{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T14:59:31Z","timestamp":1770994771872,"version":"3.50.1"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T00:00:00Z","timestamp":1635206400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T00:00:00Z","timestamp":1635206400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100015832","name":"effat university","doi-asserted-by":"crossref","award":["UC#9\/29 April.2020\/7.1-22 (2)4"],"award-info":[{"award-number":["UC#9\/29 April.2020\/7.1-22 (2)4"]}],"id":[{"id":"10.13039\/501100015832","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s42979-021-00905-0","type":"journal-article","created":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T13:05:40Z","timestamp":1635253540000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Event-Driven Acquisition and Machine-Learning-Based Efficient Prediction of the Li-Ion Battery Capacity"],"prefix":"10.1007","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4268-3482","authenticated-orcid":false,"given":"Saeed","family":"Mian Qaisar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amal Essam ElDin","family":"AbdelGawad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kathiravan","family":"Srinivasan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,26]]},"reference":[{"key":"905_CR1","unstructured":"A global compact for sustainable development \u2013 business and the SDGs: acting responsibly and finding opportunities | UN Global Compact. UN Global Compact, 2015. https:\/\/www.unglobalcompact.org\/library\/2291. Accessed 24 Jan 2021."},{"key":"905_CR2","unstructured":"Global energy and CO2 emissions in 2020 \u2013 global energy review 2020 \u2013 analysis. IEA, 2020. https:\/\/www.iea.org\/reports\/global-energy-review-2020\/global-energy-and-co2-emissions-in-2020. Accessed 24 Jan 2021."},{"key":"905_CR3","unstructured":"Martin, \u201cClimate Change,\u201d United Nations Sustainable Development, 2019. https:\/\/www.un.org\/sustainabledevelopment\/climate-change\/. Accessed 23 Jan 2021."},{"key":"905_CR4","unstructured":"Martin, \u201cEnergy,\u201d United Nations Sustainable Development. https:\/\/www.un.org\/sustainabledevelopment\/energy\/. Accessed 24 Jan 2021."},{"key":"905_CR5","doi-asserted-by":"publisher","DOI":"10.3390\/en12163110","author":"AH Alattar","year":"2019","unstructured":"Alattar AH, et al. Performance enhancement of micro grid system with SMES storage system based on mine blast optimization algorithm. Energies. 2019. https:\/\/doi.org\/10.3390\/en12163110.","journal-title":"Energies"},{"key":"905_CR6","doi-asserted-by":"publisher","unstructured":"Xu D, Wang L, Yang J. Research on Li-ion battery management system. In: 2010 International conference on electrical and control engineering, Jun 2010, pp. 4106\u20139. https:\/\/doi.org\/10.1109\/iCECE.2010.998.","DOI":"10.1109\/iCECE.2010.998"},{"issue":"6","key":"905_CR7","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1016\/j.microrel.2012.12.004","volume":"53","author":"Q Miao","year":"2013","unstructured":"Miao Q, Xie L, Cui H, Liang W, Pecht M. Remaining useful life prediction of lithium-ion battery with unscented particle filter technique. Microelectron Reliab. 2013;53(6):805\u201310. https:\/\/doi.org\/10.1016\/j.microrel.2012.12.004.","journal-title":"Microelectron Reliab"},{"key":"905_CR8","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1016\/j.rser.2015.11.042","volume":"56","author":"M Berecibar","year":"2016","unstructured":"Berecibar M, Gandiaga I, Villarreal I, Omar N, Van Mierlo J, Van den Bossche P. Critical review of state of health estimation methods of Li-ion batteries for real applications. Renew Sustain Energy Rev. 2016;56:572\u201387. https:\/\/doi.org\/10.1016\/j.rser.2015.11.042.","journal-title":"Renew Sustain Energy Rev"},{"issue":"6","key":"905_CR9","doi-asserted-by":"publisher","first-page":"821","DOI":"10.1016\/j.microrel.2013.01.006","volume":"53","author":"B Long","year":"2013","unstructured":"Long B, Xian W, Jiang L, Liu Z. An improved autoregressive model by particle swarm optimization for prognostics of lithium-ion batteries. Microelectron Reliab. 