{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T22:36:51Z","timestamp":1712702211923},"reference-count":72,"publisher":"Springer Science and Business Media LLC","issue":"3-4","license":[{"start":{"date-parts":[[2021,6,27]],"date-time":"2021-06-27T00:00:00Z","timestamp":1624752000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,6,27]],"date-time":"2021-06-27T00:00:00Z","timestamp":1624752000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["New Gener. Comput."],"published-print":{"date-parts":[[2021,11]]},"DOI":"10.1007\/s00354-021-00131-5","type":"journal-article","created":{"date-parts":[[2021,6,27]],"date-time":"2021-06-27T12:02:16Z","timestamp":1624795336000},"page":"677-700","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Computer-Aided-Diagnosis as a Service on Decentralized Medical Cloud for Efficient and Rapid Emergency Response Intelligence"],"prefix":"10.1007","volume":"39","author":[{"given":"Amirhossein","family":"Peyvandi","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6309-6407","authenticated-orcid":false,"given":"Babak","family":"Majidi","sequence":"additional","affiliation":[]},{"given":"Soodeh","family":"Peyvandi","sequence":"additional","affiliation":[]},{"given":"Jagdish","family":"Patra","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,27]]},"reference":[{"key":"131_CR1","doi-asserted-by":"crossref","unstructured":"Majidi, B., Hemmati, O., Baniardalan, F., Farahmand, H., Hajitabar, A., Sharafi, S., Aghajani, K., Esmaeili, A., Manzuri, M.T.: Geo-spatiotemporal intelligence for smart agricultural and environmental eco-cyber-physical systems. In: Enabling AI Applications in Data Science, pp. 471\u2013491. Springer (2021)","DOI":"10.1007\/978-3-030-52067-0_21"},{"key":"131_CR2","doi-asserted-by":"crossref","unstructured":"Nazerdeylami, A., Majidi, B., Movaghar, A.: Smart coastline environment management using deep detection of manmade pollution and hazards. In: 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI), 2019. IEEE","DOI":"10.1109\/KBEI.2019.8735012"},{"key":"131_CR3","doi-asserted-by":"crossref","unstructured":"Abbasi, M.H., Majidi, B., Eshghi, M., Abbasi, E.H.: Deep visual privacy preserving for internet of robotic things. In: 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI), 2019. IEEE","DOI":"10.1109\/KBEI.2019.8735033"},{"issue":"1","key":"131_CR4","doi-asserted-by":"publisher","first-page":"4200","DOI":"10.1038\/s41598-021-83784-y","volume":"11","author":"FS Heldt","year":"2021","unstructured":"Heldt, F.S., Vizcaychipi, M.P., Peacock, S., Cinelli, M., McLachlan, L., Andreotti, F., Jovanovi\u00e9, S., D\u00fcrichen, R., Lipunova, N., Fletcher, R.A., Hancock, A., McCarthy, A., Pointon, R.A., Brown, A., Eaton, J., Liddi, R., Mackillop, L., Tarassenko, L., Khan, R.T.: Early risk assessment for COVID-19 patients from emergency department data using machine learning. Sci. Rep. 11(1), 4200 (2021). https:\/\/doi.org\/10.1038\/s41598-021-83784-y","journal-title":"Sci. Rep."