Paper Info
Title
Predicting Spam Messages Using Back Propagation Neural Network
Abstract
With the increase in popularity of smartphones, text-based communication has also gained popularity. Availability of messaging services at low cost has resulted into the increase in spam messages. This increase in number of spam messages has become an important issue these days. Many mobile applications are developed to detect spam messages in mobile phones but still, there is a lack of a complete solution. This paper presents an approach for the detection of spam messages. We have identified an effective feature set for text messages which classify the messages into spam or ham with high accuracy. The feature selection procedure is implemented on normalized text messages to obtain a feature vector for each message. The feature vector obtained is tested on a set of machine learning algorithms to observe their efficiency. This paper also presents a comparative analysis of different algorithms on which the features are implemented. In addition, it presents the contribution of different features in spam detection. After implementation and as per the set of features selected, Artificial Neural Network Algorithm using Back Propagation technique works in the most efficient manner.
Year
DOI
Venue
2020
10.1007/s11277-019-06734-y
Wireless Personal Communications
Keywords
Field
DocType
Spam messages, Feature selection, Text normalization, Text classification, Machine learning, Neural network
Feature vector,Normalization (statistics),Feature selection,Computer science,Popularity,Back propagation neural network,Real-time computing,Artificial intelligence,Artificial neural network,Backpropagation,Text normalization,Machine learning
Journal
Volume
Issue
ISSN
110
1
1572-834X
Citations 
PageRank 
References 
1
0.37
0
Authors
5
Name
Order
Citations
PageRank
Ankit Kumar Jain1817.77
Diksha Goel210.37
Sanjli Agarwal310.37
Yukta Singh410.37
Gaurav Bajaj510.37