A ROBUST APPROACH FOR EFFECTIVE SPAM DETECTION USING SUPERVISED LEARNING TECHNIQUES

Authors

  • V.VENKATA SATYA SURYA,T.Nagajyothi, Komminni sushma, Gadde sahitha, Baddamprajwala

Keywords:

Short Message Administration CFI, RMSEA, NFI, Monte Carlo.

Abstract

With the ascent of texting applications, Short Message Administration (SMS) has reduced in pertinence and is currently transcendently utilized by specialist co-ops, organizations, and associations for showcasing and spam. An eminent pattern in spam is the utilization of local language content written in English, which muddles identification and sifting.

Downloads

Download data is not yet available.

References

Hppy bthdy txt!, BBC, BBC News World Edition, UK, 3 December 2002, [Online]. Available: http://news.bbc.co.uk/2/hi/uk_news/253808

stm. [Accessed October 2020].

Short Message Service (SMS) Message Format, Sustainability of Digital Formats, United States of America, September

,[Online]. Available:

Downloads

Published

2023-12-24

How to Cite

V.VENKATA SATYA SURYA,T.Nagajyothi, Komminni sushma, Gadde sahitha, Baddamprajwala. (2023). A ROBUST APPROACH FOR EFFECTIVE SPAM DETECTION USING SUPERVISED LEARNING TECHNIQUES . Pegem Journal of Education and Instruction, 13(4), 615–623. Retrieved from https://www.pegegog.net/index.php/pegegog/article/view/4027

Issue

Vol. 13 No. 4 (2023): PEGEGOG

Section

Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

NonCommercial — You may not use the material for commercial purposes.

No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.