Abstract
The Short message service (SMS) has been promptly developing these days for businesses, marketing,
advertisement, and information. Because of its popularity and easy access people use it for fraud as well. A
short message device is the most common and popular communication medium through which text sends
electronically. Spams are undesired or unfavorable message in form SMS that is passed on the communication
medium. Spam causes many problems like limited memory space and also can affect computing power and
speed. Nowadays, spam messages have been overflowing in many countries. Spam is the most annoying thing
for the individual. The main issue with spam messages violates privacy. This study presents a literature review
of the machine and deep learning techniques used in the detection, classification, and spam filtering for SMS
spam. In this review, different databases were used for search including research gate, ELSEVIER, Applied
sciences, and IEEE. SMS is a more commonly used media than email. This study gives you an overview of the
machine and deep learning methods, graphical representation method, and automatic spam filtering methods
from android previously used for SMS spam detection and filtering. The main objective is to find the limitations
of the previous studies and suggestions for future work.
Keywords: Machine Learning, Support Vector Machine, Deep Learning, Graphical representation, Conventional
Neural Network.
Modules
Algorithms
Software And Hardware
• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL