Hybrid-based framework for COVID-19 prediction via federated machine learning models

The COronaVIrus Disease 2019 (COVID-19) pandemic is unfortunately highly transmissible across the people. In order to detect and track the suspected COVID-19 infected people and consequently limit the pandemic spread, this paper entails a framework integrating the machine learning (ML), cloud, fog,...

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Vydané v:	The Journal of supercomputing Ročník 78; číslo 5; s. 7078 - 7105
Hlavní autori:	Kallel, Ameni, Rekik, Molka, Khemakhem, Mahdi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.04.2022 Springer Nature B.V
Predmet:	Algorithms Compilers Computer Science Coronaviruses COVID-19 Data processing Decision making Electronic devices Internet of Things Interpreters Machine learning Network latency Pandemics Prediction models Processor Architectures Prognosis Programming Languages Quality of service architectures Response time (computers) Viral diseases Decision-making Quantitative and qualitative evaluation COVID-19 pandemic Batch/streaming data Hybrid fog-cloud federation Machine learning Real-time prediction IoT devices Federated MLaaS
ISSN:	0920-8542, 1573-0484
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Abstract	The COronaVIrus Disease 2019 (COVID-19) pandemic is unfortunately highly transmissible across the people. In order to detect and track the suspected COVID-19 infected people and consequently limit the pandemic spread, this paper entails a framework integrating the machine learning (ML), cloud, fog, and Internet of Things (IoT) technologies to propose a novel smart COVID-19 disease monitoring and prognosis system. The proposal leverages the IoT devices that collect streaming data from both medical (e.g., X-ray machine, lung ultrasound machine, etc.) and non-medical (e.g., bracelet, smartwatch, etc.) devices. Moreover, the proposed hybrid fog-cloud framework provides two kinds of federated ML as a service (federated MLaaS); (i) the distributed batch MLaaS that is implemented on the cloud environment for a long-term decision-making, and (ii) the distributed stream MLaaS, which is installed into a hybrid fog-cloud environment for a short-term decision-making. The stream MLaaS uses a shared federated prediction model stored into the cloud, whereas the real-time symptom data processing and COVID-19 prediction are done into the fog. The federated ML models are determined after evaluating a set of both batch and stream ML algorithms from the Python’s libraries. The evaluation considers both the quantitative (i.e., performance in terms of accuracy, precision, root mean squared error, and F 1 score) and qualitative (i.e., quality of service in terms of server latency, response time, and network latency) metrics to assess these algorithms. This evaluation shows that the stream ML algorithms have the potential to be integrated into the COVID-19 prognosis allowing the early predictions of the suspected COVID-19 cases.
AbstractList	The COronaVIrus Disease 2019 (COVID-19) pandemic is unfortunately highly transmissible across the people. In order to detect and track the suspected COVID-19 infected people and consequently limit the pandemic spread, this paper entails a framework integrating the machine learning (ML), cloud, fog, and Internet of Things (IoT) technologies to propose a novel smart COVID-19 disease monitoring and prognosis system. The proposal leverages the IoT devices that collect streaming data from both medical (e.g., X-ray machine, lung ultrasound machine, etc.) and non-medical (e.g., bracelet, smartwatch, etc.) devices. Moreover, the proposed hybrid fog-cloud framework provides two kinds of federated ML as a service (federated MLaaS); (i) the distributed batch MLaaS that is implemented on the cloud environment for a long-term decision-making, and (ii) the distributed stream MLaaS, which is installed into a hybrid fog-cloud environment for a short-term decision-making. The stream MLaaS uses a shared federated prediction model stored into the cloud, whereas the real-time symptom data processing and COVID-19 prediction are done into the fog. The federated ML models are determined after evaluating a set of both batch and stream ML algorithms from the Python’s libraries. The evaluation considers both the quantitative (i.e., performance in terms of accuracy, precision, root mean squared error, and F 1 score) and qualitative (i.e., quality of service in terms of server latency, response time, and network latency) metrics to assess these algorithms. This evaluation shows that the stream ML algorithms have the potential to be integrated into the COVID-19 prognosis allowing the early predictions of the suspected COVID-19 cases. The COronaVIrus Disease 2019 (COVID-19) pandemic is unfortunately highly transmissible across the people. In order to detect and track the suspected COVID-19 infected people and consequently limit the pandemic spread, this paper entails a framework integrating the machine learning (ML), cloud, fog, and Internet of Things (IoT) technologies to propose a novel smart COVID-19 disease monitoring and prognosis system. The proposal leverages the IoT devices that collect streaming data from both medical (e.g., X-ray machine, lung ultrasound machine, etc.) and non-medical (e.g., bracelet, smartwatch, etc.) devices. Moreover, the proposed hybrid fog-cloud framework provides two kinds of federated ML as a service (federated MLaaS); (i) the distributed batch MLaaS that is implemented on the cloud environment for a long-term decision-making, and (ii) the distributed stream MLaaS, which is installed into a hybrid fog-cloud environment for a short-term decision-making. The stream MLaaS uses a shared federated prediction model stored into the cloud, whereas the real-time symptom data