},{"key":"131_CR5","doi-asserted-by":"publisher","first-page":"196299","DOI":"10.1109\/ACCESS.2020.3034032","volume":"8","author":"E Casiraghi","year":"2020","unstructured":"Casiraghi, E., Malchiodi, D., Trucco, G., Frasca, M., Cappelletti, L., Fontana, T., Esposito, A.A., Avola, E., Jachetti, A., Reese, J., Rizzi, A., Robinson, P.N., Valentini, G.: Explainable machine learning for early assessment of COVID-19 risk prediction in emergency departments. IEEE Access 8, 196299\u2013196325 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3034032","journal-title":"IEEE Access"},{"issue":"4","key":"131_CR6","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1080\/19466315.2020.1797867","volume":"12","author":"WR Zame","year":"2020","unstructured":"Zame, W.R., Bica, I., Shen, C., Curth, A., Lee, H.-S., Bailey, S., Weatherall, J., Wright, D., Bretz, F., van der Schaar, M.: Machine learning for clinical trials in the era of COVID-19. Stat. Biopharm. Res. 12(4), 506\u2013517 (2020). https:\/\/doi.org\/10.1080\/19466315.2020.1797867","journal-title":"Stat. Biopharm. Res."},{"issue":"19","key":"131_CR7","doi-asserted-by":"publisher","first-page":"10484","DOI":"10.1073\/pnas.2004978117","volume":"117","author":"M Gatto","year":"2020","unstructured":"Gatto, M., Bertuzzo, E., Mari, L., Miccoli, S., Carraro, L., Casagrandi, R., Rinaldo, A.: Spread and dynamics of the COVID-19 epidemic in Italy: effects of emergency containment measures. Proc. Natl. Acad. Sci. 117(19), 10484 (2020). https:\/\/doi.org\/10.1073\/pnas.2004978117","journal-title":"Proc. Natl. Acad. Sci."},{"key":"131_CR8","doi-asserted-by":"publisher","unstructured":"de Moraes Batista, A.F., Miraglia, J.L., Rizzi Donato, T.H., Porto Chiavegatto Filho, A.D.: COVID-19 diagnosis prediction in emergency care patients: a machine learning approach. medRxiv (2020). https:\/\/doi.org\/10.1101\/2020.04.04.20052092","DOI":"10.1101\/2020.04.04.20052092"},{"issue":"2","key":"131_CR9","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1007\/s00330-020-07154-4","volume":"31","author":"V Ducray","year":"2021","unstructured":"Ducray, V., Vlachomitrou, A.S., Bouscambert-Duchamp, M., Si-Mohamed, S., Gouttard, S., Mansuy, A., Wickert, F., Sigal, A., Gaymard, A., Talbot, F., Michel, C., Perpoint, T., Pialat, J.-B., Rouviere, O., Milot, L., Cotton, F., Douek, P., Rabilloud, M., Boussel, L., Argaud, L., Aubrun, F., Bohe, J., Bonnefoy, M., Chapurlat, R., Chassard, D., Chidiac, C., Chuzeville, M., Confavreux, C., Couraud, S., Devouassoux, G., Durieu, I., Fellahi, J.-L., Gaujard, S., Gaymard, A., Hot, A., Krolak-Salmon, P., Lantelme, P., Lina, B., Luaute, J., Lukaszewicz, A.C., Martin-Gaujard, G., Mornex, J.F., Potinet, V., Rimmele, T., Rode, G., S\u00e8ve, F.P., Sigal, A., Zoulim, F.: Chest CT for rapid triage of patients in multiple emergency departments during COVID-19 epidemic: experience report from a large French university hospital. Eur. Radiol. 31(2), 795\u2013803 (2021). https:\/\/doi.org\/10.1007\/s00330-020-07154-4","journal-title":"Eur. Radiol."},{"key":"131_CR10","doi-asserted-by":"publisher","unstructured":"Imran, A., Posokhova, I., Qureshi, H.N., Masood, U., Riaz, M.S., Ali, K., John, C.N., Hussain, M.D.I., Nabeel, M.: AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app. Informatics in Medicine Unlocked (2020). https:\/\/doi.org\/10.1016\/j.imu.2020.100378","DOI":"10.1016\/j.imu.2020.100378"},{"key":"131_CR11","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3049141","author":"KP Yu","year":"2021","unstructured":"Yu, K.P., Tan, L., Aloqaily, M., Yang, H., Jararweh, Y.: Blockchain-enhanced data sharing with traceable and direct revocation in IIoT. IEEE Trans. Ind. Inf. (2021). https:\/\/doi.org\/10.1109\/TII.2021.3049141","journal-title":"IEEE Trans. Ind. Inf."