{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T07:15:45Z","timestamp":1742973345926,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031709050"},{"type":"electronic","value":"9783031709067"}],"license":[{"start":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T00:00:00Z","timestamp":1729209600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T00:00:00Z","timestamp":1729209600000},"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":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-70906-7_17","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T09:02:51Z","timestamp":1729155771000},"page":"195-210","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Estimating the Concrete Compressive Strength of Regression Model for Machine Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8530-7711","authenticated-orcid":false,"given":"Anagha","family":"Vaidya","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7146-6049","authenticated-orcid":false,"given":"Pranjal","family":"Vaidya","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0223-8576","authenticated-orcid":false,"given":"Sarika","family":"Sharma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,18]]},"reference":[{"issue":"1","key":"17_CR1","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1353\/ks.2023.a908625","volume":"47","author":"J Cha","year":"2023","unstructured":"Cha, J.: Big data studies: the humanities in uncharted waters. Korean Stud. 47(1), 274\u2013299 (2023)","journal-title":"Korean Stud."},{"key":"17_CR2","doi-asserted-by":"crossref","unstructured":"Rong, S., Bao-Wen, Z.: The research of regression model in machine learning field. In: MATEC Web of Conferences, vol. 176, p. 01033. EDP Sciences (2018)","DOI":"10.1051\/matecconf\/201817601033"},{"key":"17_CR3","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","volume":"2","author":"IH Sarker","year":"2021","unstructured":"Sarker, I.H.: Machine learning: algorithms, real-world applications and research directions. SN Comput. Sci. 2, 160 (2021)","journal-title":"SN Comput. Sci."},{"issue":"2","key":"17_CR4","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0229345","volume":"15","author":"DG Jenkins","year":"2020","unstructured":"Jenkins, D.G., Quintana-Ascencio, P.F.: A solution to minimum sample size for regressions. PLoS ONE 15(2), e0229345 (2020)","journal-title":"PLoS ONE"},{"issue":"1","key":"17_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0219-y","volume":"6","author":"IH Sarker","year":"2019","unstructured":"Sarker, I.H., Watters, P., Kayes, A.S.M.: Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage. J. Big Data 6(1), 1\u201328 (2019)","journal-title":"J. Big Data"},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Doan, T., Kalita, J.: Selecting machine learning algorithms using regression models. In: 2015 IEEE International Conference on Data Mining Workshop (ICDMW), pp. 1498\u20131505. IEEE (2015)","DOI":"10.1109\/ICDMW.2015.43"},{"issue":"4","key":"17_CR7","doi-asserted-by":"publisher","first-page":"140","DOI":"10.38094\/jastt1457","volume":"1","author":"D Maulud","year":"2020","unstructured":"Maulud, D., Abdulazeez, A.M.: A review on linear regression comprehensive in machine learning. J. Appl. Sci. Technol. Trends 1(4), 140\u2013147 (2020)","journal-title":"J. Appl. Sci. Technol. Trends"},{"key":"17_CR8","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.neucom.2015.12.114","volume":"192","author":"A De Myttenaere","year":"2016","unstructured":"De Myttenaere, A., Golden, B., Le Grand, B., Rossi, F.: Mean absolute percentage error for regression models. Neurocomputing 192, 38\u201348 (2016)","journal-title":"Neurocomputing"},{"key":"17_CR9","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.jclinepi.2020.03.005","volume":"122","author":"BY Gravesteijn","year":"2020","unstructured":"Gravesteijn, B.Y., et al.: Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury. J. Clin. Epidemiol. 122, 95\u2013107 (2020)","journal-title":"J. Clin. Epidemiol."},{"issue":"5","key":"17_CR10","doi-asserted-by":"publisher","first-page":"e148","DOI":"10.1002\/mp.13649","volume":"47","author":"H Seo","year":"2020","unstructured":"Seo, H., et al.: Machine learning techniques for biomedical image segmentation: an overview of technical aspects and introduction to state-of-art applications. Med. Phys. 47(5), e148\u2013e167 (2020)","journal-title":"Med. Phys."