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@article{195538,
        author = {Umamaheswararao Mogili},
        title = {An Intelligent Email Spam Detection System Using Machine Learning and Natural Language Processing},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {334-341},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195538},
        abstract = {Nowadays communication plays a major role in everything be it professional or personal. Email communication service is being used extensively because of its free use services, low-cost operations, accessibility, and popularity. Emails have one major security flaw that is anyone can send an email to anyone just by getting their unique user id. This security flaw is being exploited by some businesses and ill motivated persons for advertising, phishing, malicious purposes, and finally fraud. This produces a kind of email category called SPAM. Spam refers to any email that contains an advertisement, unrelated and frequent emails. These emails are increasing day by day in numbers. Studies show that around 55 percent of all emails are some kinds of spam. A lot of effort is being put into this by service providers. Spam is evolving by changing the obvious markers of detection. Moreover, the spam detection of service providers can never be aggressive with classification because it may cause potential information loss to incase of a misclassification. Spam emails pose a significant threat to cybersecurity and user productivity, flooding in boxes with unsolicited and potentially malicious content. This project aims to develop an advanced spam detection system leveraging artificial intelligence (AI) and machine learning (ML) techniques to classify emails as spam or legitimate with high accuracy. Utilizing a dataset of labeled emails, we employ algorithms such as Naive Bayes, Support Vector Machines (SVM), and deep learning models like Recurrent Neural Networks (RNNs) or Transformers for feature extraction and classification. The system incorporates natural language processing (NLP) to analyze text patterns, sender metadata, and behavioral indicators. Experimental results demonstrate improved precision and recall compared to traditional rule-based filters, with potential for real-time deployment. This approach not only enhances email security but also adapts to evolving spam tactics through continuous learning, contributing to broader AI applications in cyber security.},
        keywords = {Email Spam Detection, Artificial Intelligence, Machine Learning, Natural Language Processing, TF-IDF, Cyber security.},
        month = {April},
        }