2013;53(6):821\u201331. https:\/\/doi.org\/10.1016\/j.microrel.2013.01.006.","journal-title":"Microelectron Reliab"},{"key":"905_CR10","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1016\/j.microrel.2017.12.036","volume":"81","author":"H Zhang","year":"2018","unstructured":"Zhang H, Miao Q, Zhang X, Liu Z. An improved unscented particle filter approach for lithium-ion battery remaining useful life prediction. Microelectron Reliab. 2018;81:288\u201398. https:\/\/doi.org\/10.1016\/j.microrel.2017.12.036.","journal-title":"Microelectron Reliab"},{"issue":"5","key":"905_CR11","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/j.mattod.2014.10.040","volume":"18","author":"N Nitta","year":"2015","unstructured":"Nitta N, Wu F, Lee JT, Yushin G. Li-ion battery materials: present and future. Mater Today. 2015;18(5):252\u201364. https:\/\/doi.org\/10.1016\/j.mattod.2014.10.040.","journal-title":"Mater Today"},{"issue":"5","key":"905_CR12","doi-asserted-by":"publisher","first-page":"1803","DOI":"10.1016\/j.ymssp.2010.11.018","volume":"25","author":"JZ Sikorska","year":"2011","unstructured":"Sikorska JZ, Hodkiewicz M, Ma L. Prognostic modelling options for remaining useful life estimation by industry. Mech Syst Signal Process. 2011;25(5):1803\u201336. https:\/\/doi.org\/10.1016\/j.ymssp.2010.11.018.","journal-title":"Mech Syst Signal Process"},{"key":"905_CR13","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.apenergy.2014.04.077","volume":"129","author":"C Hu","year":"2014","unstructured":"Hu C, Jain G, Zhang P, Schmidt C, Gomadam P, Gorka T. Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery. Appl Energy. 2014;129:49\u201355. https:\/\/doi.org\/10.1016\/j.apenergy.2014.04.077.","journal-title":"Appl Energy"},{"key":"905_CR14","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1016\/j.apenergy.2015.08.119","volume":"159","author":"MA Patil","year":"2015","unstructured":"Patil MA, et al. A novel multistage support vector machine based approach for Li ion battery remaining useful life estimation. Appl Energy. 2015;159:285\u201397. https:\/\/doi.org\/10.1016\/j.apenergy.2015.08.119.","journal-title":"Appl Energy"},{"key":"905_CR15","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.jpowsour.2018.08.064","volume":"401","author":"M Lucu","year":"2018","unstructured":"Lucu M, Martinez-Laserna E, Gandiaga I, Camblong H. A critical review on self-adaptive Li-ion battery ageing models. J Power Sources. 2018;401:85\u2013101. https:\/\/doi.org\/10.1016\/j.jpowsour.2018.08.064.","journal-title":"J Power Sources"},{"key":"905_CR16","doi-asserted-by":"publisher","first-page":"106034","DOI":"10.1016\/j.cmpb.2021.106034","volume":"203","author":"SM Qaisar","year":"2021","unstructured":"Qaisar SM, Hussain SF. Effective epileptic seizure detection by using level-crossing EEG sampling sub-bands statistical features selection and machine learning for mobile healthcare. Comput Methods Programs Biomed. 2021;203:106034. https:\/\/doi.org\/10.1016\/j.cmpb.2021.106034.","journal-title":"Comput Methods Programs Biomed"},{"key":"905_CR17","unstructured":"Qaisar SM, Fesquet L, Renaudin M. Effective resolution of an adaptive rate ADC. 2009. Special-session."},{"issue":"1","key":"905_CR18","first-page":"35","volume":"9","author":"S Mina Qaisar","year":"2018","unstructured":"Mina Qaisar S, Sidiya D, Akbar M, Subasi A. An event-driven multiple objects surveillance system. Int J Electr Comput Eng Syst. 2018;9(1):35\u201344.","journal-title":"Int J Electr Comput Eng Syst"},{"key":"905_CR19","doi-asserted-by":"publisher","first-page":"106462","DOI":"10.1016\/j.compeleceng.2019.106462","volume":"79","author":"SM Qaisar","year":"2019","unstructured":"Qaisar SM. Efficient mobile systems based on adaptive rate signal processing. Comput Electr Eng. 2019;79:106462.","journal-title":"Comput Electr Eng"},{"key":"905_CR20","doi-asserted-by":"publisher","first-page":"50587","DOI":"10.1109\/ACCESS.2018.2858856","volume":"6","author":"L Ren","year":"2018","unstructured":"Ren L, Zhao L, Hong S, Zhao S, Wang H, Zhang L. Remaining useful life prediction for lithium-ion battery: a deep learning approach. IEEE Access. 