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 Articles Article Faculty Publications -Articles 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 FL-XGBTC: federated learning inspired with XG-boost tuned classifier for YouTube spam content detection Subject The topic of the resource Character; Classifiers; Federated learning; Ham; Machine learning YouTube; NLP; Sentiment analysis; Spam Description An account of the resource The problem of spam content in YouTube comments is an ongoing issue, and detecting such content is a critical task to maintain the quality of user experience on the platform. In this study, we propose a Federated Learning Inspired XG-Boost Tuned Classifier, FL-XGBTC, for YouTube spam content detection. The proposed model leverages the advantages of federated learning, which enables the training of a model collaboratively across multiple devices without sharing raw data. The FL-XGBTC model is based on the XGBoost algorithm, which is a powerful and widely used ensemble learning algorithm for classification tasks. The proposed model was trained on a large and diverse dataset of YouTube comments, which includes both spam and non-spam comments. The results demonstrate that the FL-XGBTC model achieved a high level of accuracy in detecting spam content in YouTube comments, outperforming several baseline models. Additionally, the proposed model provides the benefit of preserving user privacy, which is a critical consideration in modern machine-learning applications. Overall, the proposed Federated Learning Inspired XG-Boost Tuned Classifier provides a promising solution for YouTube spam content detection that leverages the benefits of federated learning and ensemble learning algorithms. The major contribution of this work is to demonstrate and propose a framework for showing a distributed federated classifier for the multiscale classification of youtube spam comments using the Ensemble learning method. The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2024. Creator An entity primarily responsible for making the resource Sharma V.; Sinha A.; Alkhayyat A.; Agarwal A.; Nikitha P.; Ramkumar S.; Rathee T.; Bhargavi M.; Kumar N. Source A related resource from which the described resource is derived International Journal of System Assurance Engineering and Management, Vol-15, No. 10, pp. 4923-4946. Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s13198-024-02502-9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s13198-024-02502-9</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204123725&amp;doi=10.1007%2Fs13198-024-02502-9&amp;partnerID=40&amp;md5=78474336a186f1db65a5719e08aecdb2" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204123725&amp;doi=10.1007%2fs13198-024-02502-9&amp;partnerID=40&amp;md5=78474336a186f1db65a5719e08aecdb2</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 9756809 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sharma V., Computer Science Department, Christ University, Bengaluru, India; Sinha A., Computer Science Department, ICFAI Tech School, ICFAI University, Ranchi, India; Alkhayyat A., College of Technical Engineering, The Islamic University, Najaf, Iraq; Agarwal A., Dept of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Tamil Nadu, Krishnankoil, Virudhunagar, India; Nikitha P., Dept of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Tamil Nadu, Krishnankoil, Virudhunagar, India; Ramkumar S., Dept of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Tamil Nadu, Krishnankoil, Virudhunagar, India; Rathee T., Department of Information Technology, Maharaja Surajmal Institute of Technology, New Delhi, India; Bhargavi M., Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, India; Kumar N., Lovely Professional University, PB, Phagwara, India