processing and COVID-19 prediction are done into the fog. The federated ML models are determined after evaluating a set of both batch and stream ML algorithms from the Python's libraries. The evaluation considers both the quantitative (i.e., performance in terms of accuracy, precision, root mean squared error, and 1 score) and qualitative (i.e., quality of service in terms of server latency, response time, and network latency) metrics to assess these algorithms. This evaluation shows that the stream ML algorithms have the potential to be integrated into the COVID-19 prognosis allowing the early predictions of the suspected COVID-19 cases. The COronaVIrus Disease 2019 (COVID-19) pandemic is unfortunately highly transmissible across the people. In order to detect and track the suspected COVID-19 infected people and consequently limit the pandemic spread, this paper entails a framework integrating the machine learning (ML), cloud, fog, and Internet of Things (IoT) technologies to propose a novel smart COVID-19 disease monitoring and prognosis system. The proposal leverages the IoT devices that collect streaming data from both medical (e.g., X-ray machine, lung ultrasound machine, etc.) and non-medical (e.g., bracelet, smartwatch, etc.) devices. Moreover, the proposed hybrid fog-cloud framework provides two kinds of federated ML as a service (federated MLaaS); (i) the distributed batch MLaaS that is implemented on the cloud environment for a long-term decision-making, and (ii) the distributed stream MLaaS, which is installed into a hybrid fog-cloud environment for a short-term decision-making. The stream MLaaS uses a shared federated prediction model stored into the cloud, whereas the real-time symptom data processing and COVID-19 prediction are done into the fog. The federated ML models are determined after evaluating a set of both batch and stream ML algorithms from the Python's libraries. The evaluation considers both the quantitative (i.e., performance in terms of accuracy, precision, root mean squared error, and F1 score) and qualitative (i.e., quality of service in terms of server latency, response time, and network latency) metrics to assess these algorithms. This evaluation shows that the stream ML algorithms have the potential to be integrated into the COVID-19 prognosis allowing the early predictions of the suspected COVID-19 cases.The COronaVIrus Disease 2019 (COVID-19) pandemic is unfortunately highly transmissible across the people. In order to detect and track the suspected COVID-19 infected people and consequently limit the pandemic spread, this paper entails a framework integrating the machine learning (ML), cloud, fog, and Internet of Things (IoT) technologies to propose a novel smart COVID-19 disease monitoring and prognosis system. The proposal leverages the IoT devices that collect streaming data from both medical (e.g., X-ray machine, lung ultrasound machine, etc.) and non-medical (e.g., bracelet, smartwatch, etc.) devices. Moreover, the proposed hybrid fog-cloud framework provides two kinds of federated ML as a service (federated MLaaS); (i) the distributed batch MLaaS that is implemented on the cloud environment for a long-term decision-making, and (ii) the distributed stream MLaaS, which is installed into a hybrid fog-cloud environment for a short-term decision-making. The stream MLaaS uses a shared federated prediction model stored into the cloud, whereas the real-time symptom data processing and COVID-19 prediction are done into the fog. The federated ML models are determined after evaluating a set of both batch and stream ML algorithms from the Python's libraries. The evaluation considers both the quantitative (i.e., performance in terms of accuracy, precision, root mean squared error, and F1 score) and qualitative (i.e., quality of service in terms of server latency, response time, and network latency) metrics to assess these algorithms. This evaluation shows that the stream ML algorithms have the potential to be integrated into the COVID-19 prognosis allowing the early predictions of the suspected COVID-19 cases. The COronaVIrus Disease 2019 (COVID-19) pandemic is unfortunately highly transmissible across the people. In order to detect and track the suspected COVID-19 infected people and consequently limit the pandemic spread, this paper entails a framework integrating the machine learning (ML), cloud, fog, and Internet of Things (IoT) technologies to propose a novel smart COVID-19 disease monitoring and prognosis system. The proposal leverages the IoT devices that collect streaming data from both medical (e.g., X-ray machine, lung ultrasound machine, etc.) and non-medical (e.g., bracelet, smartwatch, etc.) devices. Moreover, the proposed hybrid fog-cloud framework provides two kinds of federated ML as a service (federated MLaaS); (i) the distributed batch MLaaS that is implemented on the cloud environment for a long-term decision-making, and (ii) the distributed stream MLaaS, which is installed into a hybrid fog-cloud environment for a short-term decision-making. The stream MLaaS uses a shared federated prediction model stored into the cloud, whereas the real-time symptom data processing and COVID-19 prediction are done into the fog. The federated ML models are determined after evaluating a set of both batch and stream ML algorithms from the Python’s libraries. The evaluation considers both the quantitative (i.e., performance in terms of accuracy, precision, root mean squared error, and F1 score) and qualitative (i.e., quality of service in terms of server latency, response time, and network latency) metrics to assess these algorithms. This evaluation shows that the stream ML algorithms have the potential to be integrated into the COVID-19 prognosis allowing the early predictions of the suspected COVID-19 cases.
