{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T21:04:03Z","timestamp":1742936643401,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030767754"},{"type":"electronic","value":"9783030767761"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-76776-1_1","type":"book-chapter","created":{"date-parts":[[2021,5,19]],"date-time":"2021-05-19T19:03:16Z","timestamp":1621450996000},"page":"3-16","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Data Balancing for Credit Card Fraud Detection Using Complementary Neural Networks and SMOTE Algorithm"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8010-8246","authenticated-orcid":false,"given":"Vrushal","family":"Shah","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7155-7901","authenticated-orcid":false,"given":"Kalpdrum","family":"Passi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,5,20]]},"reference":[{"key":"1_CR1","doi-asserted-by":"publisher","unstructured":"John, O.A., et al.: Credit card fraud detection using machine learning techniques: a comparative analysis. In: 2017 International Conference on Computing Networking and Informatics (ICCNI) (2017). https:\/\/doi.org\/10.1109\/iccni.2017.8123782","DOI":"10.1109\/iccni.2017.8123782"},{"key":"1_CR2","doi-asserted-by":"publisher","unstructured":"Kuldeep, R, et al.: Credit card fraud detection using adaboost and majority voting. IEEE Access 6, 14277\u201314284 (2018). https:\/\/doi.org\/10.1109\/access.2018.2806420","DOI":"10.1109\/access.2018.2806420"},{"key":"1_CR3","doi-asserted-by":"crossref","unstructured":"Jeatrakul, P., Wong, K.W., Fung, C.C.: Classification of imbalanced data by combining the complementary neural network and SMOTE algorithm. In: 17th International Conference on Neural Information Processing, ICONIP 2010, 22\u201325 November, Sydney (2010)","DOI":"10.1007\/978-3-642-17534-3_19"},{"key":"1_CR4","unstructured":"Promrak, J., Kraipeerapun, P., Amornsamankul, S.: combining complementary neural network and error-correcting output codes for multiclass classification problems. In Proceedings of the 10th WSEAS International Conference on Applied Computer and Applied Computational Science (2011)"},{"key":"1_CR5","doi-asserted-by":"publisher","unstructured":"Emad, M., Far, B.: Supervised machine learning algorithms for credit card fraudulent transaction detection: a comparative study. IEEE Annals of the History of Computing, IEEE, 1 July 2018. doi.ieeecomputersociety.org\/https:\/\/doi.org\/10.1109\/iri.2018.00025","DOI":"10.1109\/iri.2018.00025"},{"key":"1_CR6","doi-asserted-by":"publisher","unstructured":"Abhimanyu, R., et al.: \u201cDeep learning detecting fraud in credit card transactions. In: 2018 Systems and Information Engineering Design Symposium (SIEDS) (2018). https:\/\/doi.org\/10.1109\/sieds.2018.8374722","DOI":"10.1109\/sieds.2018.8374722"},{"key":"1_CR7","doi-asserted-by":"publisher","unstructured":"Shiyang, X., et al.: Random forest for credit card fraud detection. In: 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC) (2018). https:\/\/doi.org\/10.1109\/icnsc.2018.8361343","DOI":"10.1109\/icnsc.2018.8361343"},{"issue":"3","key":"1_CR8","first-page":"29","volume":"98","author":"Vaishali","year":"2014","unstructured":"Vaishali, et al.: Fraud detection in credit card by clustering approach. Int. J. Comput. Appl. 98(3), 29\u201332 (2014)","journal-title":"Int. J. Comput. Appl."},{"key":"1_CR9","unstructured":"Stolfo, S., Fan, D.W., Lee, W., Prodromidis, A., Chan, P.: Credit card fraud detection using meta-learning: Issues and initial results. In: AAAI-97 Workshop on Fraud Detection and Risk Management (1997)"},{"key":"1_CR10","unstructured":"Pun, J.K.F.: Improving Credit Card Fraud Detection using a Meta-Learning Strategy. Doctoral dissertation, University of Toronto (2011)"},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"Duman, E., Buyukkaya, A., Elikucuk, I.: A novel and successful credit card fraud detection system implemented in a turkish bank. In: 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW), pp. 162\u2013171. IEEE (2013)","DOI":"10.1109\/ICDMW.2013.168"},{"key":"1_CR12","doi-asserted-by":"publisher","first-page":"2845","DOI":"10.1016\/j.neucom.2008.07.017","volume":"72","author":"P Kraipeerapun","year":"2009","unstructured":"Kraipeerapun, P., Fung, C.C.: Binary classification using ensemble neural networks and interval neutrosophic sets. Neurocomputing 72, 2845\u20132856 (2009)","journal-title":"Neurocomputing"},{"key":"1_CR13","first-page":"463","volume":"6","author":"P Kraipeerapun","year":"2007","unstructured":"Kraipeerapun, P., Fung, C.C., Wong, K.W.: Uncertainty assessment using neural networks and interval neutrosophic sets for multiclass classification problems. WSEAS Trans. Comput. 6, 463\u2013470 (2007)","journal-title":"WSEAS Trans. Comput."},{"key":"1_CR14","doi-asserted-by":"crossref","unstructured":"Pozzolo, A.D., Caelen, O., Johnson, R.A., Bontempi, G.: Calibrating probability with undersampling for unbalanced classification. In: Symposium on Computational Intelligence and Data Mining (CIDM), IEEE (2015)","DOI":"10.1109\/SSCI.2015.33"},{"key":"1_CR15","unstructured":"https:\/\/www.kaggle.com\/mlg-ulb\/creditcardfraud"},{"key":"1_CR16","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1007\/s11280-012-0178-0","volume":"16","author":"W Wei","year":"2013","unstructured":"Wei, W., Li, J., Cao, L., et al.: Effective detection of sophisticated online banking fraud on extremely imbalanced data. World Wide Web 16, 449\u2013475 (2013). https:\/\/doi.org\/10.1007\/s11280-012-0178-0","journal-title":"World Wide Web"},{"key":"1_CR17","unstructured":"Bolton, R.J., Hand, D.J.: Unsupervised profiling methods for fraud detection. Credit Scoring and Credit Control VII\u201d, pp. 235\u2013255 (2001)"}],"container-title":["Communications in Computer and Information Science","Computing Science, Communication and Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-76776-1_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T00:57:53Z","timestamp":1672189073000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-76776-1_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030767754","9783030767761"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-76776-1_1","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"20 May 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"COMS2","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computing Science, Communication and Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mehsana","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 February 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 February 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"coms22021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/coms2.gnu.ac.in\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"105","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"19","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"18% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the COVID-19 pandemic the conference was held online.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}