},{"key":"131_CR12","doi-asserted-by":"publisher","first-page":"103517","DOI":"10.1016\/j.csi.2021.103517","volume":"76","author":"L Tan","year":"2021","unstructured":"Tan, L., Xiao, H., Yu, K., Aloqaily, M., Jararweh, Y.: A blockchain-empowered crowdsourcing system for 5G-enabled smart cities. Comput. Standards & Interfaces 76, 103517 (2021). https:\/\/doi.org\/10.1016\/j.csi.2021.103517","journal-title":"Comput. Standards & Interfaces"},{"key":"131_CR13","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/978-981-15-9547-9_4","volume-title":"Applications of blockchain in healthcare","author":"M Gupta","year":"2021","unstructured":"Gupta, M., Jain, R., Kumari, M., Narula, G.: Securing healthcare data by using blockchain. In: Namasudra, S., Deka, G.C. (eds.) Applications of blockchain in healthcare, pp. 93\u2013114. Springer Singapore, Singapore (2021)"},{"key":"131_CR14","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/978-981-15-9547-9_7","volume-title":"Applications of blockchain in healthcare","author":"P Sharma","year":"2021","unstructured":"Sharma, P., Jindal, R., Borah, M.D.: Healthify: a blockchain-based distributed application for health care. In: Namasudra, S., Deka, G.C. (eds.) Applications of blockchain in healthcare, pp. 171\u2013198. Springer Singapore, Singapore (2021)"},{"key":"131_CR15","first-page":"1","volume-title":"Applications of blockchain in healthcare","author":"S Bittins","year":"2021","unstructured":"Bittins, S., Kober, G., Margheri, A., Masi, M., Miladi, A., Sassone, V.: Healthcare data management by using blockchain technology. In: Namasudra, S., Deka, G.C. (eds.) Applications of blockchain in healthcare, pp. 1\u201327. Springer Singapore, Singapore (2021)"},{"key":"131_CR16","doi-asserted-by":"crossref","unstructured":"Malina, L., Srivastava, G., Dzurenda, P., Hajny, J., Ricci, S.: A privacy-enhancing framework for internet of things services. In: International Conference on Network and System Security, pp. 77\u201397. Springer International Publishing, in Network and System Security, Cham (2019)","DOI":"10.1007\/978-3-030-36938-5_5"},{"issue":"5","key":"131_CR17","doi-asserted-by":"publisher","first-page":"7702","DOI":"10.1109\/JIOT.2019.2901840","volume":"6","author":"M Shen","year":"2019","unstructured":"Shen, M., Tang, X., Zhu, L., Du, X., Guizani, M.: Privacy-preserving support vector machine training over blockchain-based encrypted IoT data in smart cities. IEEE Internet Things J. 6(5), 7702\u20137712 (2019)","journal-title":"IEEE Internet Things J."},{"key":"131_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2019.101653","author":"I Makhdoom","year":"2020","unstructured":"Makhdoom, I., Zhou, I., Abolhasan, M., Lipman, J., Ni, W.: PrivySharing: a blockchain-based framework for privacy-preserving and secure data sharing in smart cities. Comput. & Secur. (2020). https:\/\/doi.org\/10.1016\/j.cose.2019.101653","journal-title":"Comput. & Secur."},{"issue":"14","key":"131_CR19","doi-asserted-by":"publisher","first-page":"4958","DOI":"10.3390\/app10144958","volume":"10","author":"C-L Chen","year":"2020","unstructured":"Chen, C.-L., Deng, Y.