Author	Khemakhem, Mahdi Rekik, Molka Kallel, Ameni
Author_xml	– sequence: 1 givenname: Ameni orcidid: 0000-0002-7354-1276 surname: Kallel fullname: Kallel, Ameni email: kallel.ameni@gmail.com organization: Data Engineering and Semantics Research Unit, Faculty of Sciences of Sfax, University of Sfax, Département Technologies de l’Informatique, Higher Institute of Technological Studies (ISET) – sequence: 2 givenname: Molka surname: Rekik fullname: Rekik, Molka organization: Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Data Engineering and Semantics Research Unit, Faculty of Sciences of Sfax, University of Sfax – sequence: 3 givenname: Mahdi surname: Khemakhem fullname: Khemakhem, Mahdi organization: Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Data Engineering and Semantics Research Unit, Faculty of Sciences of Sfax, University of Sfax
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Cites_doi	10.1016/j.future.2019.10.043 10.1016/j.compbiomed.2020.103792 10.1002/spe.3011 10.3390/ijerph17186933 10.3390/s19112451 10.1002/spe.2924 10.7861/futurehosp.6-2-94 10.1016/j.irbm.2020.05.003 10.1001/jamainternmed.2020.0994 10.1023/A:1010933404324 10.1016/j.measurement.2020.108288 10.1109/21.97458 10.18280/ts.370313 10.1007/978-3-642-34156-4_29 10.1109/MNET.011.2000458 10.1109/CVPRW50498.2020.00118 10.1016/S0140-6736(20)30183-5 10.1007/978-3-642-40160-2_5 10.3390/electronics9091439 10.1016/j.future.2020.08.046 10.1109/ATNAC.2014.7020884 10.1016/j.procs.2019.11.087 10.3928/19382359-20200219-01 10.3892/etm.2020.8797 10.1016/j.jss.2019.04.050 10.1016/j.bspc.2020.102149 10.1016/S0893-6080(98)00031-8 10.3390/s20185236 10.1109/ASCC.2015.7244654 10.1214/aos/1013203451
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Keywords	Decision-making Quantitative and qualitative evaluation COVID-19 pandemic Batch/streaming data Hybrid fog-cloud federation Machine learning Real-time prediction IoT devices Federated MLaaS
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References	OzturkTTaloMYildirimEABalogluUBYildirimOAcharyaURAutomated detection of covid-19 cases using deep neural networks with x-ray imagesComput Biol Med202012110379210.1016/j.compbiomed.2020.103792 Singh A, Kaur A, Dhillon A, Ahuja S, Vohra H (2021) Software system to predict the infection in covid-19 patients using deep learning and web of things. Softw Pract Exp https://doi.org/10.1002/spe.3011 Anguita D, Ghelardoni L, Ghio A, Oneto L, Ridella S (2012) The ’k’ in k-fold cross validation. In: ESANN Montiel J, Halford M, Mastelini SM, Bolmier G, Sourty R, Vaysse R, Zouitine A, Gomes HM, Read J, Abdessalem T et al (2020) River: machine learning for streaming data in python. arXiv preprint arXiv:2012.04740 BreimanLRandom forestsMach Learn200145153210.1023/A:1010933404324 Pathak Y, Shukla PK, Tiwari A, Stalin S, Singh S, Shukla PK (2020) Deep transfer learning based classification model for covid-19 disease. IRBM Read J, Bifet A, Pfahringer B, Holmes G (2012) Batch-incremental versus instance-incremental learning in dynamic and evolving data. In: International symposium on intelligent data analysis. Springer, pp 313–323 Zwattendorfer B, Stranacher K, Tauber A (2013) Towards a federated identity as a service model. In: International conference on electronic government and the information systems perspective. Springer, pp 43–57 MunirMSiddiquiSAChatthaMADengelAAhmedSFusead: unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning modelsSensors20191911245110.3390/s19112451 Mitchell TM et al (1997) Machine learning. McGraw-Hill, New York, Tech. Rep. KumarASharmaKSinghHNaugriyaSGGillSSBuyyaRA