
International Journal For Multidisciplinary Research
E-ISSN: 2582-2160
•
Impact Factor: 9.24
A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal
Home
Research Paper
Submit Research Paper
Publication Guidelines
Publication Charges
Upload Documents
Track Status / Pay Fees / Download Publication Certi.
Editors & Reviewers
View All
Join as a Reviewer
Get Membership Certificate
Current Issue
Publication Archive
Conference
Publishing Conf. with IJFMR
Upcoming Conference(s) ↓
WSMCDD-2025
GSMCDD-2025
Conferences Published ↓
ICCE (2025)
RBS:RH-COVID-19 (2023)
ICMRS'23
PIPRDA-2023
Contact Us
Plagiarism is checked by the leading plagiarism checker
Call for Paper
Volume 7 Issue 3
May-June 2025
Indexing Partners



















Smart Spam Detection: An AI-Based Machine Learning
Author(s) | Ms. Prithika S, Prof. Janakiraman S |
---|---|
Country | India |
Abstract | Email has become an essential communication tool, seamlessly facilitating information exchange across personal and professional spheres. While its convenience and global accessibility are unparalleled, email systems have increasingly become targets for cybercriminals exploiting sophisticated spam tactics to breach government networks, corporate systems, and individual accounts. These threats are characterized by their complexity and scale, outpacing traditional detection mechanisms and emphasizing the need for innovative and adaptive solutions to combat emerging cyber risks effectively. This project proposes an advanced system for classifying large-scale email datasets into four distinct categories: Normal, Fraudulent, Harassment, and Suspicious. The approach integrates Natural Language Processing (NLP) with Bidirectional Long Short-Term Memory (BiLSTM) networks to capture nuanced patterns and semantic meanings within email content. The methodology includes a sample expansion phase to enhance training data diversity and a robust testing stage to ensure high accuracy under varied conditions. This innovative system enables effective forensic analysis by extracting and analysing meaningful information from email communications. Through extensive experimentation, the proposed system demonstrates a significant improvement over existing machine learning techniques, achieving a remarkable classification accuracy of 99.1%. The use of BiLSTM with recurrent gradient units ensures reliable performance across diverse email topics and complex scenarios. By offering a highly accurate and robust solution, this project contributes to advancing email security and strengthens the defence mechanisms against evolving cyber threats in today's interconnected digital environment. |
Keywords | Natural Language Processing(NLP),Supervised Learning,Spam Classification,Feature Extraction,Text Preprocessing |
Field | Computer Applications |
Published In | Volume 7, Issue 3, May-June 2025 |
Published On | 2025-06-08 |
DOI | https://doi.org/10.36948/ijfmr.2025.v07i03.47338 |
Short DOI | https://doi.org/g9pz3n |
Share this

E-ISSN 2582-2160

CrossRef DOI is assigned to each research paper published in our journal.
IJFMR DOI prefix is
10.36948/ijfmr
Downloads
All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.
