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@article{181650,
        author = {GOKULNATH S and JAGATHISAN R and RASIKA SK},
        title = {IMPROVING EMAIL STORAGE WITH THE USE OF NAIVE BAYES ALGORITHM TO DETECT AND ELIMINATE JUNK MAILS IN MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {6077-6083},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181650},
        abstract = {Email spam remains a significant challenge, impacting user privacy, productivity, and security. The increasing sophistication of spam tactics necessitates advanced detection mechanisms. Machine learning (ML) has emerged as an effective tool for automating spam classification by learning patterns and adapting to new spamming techniques.This study presents a hybrid ML approach that leverages the strengths of both Random Forest (RF) and Gradient Boosting (GB) algorithms. RF, known for its ensemble learning capabilities, enhances model stability, while GB improves classification through iterative boosting. By combining these two algorithms, the hybrid model aims to improve classification accuracy and robustness.The proposed framework consists of feature extraction, data preprocessing, and model training using the RF-GB hybrid approach. The model's performance is evaluated using key metrics, including accuracy, precision, recall, and F1-score, and is compared against individual RF and GB classifiers. Experimental results demonstrate that the hybrid approach outperforms standalone models, achieving superior spam detection rates.The findings of this study indicate that integrating ensemble learning with boosting techniques provides a promising solution for combating email spam in real-world applications. The hybrid approach enhances detection efficiency, reduces false positives, and improves overall security, making it a viable advancement in automated spam filtering systems.},
        keywords = {Student Misconceptions Analysis, Performance Analytics, Automated Quiz Generation, Student Engagementand Comprehension, Chain-of-Thought Prompting},
        month = {July},
        }