Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Lalitha, P.; Nooka Raju, Guntu; Sindhu, V.; Sujatha, Capt. Kambhampati; Mandal, Pawan; Arulkarthick, E.K. Title A name given to the resource Multilevel CNN Based Hybrid Framework for Adaptive Credit Card Fraud Detection Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 IEEE 2nd International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICITEICS64870.2025.11340855" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICITEICS64870.2025.11340855</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033217840?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033217840?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Lalitha P., Geetanjali College of Engineering and Technology, Department of Computer Science and Engineering, Hyderabad, India; Nooka Raju G., GMR Institute of Technology, Department of Electronics and Communication Engineering, Rajam, Andhra Pradesh, Vizianagaram, India; Sindhu V., Christ University, Department of Computer Science, Karnataka, India; Sujatha C.K., St. Joseph's College for Women(A), Department of Mathematics, Andhra Pradesh, Visakhapatnam, India; Mandal P., RIMT University, Department of Forensic Science, Punjab, Mandi Gobindgarh, India; Arulkarthick E.K., Nandha Engineering College, Department of Electronics and Communication Engineering, Tamil Nadu, India Description An account of the resource Credit card fraud presents a substantial problem to financial organizations, as fast changing fraudulent activities necessitate advanced detection techniques. Conventional machine learning methods frequently encounter challenges with adaptability and precision in imbalanced datasets. This study presents a multilevel CNN-based hybrid architecture that combines deep convolutional networks with traditional ensemble classifiers for adaptive credit card fraud detection. The platform includes an adaptive learning module that facilitates ongoing model upgrades, guaranteeing responsiveness to emerging fraud trends. The system, evaluated using a benchmark Kaggle dataset, attained an accuracy of 99.48%, precision of 98.76%, recall of 99.05%, F1-score of 98.90%, and AUC-ROC of 99.91%, outperforming established baseline models such as Logistic Regression, Random Forest, and XGBoost. The suggested system's capacity to integrate deep feature extraction with hybrid classifiers yields enhanced detection efficiency, reduced false positives, and improved generalization. This research enhances fraud detection by overcoming the constraints of static models, rendering it applicable for real-time financial applications and adaptable to emerging threats. 2025 IEEE. Subject The topic of the resource Adaptive Learning; Anomaly Detection; Credit Card Faud Detection; Deep Learning; Ensemble Methods; Feature Extraction; Financial Security; Hybrid Classifiers; Imbalanced Data; Incremental Learning; Multilevel Convolutional Neural Network; Real-Time Fraud Detection Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833150784-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online