## 5 Conclusions and Future Scope  
In this study, we introduced an improved transformer-based architecture for FND, developed through comprehensive ablation studies, component-level analysis, and empirical evaluation on benchmark datasets. By integrating advanced multi-head attention, dynamic positional encoding, and a refined classification head, the proposed model achieved strong performance while maintaining computational efficiency with only 3-4 million parameters. Attention mechanisms were found to be the most critical component, with their removal leading to significant performance degradation. Additional stability techniques, such as label smoothing, learning rate warm-up, and reproducible training protocols, further improved training consistency. The model achieved an average accuracy of 79.85% and an F1 score of 79.84%. The accuracies of 80%, 100%, and 59.56% were achieved on FakeNewsNet, ISOT, and LIAR, respectively. It operates with only 3.1 to 4.3 million parameters, representing an 85% reduction in the model size compared to full-sized BERT architectures. These results demonstrate that such FND models are suitable for deployment in resource-constrained environments. In terms of improved performance, its implications in the real world could have been better illustrated with access to a larger volume of labeled data. FND models are very unlikely to be flawless because the problem itself is inherently dynamic, context-dependent, linguistically complex, etc., and any automated system must balance fairness, adaptability, and ethical constraints. To address these limitations further, we aim to explore multilingual FND, cross-domain adaptation, explainable predictions, and integration with multimodal data for comprehensive misinformation detection.  
Conceptualization, J.R., M.M. and M.J.S.; methodology, J.R. and M.M.; software, J.R.; validation, M.M. and M.J.S.; formal analysis, J.R.; investigation, M.J.S.; resources, M.M. and M.J.S.; data curation, J.R.; writing--original draft preparation, J.R. and M.M.; writing--review and editing, M.J.S.; visualization, J.R.; supervision, M.J.S.; project administration, M.M. and M.J.S.; funding acquisition, M.J.S. All authors have read and agreed to the published version of the manuscript. This research and the APC were funded by Biomedical Sensors and Systems Lab, University of Memphis, Memphis, TN 38152, USA.  
Not applicable.  
Data Availability Statement: The original datasets presented in the study are openly available on Kaggle at  
FakeNewsNet: [[https://www.kaggle.com/datasets/mdepak/fakenewsnet](https://www.kaggle.com/datasets/mdepak/fakenewsnet)]([https://www.kaggle.com/datasets/mdepak/fakenewsnet](https://www.kaggle.com/datasets/mdepak/fakenewsnet)) (accessed on 19 June 2025)  
ISOT: [[https://www.kaggle.com/datasets/csmalarkodi/isot-fake-news-dataset](https://www.kaggle.com/datasets/csmalarkodi/isot-fake-news-dataset)]([https://www.kaggle.com/datasets/csmalarkodi/isot-fake-news-dataset](https://www.kaggle.com/datasets/csmalarkodi/isot-fake-news-dataset)) (accessed on 19 June 2025)  
LIAR: [[https://www.kaggle.com/datasets/doanquanvietnamca/liar-dataset](https://www.kaggle.com/datasets/doanquanvietnamca/liar-dataset)]([https://www.kaggle.com/datasets/doanquanvietnamca/liar-dataset](https://www.kaggle.com/datasets/doanquanvietnamca/liar-dataset)) (accessed on 19 June 2025)  
Conflicts of Interest: The authors declare no conflicts of interest.