Download Association Rule Mining in Discovering Travel Pattern in

Praveen Rani et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (4) , 2014, 5015-5019
Association Rule Mining in Discovering Travel
Pattern in Passport Data Analysis
Praveen Rani#1 , Dr. Rajan Vohra*2 , Anju Gulia#3
# 1,3
Students, Computer Science & Engineering Deptt.,
P.D.M College Of Engineering,Bhadurgarh, Haryana(India)
*2
Head of Deptt., Computer Science & Engineering Deptt.,
P.D.M College Of Engineering,Bhadurgarh , Hrayana(India)
Abstract— This paper presents a comprehensive statistical
experiment to identify the travel patterns by analysing passport and
visa database. This study, provide information about the choice of
destination of Indian travellers, they choose for the fulfilment of
their specific purpose. Data mining system discovers patterns and
relationships hidden in data, and actually is a part of a larger
process called “knowledge discovery” which describes the steps that
must be taken to ensure meaningful results. The presented work
focus on implementing different data mining approaches on
passport database which is simulated from embassy and visa
database. This paper combines two major approaches to discover
travel patterns by association rules and then validate this result with
the result comes from another research work. In that research work
travel patterns was discovered by using clustering in data mining.
According to defined approach, association rule technique is applied
on the simulated primary database of passport and visa, to discover
the travel patterns in different segments. These segments are
already found out in previous research work. After discovering
travel patterns via association rules, its result will compare from the
result of discovering travel pattern via clustering, which are already
find out in previous research work. So this step will validate the
result of this research work also.
Key Words: Travellers, Travel pattern, Data mining, Association
Rule, Apriori algorithm, Passport, Visa, Weka tool.
I. INTRODUCTION
Data mining is the exploration and analysis of large
quantities of data in order to discover valid, novel,
potentially useful, and ultimately understandable patterns
in data [1]. Data mining is a process that uses a variety of
data analysis tools to discover patterns and relationships
in data that may be used to make valid predictions [2].
Data mining can be viewed as a result of the natural
evolution of information technology [3]. Data mining is
the process of discovering interesting patterns and
knowledge from large amounts of data. The data sources
can include databases, data warehouses, the Web, other
information repositories, or data that are streamed into the
system dynamically [4]. Data Mining is the discovery of
hidden information found in databases and can be viewed
as a step in the knowledge discovery process [5] [6].
Travel is the movement of people between relatively
distant geographical locations [7], and can involve travel
by foot, bicycle, automobile, train, boat, airplane, or other
means, with or without luggage, and can be one way or
round trip. Tourism embraces nearly all aspects of our
society. Apart from its importance to economic changes,
human socio-cultural activities and environmental
development, tourism is related to other academic subjects
such as geography, economics, history, languages,
psychology, marketing, business and law, etc [8]. For
www.ijcsit.com
travelling outside the countries traveller must have their
identity check or passport.
A Passport is an essential travel document for those who
are travelling abroad for education, tourism, pilgrimage,
medical attendance, business purposes and family visits
[9]. It is an official document issued by a government,
certifying the holder's identity and citizenship and
entitling them to travel under its protection to and from
foreign countries. Most nations require entering travellers
to obtain a visa, an endorsement on the passport showing
that the proper authorities have examined it and
permitting the bearer to enter the country and remain for a
specified period [10].
A Visa is a certificate issued or a stamp marked (on the
applicant's passport) by the immigration authorities of a
country to indicate that the applicant's credentials have
been verified and he or she has been granted permission to
enter the country for a temporary stay within a specified
period. This permission, however, is provisional and
subject to the approval of the immigration officer at the
entry point [11].
A. Significance of the problem
The questions this research work can provide the solutions
to, can be given as follows:
1) How many Indian Travellers travel from India to
other foreign countries?
2) Which country is dominantly chosen by Indian
Travellers for achieving their specific purpose?
3) Is this result is validate by different approach?
Today most of peoples leave their home country and
move to foreign countries for their specific purpose. It
becomes necessary to know the reasons behind their
leaving, whether it is kind of permanent leaving or addock leaving. This research is basically focused to
discover the foreign country chosen by most of travellers
in each category and validate this result by comparing it
with the result of previously known work in which travel
patterns was discovered by using clustering method [12].
II. RESEARCH BACKGROUND
In year 2005, Junyi Zhang, Akimasa Fujiwara & Makoto
Chikaraishi, a study was done to identify travel patterns
and examine their influential factors in the context of
developing countries and a comparative analysis is
conducted in order to explore the similarities and
differences among travel patterns in different cities [13].
5015
Praveen Rani et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (4) , 2014, 5015-5019
In year 2013, Pairaya Juwattanasamran et al., provide the
rules in database is capable to utilize to be a database
which is plugged into a server when the user registered to
the system his/her profile will be collected in database and
the system will select the suitable pattern for that user and
when the user clicks to search any item, the system will
deliver the alternative items which relates to the first
selected choice. User will get many alternatives choices
and suppliers like hotels, restaurants and tourist attractions
also have more chance to promote their products and
services [14].
The innovative system contributes new knowledge which
gathers increasingly on database. While new user is
searching for travel information on mobile devices, the
system will learn user behavior transaction which user
clicks. The system will collect new data and analyze them
then interpret to user. The system will learn increasingly
when several users click more on mobile application. It
will collect more data and repeatedly analyze the newly
obtained data. This presents that the knowledge discovery
in database will get bigger. Their framework implies to
increase learning model. If the travelers’ behavior
changes, the pattern in database also changes. The same
as tourism products and services changed, the knowledge
in database will change to new rules as well. Therefore,
the database developed in this study is capable to
accumulate new knowledge and update all the time. The
system will operate more accurate and work effectively
along with the dynamics of the world.