drone-based networked system and methods for combating coronavirus disease (covid-19) pandemicFuture Gener Comput Syst202011511910.1016/j.future.2020.08.046 OtoomMOtoumNAlzubaidiMAEtoomYBanihaniRAn IoT-based framework for early identification and monitoring of covid-19 casesBiomed Signal Process Control20206210214910.1016/j.bspc.2020.102149 SafavianSRLandgrebeDA survey of decision tree classifier methodologyIEEE Trans Syst Man Cybern1991213660674113073110.1109/21.97458 Gia TN, Jiang M, Sarker VK, Rahmani AM, Westerlund T, Liljeberg P, Tenhunen H (2017) Low-cost fog-assisted health-care IoT system with energy-efficient sensor nodes. In: 2017 13th international wireless communications and mobile computing conference (IWCMC). IEEE, pp 1765–1770 Stojmenovic I (2014) Fog computing: A cloud to the ground support for smart things and machine-to-machine networks. In: 2014 Australasian telecommunication networks and applications conference (ATNAC). IEEE, pp 117–122 Egmont-PetersenMTalmonJLHasmanAAmbergenAWAssessing the importance of features for multi-layer perceptronsNeural Netw199811462363510.1016/S0893-6080(98)00031-8 DavenportTKalakotaRThe potential for artificial intelligence in healthcareFuture Healthc J2019629410.7861/futurehosp.6-2-94 Nematzadeh Z, Ibrahim R, Selamat A (2015) Comparative studies on breast cancer classifications with k-fold cross validations using machine learning techniques. In: 2015 10th Asian Control Conference (ASCC). IEEE, pp 1–6 HuangCWangYLiXRenLZhaoJHuYZhangLFanGXuJGuXClinical features of patients infected with 2019 novel coronavirus in Wuhan, ChinaLancet20203951022349750610.1016/S0140-6736(20)30183-5 Chowdary GJ, Punn NS, Sonbhadra SK, Agarwal S (2020) Face mask detection using transfer learning of inceptionv3. arXiv preprint arXiv:2009.08369 DebaucheOMahmoudiSMannebackPAssilaAFog IoT for health: a new architecture for patients and elderly monitoringProcedia Comput Sci201916028929710.1016/j.procs.2019.11.087 KallelARekikMKhemakhemMIoT-fog-cloud based architecture for smart systems: prototypes of autism and covid-19 monitoring systemsSoftw Pract Exp20205119111610.1002/spe.2924 TuliSMahmudRTuliSBuyyaRFogbus: a blockchain-based lightweight framework for edge and fog computingJ Syst Softw2019154223610.1016/j.jss.2019.04.050 TsiknakisNTrivizakisEVassalouEEPapadakisGZSpandidosDATsatsakisASánchez-GarcíaJLópez-GonzálezRPapanikolaouNKarantanasAHInterpretable artificial intelligence framework for covid-19 screening on chest X-raysExp Ther Med202020272773510.3892/etm.2020.8797 Rojas R et al (2009) Adaboost and the super bowl of classifiers a tutorial introduction to adaptive boosting, Freie University, Berlin, Tech. Rep Jagirdar NM (2018) Online machine learning algorithms review and comparison in healthcare, Ph.D. dissertation, University of Tennessee, The address of the publisher, 12, an optional note HagemanJRThe coronavirus disease 2019 (covid-19)Pediatr Ann2020493e99e100405959510.3928/19382359-20200219-01 El-RashidyNEl-SappaghSIslamSEl-BakryHMAbdelrazekSEnd-to-end deep learning framework for coronavirus (covid-19) detection and monitoringElectronics202099143910.3390/electronics9091439 Tartaglione E, Barbano CA, Berzovini C, Calandri M, Grangetto M (2020) Unveiling covid-19 from chest X-ray with deep learning: a hurdles race with small data. arXiv preprint arXiv:2004.05405 LoeyMManogaranGTahaMHNKhalifaNEMA hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the covid-19 pandemicMeasurement202016710828810.1016/j.measurement.2020.108288 MurphyKPNaive bayes classifiersUniv B C20061860 YasserITwakolAEl-KhalekASamrahASalamaACovid-x: novel health-fog framework based on neutrosophic classifier for confrontation covid-19Neutrosophic Sets Syst20203511 Tang Y (2013) Deep learning using linear support vector machines. arXiv preprint arXiv:1306.0239 Wu C, Chen X, Cai Y, Zhou X, Xu S, Huang H, Zhang L, Zhou X, Du C, Zhang Y et al (2020) Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China. JAMA Intern Med HossainMSMuhammadGGuizaniNExplainable AI and mass surveillance system-based healthcare framework to combat covid-i9 like pandemicsIEEE Netw202034412613210.1109/MNET.011.2000458 Hayes TL, Kanan C (2020) Lifelong machine learning with deep streaming linear discriminant analysis. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pp 220–221 TuliSBasumataryNGillSSKahaniMAryaRCWanderGSBuyyaRHealthfog: an ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated IoT and fog computing environmentsFutur Gener Comput Syst202010418720010.1016/j.future.2019.10.043 Barstugan M, Ozkaya U, Ozturk S (2020) Coronavirus (covid-19) classification using CT images by machine learning methods. arXiv preprint arXiv:2003.09424 FriedmanJHGreedy function approximation: a gradient boosting machineAnn Stat20012911891232187332810.1214/aos/1013203451 QinBLiDIdentifying facemask-wearing condition using image super-resolution with classification network to prevent covid-19Res Sensors20202018523610.3390/s20185236 YildirimMCinarAA deep learning based hybrid approach for covid-19 disease detectionsTraitement du Signal202037346146810.18280/ts.370313 PedregosaFVaroquauxGGramfortAMichelVThirionBGriselOBlondelMPrettenhoferPWeissRDubourgVScikit-learn: machine learning in pythonJ Mach Learn Res2011122825283028543481280.68189 4166_CR32 4166_CR33 4166_CR12 4166_CR34 4166_CR35 S Tuli (4166_CR7) 2020; 104 4166_CR15 4166_CR16 N El-Rashidy (4166_CR13) 2020; 9 T Davenport (4166_CR5) 2019; 6 T Ozturk (4166_CR14) 2020; 121 MS Hossain (4166_CR19) 2020; 34 C Huang (4166_CR31) 2020; 395 L Breiman (4166_CR37) 2001; 45 M Loey (4166_CR10) 2020; 167 B Qin (4166_CR11) 2020; 20 4166_CR21 M Munir (4166_CR8) 2019; 19 4166_CR24 S Tuli (4166_CR2) 2019; 154 4166_CR25 4166_CR26 M Egmont-Petersen (4166_CR40) 1998; 11 A Kallel (4166_CR22) 2020; 51 4166_CR27 M Yildirim (4166_CR41) 2020; 37 4166_CR28 4166_CR29 A Kumar (4166_CR20) 2020; 115 I Yasser (4166_CR30) 2020; 35 N Tsiknakis (4166_CR17) 2020; 20 KP Murphy (4166_CR38) 2006; 18 O Debauche (4166_CR4) 2019; 160 JR Hageman (4166_CR9) 2020; 49 M Otoom (4166_CR18) 2020; 62 SR Safavian (4166_CR36) 1991; 21 F Pedregosa (4166_CR23) 2011; 12 4166_CR1 4166_CR3 4166_CR6 JH Friedman (4166_CR39) 2001; 29 4166_CR42
References_xml	– reference: YasserITwakolAEl-KhalekASamrahASalamaACovid-x: novel health-fog framework based on neutrosophic classifier for confrontation covid-19Neutrosophic Sets Syst20203511 – reference: PedregosaFVaroquauxGGramfortAMichelVThirionBGriselOBlondelMPrettenhoferPWeissRDubourgVScikit-learn: machine learning in pythonJ Mach Learn Res2011122825283028543481280.68189 – reference: OzturkTTaloMYildirimEABalogluUBYildirimOAcharyaURAutomated detection of covid-19 cases using deep neural networks with x-ray imagesComput Biol Med202012110379210.1016/j.compbiomed.2020.103792 – reference: Montiel J, Halford M, Mastelini SM, Bolmier G, Sourty R, Vaysse R, Zouitine A, Gomes HM, Read J, Abdessalem T et al (2020) River: machine learning for streaming data in python. arXiv preprint arXiv:2012.04740 – reference: TuliSBasumataryNGillSSKahaniMAryaRCWanderGSBuyyaRHealthfog: an ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated IoT and fog computing environmentsFutur Gener