-Y., Weng, W., Sun, H., Zhou, M.: A blockchain-based secure inter-hospital EMR sharing system. Appl. Sci. 10(14), 4958 (2020)","journal-title":"Appl. Sci."},{"key":"131_CR20","doi-asserted-by":"crossref","unstructured":"Dai, H.-N., Imran, M., Haider, N.: Blockchain-enabled Internet of Medical Things to Combat COVID-19. arXiv preprint arXiv:2008.09933 (2020)","DOI":"10.1109\/IOTM.0001.2000087"},{"issue":"5","key":"131_CR21","doi-asserted-by":"publisher","first-page":"1521","DOI":"10.3390\/s20051521","volume":"20","author":"H Shu","year":"2020","unstructured":"Shu, H., Qi, P., Huang, Y., Chen, F., Xie, D., Sun, L.: An efficient certificateless aggregate signature scheme for blockchain-based medical cyber physical systems. Sensors 20(5), 1521 (2020)","journal-title":"Sensors"},{"key":"131_CR22","doi-asserted-by":"publisher","first-page":"132302","DOI":"10.1109\/ACCESS.2020.3009783","volume":"8","author":"A Jaleel","year":"2020","unstructured":"Jaleel, A., Mahmood, T., Hassan, M.A., Bano, G., Khurshid, S.K.: Towards medical data interoperability through collaboration of healthcare devices. IEEE Access 8, 132302\u2013132319 (2020)","journal-title":"IEEE Access"},{"issue":"2","key":"131_CR23","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1109\/MCE.2020.3035520","volume":"10","author":"K Yu","year":"2021","unstructured":"Yu, K., Tan, L., Shang, X., Huang, J., Srivastava, G., Chatterjee, P.: Efficient and privacy-preserving medical research support platform against COVID-19: a blockchain-based approach. IEEE Consumer Electron. Magazine 10(2), 111\u2013120 (2021). https:\/\/doi.org\/10.1109\/MCE.2020.3035520","journal-title":"IEEE Consumer Electron. Magazine"},{"issue":"12","key":"131_CR24","doi-asserted-by":"publisher","first-page":"10354","DOI":"10.1007\/s11227-020-03251-9","volume":"76","author":"A Fadaeddini","year":"2020","unstructured":"Fadaeddini, A., Majidi, B., Eshghi, M.: Secure decentralized peer-to-peer training of deep neural networks based on distributed ledger technology. J. Supercomput. 76(12), 10354\u201310368 (2020)","journal-title":"J. Supercomput."},{"key":"131_CR25","doi-asserted-by":"crossref","unstructured":"Fadaeddini, A., Majidi, B., Eshghi, M.: Privacy preserved decentralized deep learning: A blockchain based solution for secure ai-driven enterprise. In: International Congress on High-Performance Computing and Big Data Analysis. Springer (2019)","DOI":"10.1007\/978-3-030-33495-6_3"},{"key":"131_CR26","doi-asserted-by":"crossref","unstructured":"Mohanta, B.K., Panda, S.S., Jena, D.: An overview of smart contract and use cases in blockchain technology. In: 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1\u20134 (2018)","DOI":"10.1109\/ICCCNT.2018.8494045"},{"key":"131_CR27","doi-asserted-by":"publisher","first-page":"150184","DOI":"10.1109\/ACCESS.2019.2946988","volume":"7","author":"Y Huang","year":"2019","unstructured":"Huang, Y., Bian, Y., Li, R., Zhao, J.L., Shi, P.: Smart contract security: a software lifecycle perspective. IEEE Access 7, 150184\u2013150202 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2946988","journal-title":"IEEE Access"},{"key":"131_CR28","doi-asserted-by":"crossref","unstructured":"Bhargavan, K., Delignat-Lavaud, A., Fournet, C., Gollamudi, A., Gonthier, G., Kobeissi, N., Kulatova, N., Rastogi, A., Sibut-Pinote, T., Swamy, N.: Formal verification of