III. RESEARCH METHODOLOGY
For solving the above two problems some research
techniques and methodologies are used for obtaining the
desired result. Some tools and algorithms are required for
obtaining the result. Main steps under the research
methodologies are:Review literature or research papers – first of all
literatures and research papers were reviewed for getting
more information about the problem and knowing which
type of work was done by others on this topic and by
which method.
Identify tools – then tools required for solving the
problem were identified and the best tool – “WEKA” was
selected from all.
Study database attributes and data structure –
attributes and structure of the database was thoroughly
studied for finding out useful attributes from the passport.
For critical attributes used in the database first and last
page of the passport was studied.
Organize filed visits to NIC (NATIONAL
INFORMATICS CENTRE) that process and generate
the passports. From there we get information about the
passport holders, visa services and information needed at
the time of visa generation.
Study the work flow in the work centers like NIC and
TCS.
Determine nature and definition of research problem
and work flow of the problem for getting accurate and
desired result.
www.ijcsit.com
Organize the database [15] with useful attributes and
populate it then perform data analysis using suitable tool
e.g., WEKA in order to generate the result.
IV. CONCEPTUAL FRAMEWORK
Association rules mining has been a popular mining
approach for discovering positive associations given a set
I = {I1, I2, …, Ik} of items (k-item sets) in a transactional
database D. Each transaction T ∈ D is a subset of I. It is
often called frequent pattern mining since it discovers
frequent patterns [16]. An association rule is an
expression in the form of X => Y (X ∩ Y = φ) where X is
called antecedent and Y is called consequent. Association
rules mining involves two estimates: support and
confidence. The support of an item set X ∈ D is the
number of transactions in D that have X in them. That is,
it is the probability X and Y being in the dataset. The
confidence of an association rule X => Y is the
conditional probability of having Y contained in a
transaction given that X is in that transaction.
The Apriori algorithm developed by [17] is a great
achievement in the history of mining association rules
[18]. It is by far the most well-known association rule
algorithm. This technique uses the property that any
subset of a large item set must be a large item set. Also,
it is assumed that items within an item set are kept in
lexicographic order.
This paper presents solution to two main problems related
to discovering travel patterns in passport analysis. These
problems are:
A. Discovering Travel Pattern Via Association Rule
By using association rules, link between segments and
countries will be discovered. It also finds out the
dominant country for each segment by using Apriori
algorithm. It will generate the different rules according to
the visa service and destination of visa holders like as:
Rule 1. Visa type  Destination
Rule 2. Destination Visa type
Both rules support each other and having confidence
value acc. to the number of entries in database. 0.1
confidence value for each 55 entries. The value of
confidence for supporting the rule should lies in between
0 – 1. The support supp(X) of an item set X is defined as
the proportion of transactions in the data set which
contain the item set. The confidence of a rule is defined
conf (X Y) = supp (X U Y)/ supp (X) Then choose the
strongest and more confident link among all links and
define that link as a rule.
Fig. 1 Diagrammatic view
5016
Praveen Rani et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (4) , 2014, 5015-5019
B. Validation of Travel Pattern
After getting results from Association Mining technique,
the result will be analyzed and compared with the already
known travel patterns discovered clustering technique
[12] to see whether the results are same and supporting
each other or not. If results will same then rules generated
by association rule will valid and provide the correct
travel patterns.
Fig. 2 Validation of results
V. RESULTS AND DISCUSSION
The data analysis is processed using WEKA data mining
tool for exploratory data analysis. For discovering travel
patterns and its validation same data set [15] is used
which as used to discovered travel patterns by clustering
[12]. A database of 550 records/entries is collected from
embassy and visa database. Primary data collected by
random sampling, is used to solve the problems.
A. Discovering Travel Pattern Via Association Rule
In this part, the whole data of passport and visa is refined
and input to Weka tool for mining. After loaded the
database in Weka all other attributes are removed and
keep only two attributes named visa type & destination
to get the accurate link between visa i.e purpose and
destination. In this visa type is based on the purpose for
which traveller want to move in foreign country. Here,
travellers of different profiles or same segment are linked
with dominant destination on basis of value of support
and confidence to form a rule.
There are total 10 rules supporting each other according to
their dominant category. these rules are generated by
setting some values of Apriori algorithm in Weka, such as
choose lift as metric type, set delta at 0.01, minMetric at
1.1, lowerBoundMinSupport at 0.0, etc. These rules
having some confidence values as shown in Fig. 3, this
supports them.
Fig. 3 Association rule
Fig. 3 represents 10 rules. These are as follows:
• Rule 1. visa type = Student 80  destination = UK
34 means there are total 80 Students and maximum
Students go to UK (34 out of 80), rest of the Students
go anywhere else but not more than UK.
• Rule 2. Only 34 people come to UK and they all are
Students.
• Rule 3. There are 114 Tourist type visa holders, out
of which 35 visa holders go to France for travelling.
• Rule 4. It support the above rule as, In France there
are 35 Indian travellers having tourist visa.
• Rule 5. There are 81 I.T Worker type visa holders,
out of which 54 visa holders go to USA
www.ijcsit.com
•
Rule 6. It supports the above rule as, In USA there
are 81 Indian travellers having I.T Worker visa.
• Rule 7. There are 124 Business type visa holders, out
of which 60 visa holders go to Ireland.
• Rule 8. It support the above rule as, In Ireland there
are 60 Indian travellers, they all are having Business
visa.
• Rule 9. There are 151 Labor type visa holders, out of
which 80 visa holders go to Dubai.
• Rule 10. It supports the above rule as, In Dubai there
are 80 Indian travellers they all are having Labor
visa.
In Table1, the above said result is nut shelled.