Comput Syst202010418720010.1016/j.future.2019.10.043 – reference: KumarASharmaKSinghHNaugriyaSGGillSSBuyyaRA drone-based networked system and methods for combating coronavirus disease (covid-19) pandemicFuture Gener Comput Syst202011511910.1016/j.future.2020.08.046 – reference: Chowdary GJ, Punn NS, Sonbhadra SK, Agarwal S (2020) Face mask detection using transfer learning of inceptionv3. arXiv preprint arXiv:2009.08369 – reference: Tartaglione E, Barbano CA, Berzovini C, Calandri M, Grangetto M (2020) Unveiling covid-19 from chest X-ray with deep learning: a hurdles race with small data. arXiv preprint arXiv:2004.05405 – reference: El-RashidyNEl-SappaghSIslamSEl-BakryHMAbdelrazekSEnd-to-end deep learning framework for coronavirus (covid-19) detection and monitoringElectronics202099143910.3390/electronics9091439 – reference: Nematzadeh Z, Ibrahim R, Selamat A (2015) Comparative studies on breast cancer classifications with k-fold cross validations using machine learning techniques. In: 2015 10th Asian Control Conference (ASCC). IEEE, pp 1–6 – reference: Barstugan M, Ozkaya U, Ozturk S (2020) Coronavirus (covid-19) classification using CT images by machine learning methods. arXiv preprint arXiv:2003.09424 – reference: Read J, Bifet A, Pfahringer B, Holmes G (2012) Batch-incremental versus instance-incremental learning in dynamic and evolving data. In: International symposium on intelligent data analysis. Springer, pp 313–323 – reference: DebaucheOMahmoudiSMannebackPAssilaAFog IoT for health: a new architecture for patients and elderly monitoringProcedia Comput Sci201916028929710.1016/j.procs.2019.11.087 – reference: HuangCWangYLiXRenLZhaoJHuYZhangLFanGXuJGuXClinical features of patients infected with 2019 novel coronavirus in Wuhan, ChinaLancet20203951022349750610.1016/S0140-6736(20)30183-5 – reference: DavenportTKalakotaRThe potential for artificial intelligence in healthcareFuture Healthc J2019629410.7861/futurehosp.6-2-94 – reference: TsiknakisNTrivizakisEVassalouEEPapadakisGZSpandidosDATsatsakisASánchez-GarcíaJLópez-GonzálezRPapanikolaouNKarantanasAHInterpretable artificial intelligence framework for covid-19 screening on chest X-raysExp Ther Med202020272773510.3892/etm.2020.8797 – reference: Hayes TL, Kanan C (2020) Lifelong machine learning with deep streaming linear discriminant analysis. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pp 220–221 – reference: Rojas R et al (2009) Adaboost and the super bowl of classifiers a tutorial introduction to adaptive boosting, Freie University, Berlin, Tech. Rep – reference: Pathak Y, Shukla PK, Tiwari A, Stalin S, Singh S, Shukla PK (2020) Deep transfer learning based classification model for covid-19 disease. IRBM – reference: Gia TN, Jiang M, Sarker VK, Rahmani AM, Westerlund T, Liljeberg P, Tenhunen H (2017) Low-cost fog-assisted health-care IoT system with energy-efficient sensor nodes. In: 2017 13th international wireless communications and mobile computing conference (IWCMC). IEEE, pp 1765–1770 – reference: Tang Y (2013) Deep learning using linear support vector machines. arXiv preprint arXiv:1306.0239 – reference: Jagirdar NM (2018) Online machine learning algorithms review and comparison in healthcare, Ph.D. dissertation, University of Tennessee, The address of the publisher, 12, an optional note – reference: FriedmanJHGreedy function approximation: a gradient boosting machineAnn Stat20012911891232187332810.1214/aos/1013203451 – reference: HagemanJRThe coronavirus disease 2019 (covid-19)Pediatr Ann2020493e99e100405959510.3928/19382359-20200219-01 – reference: SafavianSRLandgrebeDA survey