TY  - JOUR
T1  - A Comparative Study of Machine Learning Classifiers for Different Language Spam SMS Detection: Performance Evaluation and Analysis
TT  - A Comparative Study of Machine Learning Classifiers for Different Language Spam SMS Detection: Performance Evaluation and Analysis
AU  - Dev Sharma, Samrat Kumar
PY  - 2024
DA  - December
Y2  - 2024
DO  - 10.54569/aair.1549781
JF  - Advances in Artificial Intelligence Research
JO  - Adv. Artif. Intell. Res.
PB  - Osman ÖZKARACA
WT  - DergiPark
SN  - 2757-7422
SP  - 69
EP  - 77
VL  - 4
IS  - 2
LA  - en
AB  - With the continuous rise in the number of mobile device users, SMS (Short Message Service) remains a prevalent communication tool accessible on both smartphones and basic phones. Consequently, SMS traffic has experienced a significant surge. This increase has also led to a rise in spam messages, as spammers seek financial or business gains through activities like marketing promotions, lottery scams, and credit card information theft. Consequently, spam classification has become a focal point of research. In this paper, we explore the effectiveness of 11 machine learning algorithms for SMS spam detection, including multinomial Naïve Bayes, K-Nearest Neighbors (KNN), and Random Forest, among others. Utilizing datasets from UCI and Bangla SMS collections, our experimental results reveal that the multinomial Naïve Bayes algorithm surpasses previous models in spam detection, achieving accuracies of 98.65% and 89.10% in the respective datasets.
KW  - Spam SMS Detection
KW  - NLP
KW  - Machine Learning
KW  - Deep Learning
KW  - Naïve Bayes
N2  - With the continuous rise in the number of mobile device users, SMS (Short Message Service) remains a prevalent communication tool accessible on both smartphones and basic phones. Consequently, SMS traffic has experienced a significant surge. This increase has also led to a rise in spam messages, as spammers seek financial or business gains through activities like marketing promotions, lottery scams, and credit card information theft. Consequently, spam classification has become a focal point of research. In this paper, we explore the effectiveness of 11 machine learning algorithms for SMS spam detection, including multinomial Naïve Bayes, K-Nearest Neighbors (KNN), and Random Forest, among others. Utilizing datasets from UCI and Bangla SMS collections, our experimental results reveal that the multinomial Naïve Bayes algorithm surpasses previous models in spam detection, achieving accuracies of 98.65% and 89.10% in the respective datasets.
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UR  - https://doi.org/10.54569/aair.1549781
L1  - https://dergipark.org.tr/en/download/article-file/4213091
ER  -