5017
Praveen Rani et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (4) , 2014, 5015-5019
TABLE 1 TRAVEL PATTERN VIA ASSOCIATION RULE
Segments
All Destinations
USA
UK
New Zealand
Denmark
Norway
Switzerland
France
U.A.E
China
Spain
Italy
Malaysia
Ireland
Australia
New Zealand
Hong Kong
UAE
Dubai
Saudi Arab
U.S.A
China
Japan
Student
Tourist
Business
Labor
I.T Worker
Count Of
Destinations
27
34
9
6
4
10
35
02
27
19
15
06
60
14
30
20
31
80
40
54
7
20
B. Validation of Travel Pattern
In this part, two results from two different studies (while
both using same dataset) are compared and check whether
they supporting each other on both the results are
different. If both the results are same that means the travel
patterns discovered from two different techniques are
validate and accurate.
1) Result of Discovering Travel Pattern via
Clustering [12]
TABLE 2
Cluster
Number
Cluster 0
Cluster 1
Cluster 2
Cluster 3
Cluster 4
TRAVEL PATTERN VIA CLUSTERING
Segment
Dominant Destination
Student
Tourist
Business
I.T Worker
Labor
UK
France
Ireland
USA
Dubai
From table 2, we get the dominant destination in each
segment by clustering technique. Now this result is
compared with result of this study, which comes by using
association rule mining.
2) Result of validation
TABLE 3.
ANALYSIS OF RESULT
Segments
Dominant
Destination
Via Clustering
Student
Tourist
Business
Labor
I.T Worker
U.K
France
Ireland
Dubai
U.S.A
www.ijcsit.com
Dominant
Destination
Via Association
Rule
U.K
France
Ireland
Dubai
U.S.A
Dominant Destination
Rule Formed
UK
Student  U.K
France
Tourist  France
Ireland
Business  Ireland
Dubai
Labor  Dubai
U.S.A
I.T Worker  U.S.A
Table 3 contain dominant destination in each segment
which is an output of two different approaches say
clustering and association rule. As, the dominant
destinations for all segments are same in both studies, it
proves that the result of this study is accurate and valid.
Hence, this study discover accurate travel pattern by using
association rules.
VI. FUTURE WORK
There is always remains scope of improvement in any
research and so is with this research also. This work used
simulated database and not real so it is a kind of
algorithm by using which real world problem will solved
in future if real world data will available. The other scope
is for airline. The airlines database will use to find out
frequent customers of those airlines so that the airline will
move one step ahead to increase revenue and open a door
for its success.
Profiling of travellers in Airlines database are:• Frequent travellers
• Economy Flyers
• Premium Flyers
• Identifying profitable Routes and Destination
• Travel industry shareholders e.g. Hotels,
Transport sector etc.
ACKNOWLEDGEMENT
Authors would like to thanks to their head Dr. Rajan
Vohra, HOD of CSE & I.T department, PDMCE,
Bahadurgarh, for his valuable support and help.
[1]
[2]
REFERENCES
Ramakrishnan and Gehrke. Database Management Systems, 3rd
Edition.
Introduction to Data Mining and Knowledge Discovery, Third
Edition, ISBN: 1-892095-02-5, Two Crows Corporation
5018
Praveen Rani et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (4) , 2014, 5015-5019
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
Marek Maurizio, “Data Mining Concepts And Techniques”,
domenica 20 marzo 2011.
Jiawei Han, Micheline Kamber “Data Mining:
Concepts
and
Techniques”.
Fayyad, U., “Data Mining and Knowledge Discovery: Making
Sense out of Data”, IEEE Expert, Oct. 20-25, 1996.
Ming-Syan Chen, Jiawei Han and Philip S. Yu, “Data Mining: An
Overview from a Database Perspective”, IEEE Transactions on
Knowledge and Data Engineering, Vol. 8, No. 6, pp. 866-883,
1996.
Marion Libreros, “Tourism Statistics: Challenges and Good
Practices”, UNWTO/UNSD WS Vientiane June 16-19 2009.
Personal, Social and Humanities Education Section Education
Bureau “Introduction to Tourism” 13/F, Room 1319, Wu Chung
House, 213 Queen’s Road East, Wan Chai Hong Kong.
http://www.passportindia.gov.in/AppOnlineProject/online/knowPa
ssportSeva
http://www.merriam-webster.com/dictionary/passport
http://www.businessdictionary.com/definition/visa.html
Praveen Rani, Dr. Rajan Vohra, Anju Gulia, “Discovering Travel
Pattern In Passport Data Analysis”, IJIRS, in communicating.
Junyi Zhang, Akimasa Fujiwara, Makoto Chikaraish,
“Comparative Analysis Of Travel Patterns In The Developing
Cities Based On A Hybrid Model”, Journal Of The Eastern Asia
Society For Transportation Studies, Vol. 6, Pp. 4333 - 4348, 2005.
Pairaya Juwattanasamran, Sarawut Supattranuwong, Sukree
Sinthupinyo, “Applying
Data Mining to Analyze Travel Pattern
in Searching Travel Destination Choices”, The International
Journal Of Engineering And Science (IJES) ||Volume||2 ||Issue|| 4
||Pages|| 38-44||2013|| ISSN(e): 2319 – 1813 ISSN(p): 2319 –
1805.
Database - “Travel Pattern”.
Ickjai Lee, Guochen Cai, Kyungmi Lee, “Mining Points-of-Interest
Association Rules from Geo-tagged Photos”.
Rakesh Agrawal and Ramakrishnan Srikant, Fast Algorithms for
Mining Association Rules in Large Databases, Proceedings of the
Twentieth International Conference on Very Large Databases, pp.
487-499, Santiago, Chile, 1994.
