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Daedalus, 146(4), 71-84" } ], "record": { "registrant": "Longman Publishers", "registered": "2026-09-18", "updated": "2026-09-21", "issue_number": 1, "source": "crossref-api", "source_agency": "Crossref (member 25296)" } }, "url_after": "https://www.jst.org.in/index.php/pub/article/view/1175", "sha256_before": null, "sha256_after": "80e35d0bab0f02cd8a9d51360ecee225fd58673395e4f1e934599da4a65215dc" }, { "seq": 2, "kind": "update", "at": "2026-09-30 00:00:01", "by": "Administrator (admin)", "by_kind": "user", "note": "record re-read from api.crossref.org", "changes": [ { "path": "container.titles.0.value", "from": "Journal of Science & Technology", "to": "Journal of Science & Technology" } ], "record_after": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jst.2022.v7.i04pp221-234", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "Harnessing Generative Adversarial Networks and AI-Oriented Anomaly Detection Mechanisms for Resilient Fraud and Crisis Mitigation Amidst Pandemic Challenges", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jst.2022.v7.i04pp221-234" } ], "agents": [ { "role": "author", "name": { "given": "SRI HARSHA", "family": "GRANDHI" }, "sequence": "first" }, { "role": "author", "name": { "given": "SUNDARAPANDIAN", "family": "MURUGESAN" }, "sequence": "additional" }, { "role": "author", "name": { "given": "RAJYA", "family": "LAKSHMI GUDIVAKA" }, "sequence": "additional" }, { "role": "author", "name": { "given": "RAJ KUMAR", "family": "GUDIVAKA" }, "sequence": "additional" }, { "role": "author", "name": { "given": "BASAVA", "family": "RAMANJANEYULU GUDIVAKA" }, "sequence": "additional" }, { "role": "author", "name": { "given": "DINESH KUMAR", "family": "REDDY BASANI" }, "sequence": "additional" }, { "role": "publisher", "name": { "org": "Longman Publishers" } } ], "dates": { "published": "2022-04-29", "date_type": "PublicationDate", "online": "2022-04-29" }, "language": "en", "container": { "type": "Journal", "titles": [ { "value": "Journal of Science & Technology", "type": "PrincipalTitle" } ], "identifiers": [ { "type": "ISSN", "value": "2456-5660", "medium": "electronic" } ], "volume": "07", "issue": "04", "pages": { "first": "221", "last": "234" } }, "links": [ { "url": "https://www.jst.org.in/index.php/pub/article/view/1175", "return_type": "text/html", "primary": true }, { "url": "https://www.jst.org.in/index.php/pub/article/download/1175/951", "purpose": "text-mining", "return_type": "application/pdf" }, { "url": "https://www.jst.org.in/index.php/pub/article/download/1175/4114", "purpose": "text-mining", "return_type": "application/xml" }, { "url": "https://www.jst.org.in/index.php/pub/article/view/1175/951", "purpose": "similarity-checking" } ], "abstract": { "value": "Background Information: Resilient solutions are required because the COVID-19 pandemic has escalated fraud and system vulnerabilities across industries. In order to reduce fraud and successfully handle crises, this study combines Generative Adversarial Networks (GANs) with AI-driven anomaly detection techniques. We tackle the problems of changing threats, unbalanced data, and instantaneous adaptation in a changing environment. Objectives: In order to improve system resilience against fraud and crises, this project intends to use GANs to generate fraud scenarios, integrate AI for real-time anomaly detection, and create a hybrid framework. Achieving scalability, accuracy, and adaptability for a variety of applications amid pandemic-related challenges is its main goal.", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0/", "start": "2022-04-29", "applies_to": "vor" }, "references": [ { "key": "ref1", "doi": "10.1080/17453674.2019.1600125", "unstructured": "Gan, K., Xu, D., Lin, Y., Shen, Y., Zhang, T., Hu, K.,... & Liu, Y. (2019). 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