smart contracts: Short paper. In: Proceedings of the 2016 ACM Workshop on Programming Languages and Analysis for Security, pp. 91\u201396 (2016)","DOI":"10.1145\/2993600.2993611"},{"key":"131_CR29","doi-asserted-by":"crossref","unstructured":"Tsankov, P., Dan, A., Drachsler-Cohen, D., Gervais, A., Buenzli, F., Vechev, M.: Securify: practical security analysis of smart contracts. In: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, pp. 67\u201382 (2018)","DOI":"10.1145\/3243734.3243780"},{"key":"131_CR30","doi-asserted-by":"crossref","unstructured":"Kalra, S., Goel, S., Dhawan, M., Sharma, S.: ZEUS: analyzing safety of smart contracts. In: NDSS, pp. 1\u201312 (2018)","DOI":"10.14722\/ndss.2018.23082"},{"key":"131_CR31","unstructured":"OpenZeppelin. https:\/\/openzeppelin.com\/contracts\/. Accessed 27 Feb 2021"},{"key":"131_CR32","unstructured":"SmartDec. https:\/\/smartcontracts.smartdec.net\/. Accessed 27 Feb 2021"},{"key":"131_CR33","doi-asserted-by":"crossref","unstructured":"Alt L., Reitwie\u00dfner, C.: SMT-based verification of solidity smart contracts. In: International Symposium on Leveraging Applications of Formal Methods, pp. 376\u2013388. Springer (2018)","DOI":"10.1007\/978-3-030-03427-6_28"},{"key":"131_CR34","unstructured":"Benet, J.: InterPlanetary File System. https:\/\/ipfs.io\/. Accessed 17 Dec 2020"},{"key":"131_CR35","unstructured":"Entriken, W.: Introduction to smart contracts. https:\/\/ethereum.org\/en\/developers\/docs\/smart-contracts\/. Accessed 30 Nov 2020"},{"key":"131_CR36","unstructured":"MetaMask. https:\/\/metamask.io\/. Accessed 17 Dec 2020"},{"key":"131_CR37","unstructured":"Solidity. https:\/\/docs.soliditylang.org\/en\/v0.5.0\/resources.html. Accessed 17 Dec 2020"},{"key":"131_CR38","unstructured":"Truffle Suite. https:\/\/www.trufflesuite.com\/. Accessed 17 Dec 2020"},{"key":"131_CR39","unstructured":"Ganache. https:\/\/www.trufflesuite.com\/ganache. Accessed 17 Dec 2020"},{"key":"131_CR40","unstructured":"web3.js. https:\/\/web3js.readthedocs.io\/en\/v1.3.0\/. Accessed 17 Dec 2020"},{"key":"131_CR41","unstructured":"OpenZeppelin\u2019s AccessControl Module. https:\/\/github.com\/OpenZeppelin\/openzeppelin-contracts\/blob\/master\/contracts\/access\/AccessControl.sol. Accessed 2 Mar 2021"},{"key":"131_CR42","unstructured":"Kovan Testnet. https:\/\/kovan-testnet.github.io\/website\/. Accessed 15 Dec 2020"},{"key":"131_CR43","unstructured":"Infura. https:\/\/infura.io\/. Accessed 15 Dec 2020"},{"key":"131_CR44","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.inffus.2019.12.001","volume":"57","author":"T Meng","year":"2020","unstructured":"Meng, T., Jing, X., Yan, Z., Pedrycz, W.: A survey on machine learning for data fusion. Inf. Fusion 57, 115\u2013129 (2020). https:\/\/doi.org\/10.1016\/j.inffus.2019.12.001","journal-title":"Inf. Fusion"},{"issue":"12","key":"131_CR45","doi-asserted-by":"publisher","first-page":"0242958","DOI":"10.1371\/journal.pone.0242958","volume":"15","author":"I Arevalo-Rodriguez","year":"2020","unstructured":"Arevalo-Rodriguez, I., Buitrago-Garcia, D., Simancas-Racines, D., Zambrano-Achig, P., Del Campo, R., Ciapponi, A., Sued, O., Martinez-Garcia, L., Rutjes, A.W., Low, N.: False-negative results of initial RT-PCR assays for COVID-19: a systematic review. PloS One 15(12), 0242958 (2020)","journal-title":"PloS