David Wai-Lok Cheung, Vincent T. Ng, Ada Wai-Chee Fu, and
Yongjian Fu, Efficient Mining of Association Rules in Distributed
Databases, IEEE Transactions on Knowledge and Data
Engineering, Vol. 8, No. 6, pp. 911-922, December 1996.
www.ijcsit.com
[19] Osmar R. Zaïane, “Principles of Knowledge Discovery in
Databases - Introduction to Data Mining”, CMPUT690, 1999.
[20] Weka online documentation:
http://www.cs.waikato.ac.nz/ml/weka/index_doc umentation.html
[21] YAN-TAO ZHENG, Mining Travel Patterns from Geotagged
Photos, ACM Transactions on Intelligent Systems and
Technology, Vol. V, No. N, Month 20YY.
[22] Recurrent sources in sequences, F., 2003: A. gionis and h. mannila.
RECOMB.
[23] Chakrabarti, S., S. Sarawagi, and B. Dom, 1998: Mining
surprising temporal patterns. Proc. of the Twenty fourth Int’l conf.
on Very Large Databases (VLDB), New York, USA.
[24] Hand, D., Mannila, H., Smyth, P., Principles of Data
Mining,
Prentice Hall of India 2001
[25] Tan, C. L., Quah, T. S. and Teh, H. H., An Artificial Neural
Network that models Human Decision making, IEEE Computer,
64- 70, 1996
[26] Goldberg, D.E. Genetic Algorithms in Search
Optimization
and Machine
Learning, Addison - Wesley, 1989
[27] Fraley, Andrew, and Thearting, Kurt (1999). Increasing customer
value by integrating data mining and campaign management
software. Data Management, 49–53.
[28] I.Krishna Murthy, “Data Mining- Statistics
Applications: A
Key to Managerial
Decision Making”,
Article/Report
indiastat.com, AprilMay 2010.
[29] Dong-sheng Liu and Shu-jiang Fan, Tourist Behavior Pattern
Mining Model Based on Context, Hindawi Publishing
Corporation Discrete Dynamics in Nature and Society ,Volume
2013, Article ID 108062, 12 pageS.
[30] Jiawei Han , Hong Cheng , Dong Xin , Xifeng Yan, Frequent
pattern mining: current status and future directions, Received: 22
June 2006 / Accepted: 8 November 2006 / Published online: 27
January 2007, Springer
Science+Business
Media,
LLC
2007.
[31] Zhu F, Yan X, Han J, Yu PS, Cheng H (2007)
Mining colossal
frequent patterns
by core
pattern
fusion.
In:
Proceeding of the 2007
international
conference on
data engineering
(ICDE’07),Istanbul, Turkey
[32] Margaret H. Dunham, Yongqiao Xiao, Le Gruenwald, Zahid
Hossain “A Survey Of Association Rules”.
[33] Rakesh Agrawal, Ramakrishnan Srikant, “Fast
Algorithms for
Mining Association Rules”.
5019
Praveen Rani et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (4) , 2014, 5015-5019
Name
ANJU
JYOTI SANGWAN
JYOTI DAHIYA
KUSUM
NISHA
PAYAL
PRAVEEN
RAJEEV
RUCHI SANGWAN
RUCHIKA
SONIA
RADHIKA
SUCHETA
HARPREET SINGH
DEEPIKA SINGH
RAM KUMAR
SHWEETA
RENUKA
AMIT
VANDANA
ANU
MEENAKSHI
AJAY
MUKESH
ANSHULA
PRIYANKA GERA
PRIYANKA KATHURIA
ROHIT
RAKSHIT
ANSHUL
SWATI
Era Jain
Siya shokeen
Vikram Gulia
Ravinder Goyal
Amit Goyal
Adrash Gupta
Beenu
Vivek Tetarwal
Preeti Dagar
Sujata Saini
Mohit
Tarun Kumar
Manju Birhman
Gunjann Shah
Anjana Jha
Himender Rao
Rani Roy
Sonam Verma
Sagar Drall
Anju
Praveen
Gimpy
Jyoti Sangwan
Jyoti Dahiya
Kusum Dahiya
Nisha
Payal
Rajiv Dhawan
Ruchika
Ruchi
Sonia
Sucheta
Amar
Deepika Singh
Renuka
Priyanka Gera
Swati
Arya Dahiya
Age
45
60
56
48
57
23
46
32
24
60
22
36
20
47
56
39
38
44
33
25
34
36
31
30
20
26
48
42
27
28
30
65
62
62
58
72
46
26
29
27
55
57
72
64
74
61
25
38
33
40
30
56
46
48
47
45
62
42
50
85
35
21
40
32
55
45
34
38
38
ANNEXURE- TRAVEL PATTERN
Gender Profession
visa type
F
Business
Business
F
Govt. servant
Tourist
F
Docter
I.T Workers
F
C.A
Business
F
C.S
Business
F
student
student
F
Purchase Engg.
