Cite this DOI
10.46243/jst.2022.v7.i04pp221-234 · Harnessing Generative Adversarial Networks and AI-Oriented Anomaly Detection Mechanisms for Resilient Fraud and Crisis Mitigation Amidst Pandemic Challenges
APA (7th edition)
GRANDHI, S. H., MURUGESAN, S., LAKSHMI GUDIVAKA, R., GUDIVAKA, R. K., RAMANJANEYULU GUDIVAKA, B., & REDDY BASANI, D. K. (2022). Harnessing Generative Adversarial Networks and AI-Oriented Anomaly Detection Mechanisms for Resilient Fraud and Crisis Mitigation Amidst Pandemic Challenges. *Journal of Science & Technology*, *07*(04), 221–234. https://doi.org/10.46243/jst.2022.v7.i04pp221-234
⬇ text Italics are shown as *asterisks* in plain text — the journal or book title and the volume.
BibTeX
@article{grandhi2022harnessing,
author = {GRANDHI, SRI HARSHA and MURUGESAN, SUNDARAPANDIAN and LAKSHMI GUDIVAKA, RAJYA and GUDIVAKA, RAJ KUMAR and RAMANJANEYULU GUDIVAKA, BASAVA and REDDY BASANI, DINESH KUMAR},
title = {{Harnessing Generative Adversarial Networks and AI-Oriented Anomaly Detection Mechanisms for Resilient Fraud and Crisis Mitigation Amidst Pandemic Challenges}},
journal = {Journal of Science \& Technology},
year = {2022},
month = {apr},
volume = {07},
number = {04},
pages = {221--234},
publisher = {Longman Publishers},
issn = {2456-5660},
doi = {10.46243/jst.2022.v7.i04pp221-234},
url = {https://doi.org/10.46243/jst.2022.v7.i04pp221-234},
language = {en},
abstract = {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.}
}RIS (EndNote, Zotero, Mendeley)
TY - JOUR TI - Harnessing Generative Adversarial Networks and AI-Oriented Anomaly Detection Mechanisms for Resilient Fraud and Crisis Mitigation Amidst Pandemic Challenges AU - GRANDHI, SRI HARSHA AU - MURUGESAN, SUNDARAPANDIAN AU - LAKSHMI GUDIVAKA, RAJYA AU - GUDIVAKA, RAJ KUMAR AU - RAMANJANEYULU GUDIVAKA, BASAVA AU - REDDY BASANI, DINESH KUMAR JO - Journal of Science & Technology PY - 2022 DA - 2022/04/29/ VL - 07 IS - 04 SP - 221 EP - 234 PB - Longman Publishers SN - 2456-5660 LA - en AB - 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. DO - 10.46243/jst.2022.v7.i04pp221-234 UR - https://doi.org/10.46243/jst.2022.v7.i04pp221-234 ER -
CSL-JSON
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} ⬇ .json What citeproc and reference managers read; the DOI system hands it out for Accept: application/vnd.citationstyles.csl+json, and so does this registry's resolver.
From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2022.v7.i04pp221-234 gives all four in one JSON answer.