One"},{"key":"131_CR46","doi-asserted-by":"crossref","unstructured":"Watson, J., Whiting, P. F., Brush, J. E.: Interpreting a COVID-19 test result. BMJ 369 (2020)","DOI":"10.1136\/bmj.m1808"},{"issue":"3","key":"131_CR47","doi-asserted-by":"publisher","first-page":"1690","DOI":"10.1007\/s10489-020-01902-1","volume":"51","author":"R Jain","year":"2021","unstructured":"Jain, R., Gupta, M., Taneja, S., Hemanth, D.J.: Deep learning based detection and analysis of COVID-19 on chest X-ray images. Appl. Intell. 51(3), 1690\u20131700 (2021). https:\/\/doi.org\/10.1007\/s10489-020-01902-1","journal-title":"Appl. Intell."},{"key":"131_CR48","doi-asserted-by":"publisher","DOI":"10.1001\/jamacardio.2020.0950","volume-title":"Association of cardiac injury with mortality in hospitalized patients with COVID-19 in Wuhan","author":"S Shi","year":"2020","unstructured":"Shi, S., Qin, M., Shen, B., Cai, Y., Liu, T., Yang, F., Gong, W., Liu, X., Liang, J., Zhao, Q.: Association of cardiac injury with mortality in hospitalized patients with COVID-19 in Wuhan. JAMA cardiology, China (2020)"},{"issue":"3","key":"131_CR49","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1109\/51.932724","volume":"20","author":"GB Moody","year":"2001","unstructured":"Moody, G.B., Mark, R.G.: The impact of the MIT-BIH Arrhythmia database. IEEE Eng. Med. Biol. Mag. 20(3), 45\u201350 (2001)","journal-title":"IEEE Eng. Med. Biol. Mag."},{"key":"131_CR50","doi-asserted-by":"crossref","unstructured":"Kachuee, M., Fazeli, S., Sarrafzadeh, M.: ECG Heartbeat Classification: a deep transferable representation. In: IEEE International Conference on Healthcare Informatics (ICHI) 2018, 443\u2013444 (2018)","DOI":"10.1109\/ICHI.2018.00092"},{"key":"131_CR51","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1016\/j.compbiomed.2017.08.022","volume":"89","author":"UR Acharya","year":"2017","unstructured":"Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adam, M., Gertych, A., San Tan, R.: A deep convolutional neural network model to classify heartbeats. Comput. Biol. Med. 89, 389\u2013396 (2017)","journal-title":"Comput. Biol. Med."},{"key":"131_CR52","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-020-10150-x","author":"R Kaspal","year":"2020","unstructured":"Kaspal, R., Alsadoon, A., Prasad, P.W.C., Al-Saiyd, N.A., Nguyen, T.Q.V., Pham, D.T.H.: A novel approach for early prediction of sudden cardiac death (SCD) using hybrid deep learning. Multimedia Tools Appl. (2020). https:\/\/doi.org\/10.1007\/s11042-020-10150-x","journal-title":"Multimedia Tools Appl."},{"key":"131_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.jelectrocard.2020.11.014","author":"J Malik","year":"2020","unstructured":"Malik, J., Loring, Z., Piccini, J.P., Wu, H.T.: Interpretable morphological features for efficient single-lead automatic ventricular ectopy detection. J. Electrocardiol. (2020). https:\/\/doi.org\/10.1016\/j.jelectrocard.2020.11.014","journal-title":"J. Electrocardiol."