Business
M
Manager
Tourist
F
student
student
F
General Manager
Business
F
student
student
F
Labour
Labour
F
student
student
M
Labour
Labour
F
Software Tester
Business
M
Labour
Labour
F
Labour
Labour
F
Labour
Tourist
M
proffesor
Tourist
F
Research Scholar
Tourist
F
vice chanseller
student
F
Accountant
Business
M
PRO
Business
M
Income tax Officer I.T Workers
F
Sales tax officer
I.T Workers
F
Research Scholar
student
F
J.E
Business
M
Docter
I.T Workers
M
Labour
Labour
M
Architect
Labour
F
Web Designer
Business
F
i.t worker
I.T Workers
F
Labour
Tourist
M
Enggineering
I.T Workers
M
Banking
Business
M
Defence Services
Tourist
M
Driver
Labour
F
teacher
Tourist
M
Delhi Police
Tourist
F
student
student
F
Real Estate
Business
M
teacher
Tourist
M
Politics
Tourist
F
i.t worker
I.T Workers
F
Bussiness
Business
F
Labour
Labour
M
Govt. sector
Tourist
F
Labour
Labour
F
Salesman
Labour
M
Labour
Labour
F
i.t worker
I.T Workers
M
Salesman
Labour
F
Defence Services
Tourist
F
i.t worker
I.T Workers
F
Enggineering
I.T Workers
F
Banking
Business
F
Driver
Labour
F
Defence Services
Tourist
M
Banking
Business
F
i.t worker
I.T Workers
F
Enggineering
I.T Workers
F
Salesman
Business
F
i.t worker
I.T Workers
M
i.t worker
I.T Workers
F
Driver
Labour
F
Real Estate
Business
F
i.t worker
I.T Workers
F
Banking
Business
M
Enggineering
I.T Workers
www.ijcsit.com
Source
Mumbai
Delhi
Chandigarh
Mumbai
Delhi
Delhi
Delhi
Mumbai
Calcutta
Banglore
Banglore
Calcutta
Calcutta
Banglore
Mumbai
Delhi
Delhi
Delhi
Mumbai
Mumbai
Mumbai
Chandigarh
Chandigarh
Banglore
Banglore
Banglore
Mumbai
Delhi
Mumbai
Mumbai
Delhi
Chandigarh
Mumbai
Delhi
Delhi
Delhi
Mumbai
Calcutta
Banglore
Banglore
Calcutta
Calcutta
Banglore
Mumbai
Delhi
Delhi
Delhi
Mumbai
Mumbai
Mumbai
Chandigarh
Chandigarh
Banglore
Banglore
Banglore
Mumbai
Delhi
Mumbai
Delhi
Chandigarh
Delhi
Mumbai
Calcutta
chennai
Banglore
hyderabad
ahmedabad
puna
surat
Destination
Ireland
Switzerland
USA
Ireland
Australia
UK
Australia
France
UK
Australia
UK
UAE
New Zealand
UAE
Australia
UAE
UAE
UAE
China
Switzerland
Denmark
Australia
Australia
USA
USA
Norway
Australia
USA
UAE
UAE
Australia
USA
UAE
USA
Australia
France
UAE
China
France
UK
Australia
China
France
USA
Hong Kong
UAE
France
Dubai
Dubai
Dubai
USA
Dubai
France
USA
USA
Hong Kong
Dubai
China
Hong Kong
USA
USA
Hong Kong
USA
USA
Dubai
Hong Kong
USA
Australia
USA
Praveen Rani et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (4) , 2014, 5015-5019
Name
Anshul Bhutani
Amit Maan
Anshul
Divya
Ajay Kumar
Anshul Arora
Akshay
Harpreet Sehgel
Meenakshi
Anu Dahiya
Vandana Tehlan
Nidhi Mathur
Vinay
Barun Sobti
Khusi Kumari
Rishab Kundra
Muskaan
Piyali
Priyanka Kathuriya
Mohit Dahiya
Karan Singh
Arun Kumar
Neeraj Gulia
Vikas Malik
Morris
Yogesh Drall
Jyoti Palarwal
Ankit Yadav
Nisha dahiya
Gautam Bansal
Himanshu Kapoor
Rahul Shrama
Basant yadav
Karuna Goyal
kiran Gaur
Faizal
Simeer Kaur
Simran Wadhwa
Vikrant Suryavanshi
Sarita Saroha
Kunal Kapoor
Koyal
Nishika Gulia
Shabnam Sangwan
Shweta Chhikara
Sonia Garg
Ritik Sangwan
Preeti Lochab
Vinita Solanki
Kamal
Ritu Rahar
Surrabh Rana
Shikha Dabass
Mahima Shrama
Sachin Yadav
Ravneet Kaur
Malvika
Shwetika Kalia
Ishha Rana
Bhwana Kataria
Anu Kadain
Rahul Dua
Rohit Jain
Varun Dhawan
Sidarnth Malhotra
Preedhi
Yash Saroha
Vickey Joon
Ishita Ayyer
Mihika
Age
42
42
33
48
51
64
31
58
58
57
57
57
54
37
66
60
29
75
75
52
68
29
31
68
70
58
58
29
49
33
32
34
33
58
48
60
60
60
60
60
60
75
39
39
28
38
27
27
57
55
63
36
36
36
36
36
24
48
27
74
50
50
48
32
32
32
32
32
58
64
Gender
M
M
M
F
M
M
M
M
F
F
F
F
M
M
F
M
F
F
F
M
M
M
M
M
M
M
F
M
F
M
M
M
M
F
F
M
F
F
M
F
M
F
F
F
F
F
M
F
F
M
F
M
F
F
M
F
F
F
F
F
F
M
M
M
M
F
M
M
F
F
Profession
Salesman
Accounting
Driver
Banking
Delhi Police
student
Defence Services
Banking
Salesman
i.t worker
Bussiness
Labour
Govt. sector
Labour
Salesman
Labour
teacher
Salesman
Defence Services
student
Enggineering
Banking
Driver
Enggineering
Banking
