A Robust Keywords Based Document Retrieval by Utilizing Advanced Encryption System

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 06 | June -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 574
A Robust Keywords Based Document Retrieval by Utilizing Advanced
Encryption System
F Ajaykumar Mourya1, Asst. Prof. Chinmay Bhatt 2
1 M.Tech. Scholar Department of Computer Science & Engineering R.K.D.F. I.S.T. Bhopa, India
2Assistant Professor Department of Computer Science & Engineering R.K.D.F.I.S.T. Bhopal, India
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Abstract - As the digital data increases on servers different
researcher have focused on this field. As various issues are
arise on the server such as data handling, security,
maintenance, etc. In this paper document retrieval was
proposed that efficiently the fetch document as per query.
Here hash based indexing of the dataset document was done
by utilizing term features. In order to provide privacy for the
terms each of this is identified by a unique number and each
document has its hash index key for identification. Experiment
was done on real and artificial dataset. Results shows that
NDCG, precision, recall parameter of the work is better as
compare to previous work on different size of datasets.
Key Words: Information Retrieval, TextFeature, TextMining,
Text Ontology .
1. INTRODUCTION
Data fetching is a field that has been creating in parallel with
database frameworks for a long time. Not at all like the field
of database frameworks, which has concentrated on query
and transaction handling of organized information, data
fetching is worried with the association and fetching of data
from documents. Since data fetching and database
frameworks each handle various types of information,some
database framework issues are normally not presentindata
fetching frameworks, for example, concurrency control,
recovery, transaction management, etc. Additionally, some
regular data fetching issues are generally not experienced in
customary database frameworks, for example,unstructured
reports, tentative search by use of keywords and the idea of
relevance. Because of the wealth of content data, data
fetching has discovered numerous applications. There exist
numerous data fetching frameworks, for example, on-line
library list frameworks, on-line record administration
frameworks, and the web crawlers. An ordinary data
fetching issue is to find relevant documents in an archive
accumulation in light of a client's query, which is regularly a
few keywords depicting a information need, in spite of the
fact that it could likewise be anillustrationimportantrecord.
In such a pursuit issue, a client steps up with regards to pul
the significant data out from the gathering; this is most
suitable when a client has some impromptu data need, for
example, discovering data to purchase an car. At the point
when a client has a long data require , a fetching framework
may likewise step up with regards to ―push‖ any recently
arrived data thing to a client if the thing is judged as being
significant to the client's data require. Such a data get to
process is called data filtering, and the comparing
frameworks are regularly called filtering frameworks or
recommender frameworks.
Text mining is a minor departure from a field called
information mining that tries to discover intriguing
examples from vastdatabases.Contentdatabasesarequickly
becoming because of the expanding measure of data
accessible in electronic shape, for example, electronic
productions, different sorts of electronic records, email, and
the World Wide Web. These days the vast majority of the
data in government, industry, business, and different
organizations are put away electronically, as content
databases. Information put away in most content databases
are semi organized information in that they are neither
totally unstructured nor totally organized. For instance, an
archive may contain a couple organized fields, for example,
title, creators, distribution date, and class, et cetera,
additionally contain some to a greatextentunstructuredtext
data, for example, summary and conclusions.
2. RELATED WORK
Yang et al., [35] proposed a new approach that is L2,1 -norm
regularized Unsupervised Discriminative Feature Selection
(UDFS). The algorithm chooses the most discriminative
feature subset from the entire feature set in batch mode.
UDFS outclasses the existing unsupervised feature selection
algorithms and selects discriminative features for data
representation. The performance is sensitive to the number
of selected features and is data dependent.
Cai et al., [36] presented a novel algorithm, called Graph
regularized Nonnegative Matrix Factorization (GNMF) [37],
which explicitly considers the local invariance. In GNMF, the
geometrical information of the data spaceispre-arrangedby
building a nearest neighborgraphandgathering parts-based
representation space in which two data points are
adequately close to each other, if they are connected in the
graph. GNMF models the data space asa submanifoldrooted
in the ambient space and achieves more discriminating
power than the ordinary NMF approach.
Fan et al., [38] suggested a principled vibrational framework
for unsupervised feature selection using the non Gaussian
data which is subjective to several applications that range

from several diversified domains to disciplines. The
vibrational frameworks provides a deterministic alternative
for Bayesian approximation by the maximization of a lower
bound on the marginal probability which has an advantage
of computational efficiency. 2) Text summarization and
Dataset: Several approaches have been developed till date
for automatic summarization by identifying importanttopic
from single document or clustered documents.
Gupta et al., [39] describes a topic representation approach
that captures the topic and frequency drivenapproachusing
word probability which gives reasonable performance and
conceptual simplicity.
Negi et al., [40] developed a system that summarizes the
information from a clump of documents. The proposed
system constructs identifiers that are useful for useful for
retrieving the important the information fromthegiventext.
It achieves high accuracy but cannot calculate the relevance
of the document.
3. PROPOSED METHODOLOGY
The content mining is done in this work by the proposed
technique for recovery of the record or articles in the data-
set without having any earlier learning of the archives.
Entire work is clarified in fig 1 and 2.
3.1 Stop Word Removal
Stop Word Removal is a procedure utilized for
transformation of record into feature vector. Content stop
word removal is comprising of words which are in charge of
bringing down the execution time of learning models. Stop
word removal reduces the span of the info present in
articles. Stop-words are practical words which happen as
often as possible in the language of the content (for instance
a, the, an, of and so forth in English language), with the goal
that they are not valuable for grouping [3, 10, 13]. Let D is
archive [India is an incredible nation. Its a nation of various
religion and caste.], Stop-word S is [a, are, an, and, am, for, is,
its, when, where, etc.]. At that point in pre-handling
subtraction operation is done on these sets. Here D-S =
[India, incredible, nation, diverse, religion, caste].
Fig.1 Block diagram of proposed Learning Model.
Stop word Removal
Term Frequency
Document Code Key
Unique Term number
Advanced Encryption
system
Robust Data Set
Each Document
Training Data Set

