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@article{200936,
        author = {Mrs. S. Sharmila and Thirumalai K and Suriya M and Vikram K},
        title = {Intelligent Spam Email Detection System Using Logistic Regression},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {no},
        pages = {55-61},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200936},
        abstract = {In today's digital era, email communication has become an indispensable tool for personal and professional use. The growing volume of spam emails poses significant challenges such as security risks, phishing attacks, and productivity loss, making automated and intelligent detection systems a critical necessity.
This paper proposes a machine learning-based spam detection system using Logistic Regression, leveraging labeled datasets to distinguish spam from legitimate emails. The system integrates text preprocessing techniques including tokenization, stop-word removal, and stemming, combined with TF-IDF or Bag of Words feature extraction, to deliver adaptive, reliable, and scalable email filtering.},
        keywords = {Spam Detection, Logistic Regression, Machine Learning, TF-IDF, Text Preprocessing, Email Filtering, Bag of Words, Natural Language Processing, Binary Classification, Cybersecurity.},
        month = {May},
        }