i.t worker
Enggineering
Salesman
student
i.t worker
Driver
student
i.t worker
Labour
Accounting
Salesman
Defence Services
Driver
Labour
Delhi Police
student
Real Estate
Labour
Politics
i.t worker
Enggineering
Labour
Govt. sector
Labour
Salesman
Labour
teacher
Salesman
Defence Services
Banking
Enggineering
Salesman
Driver
Defence Services
Banking
i.t worker
Enggineering
Salesman
Labour
i.t worker
Driver
student
i.t worker
Labour
Enggineering
www.ijcsit.com
visa type
Business
Business
Labour
Business
Business
student
Tourist
Business
Business
I.T Workers
Business
Labour
Tourist
Labour
Business
Labour
Tourist
Business
Tourist
student
I.T Workers
Business
Labour
Tourist
Business
I.T Workers
I.T Workers
Labour
student
I.T Workers
Labour
student
I.T Workers
Labour
Business
Labour
Tourist
Labour
Labour
Tourist
student
Business
Labour
Tourist
I.T Workers
I.T Workers
Labour
Tourist
Labour
Business
Labour
Tourist
Business
Tourist
Business
I.T Workers
Labour
Labour
Tourist
Business
I.T Workers
I.T Workers
Labour
Labour
I.T Workers
Labour
student
I.T Workers
Labour
I.T Workers
Source
patna
Mumbai
Delhi
Chandigarh
Mumbai
Delhi
Delhi
Delhi
Mumbai
Calcutta
Banglore
Banglore
Calcutta
Calcutta
Banglore
Mumbai
Delhi
Delhi
Delhi
Mumbai
Mumbai
Mumbai
Chandigarh
Chandigarh
Banglore
Banglore
Banglore
Mumbai
Delhi
Mumbai
Mumbai
Delhi
Chandigarh
Mumbai
Delhi
Delhi
Delhi
Mumbai
Calcutta
Banglore
Banglore
Calcutta
Calcutta
Banglore
Mumbai
Delhi
Delhi
Delhi
Mumbai
Mumbai
Mumbai
Chandigarh
Chandigarh
Banglore
Banglore
Banglore
Mumbai
Delhi
Mumbai
Delhi
Chandigarh
Delhi
Mumbai
Calcutta
chennai
Banglore
hyderabad
ahmedabad
puna
Mumbai
Destination
Australia
Australia
Dubai
Australia
New Zealand
UK
China
New Zealand
New Zealand
USA
New Zealand
Dubai
France
Dubai
New Zealand
Dubai
China
Hong Kong
France
UK
USA
New Zealand
Dubai
China
Hong Kong
USA
USA
Dubai
UK
USA
Dubai
UK
Japan
Dubai
New Zealand
Dubai
France
Dubai
Dubai
China
New Zealand
Hong Kong
Dubai
China
USA
Japan
Dubai
China
Dubai
New Zealand
Dubai
Spain
Hong Kong
Spain
New Zealand
USA
Dubai
Dubai
France
Hong Kong
Japan
USA
Dubai
Dubai
Japan
Dubai
Denmark
USA
Dubai
Japan
Praveen Rani et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (4) , 2014, 5015-5019
Name
Mihir Walia
Ruhi Kundra
Karamvir Drall
Himishka Dabas
Lalit Dagar
Naveen Antil
Indu Dahiya
Kanishka Rathi
Reena Joon
Rajender Singh
Mayak Kapil
Adhayan Suman
Pradeep Suri
Veer Joon
Jigar Mehta
Abhijeet
Aman Dureja
Jyoti Arora
Sunita Sangwan
Rajat Vohra
Parveen Bano
Priyanka Saroha
Amita Rathi
Kuldeep Kaushik
Dheeraj Kumar
Bharat Bhusham
Pramjeet
Amarjeet
Saroop Gulia
Dev
Ajay Duraja
Aaditya Juneja
Prem Chopra
Ranjit Singh
Ranjeet Kapoor
Pritam Kumar
Shaan
Vishal Dadlani
Shekher
Vishal Bharadwaj
Jimmy Bhagnani
Adnan Sami
Akshay Khanna
RK Mehta
Mukul Mittal
Anshu Lochab
Sachi Kapoor
Priyanshu
Neil Mukesh
Nitin Mukesh
Bipin Pal
Mahender Pal
Harpal Dahiya
Ajeet Rana
Rajkumar
Tina
Twinkle Khanna
Rajiv Bweja
Harman Bweja
Aryaman
Abhiman Suryavanshi
Daksh Gulia
Tanish Gulia
Sonu Gurawalia
Tansh Walia
Rishab Malik
Anil Yadav
Dharmender Kohla
Prakash Yadav
Shivlata
Age
28
60
48
64
58
45
45
70
30
53
40
66
46
34
43
56
60
66
30
30
45
65
66
65
50
60
56
50
46
52
34
34
32
72
72
50
60
60
60
39
39
48
55
47
60
60
72
44
55
31
31
31
55
75
75
75
75
75
65
40
64
38
60
60
60
48
60
60
60
49
Gender
M
F
M
F
M
M
F
F
F
M
M
M
M
M
M
M
M
F
F
M
M
F
F
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
F
M
M
M
M
M
M
M
M
F
F
M
M
M
M
M
M
M
M
M
M
M
M
F
Profession
Banking
Defence Services
Driver
Labour
Enggineering
student
Accounting
student
Politics
i.t worker
Bussiness
Labour
Salesman
Labour
Salesman
Labour
student
Salesman
Real Estate
student
Enggineering
Banking
Driver
Defence Services
Banking
i.t worker
Enggineering
Salesman
student
i.t worker
Driver
student
i.t worker
Enggineering
Enggineering
Accounting
Defence Services
Driver
teacher
Delhi Police
student
Driver
Labour
Politics
