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 Kiruthika, D.; Fulgen, Maria; Nambiraj, L. Title A name given to the resource Deep Learning Driven Predictive Analytics Framework for Assessing Customer Satisfaction in Health Insurance Services 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 2nd International Conference on Intelligent Systems for Cybersecurity, ISCS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ISCS69371.2025.11386199" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ISCS69371.2025.11386199</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034869226?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034869226?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 Kiruthika D., Dr. SNS Rajalakshmi College of Arts and Science (Autonomous), Department of Commerce with Computer Applications, Tamil Nadu, Coimbatore, India; Fulgen M., Christ Deemed to Be University, Department of Commerce, Bengaluru, India; Nambiraj L., Dr. SNS Rajalakshmi College of Arts and Science (Autonomous), Department of Commerce with Computer Applications, Tamil Nadu, Coimbatore, India Description An account of the resource The paper aims to design a predictive analytics platform based on deep learning that can measure the satisfaction of health insurance policyholders with accuracy. One of the major challenges in predicting customer satisfaction is the use of multiple sources of data. These sources also include unstructured consumer sentiments and official demographic and policy measures. The system that is suggested integrates a 1-D Convolutional Neural Network with TabNet, a deep attention-based neural network that is specifically good at handling tabular data. The two are combined to deal with inputs that are highly sentiment-loaded. To enable joint feature learning while maintaining interpretability, the dual-path architecture leverages feature attribution. The suggested method surpasses standard machine learning and isolated deep learning baselines accuracy more than 97% through experimental evaluation on a real-world LIC health insurance dataset. The findings provide the basis for an interpretable and scalable customer-oriented decision-making framework in health insurance. 2025 IEEE. Subject The topic of the resource Attention Mechanism; CNN; Customer Satisfaction; Deep Learning; Health Insurance; Hybrid Model; Interpretability; Predictive Analytics; Sentiment Analysis; TabNet Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833157243-3; 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