{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T00:34:56Z","timestamp":1767141296376,"version":"build-2238731810"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T00:00:00Z","timestamp":1697500800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T00:00:00Z","timestamp":1697500800000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-17330-5","type":"journal-article","created":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T03:02:10Z","timestamp":1697511730000},"page":"44047-44066","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Analyzing distributed Spark MLlib regression algorithms for accuracy, execution efficiency and scalability using best subset selection approach"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9975-0906","authenticated-orcid":false,"given":"Piyush","family":"Sewal","sequence":"first","affiliation":[]},{"given":"Hari","family":"Singh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,17]]},"reference":[{"issue":"September","key":"17330_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3233\/978-1-61499-814-3-1","volume":"29","author":"H Singh","year":"2018","unstructured":"Singh H, Vasuja R, Sharma R (2018) A Survey of Diversified Domain of Big Data Technologies. Adv Parallel Comput 29(September):1\u201327. https:\/\/doi.org\/10.3233\/978-1-61499-814-3-1","journal-title":"Adv Parallel Comput"},{"key":"17330_CR2","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.future.2017.03.028","volume":"73","author":"H Singh","year":"2017","unstructured":"Singh H, Bawa S (2017) A MapReduce-based scalable discovery and indexing of structured big data. Futur Gener Comput Syst 73:32\u201343. https:\/\/doi.org\/10.1016\/j.future.2017.03.028","journal-title":"Futur Gener Comput Syst"},{"key":"17330_CR3","doi-asserted-by":"publisher","first-page":"101517","DOI":"10.1016\/j.tele.2020.101517","volume":"57","author":"S BazzazAbkenar","year":"2021","unstructured":"BazzazAbkenar S, HaghiKashani M, Mahdipour E, Jameii SM (2021) Big data analytics meets social media A systematic review of techniques, open issues, and future directions. Telemat Informatics 57:101517. https:\/\/doi.org\/10.1016\/j.tele.2020.101517","journal-title":"Telemat Informatics"},{"issue":"March","key":"17330_CR4","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.ijmedinf.2018.03.013","volume":"114","author":"N Mehta","year":"2018","unstructured":"Mehta N, Pandit A (2018) Concurrence of big data analytics and healthcare: A systematic review. Int J Med Inform 114(March):57\u201365. https:\/\/doi.org\/10.1016\/j.ijmedinf.2018.03.013","journal-title":"Int J Med Inform"},{"key":"17330_CR5","doi-asserted-by":"publisher","unstructured":"Le TM, Liaw SY (2017) Effects of pros and cons of applying big data analytics to consumers\u2019 responses in an e-commerce context. Sustain 9(5). https:\/\/doi.org\/10.3390\/su9050798","DOI":"10.3390\/su9050798"},{"key":"17330_CR6","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.knosys.2014.11.007","volume":"79","author":"R Agerri","year":"2015","unstructured":"Agerri R, Artola X, Beloki Z, Rigau G, Soroa A (2015) Big data for Natural Language Processing: A streaming approach. Knowledge-Based Syst 79:36\u201342. https:\/\/doi.org\/10.1016\/j.knosys.2014.11.007","journal-title":"Knowledge-Based Syst"},{"key":"17330_CR7","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1007\/978-3-319-25013-7","volume":"3","author":"M Janssen","year":"2015","unstructured":"Janssen M et al (2015) Open and Big Data Management and Innovation. Lect Notes Comput Sci 3:200\u2013211. https:\/\/doi.org\/10.1007\/978-3-319-25013-7","journal-title":"Lect Notes Comput Sci"},{"key":"17330_CR8","doi-asserted-by":"publisher","unstructured":"Sewal P, Singh H (2021) A Critical Analysis of Apache Hadoop and Spark for Big Data Processing, in 2021 6th International Conference on Signal Processing, Computing and Control (ISPCC). pp. 308\u2013313. https:\/\/doi.org\/10.1109\/ISPCC53510.2021.9609518","DOI":"10.1109\/ISPCC53510.2021.9609518"},{"key":"17330_CR9","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1109\/PDGC56933.2022.10053356","volume-title":"PDGC 