SMS Spam Detection using Machine Learning & Deep Learning Approaches

Registration ID: IJNRD_225601

Published ID: IJNRD2407463

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K.Suryateja Reddy , Dr.R.Suresh

Multinomial Naïve Bayes, Bi-LSTM, spam

SMS, a widely used and rapidly expanding GSM value-added service globally, has increasingly become a target for unwanted messages, commonly referred to as SMS spam. The impact of SMS spam is considerable, as it undermines user trust and poses significant challenges for service providers. This study evaluates the performance of three models for SMS spam classification: Multinomial Naive Bayes (MultinomialNB), a Custom Vector Embedding bidirectional long short-term memory (BiLSTM) model as well as. The models were evaluated on exactness, accuracy, review, and F1-score. TheMultinomialNB model achieved 96.23% accuracy, 100% precision, 72.00% recall, and an F1-score of 83.72%. The Custom Vector Embedding model recorded 98.21% accuracy, 97.79% precision, 88.67% recall, and a 93.01% F1-score. The BiLSTM model showed 98.21% accuracy, 97.10% precision, 89.33% recall, and a 93.06% F1-score. Results indicate that the Custom Vector Embedding and BiLSTM models outperform the Multinomial NB model, highlighting the effectiveness of deep learning approaches for SMS spam detection.

Published Paper   E-Certificate

K.Suryateja Reddy & Dr.R.Suresh (July-2024). SMS Spam Detection using Machine Learning & Deep Learning Approaches. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(7), f619-f626. https://ijnrd.org/papers/IJNRD2407463.pdf

Volume 9 Issue 7, July-2024

Pages : f619-f626

Paper Reg. ID: IJNRD_225601

Published Paper Id: IJNRD2407463

Downloads: 000121985

Research Area: Computer Engineering 

Country: CHITTOR, AP, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2407463.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2407463

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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