i.t worker
Bussiness
Labour
Govt. sector
Labour
Salesman
Labour
teacher
Salesman
Defence Services
student
Enggineering
Banking
Driver
Defence Services
Banking
i.t worker
Driver
Accounting
Banking
Salesman
Driver
student
i.t worker
Banking
Enggineering
www.ijcsit.com
visa type
Business
Tourist
Labour
Labour
I.T Workers
student
Business
student
Business
I.T Workers
Business
Labour
Business
Labour
Business
Labour
student
Labour
Business
student
student
Business
Labour
Tourist
Business
student
student
student
student
I.T Workers
Labour
student
I.T Workers
Business
Business
Business
Tourist
Labour
Tourist
Tourist
student
Labour
Labour
Tourist
I.T Workers
Business
Labour
Tourist
Labour
Business
Labour
Tourist
Business
Tourist
student
I.T Workers
Business
Labour
Tourist
I.T Workers
I.T Workers
Labour
Business
I.T Workers
Labour
Labour
student
I.T Workers
Business
I.T Workers
Source
Delhi
Chandigarh
Mumbai
Delhi
Delhi
Delhi
Mumbai
Calcutta
Banglore
Banglore
Calcutta
Calcutta
Banglore
Mumbai
Delhi
Delhi
Delhi
Mumbai
Mumbai
Mumbai
Chandigarh
Chandigarh
Banglore
Banglore
Banglore
Mumbai
Delhi
Mumbai
Mumbai
Delhi
Chandigarh
Mumbai
Delhi
Delhi
Delhi
Mumbai
Calcutta
Banglore
Banglore
Calcutta
Calcutta
Banglore
Mumbai
Delhi
Delhi
Delhi
Mumbai
Mumbai
Mumbai
Chandigarh
Chandigarh
Banglore
Banglore
Banglore
Mumbai
Delhi
Mumbai
Delhi
Chandigarh
Delhi
Mumbai
Calcutta
chennai
Banglore
hyderabad
ahmedabad
puna
surat
patna
Mumbai
Destination
New Zealand
China
Dubai
Dubai
USA
Norway
Ireland
USA
Ireland
Japan
Ireland
Dubai
Ireland
Dubai
Ireland
Dubai
USA
Dubai
Ireland
USA
USA
Ireland
Dubai
France
Ireland
New Zealand
Denmark
New Zealand
Norway
USA
Dubai
USA
Japan
Ireland
New Zealand
Ireland
France
Dubai
Italy
Italy
UK
Dubai
Dubai
Italy
USA
New Zealand
Dubai
Spain
Dubai
Ireland
Soudi Arab
China
New Zealand
France
UK
Japan
Ireland
Soudi Arab
France
USA
Japan
Soudi Arab
New Zealand
USA
Soudi Arab
Soudi Arab
Denmark
Japan
Ireland
China
Praveen Rani et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (4) , 2014, 5015-5019
Name
Shmita Shethi
Sunil Deshwal
Shalu Bansal
Amarjeet Joon
Anita Sangwan
Amita Dahiya
Madhubala
Jia Khan
Zoya Khan
Asad Khan
Ayan Gulia
Rithik Ansari
Farid Ansari
Ankit Tripathi
Kunal Kundra
Richa
Nancy
Dolly David
Anya Bachan
Aamir Ali
Rohan Roy
Rohit Roy
Jaya Deshmukh
Ritesh Deshmukh
Vivek Oberoi
Suresh Oberoi
Kamal Kumar
Jeent Amman
Praveen Marya
Harsh Joon
Harshit Shrama
Arpit Kohli
Natik Ganguli
Dhiksha Saroha
Drisha Shokeen
Dia Yadav
Shikha Chauhan
Kamakshi Pundhir
Pooja Juneja
Renu Yadav
Amit Panwar
Manish Rawat
Aman Shehrawat
Gouri Khan
Mallika Khan
Amrita Arora
Shraman Jhoshi
Atif Aslam
Pooja Dallal
Sunidhi Chauhan
Sunishi
Shreya Ghosal
Mohit Chauhan
Arsh Padukone
Akshat Antil
Moni Verma
Sumit Kumar
Kumkum
Bani Walia
Bablu Dahiya
Anju Dahiya
Sumitra
Preeti Gurawalia
Meenu Gulia
Anurag Basu
Bipasha Jain
Virat Kohli
Ishant Singh
Shilpa Sheti
Viraj Ganguli
Age
49
60
60
26
41
46
49
49
38
45
37
45
40
40
70
45
28
42
22
38
38
66
55
49
66
37
37
47
47
50
70
26
35
68
65
46
61
61
50
33
40
60
27
35
35
40
48
51
29
28
54
54
55
55
40
33
33
33
65
35
38
38
50
44
36
42
42
33
45
38
Gender
F
M
F
M
F
F
F
F
F
M
M
M
M
M
M
F
F
F
F
M
M
M
F
M
M
M
M
F
F
M
M
M
M
F
F
F
F
F
F
F
M
M
M
F
F
F
M
M
F
F
F
F
M
M
M
M
M
F
F
M
F
F
F
F
M
F
M
M
F
M
Profession
Banking
Defence Services
Driver
student
Delhi Police
student
Defence Services
i.t worker
Politics
Enggineering
Real Estate
Labour
Govt. sector
Labour
Salesman
Labour
i.t worker
Salesman
Driver
student
Accounting
Banking
Driver
Defence Services
Banking
i.t worker
Enggineering
Salesman
student
i.t worker
Driver
student
i.t worker
teacher
Enggineering
Banking
Defence Services
Driver
Bussiness
Delhi Police
student
Enggineering
Bussiness
Real Estate
i.t worker
Bussiness
Labour
Govt. sector
Labour
Salesman
Labour
Bussiness
Salesman
Defence Services
Bussiness
Enggineering
Banking
Driver
Defence Services
Banking
i.t worker
