## 4 Experimental Results
### Spam Detection Models and Results
The experimental results demonstrated that our ensemble model significantly outperformed the individual models, achieving the highest classification accuracy of 99.28% on the SMS Spam Collection dataset, 99.24% on the Email Spam dataset, 99.00% on the Enron-Spam dataset, 98.71% on the Super SMS dataset, and 95.09% on UtkMI's Twitter Spam dataset. These results exceed those reported in the literature, emphasizing the model's superior performance. The inclusion of additional statistical features derived from message texts further enhanced the model's effectiveness, particularly in MLP models, where the Term Frequency-Inverse Document Frequency (TF-IDF) vectorization method provided a slight improvement over CountVectorizer. The ensemble approach effectively captured the complex patterns inherent in spam messages by leveraging the capabilities of different machine learning architectures and data preprocessing techniques. The weighted majority voting mechanism proved especially beneficial on datasets with unbalanced class distributions, reinforcing the model's robustness and generalization ability. Our findings contribute to ongoing efforts to combat the persistent threat of spam messages, which pose significant challenges to digital communication security and the user experience. The proposed hybrid model not only improves detection accuracy but also demonstrates adaptability across various datasets, making it a valuable tool for enhancing spam-filtering mechanisms in diverse applications. In addition to SMS data, the models created in the study were also tested using e-mail and Twitter messages. It was observed that higher success rates were achieved than many studies in the literature. A successful model has been put forward in which different artificial intelligence models such as MLP, GRU, and hybrid autoencoder, which are not very similar in the literature, work together with ensemble learning. The advantages of these models and different approaches are brought together in this way. However, since the EGMA model we created includes an autoencoder model that works by increasing the dimensions and, therefore, contains a large number of parameters, the model-training times are relatively high. However, this is only the case in the model's training process. The runtime of the model is much shorter. Future work could explore integrating additional deep learning architectures and feature extraction methods to improve performance further. Extending the model to handle multilingual datasets and real-time spam detection could provide a broader applicability and strengthen defenses against evolving spam techniques. Conceptualization, M.K. ; methodology, Y.B. and M.K. ; software, Y.B. ; validation, M.K. ; investigation, Y.B. and M.K. ; writing--original draft preparation, Y.B. and M.K. ; writing--review and editing, Y.B. and M.K. All authors have read and agreed to the published version of the manuscript. ## Funding:

This research received no external funding. ## Institutional Review Board Statement:

Not applicable. ## Informed Consent Statement:

Not applicable. ## Data Availability Statement:

The data presented in this study are included in the article; further inquiries can be directed to the corresponding author. ## Acknowledgments:

This article originates from [PERSON]'s Master's thesis, \"Detection of Spam Messages with Machine Learning Methods\", supervised by [PERSON] at Siirt University, Institute of Science and Technology, Department of Computer Engineering. ## Conflicts of Interest:

The authors declare no conflicts of interest. ## References

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