2022 - 2022 7th Int. Conf. Parallel, Distrib. Grid Comput.","author":"P Sewal","year":"2022","unstructured":"Sewal P, Singh H (2022) A Machine Learning Approach for Predicting Execution Statistics of Spark Application. PDGC 2022 - 2022 7th Int. Conf. Parallel, Distrib. Grid Comput. pp 331\u2013336. https:\/\/doi.org\/10.1109\/PDGC56933.2022.10053356"},{"issue":"8","key":"17330_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/gigascience\/giy098","volume":"7","author":"R Guo","year":"2018","unstructured":"Guo R, Zhao Y, Zou Q, Fang X, Peng S (2018) Bioinformatics applications on Apache Spark. Gigascience 7(8):1\u201310. https:\/\/doi.org\/10.1093\/gigascience\/giy098","journal-title":"Gigascience"},{"issue":"2","key":"17330_CR11","doi-asserted-by":"publisher","first-page":"e13368","DOI":"10.1016\/j.heliyon.2023.e13368","volume":"9","author":"A Manconi","year":"2023","unstructured":"Manconi A, Gnocchi M, Milanesi L, Marullo O, Armano G (2023) Framing Apache Spark in life sciences. Heliyon 9(2):e13368. https:\/\/doi.org\/10.1016\/j.heliyon.2023.e13368","journal-title":"Heliyon"},{"issue":"7","key":"17330_CR12","doi-asserted-by":"publisher","first-page":"e1011272","DOI":"10.1371\/journal.pcbi.1011272","volume":"19","author":"D Chicco","year":"2023","unstructured":"Chicco D, Ferraro Petrillo U, Cattaneo G (2023) Ten quick tips for bioinformatics analyses using an Apache Spark distributed computing environment. PLoS Comput Biol 19(7):e1011272. https:\/\/doi.org\/10.1371\/journal.pcbi.1011272","journal-title":"PLoS Comput Biol"},{"key":"17330_CR13","doi-asserted-by":"publisher","unstructured":"Arpaci I, Al-Emran M, Al-Sharafi MA, Marques G (2021) Emerging Technologies During the Era of COVID-19 Pandemic. Studies in Systems, Decision and Control, 348. [Online]. Available:\u00a0https:\/\/doi.org\/10.1007\/978-3-030-67716-9","DOI":"10.1007\/978-3-030-67716-9"},{"key":"17330_CR14","doi-asserted-by":"publisher","unstructured":"Kamalov F, Cherukuri AK, Sulieman H, Thabtah F, Hossain A (2022) Machine learning applications for COVID-19: a state-of-the-art review, in Data Science for Genomics, Academic Press. pp. 277\u2013289.\u00a0https:\/\/doi.org\/10.1016\/B978-0-323-98352-5.00010-0","DOI":"10.1016\/B978-0-323-98352-5.00010-0"},{"key":"17330_CR15","unstructured":"Zaharia M et al. (2012) Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing, in Proceedings of NSDI 2012: 9th USENIX Symposium on Networked Systems Design and Implementation. pp. 15\u201328"},{"key":"17330_CR16","doi-asserted-by":"publisher","unstructured":"Han S, Choi W, Muwafiq R, Nah Y (2017) Impact of Memory Size on Bigdata Processing based on Hadoop and Spark, in Proceedings of the International Conference on Research in Adaptive and Convergent Systems. 2017:275\u2013280.\u00a0https:\/\/doi.org\/10.1145\/3129676.3129688","DOI":"10.1145\/3129676.3129688"},{"issue":"1","key":"17330_CR17","doi-asserted-by":"publisher","first-page":"8","DOI":"10.5120\/19788-0531","volume":"113","author":"S Gopalani","year":"2015","unstructured":"Gopalani S, Arora R (2015) Comparing Apache Spark and Map Reduce with Performance Analysis using K-Means. Int J Comput Appl 113(1):8\u201311. https:\/\/doi.org\/10.5120\/19788-0531","journal-title":"Int J Comput Appl"},{"issue":"4","key":"17330_CR18","first-page":"23","volume":"5","author":"T Sharma","year":"2016","unstructured":"Sharma T, Shokeen DV, Mathur DS (2016) Multiple K Means++ Clustering of Satellite Image Using Hadoop MapReduce and Spark. Int J Adv Stud Comput Sci Eng 5(4):23\u201331 (Available: http:\/\/arxiv.org\/abs\/1605.01802)","journal-title":"Int J Adv Stud Comput Sci Eng"},{"key":"17330_CR19","doi-asserted-by":"publisher","unstructured":"Lin X, Wang P, Wu B (2013) Log analysis in cloud computing environment with Hadoop and Spark, Proc. 2013 5th IEEE Int. Conf. Broadband Netw. Multimed. Technol. IEEE IC-BNMT. pp. 273\u2013276.