},{"key":"131_CR54","doi-asserted-by":"publisher","unstructured":"Khan, M.M.R., Siddique, M.A.B., Sakib, S., Aziz, A., Tanzeem, A.K., Hossain, Z.: Electrocardiogram heartbeat classification using convolutional neural networks for the detection of cardiac Arrhythmia. In: 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pp. 915\u2013920 (2020). https:\/\/doi.org\/10.1109\/I-SMAC49090.2020.9243474","DOI":"10.1109\/I-SMAC49090.2020.9243474"},{"key":"131_CR55","doi-asserted-by":"publisher","DOI":"10.3389\/fphy.2019.00103","author":"M Alfaras","year":"2019","unstructured":"Alfaras, M., Soriano, M.C., Ort\u00edn, S.: A fast machine learning model for ECG-based heartbeat classification and Arrhythmia detection, (in English). Front. Phys. (2019). https:\/\/doi.org\/10.3389\/fphy.2019.00103","journal-title":"Front. Phys."},{"key":"131_CR56","unstructured":"COVID-19 Cough Recordings. https:\/\/www.kaggle.com\/himanshu007121\/coughclassifier-trial. Accessed 2 Mar 2021"},{"key":"131_CR57","doi-asserted-by":"crossref","unstructured":"Sharma, N., Krishnan, P., Kumar, R., Ramoji, S., Chetupalli, S.R., Ghosh, P.K., Ganapathy, S.: Coswara\u2013A database of breathing, cough, and voice sounds for COVID-19 Diagnosis. arXiv preprint arXiv:2005.10548 (2020)","DOI":"10.21437\/Interspeech.2020-2768"},{"key":"131_CR58","unstructured":"Coswara dataset. https:\/\/github.com\/iiscleap\/Coswara-Data. Accessed 3 Mar 2021"},{"issue":"1","key":"131_CR59","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1016\/j.aej.2020.09.032","volume":"60","author":"DS Vijayakumar","year":"2021","unstructured":"Vijayakumar, D.S., Sneha, M.: Low cost Covid-19 preliminary diagnosis utilizing cough samples and keenly intellective deep learning approaches. Alexandria Eng. J. 60(1), 549\u2013557 (2021). https:\/\/doi.org\/10.1016\/j.aej.2020.09.032","journal-title":"Alexandria Eng. J."},{"issue":"1","key":"131_CR60","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1007\/s42979-020-00422-6","volume":"2","author":"P Mouawad","year":"2021","unstructured":"Mouawad, P., Dubnov, T., Dubnov, S.: Robust detection of COVID-19 in cough sounds. SN Comput. Sci. 2(1), 34 (2021). https:\/\/doi.org\/10.1007\/s42979-020-00422-6","journal-title":"SN Comput. Sci."},{"key":"131_CR61","unstructured":"Chest X-ray (Covid-19 & Pneumonia). https:\/\/www.kaggle.com\/prashant268\/chest-xray-covid19-pneumonia. Accessed 2 Mar 2021"},{"key":"131_CR62","doi-asserted-by":"publisher","first-page":"103792","DOI":"10.1016\/j.compbiomed.2020.103792","volume":"121","author":"T Ozturk","year":"2020","unstructured":"Ozturk, T., Talo, M., Yildirim, E.A., Baloglu, U.B., Yildirim, O., Acharya, U.R.: Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput. Biol. Med. 121, 103792 (2020)","journal-title":"Comput. Biol. Med."},{"key":"131_CR63","doi-asserted-by":"publisher","first-page":"427","DOI":"10.3389\/fmed.2020.00427","volume":"7","author":"SH Yoo","year":"2020","unstructured":"Yoo, S.H., Geng, H., Chiu, T.L., Yu, S.K., Cho, D.C., Heo, J., Choi, M.S., Choi, I.H., Van Cung, C., Nhung, N.V.: Deep learning-based decision-tree classifier for COVID-19 diagnosis from chest X-ray imaging. Front. Med. 7, 427 (2020)","journal-title":"Front. Med."},{"key":"131_CR64","doi-asserted-by":"crossref","unstructured":"Sethy, P.K., Behera, S.K., Ratha, P.K., Biswas, P.: Detection of coronavirus disease (COVID-19) based on deep features and support vector machine. Preprints (2020)","DOI":"10.20944\/preprints202003.0300.v1"},{"key":"131_CR65","doi-asserted-by":"publisher","first-page":"109944","DOI":"10.1016\/j.chaos.2020.109944","volume":"138","author":"H Panwar","year":"2020","unstructured":"Panwar, H., Gupta, P.K., Siddiqui, M.K., Morales-Menendez, R., Singh, V.: Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet. Chaos, Solitons & Fractals 138, 109944 (2020)","journal-title":"Chaos, Solitons & Fractals"},{"key":"131_CR66","unstructured":"C.