},{"key":"17_CR11","doi-asserted-by":"publisher","first-page":"150360","DOI":"10.1109\/ACCESS.2020.3016715","volume":"8","author":"N Fatima","year":"2020","unstructured":"Fatima, N., Liu, L., Hong, S., Ahmed, H.: Prediction of breast cancer, comparative review of machine learning techniques, and their analysis. IEEE Access 8, 150360\u2013150376 (2020)","journal-title":"IEEE Access"},{"key":"17_CR12","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.compchemeng.2017.06.011","volume":"106","author":"M Nilashi","year":"2017","unstructured":"Nilashi, M., bin Ibrahim, O., Ahmadi, H., Shahmoradi, L.: An analytical method for diseases prediction using machine learning techniques. Comput. Chem. Eng. 106, 212\u2013223 (2017)","journal-title":"Comput. Chem. Eng."},{"key":"17_CR13","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1016\/j.eswa.2019.01.012","volume":"124","author":"BM Henrique","year":"2019","unstructured":"Henrique, B.M., Sobreiro, V.A., Kimura, H.: Literature review: machine learning techniques applied to financial market prediction. Expert Syst. Appl. 124, 226\u2013251 (2019)","journal-title":"Expert Syst. Appl."},{"issue":"5","key":"17_CR14","doi-asserted-by":"publisher","first-page":"2199","DOI":"10.30534\/ijeter\/2020\/117852020","volume":"8","author":"KB Prakash","year":"2020","unstructured":"Prakash, K.B., Imambi, S.S., Ismail, M., Kumar, T.P., Pawan, Y.N.: Analysis, prediction and evaluation of COVID-19 datasets using machine learning algorithms. Int. J. Emerg. Trends Eng. Res. 8(5), 2199\u20132204 (2020)","journal-title":"Int. J. Emerg. Trends Eng. Res."},{"key":"17_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2023.101382","volume":"42","author":"SM Memon","year":"2023","unstructured":"Memon, S.M., Wamala, R., Kabano, I.H.: A comparison of imputation methods for categorical data. Inform. Med. Unlocked 42, 101382 (2023)","journal-title":"Inform. Med. Unlocked"},{"issue":"4","key":"17_CR16","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1109\/TEVC.2015.2504420","volume":"20","author":"B Xue","year":"2015","unstructured":"Xue, B., Zhang, M., Browne, W.N., Yao, X.: A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evol. Comput. 20(4), 606\u2013626 (2015)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"17_CR17","volume-title":"Forecasting: Principles and Practice","author":"RJ Hyndman","year":"2020","unstructured":"Hyndman, R.J., Athanasopoulos, G.: Forecasting: Principles and Practice, 3rd edn. OTexts, Melbourne (2020)","edition":"3"},{"key":"17_CR18","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.623","volume":"7","author":"D Chicco","year":"2021","unstructured":"Chicco, D., Warrens, M.J., Jurman, G.: The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Comput. Sci. 7, e623 (2021)","journal-title":"PeerJ Comput. Sci."},{"key":"17_CR19","unstructured":"Jadon, A., Patil, A., Jadon, S.: A comprehensive survey of regression based loss functions for time series forecasting. arXiv preprint arXiv:2211.02989 (2022)"},{"key":"17_CR20","doi-asserted-by":"crossref","unstructured":"Sarkar, A., Yang, Y., Vihinen, M.: Variation benchmark datasets: update, criteria, quality and applications. Database 2020, baz117 (2020)","DOI":"10.1093\/database\/baz117"},{"issue":"2","key":"17_CR21","first-page":"105","volume":"26","author":"C Feng","year":"2014","unstructured":"Feng, C., et al.: Log-transformation and its implications for data analysis. Shanghai Arch. Psychiatry 26(2), 105\u2013109 (2014)","journal-title":"Shanghai Arch. Psychiatry"}],"container-title":["Communications in Computer and Information Science","Advances in Computing and Data Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70906-7_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T09:16:19Z","timestamp":1729156579000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70906-7_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,18]]},"ISBN":["9783031709050","9783031709067"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70906-7_17","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024,10,18]]},"assertion":[{"value":"18 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICACDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advances in Computing and Data Sciences","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Velizy","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 May 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 May 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icacds2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icacds.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}