3.2 Term Frequency
The vector which contains the first step processed data is
use for collecting feature of that document. This is done by
comparing the vector with vector KEY (collection of
keywords) of the ontology of different area. So the refined
vector will act as the feature vector for that document [11,
14].
So the list of words which are crossing the threshold are
consider as the keywords or feature of that document.
[feature ] = mini_threshold ( [processed_text] )---(1)
In this way term feature vector is created from the
document.
3.3 Unique Term Number
Now assign number to each term of the different document.
So that a dictionary of words with there number is created
where each text is identified by separate number. Here
words coming from different document which are already
present in the dictionary is not updated. So those terms
which are not present in the dictionary is insert in the
dictionary with unique termed.
3.4 Document Code Key
In this step document index is decide based on the terms
collected from the document. Here all the term are arrange
in decreasing order as per the terms frequency value of the
terms in the document. So new order of the document term
is 918465 this is based on the decreasing order of the term
frequency. So from above table one has number is generate
for selected document in similar fashion other document in
the dataset get collected. Now as per the index value
document is identify.
3.5 Advanced Encryption System
Now common step for all kind of data is that each data need
to be convert into 16 element set of input. Here each input
need to be in integer data type. In case of numeric this is ok,
but in case of image gray scale will convert pixel values in
integer form. While for text unique number is assign for all
extracted words.
In this encryption algorithm four stages are perform in each
round. While final round consist of three stages only. These
steps are common in both encryption as well as decryption
algorithm where decryption algorithm is inverse of the
encryption one. So round consist of following four stages.
1. Substitute bytes
2. Shift rows
3. Mix Columns
4. Add Round Key
In final round simply all stages remain in same sequence
except Mix Columns stage.
Fig.2 Block diagram of proposed Searching Model.
In this searching model whole step of assigning unique
number is same as done in previous steps. So searching and
retrieving related document is new for searching model.For
convince words obtained from user text query is called as
keywords.
3.6 Searching
In this step as per the keywords (terms) from the user text
query have their own unique number while number are
same as present in the dataset. Due to this unique number
privacy of the user query is increases. Now all unique
number that are present in the text query is compare from
each document code key. So top most documentcodehaving
similar codes as present in the user query is rank.
Unique Term
Number
User Text
Query
Each Term
Similarity
Score
Document
Code Key
Relevant
Document

4. EXPERIMENT AND RESULT
In order to implement above algorithm for document
retrieval MATLAB 2012a tool was used. Heresamework can
be implement on other programming language as well. But
as some of the function was inbuilt in the tool which help
researcher to focus on the work. Experiment was done on
real as well as on artificial dataset. Here different set of dat
set was use for retrieving documents.
4.1 Evaluation Parameter
As various techniques evolve different steps of working for
classifying document into appropriate category. So it is
highly required that proposed techniques or existing work
need to be compare on same dataset. So following are some
of the evaluation formula shown in equation number 4, 5,
6and 7 which help to judge the classification techniques
ranking.
Precision = (True_positive/(False_positive+True_positive))
True positive Rate = (True_positive /(False_negative+
True_positive))
F-Measure = (2xPrecisionxRecall/ (Recall + Precision))
4.2Results
Table. 1. Comparison of accuracy value with previous
work [15].
Comparison of True Positive Rate
Query Proposed Work Previous work[15]
Q1 0.5 0.44
Q2 0.6 0.5
Q3 0.44 0.4
Q4 0.5 0.44
From above table 1 it is obtained that proposed work true
positive rate is higher then previous work on different
queries. As query set has good quality keywords results of
proposed work is also high.
Table. 2. Comparison of precision value with previous
work [15].
Comparison of precision values
Q1 0.7143 0.5714
Q2 0.8571 0.7143
Q3 0.5714 0.5714
Q4 0.7143 0.5714
From above table 2 it is obtained that proposed work
precision value is higher than previous work on different
Table. 3. Comparison of F-Measure value with previous
work [15].
Comparison of F-Measure values
Q1 0.5882 0.5
Q2 0.70 0.5882
Q3 0.5 0.4706
Q4 0.588 0.5
From above table 3 it is obtained that proposed work F-
Measure value is higher than previous work on different
5. CONCLUSIONS
With the drastic increase of the digital text data on the
servers, libraries it is important for researcher to work on it.
Considering this fact work has focus on one of the issue of
the document retrieval. Here many researchers has already
done lot of work but that is focus only on the content
classification where in this work document are classify.
Proposed work has increase the retrieval efficiency of the
work in all different evaluation parameters. So use of hash
based indexing provide privacy withefficiencyfordocument
retrieval. As there is always work remaining in every
because research is a never ending process, here one can
implement similar thing for different other language.
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