Spam filtering in SMS using Recurrent Neural Networks project in python

Spam filtering in SMS using Recurrent Neural Networks

 

 PROJECT ID: PYTHON26

 

PROJECT NAME: Spam filtering in SMS using Recurrent Neural Networks

 

 

 

PROJECT CATEGORY: MCA / BCA / BCCA / MCM / POLY / ENGINEERING

 

PROJECT ABSTRACT:

The upsurge in the volume of unwanted emails called spam has created an intense need for the development of more dependable and robust antispam filters. Machine learning methods of recent are being used to successfully detect and filter spam emails. We present a systematic review of some of the popular machine learning based email spam filtering approaches. Our review covers survey of the important concepts, attempts, efficiency, and the research trend in spam filtering. The preliminary discussion in the study background examines the applications of machine learning techniques to the email spam filtering process of the leading internet service providers (ISPs) like Gmail, Yahoo and Outlook emails spam filters. Discussion on general email spam filtering process, and the various efforts by different researchers in combating spam through the use machine learning techniques was done. Our review compares the strengths and drawbacks of existing machine learning approaches and the open research problems in spam filtering. We recommended deep leaning and deep adversarial learning as the future techniques that can effectively handle the menace of spam emails.

SOFTWARE REQUIREMENTS:

OS                                : Windows

Python IDE                  : Python 2.7.x and above

Language                             : Python Programming

Database                             : MYSQL

 

HARDWARE REQUIREMENTS:

 RAM                :  4GB and Higher

Processor          :  Intel i3 and above

Hard Disk         : 500GB Minimum

 

Setting up Software Environment

Python is a high level, interpreted, interactive and object-oriented scripting language.

Python is designed to be highly readable and has fewer syntactical constructions than other languages. Python is used in the development of this model. In this experiment, the following python libraries are used to develop the machine learning models:

• NLTK: It is a python package which works with human language data and provides an easy-to-use interface to different lexical resources like WordNet and text processing libraries. These lexical resources are used for classification, tokenization, stemming, tagging, parsing, and semantic reasoning [23].

• Pandas: It is a python package which acts as a data analysis tool and deals with data structures. Pandas carry out entire data analysis workflow in Python without having to switch to a more domain specific language like R [46].

• Tweepy: It is used in accessing the Twitter API by establishing the connection and to gather tweets from Twitter [24]. This module is used to stream live tweets directly from Twitter in real-time.

• Numpy: NumPy is the fundamental package for computing with Python. It is used to add support to multi-dimensional arrays and matrices, with a large collection of high-level mathematical functions [47].

• scikit-learn: It is a simple and efficient tool for data mining and data analysis [47]. • matplotlib python library which generates plots, histograms, power spectra, bar charts, etc.

In this work matplotlib.pyplot module is used to plot the metrics [47].

• Gensim It is used to automatically extract semantic topics from documents, as efficiently as possible. Gensim is designed to process raw, unstructured text data. The algorithms in Gensim, such as Word2Vec where it automatically discovers the semantic structure of phrase by examining statistical co-occurrence patterns within a corpus of training documents. These algorithms are unsupervised. Once these statistical patterns are found, any plain text documents can be succinctly expressed in the new, semantic representation and queried for topical similarity against other documents [48].

• Keras: Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research [49]

 

TABLE OF CONTENTS

·        Title Page      

·        Declaration

·        Certification Page

·        Dedication

·        Acknowledgements

·        Table of Contents

·        List of Tables

·        Abstract

 

CHAPTER SCHEME

CHAPTER ONE: INTRODUCTION

CHAPTER TWO: OBJECTIVES

CHAPTER THREE: PRELIMINARY SYSTEM ANALYSIS

·         Preliminary Investigation

·         Present System in Use

·         Flaws In Present System

·         Need Of New System

·         Feasibility Study

·         Project  Category

CHAPTER FOUR: SOFTWARE ENGINEERING AND PARADIGM APPLIED   

·         Modules

·         System / Module Chart

CHAPTER FIVE: SOFTWARE AND HARDWARE REQUIREMENT

CHAPTER SIX: DETAIL SYSTEM ANALYSIS

·         Data Flow Diagram

·         Number of modules and Process Logic

·         Data Structures  and Tables

·         Entity- Relationship Diagram

·         System Design

·         Form Design 

·         Source Code

·         Input Screen and Output Screen

CHAPTER SEVEN: TESTING AND VALIDATION CHECK

CHAPTER EIGHT: SYSTEM SECURITY MEASURES

CHAPTER NINE: IMPLEMENTATION, EVALUATION & MAINTENANCE

CHAPTER TEN: FUTURE SCOPE OF THE PROJECT

CHAPTER ELEVEN: SUGGESTION AND CONCLUSION

CHAPTER TWELE: BIBLIOGRAPHY& REFERENCES          

Other Information

 

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PROJECT WITH HARD BINDING	1850/- Only
TOTAL COST (SYNOPSIS, SOFTCOPY, HARDBOOK, and SOFTWARE, PPT)	2500/- Only
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