of decision tree classifier methodologyIEEE Trans Syst Man Cybern1991213660674113073110.1109/21.97458 – reference: YildirimMCinarAA deep learning based hybrid approach for covid-19 disease detectionsTraitement du Signal202037346146810.18280/ts.370313 – reference: MunirMSiddiquiSAChatthaMADengelAAhmedSFusead: unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning modelsSensors20191911245110.3390/s19112451 – reference: Wu C, Chen X, Cai Y, Zhou X, Xu S, Huang H, Zhang L, Zhou X, Du C, Zhang Y et al (2020) Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China. JAMA Intern Med – reference: MurphyKPNaive bayes classifiersUniv B C20061860 – reference: TuliSMahmudRTuliSBuyyaRFogbus: a blockchain-based lightweight framework for edge and fog computingJ Syst Softw2019154223610.1016/j.jss.2019.04.050 – reference: QinBLiDIdentifying facemask-wearing condition using image super-resolution with classification network to prevent covid-19Res Sensors20202018523610.3390/s20185236 – reference: Stojmenovic I (2014) Fog computing: A cloud to the ground support for smart things and machine-to-machine networks. In: 2014 Australasian telecommunication networks and applications conference (ATNAC). IEEE, pp 117–122 – reference: LoeyMManogaranGTahaMHNKhalifaNEMA hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the covid-19 pandemicMeasurement202016710828810.1016/j.measurement.2020.108288 – reference: HossainMSMuhammadGGuizaniNExplainable AI and mass surveillance system-based healthcare framework to combat covid-i9 like pandemicsIEEE Netw202034412613210.1109/MNET.011.2000458 – reference: Zwattendorfer B, Stranacher K, Tauber A (2013) Towards a federated identity as a service model. In: International conference on electronic government and the information systems perspective. Springer, pp 43–57 – reference: Singh A, Kaur A, Dhillon A, Ahuja S, Vohra H (2021) Software system to predict the infection in covid-19 patients using deep learning and web of things. Softw Pract Exp https://doi.org/10.1002/spe.3011 – reference: OtoomMOtoumNAlzubaidiMAEtoomYBanihaniRAn IoT-based framework for early identification and monitoring of covid-19 casesBiomed Signal Process Control20206210214910.1016/j.bspc.2020.102149 – reference: Anguita D, Ghelardoni L, Ghio A, Oneto L, Ridella S (2012) The ’k’ in k-fold cross validation. In: ESANN – reference: BreimanLRandom forestsMach Learn200145153210.1023/A:1010933404324 – reference: Mitchell TM et al (1997) Machine learning. McGraw-Hill, New York, Tech. Rep. – reference: Egmont-PetersenMTalmonJLHasmanAAmbergenAWAssessing the importance of features for multi-layer perceptronsNeural Netw199811462363510.1016/S0893-6080(98)00031-8 – reference: KallelARekikMKhemakhemMIoT-fog-cloud based architecture for smart systems: prototypes of autism and covid-19 monitoring systemsSoftw Pract Exp20205119111610.1002/spe.2924 – volume: 35 start-page: 1 issue: 1 year: 2020 ident: 4166_CR30 publication-title: Neutrosophic Sets Syst – volume: 104 start-page: 187 year: 2020 ident: 4166_CR7 publication-title: Futur Gener Comput Syst doi: 10.1016/j.future.2019.10.043 – volume: 121 start-page: 103792 year: 2020 ident: 4166_CR14 publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2020.103792 – ident: 4166_CR25 doi: 10.1002/spe.3011 – ident: 4166_CR29 doi: 10.3390/ijerph17186933 – ident: 4166_CR26 – ident: 4166_CR24 – volume: 19 start-page: 2451 issue: 11 year: 2019 ident: 4166_CR8 publication-title: Sensors doi: 10.3390/s19112451 – volume: 51 start-page: 91 issue: 1 year: 2020 ident: 4166_CR22 publication-title: Softw Pract Exp doi: 10.1002/spe.2924 – volume: 6 start-page: 94 issue: 2 year: 2019 ident: 4166_CR5 publication-title: Future Healthc J doi: 10.7861/futurehosp.6-2-94 – ident: 4166_CR16 doi: 10.1016/j.irbm.2020.05.003 – ident: 4166_CR32 doi: 10.1001/jamainternmed.2020.0994 – ident: 4166_CR34 – volume: 45 start-page: 5 issue: 1 year: 2001 ident: 4166_CR37 publication-title: Mach Learn doi: 10.1023/A:1010933404324 – volume: 167 start-page: 108288 year: 2020 ident: 4166_CR10 publication-title: Measurement doi: 10.1016/j.measurement.2020.108288 – volume: 21 start-page: 660 issue: 3 year: 1991 ident: 4166_CR36 publication-title: IEEE Trans Syst Man Cybern doi: 10.1109/21.97458 – volume: 37 start-page: 461 issue: 3 year: 2020 ident: 4166_CR41 publication-title: Traitement du Signal doi: 10.18280/ts.370313 – volume: 18 start-page: 60 year: 2006 ident: 4166_CR38 publication-title: Univ B C – ident: 4166_CR3 – ident: 4166_CR15 – ident: 4166_CR21 doi: 10.1007/978-3-642-34156-4_29 – volume: 34 start-page: 126 issue: 4 year: 2020 ident: 4166_CR19 publication-title: IEEE Netw doi: 10.1109/MNET.011.2000458 – ident: 4166_CR27 – ident: 4166_CR28 doi: 10.1109/CVPRW50498.2020.00118 – volume: 395 start-page: 497 issue: 10223 year: 2020 ident: 4166_CR31 publication-title: Lancet doi: 10.1016/S0140-6736(20)30183-5 – ident: 4166_CR33 doi: 10.1007/978-3-642-40160-2_5 – volume: 9 start-page: 1439 issue: 9 year: 2020 ident: 4166_CR13 publication-title: Electronics doi: 10.3390/electronics9091439 – volume: 115 start-page: 1 year: 2020 ident: 4166_CR20 publication-title: Future Gener Comput Syst doi: 10.1016/j.future.2020.08.046 – ident: 4166_CR1 doi: 10.1109/ATNAC.2014.7020884 – volume: 160 start-page: 289 year: 2019 ident: 4166_CR4 publication-title: Procedia Comput Sci doi: 10.1016/j.procs.2019.11.087 – volume: 49 start-page: e99 issue: 3 year: 2020 ident: 4166_CR9 publication-title: Pediatr Ann doi: 10.3928/19382359-20200219-01 – volume: 12 start-page: 2825 year: 2011 ident: 4166_CR23 publication-title: J Mach Learn Res – volume: 20 start-page: 727 issue: 2 year: 2020 ident: 4166_CR17 publication-title: Exp Ther Med doi: 10.3892/etm.2020.8797 – ident: 4166_CR42 – volume: 154 start-page: 22 year: 2019 ident: 4166_CR2 publication-title: J Syst Softw doi: 10.1016/j.jss.2019.04.050 – volume: 62 start-page: 102149 year: 2020 ident: 4166_CR18 publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2020.102149 – volume: 11 start-page: 623 issue: 4 year: 1998 ident: 4166_CR40 publication-title: Neural Netw doi: 10.1016/S0893-6080(98)00031-8 – volume: 20 start-page: 5236 issue: 18 year: 2020 ident: 4166_CR11 publication-title: Res Sensors doi: 10.3390/s20185236 – ident: 4166_CR12 – ident: 4166_CR6 doi: 10.1109/ASCC.2015.7244654 – ident: 4166_CR35 – volume: 29 start-page: 1189 year: 2001 ident: 4166_CR39 publication-title: Ann Stat doi: 10.1214/aos/1013203451
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Snippet	The COronaVIrus Disease 2019 (COVID-19) pandemic is unfortunately highly transmissible across the people. In order to detect and track the suspected COVID-19...
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SubjectTerms	Algorithms Compilers Computer Science Coronaviruses COVID-19 Data processing Decision making Electronic devices Internet of Things Interpreters Machine learning Network latency Pandemics Prediction models Processor Architectures Prognosis Programming Languages Quality of service architectures Response time (computers) Viral diseases
Title	Hybrid-based framework for COVID-19 prediction via federated machine learning models
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