\u00a0https:\/\/doi.org\/10.1109\/ICBNMT.2013.6823956","DOI":"10.1109\/ICBNMT.2013.6823956"},{"key":"17330_CR20","doi-asserted-by":"publisher","unstructured":"Gu L, Li H (2013) Memory or Time: Performance Evaluation for Iterative Operation on Hadoop and Spark, in 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing. pp. 721\u2013727.\u00a0https:\/\/doi.org\/10.1109\/HPCC.and.EUC.2013.106","DOI":"10.1109\/HPCC.and.EUC.2013.106"},{"issue":"2","key":"17330_CR21","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1007\/s11227-020-03328-5","volume":"77","author":"A Mostafaeipour","year":"2021","unstructured":"Mostafaeipour A, Jahangard Rafsanjani A, Ahmadi M, ArockiaDhanraj J (2021) Investigating the performance of Hadoop and Spark platforms on machine learning algorithms. J Supercomput 77(2):1273\u20131300. https:\/\/doi.org\/10.1007\/s11227-020-03328-5","journal-title":"J Supercomput"},{"key":"17330_CR22","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1007\/978-3-030-79357-9_55","volume":"76","author":"S Melenli","year":"2021","unstructured":"Melenli S, Topkaya A (2021) Real-Time Maintaining of Social Distance in Covid-19 Environment Using Image Processing and Big Data. Lect Notes Data Eng Commun Technol 76:578\u2013589. https:\/\/doi.org\/10.1007\/978-3-030-79357-9_55","journal-title":"Lect Notes Data Eng Commun Technol"},{"issue":"1","key":"17330_CR23","doi-asserted-by":"publisher","first-page":"2021","DOI":"10.3390\/bdcc5010012","volume":"5","author":"O Azeroual","year":"2021","unstructured":"Azeroual O, Fabre R (2021) Processing big data with apache hadoop in the current challenging era of COVID-19. Big Data Cogn. Comput. 5(1):2021. https:\/\/doi.org\/10.3390\/bdcc5010012","journal-title":"Big Data Cogn. Comput."},{"key":"17330_CR24","doi-asserted-by":"publisher","unstructured":"\u00c7akan\u00a0S (2020) Dynamic analysis of a mathematical model with health care capacity for COVID-19 pandemic. Chaos, Solitons and Fractals 139.\u00a0https:\/\/doi.org\/10.1016\/j.chaos.2020.110033","DOI":"10.1016\/j.chaos.2020.110033"},{"key":"17330_CR25","doi-asserted-by":"publisher","first-page":"110023","DOI":"10.1016\/j.chaos.2020.110023","volume":"138","author":"A Singhal","year":"2020","unstructured":"Singhal A, Singh P, Lall B, Joshi SD (2020) Modeling and prediction of COVID-19 pandemic using Gaussian mixture model. Chaos, Solitons Fractals 138:110023. https:\/\/doi.org\/10.1016\/j.chaos.2020.110023","journal-title":"Chaos, Solitons Fractals"},{"issue":"9","key":"17330_CR26","doi-asserted-by":"publisher","first-page":"1603","DOI":"10.1002\/jmv.25850","volume":"92","author":"N AL-Rousan","year":"2020","unstructured":"AL-Rousan N, AL-Najjar H (2020) Data analysis of coronavirus COVID-19 epidemic in South Korea based on recovered and death cases. J Med Virol 92(9):1603\u20131608. https:\/\/doi.org\/10.1002\/jmv.25850","journal-title":"J Med Virol"},{"issue":"1","key":"17330_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-78084-w","volume":"10","author":"J Sun","year":"2020","unstructured":"Sun J et al (2020) Forecasting the long-term trend of COVID-19 epidemic using a dynamic model. Sci Rep 10(1):1\u201310. https:\/\/doi.org\/10.1038\/s41598-020-78084-w","journal-title":"Sci Rep"},{"issue":"1 January","key":"17330_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0259958","volume":"17","author":"K Prieto","year":"2022","unstructured":"Prieto K (2022) Current forecast of COVID-19 in Mexico: A Bayesian and machine learning approaches. PLoS One 17(1 January):1\u201321. https:\/\/doi.org\/10.1371\/journal.pone.0259958","journal-title":"PLoS One"},{"issue":"4","key":"17330_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-020-00209-9","volume":"1","author":"GR Shinde","year":"2020","unstructured":"Shinde GR, Kalamkar AB, Mahalle PN, Dey N, Chaki J, Hassanien AE (2020) Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art. SN Comput Sci 1(4):1\u201315. https:\/\/doi.org\/10.1007\/s42979-020-00209-9","journal-title":"SN Comput Sci"},{"issue":"8","key":"17330_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-020-01597-4","volume":"44","author":"D Brinati","year":"2020","unstructured":"Brinati D, Campagner A, Ferrari D, Locatelli M, Banfi G, Cabitza F (Aug.2020) Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study. J Med Syst 44(8):1\u201312. https:\/\/doi.org\/10.1007\/s10916-020-01597-4","journal-title":"J Med Syst"},{"issue":"8","key":"17330_CR31","doi-asserted-by":"publisher","first-page":"1435","DOI":"10.1007\/s11739-020-02475-0","volume":"15","author":"D Assaf","year":"2020","unstructured":"Assaf D et al (2020) Utilization of machine-learning models to accurately predict the risk for critical COVID-19. Intern Emerg Med 15(8):1435\u20131443. https:\/\/doi.org\/10.1007\/s11739-020-02475-0","journal-title":"Intern Emerg Med"},{"key":"17330_CR32","doi-asserted-by":"publisher","unstructured":"Magdon-Ismail M (202) Machine Learning the Phenomenology of COVID-19 From Early Infection Dynamics. pp. 1\u201316. https:\/\/doi.org\/10.48550\/arXiv.2003.07602","DOI":"10.48550\/arXiv.2003.07602"},{"issue":"December 2012","key":"17330_CR33","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1016\/j.proeng.2012.09.545","volume":"48","author":"E Ostertagov\u00e1","year":"2012","unstructured":"Ostertagov\u00e1 E (2012) Modelling using polynomial regression. Procedia Eng 48(December 2012):500\u2013506. https:\/\/doi.org\/10.1016\/j.proeng.2012.09.545","journal-title":"Procedia Eng"},{"key":"17330_CR34","doi-asserted-by":"publisher","first-page":"107946","DOI":"10.1016\/j.asoc.2021.107946","volume":"113","author":"S Cui","year":"2021","unstructured":"Cui S, Wang Y, Wang D, Sai Q, Huang Z, Cheng TCE (2021) A two-layer nested heterogeneous ensemble learning predictive method for COVID-19 mortality. Appl Soft Comput 113:107946. https:\/\/doi.org\/10.1016\/j.asoc.2021.107946","journal-title":"Appl Soft Comput"},{"issue":"1","key":"17330_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00530-021-00798-2","volume":"28","author":"H Singh","year":"2021","unstructured":"Singh H, Bawa S (2021) Predicting COVID-19 statistics using machine learning regression model: Li-MuLi-Poly. Multimed Syst 28(1):1\u20138. https:\/\/doi.org\/10.1007\/s00530-021-00798-2","journal-title":"Multimed Syst"},{"key":"17330_CR36","doi-asserted-by":"publisher","unstructured":"Kwekha-Rashid AS, Abduljabbar HN, Alhayani B (2021) Coronavirus disease (COVID-19) cases analysis using machine-learning applications, Appl. Nanosci., no. 0123456789.\u00a0https:\/\/doi.org\/10.1007\/s13204-021-01868-7","DOI":"10.1007\/s13204-021-01868-7"},{"issue":"4","key":"17330_CR37","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/j.dsx.2020.03.017","volume":"14","author":"S Ghosal","year":"2020","unstructured":"Ghosal S, Sengupta S, Majumder M, Sinha B (2020) Diabetes & Metabolic Syndrome\u202f: Clinical Research & Reviews Linear Regression Analysis to predict the number of deaths in India due to SARS-CoV-2 at 6 weeks from day 0 (100 cases - March 14th. Diabetes Metab Syndr Clin Res Rev 14(4):311\u2013315. https:\/\/doi.org\/10.1016\/j.dsx.2020.03.017","journal-title":"Diabetes Metab Syndr Clin Res Rev"},{"issue":"4","key":"17330_CR38","doi-asserted-by":"publisher","first-page":"1321","DOI":"10.1007\/s41870-020-00484-y","volume":"12","author":"RS Yadav","year":"2020","unstructured":"Yadav RS (2020) Data analysis of COVID-2019 epidemic using machine learning methods: a case study of India. Int J Inf Technol 12(4):1321\u20131330. https:\/\/doi.org\/10.1007\/s41870-020-00484-y","journal-title":"Int J Inf Technol"},{"issue":"4","key":"17330_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-020-00216-w","volume":"1","author":"LJ Muhammad","year":"2020","unstructured":"Muhammad LJ, Islam MM, Usman SS, Ayon SI (2020) Predictive Data Mining Models for Novel Coronavirus (COVID-19) Infected Patients\u2019 Recovery. SN Comput Sci 1(4):1\u20137. https:\/\/doi.org\/10.1007\/s42979-020-00216-w","journal-title":"SN Comput Sci"},{"key":"17330_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.chaos.2020.110055","volume":"139","author":"Y Peng","year":"2020","unstructured":"Peng Y, Nagata MH (2020) An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data. Chaos, Solitons Fractals 139:1\u201315. https:\/\/doi.org\/10.1016\/j.chaos.2020.110055","journal-title":"Chaos, Solitons