Application of Adaptive Neuro-Fuzzy Inference Systems in Mobile’s Spam

Jyoti Chouhan¹ , Raju Barskar² , Uday Chourasia³

Section:Review Paper, Product Type: Journal
Vol.10 , Issue.4 , pp.8-11, Oct-2022

Online published on Oct 31, 2022

Copyright © Jyoti Chouhan, Raju Barskar, Uday Chourasia . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The function of science is to explain reality logically. With the increased usage of the internet, spamming is the most typical problem that we faced every day. Service of mailing and web applications are much spammed, nowadays this expansion in the number of spam messages has turned into a significant issue. Accessibility of messaging services for a minimal price has come about in the expansion of spam messages. Spam location is attempting a direct result of the necessity for semantic analysis of the compact spam messages, which overall will for the most part have overlapping polarities. In this work, a portable spam order strategy is created in light of an Adaptive Neuro Inference System(ANFIS) containing Gini record fuzzy and Back-Propagation in machine learning. The methodology includes Gini’s index models for information indexes and a back-propagation-based neural network as the AI classifier. This paper presents the assessment of the introduced system based on the error, accuracy of classification, and the number of iterations.

Key-Words / Index Term :
Mobile Spam Classification, ANFIS, Gini’s Index, Back Propagation, Training Iterations, Classification Accuracy

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