-C. Scans. https:\/\/www.kaggle.com\/andrewmvd\/covid19-ct-scans. Accessed 3 Mar 2021"},{"key":"131_CR67","doi-asserted-by":"crossref","unstructured":"Glick. COVID-19 Pneumonia. https:\/\/radiopaedia.org\/playlists\/25887. Accessed 3 Mar 2021","DOI":"10.53347\/rID-75496"},{"key":"131_CR68","unstructured":"Paiva, O.: CT scans of patients with COVID-19 from Wenzhou Medical University. https:\/\/coronacases.org\/. Accessed 3 Mar 2021"},{"key":"131_CR69","doi-asserted-by":"publisher","unstructured":"Jun, M., Cheng, G., Yixin, W., Xingle, A., Jiantao, G., Ziqi, Y., Minqing, Z., Xin, L., Xueyuan, D., Shucheng, C., Hao, W., Sen, M., Xiaoyu, Y., Ziwei, N., Chen, L., Lu, T., Yuntao, Z., Qiongjie, Z., Guoqiang, D., Jian, H.: COVID-19 CT Lung and Infection Segmentation Dataset. https:\/\/doi.org\/10.5281\/zenodo.3757475. Accessed 3 Mar 2021","DOI":"10.5281\/zenodo.3757475"},{"issue":"7","key":"131_CR70","doi-asserted-by":"publisher","first-page":"1379","DOI":"10.1007\/s10096-020-03901-z","volume":"39","author":"D Singh","year":"2020","unstructured":"Singh, D., Kumar, V., Kaur, M.: Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks. Eur. J. Clin. Microbiol. Infectious Dis. 39(7), 1379\u20131389 (2020)","journal-title":"Eur. J. Clin. Microbiol. Infectious Dis."},{"issue":"8","key":"131_CR71","doi-asserted-by":"publisher","first-page":"2615","DOI":"10.1109\/TMI.2020.2995965","volume":"39","author":"X Wang","year":"2020","unstructured":"Wang, X., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., Zheng, C.: A weakly-supervised framework for COVID-19 classification and lesion localization from chest CT. IEEE Trans. Med. Imaging 39(8), 2615\u20132625 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"131_CR72","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1007\/s10489-020-01826-w","volume":"51","author":"S Ahuja","year":"2021","unstructured":"Ahuja, S., Panigrahi, B.K., Dey, N., Rajinikanth, V., Gandhi, T.K.: Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices. Appl. Intell. 51(1), 571\u2013585 (2021)","journal-title":"Appl. Intell."}],"container-title":["New Generation Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00354-021-00131-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00354-021-00131-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00354-021-00131-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,23]],"date-time":"2021-11-23T10:31:23Z","timestamp":1637663483000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00354-021-00131-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,27]]},"references-count":72,"journal-issue":{"issue":"3-4","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["131"],"URL":"https:\/\/doi.org\/10.1007\/s00354-021-00131-5","relation":{},"ISSN":["0288-3635","1882-7055"],"issn-type":[{"value":"0288-3635","type":"print"},{"value":"1882-7055","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,27]]},"assertion":[{"value":"19 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 June 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}