (2012) Sequence-dependent prediction of recombination hotspots in Saccharomyces cerevisiae. J Theor Biol 293:49\u201354. https:\/\/doi.org\/10.1016\/j.jtbi.2011.10.004","journal-title":"J Theor Biol"},{"key":"6440_CR20","doi-asserted-by":"publisher","first-page":"1586","DOI":"10.1109\/TKDE.2019.2912815","volume":"32","author":"TT Wong","year":"2020","unstructured":"Wong TT, Yeh PY (2020) Reliable accuracy estimates from k-fold cross-validation. IEEE Trans Knowl Data Eng 32:1586\u20131594. https:\/\/doi.org\/10.1109\/TKDE.2019.2912815","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"6440_CR21","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-05933-8","author":"Yu Miao","year":"2021","unstructured":"Miao Yu, Quan T, Qinglong Peng XY, Liu L (2021) A model-based collaborate filtering algorithm based on stacked autoencoder. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-021-05933-8","journal-title":"Neural Comput Appl"},{"key":"6440_CR22","doi-asserted-by":"publisher","first-page":"581","DOI":"10.2174\/1568026615666150819104617","volume":"16","author":"G-P Zhou","year":"2015","unstructured":"Zhou G-P, Chen D, Liao S, Huang R-B (2015) Recent progress in studying helix-helix interactions in proteins by incorporating the wenxiang diagram into the NMR spectroscopy. Curr Top Med Chem 16:581\u2013590","journal-title":"Curr Top Med Chem"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06440-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06440-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06440-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T10:13:20Z","timestamp":1744193600000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06440-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,15]]},"references-count":22,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["6440"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06440-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,15]]},"assertion":[{"value":"6 February 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 August 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 September 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 declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}