Identifying spam messages for Kazakh Language using Hybrid Machine Learning Model
| dc.contributor.author | Ydyrys A. | |
| dc.date.accessioned | 2024-12-10T09:33:56Z | |
| dc.date.available | 2024-12-10T09:33:56Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | The rapid growth of digital communication has led to an increase in unwanted spam messages, which pose a significant challenge for users. While numerous machine learning techniques have been employed to combat spam in widely spoken languages, there is a lack of research on spam detection in less common languages, such as Kazakh. In this study, we propose a hybrid machine learning model for identifying spam messages in the Kazakh language. Our approach combines the strengths of different machine learning techniques to enhance the accuracy and efficiency of spam detection. To evaluate the performance of our hybrid model, we manually collected a dataset of Kazakh language messages, which were labeled as spam or non-spam. The results demonstrate that our hybrid approach surpasses traditional machine learning methods in terms of accuracy, precision, recall, and F1-score. This model not only improves the efficiency of spam filtering but also enhances the user experience for Kazakh-speaking individuals and promotes the development of languagespecific spam detection techniques for other underrepresented languages. | |
| dc.identifier.uri | https://repository.sdu.edu.kz/handle/123456789/1556 | |
| dc.language.iso | en | |
| dc.subject | Internet, spam messages, machine learning, algorithms | |
| dc.title | Identifying spam messages for Kazakh Language using Hybrid Machine Learning Model | |
| dc.type | Other |