Fractals"},{"issue":"1","key":"17330_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-020-00394-7","volume":"2","author":"LJ Muhammad","year":"2021","unstructured":"Muhammad LJ, Algehyne EA, Usman SS, Ahmad A, Chakraborty C, Mohammed IA (2021) Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset. SN Comput Sci 2(1):1\u201313. https:\/\/doi.org\/10.1007\/s42979-020-00394-7","journal-title":"SN Comput Sci"},{"issue":"4","key":"17330_CR42","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1007\/s41403-020-00165-z","volume":"5","author":"V Kumar","year":"2020","unstructured":"Kumar V, Unnati S (2020) Modeling and Forecasting of COVID - 19 Growth Curve in India. Trans Indian Natl Acad Eng 5(4):697\u2013710. https:\/\/doi.org\/10.1007\/s41403-020-00165-z","journal-title":"Trans Indian Natl Acad Eng"},{"issue":"3","key":"17330_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0230405","volume":"15","author":"C Anastassopoulou","year":"2020","unstructured":"Anastassopoulou C, Russo L, Tsakris A, Siettos C (2020) Data-based analysis, modelling and forecasting of the COVID-19 outbreak. PLoS ONE 15(3):1\u201321. https:\/\/doi.org\/10.1371\/journal.pone.0230405","journal-title":"PLoS ONE"},{"key":"17330_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.chaos.2020.110046","volume":"139","author":"KN Nabi","year":"2020","unstructured":"Nabi KN (2020) Forecasting COVID-19 pandemic: A data-driven analysis. Chaos, Solitons Fractals 139:1\u201315. https:\/\/doi.org\/10.1016\/j.chaos.2020.110046","journal-title":"Chaos, Solitons Fractals"},{"issue":"1","key":"17330_CR45","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/s42600-021-00135-6","volume":"38","author":"J Nayak","year":"2022","unstructured":"Nayak J, Naik B, Dinesh P, Vakula K, Dash PB, Pelusi D (2022) Significance of deep learning for Covid-19: state-of-the-art review. Res Biomed Eng 38(1):243\u2013266. https:\/\/doi.org\/10.1007\/s42600-021-00135-6","journal-title":"Res Biomed Eng"},{"key":"17330_CR46","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.neucom.2022.09.005","volume":"511","author":"F Kamalov","year":"2022","unstructured":"Kamalov F, Rajab K, Cherukuri AK, Elnagar A, Safaraliev M (2022) Deep learning for Covid-19 forecasting: State-of-the-art review. Neurocomputing 511:142\u2013154. https:\/\/doi.org\/10.1016\/j.neucom.2022.09.005","journal-title":"Neurocomputing"},{"key":"17330_CR47","doi-asserted-by":"publisher","unstructured":"Assefi M, Behravesh E, Liu G, Tafti AP (2017) Big data machine learning using apache spark MLlib, in Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017, 2018:3492\u20133498. https:\/\/doi.org\/10.1109\/BigData.2017.8258338","DOI":"10.1109\/BigData.2017.8258338"},{"key":"17330_CR48","unstructured":"\u201cKaggle: Your Machine Learning and Data Science Community.\u201d https:\/\/www.kaggle.com\/. Accessed 23 March 2022"}],"updated-by":[{"DOI":"10.1007\/s11042-023-17827-z","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T00:00:00Z","timestamp":1706140800000}}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17330-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-17330-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17330-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T07:25:19Z","timestamp":1714375519000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-17330-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,17]]},"references-count":48,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["17330"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-17330-5","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,17]]},"assertion":[{"value":"8 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 August 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 September 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 January 2024","order":5,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":6,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s11042-023-17827-z","URL":"https:\/\/doi.org\/10.1007\/s11042-023-17827-z","order":8,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}