2018;6:50587\u201398.","journal-title":"IEEE Access"},{"key":"905_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/B978-0-12-417029-2.00001-7","volume-title":"Methods in microbiology","author":"JS Hallinan","year":"2013","unstructured":"Hallinan JS. Chapter 1 - computational intelligence in the design of synthetic microbial genetic systems. In: Harwood C, Wipat A, editors. Methods in microbiology, vol. 40. Cambridge: Academic Press; 2013. p. 1\u201337. https:\/\/doi.org\/10.1016\/B978-0-12-417029-2.00001-7."},{"key":"905_CR22","volume-title":"Applications of pattern recognition","author":"K-S Fu","year":"2019","unstructured":"Fu K-S. Applications of pattern recognition. Boca Raton, Florida, US: CRC Press; 2019."},{"issue":"12","key":"905_CR23","doi-asserted-by":"publisher","first-page":"1955","DOI":"10.1016\/j.asr.2007.07.020","volume":"41","author":"Y Zhao","year":"2008","unstructured":"Zhao Y, Zhang Y. Comparison of decision tree methods for finding active objects. Adv Space Res. 2008;41(12):1955\u20139. https:\/\/doi.org\/10.1016\/j.asr.2007.07.020.","journal-title":"Adv Space Res"},{"key":"905_CR24","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/B978-0-12-401690-3.00003-2","volume-title":"Multi-asset risk modeling","author":"J Mun","year":"2014","unstructured":"Mun J. Chapter 3 - a primer on quantitative risk analysis. In: Glantz M, Kissell R, editors. Multi-asset risk modeling. San Diego: Academic Press; 2014. p. 63\u2013118. https:\/\/doi.org\/10.1016\/B978-0-12-401690-3.00003-2."},{"key":"905_CR25","unstructured":"Zavarella L. how to better evaluate the goodness-of-fit of regressions. Medium, Feb. 05, 2019. https:\/\/medium.com\/microsoftazure\/how-to-better-evaluate-the-goodness-of-fit-of-regressions-990dbf1c0091. Accessed 24 Feb 2021."},{"issue":"21","key":"905_CR26","doi-asserted-by":"publisher","first-page":"5600","DOI":"10.3390\/en13215600","volume":"13","author":"S Mian Qaisar","year":"2020","unstructured":"Mian Qaisar S. Event-driven coulomb counting for effective online approximation of Li-ion battery state of charge. Energies. 2020;13(21):5600.","journal-title":"Energies"},{"issue":"4","key":"905_CR27","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1049\/htl.2019.0116","volume":"7","author":"SM Qaisar","year":"2020","unstructured":"Qaisar SM. Baseline wander and power-line interference elimination of ECG signals using efficient signal-piloted filtering. Healthc Technol Lett. 2020;7(4):114\u20138.","journal-title":"Healthc Technol Lett"},{"issue":"8","key":"905_CR28","doi-asserted-by":"publisher","first-page":"2252","DOI":"10.3390\/s20082252","volume":"20","author":"S Mian Qaisar","year":"2020","unstructured":"Mian Qaisar S, Fawad Hussain S. Arrhythmia diagnosis by using level-crossing ECG sampling and sub-bands features extraction for mobile healthcare. Sensors. 2020;20(8):2252.","journal-title":"Sensors"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-021-00905-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-021-00905-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-021-00905-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T09:53:04Z","timestamp":1744192384000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-021-00905-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,26]]},"references-count":28,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["905"],"URL":"https:\/\/doi.org\/10.1007\/s42979-021-00905-0","relation":{},"ISSN":["2662-995X","2661-8907"],"issn-type":[{"value":"2662-995X","type":"print"},{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,26]]},"assertion":[{"value":"21 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 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"}}],"article-number":"15"}}