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 Saraswathi, D.; Kavitha, R. Title A name given to the resource An Optimized Approach for Spam Message Detection Using C4.5 Classifier with Stochastic Hill Climbing and Genetic Algorithm for Feature Selection Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived 2026 2nd International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2026; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICISCoIS62701.2026.11447774" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICISCoIS62701.2026.11447774</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037453215?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037453215?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 Saraswathi D., Psg College of Arts &amp; Science, Department of Computer Science, Coimbatore, India; Kavitha R., Christ University, Department of Statistics and Data Science, Bangalore, India Description An account of the resource In the mobile industry, text messaging is a popular feature that is mainly intended to make money for service providers. But spam, which is defined as unsolicited bulk messages that contain commercial content, has become a widespread problem. These spam texts are frequently used to spread phishing links or advertise goods and services in order to make money. The phone alerts the user whenever spam text messages arrive in their inbox. When the user discovers that the message is unsolicited, these unsolicited texts not only take up storage space and waste their time, but they also irritate them. Even with the development of numerous sophisticated algorithms to identify spam, users are still impacted by text message spam. Thus, the mobile sector needs to implement efficient filtering methods. The proposed study uses the C4.5 Decision Tree as the classification model and combines a Genetic Algorithm and Stochastic Hill Climbing to find optimal features in order to detect spam in text messages. This method uses metaheuristic techniques to find the best features, which are then categorized using decision trees. This hybrid model performs better than current classification methods. 2026 IEEE. Subject The topic of the resource Classification; Decision Tree; Hybrid approach; Metaheuristic Algorithm; Spam message; Stochastic Hill Climbing Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833154596-3; 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