Driver
Salesman
student
i.t worker
Driver
Real Estate
i.t worker
Human Resource
Enggineering
www.ijcsit.com
visa type
Business
Tourist
Labour
student
Tourist
student
Tourist
I.T Workers
Tourist
I.T Workers
Business
Labour
Tourist
Labour
Business
Labour
I.T Workers
student
Labour
student
Business
Business
Labour
Tourist
Business
I.T Workers
I.T Workers
Tourist
student
student
Labour
student
I.T Workers
Tourist
I.T Workers
Business
Tourist
Labour
Business
Tourist
student
student
student
Business
I.T Workers
Business
Labour
Tourist
Labour
Labour
Labour
Business
Business
Tourist
Business
student
Business
Labour
Tourist
Business
I.T Workers
Labour
Labour
student
I.T Workers
Labour
Business
I.T Workers
Tourist
student
Source
Chandigarh
Chandigarh
Banglore
Banglore
Banglore
Mumbai
Delhi
Mumbai
Delhi
Chandigarh
Delhi
Mumbai
Calcutta
chennai
Banglore
hyderabad
ahmedabad
puna
surat
patna
Chandigarh
Banglore
Banglore
Banglore
Mumbai
Delhi
Mumbai
Mumbai
Delhi
Chandigarh
Mumbai
Delhi
Delhi
Delhi
Mumbai
Calcutta
Banglore
Banglore
Calcutta
Calcutta
Banglore
Mumbai
Delhi
Delhi
Delhi
Mumbai
Mumbai
Mumbai
Chandigarh
Chandigarh
Banglore
Banglore
Banglore
Mumbai
Delhi
Mumbai
Delhi
Chandigarh
Delhi
Mumbai
Calcutta
chennai
Banglore
hyderabad
ahmedabad
puna
surat
patna
Mumbai
Chandigarh
Destination
New Zealand
China
Soudi Arab
New Zealand
Italy
New Zealand
Italy
Japan
France
China
Ireland
Soudi Arab
Spain
Soudi Arab
New Zealand
Soudi Arab
Japan
USA
Soudi Arab
USA
Ireland
New Zealand
Soudi Arab
Spain
Ireland
China
Japan
China
New Zealand
USA
Soudi Arab
USA
China
France
Japan
New Zealand
France
Soudi Arab
Ireland
China
USA
Denmark
USA
New Zealand
China
Ireland
Soudi Arab
Italy
Soudi Arab
Soudi Arab
Soudi Arab
New Zealand
Ireland
China
Ireland
Denmark
Ireland
Soudi Arab
China
Ireland
Japan
Soudi Arab
Soudi Arab
USA
China
Dubai
Ireland
Japan
Spain
USA
Praveen Rani et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (4) , 2014, 5015-5019
Name
Mahen Dhoni
Sohaan Basu
Rani Mukherjee
Salmaan
Bindu Chhikara
Narender Hooda
Anju Verma
Praveen Dhankher
Gimpy yadav
Jyoti Singh
Jyoti Dua
Kusum Gulia
Nisha Dhankher
Payal Jain
Rajiv Wadhwa
Ruchika Dhwan
Ruchi Walia
Sonia Maan
Sucheta Yadav
Maddy Dahiya
Anji Dahiya
Deepika Rathi
Renuka Pundhir
Priyanka Goyal
Swati Tehlan
Ani Dahiya
Ansh Bhutani
Amit Malik
Anshula Verma
Divya Dalal
Ajay Kumar Narwal
Anshul Ashwani Arora
Akshay Kumar
Harpreet Sangwan
Meenakshi Khatri
Anu Khana
Vandana Gulia
Nidhi Shokeen
Vinay Tripathi
Barun Vohra
Khusi Gupta
Rishab Kapur
Muskaan Solanki
Piyali Garg
Priyanka Kadian
Mohit Singh Dahiya
Karan Sobti
Arun Kumar Shrama
Neeraj Gulati
Vikas Maan
Morris Malik
Yogesh Walia
Jyoti Birhman
Ankita Rao
Nisha Bansal
Gautam Kumar
Himanshu Khatri
Rahul Shokeen
Basant Malik
Karuna Gaur
kiran Antil
Faizal Mohammad Khan
Simeerjeet Kaur
Simran Walia
Vikrant Solanki
Sarita Vohra
Kunal Arora
Koyal mehta
Nishika Antil
Shabnam
Age
38
44
62
43
40
26
37
33
43
30
33
26
35
60
45
48
58
50
50
54
34
33
44
43
50
57
45
60
45
23
22
22
74
25
31
24
58
51
50
50
55
54
48
30
45
48
51
54
27
30
26
45
44
35
38
54
30
30
36
44
60
42
36
24
43
21
26
26
26
36
Gender
M
M
F
M
F
M
F
F
F
F
F
F
F
F
M
F
F
F
F
M
F
F
F
F
F
F
M
M
F
F
M
F
M
M
F
F
F
F
M
M
F
M
F
F
F
M
M
M
M
M
M
M
F
F
F
M
M
M
M
F
F
M
F
F
M
F
M
F
F
F
Profession
Banking
Defence Services
Driver
student
Delhi Police
student
Defence Services
teacher
Politics
i.t worker
Bussiness
Labour
Govt. sector
Labour
Salesman
Labour
student
Salesman
Driver
teacher
Enggineering
Banking
Driver
Defence Services
Banking
i.t worker
Real Estate
Salesman
Bussiness
i.t worker
Driver
Human Resource
i.t worker
teacher
Enggineering
Banking
Defence Services
Driver
student
Delhi Police
student
Defence Services
student
Politics
i.t worker
Bussiness
Labour
Govt. sector
Labour
Salesman
Labour
Bussiness
Salesman
Defence Services
Labour
Enggineering
Banking
Driver
Real Estate
Banking
i.t worker
Human Resource
Salesman
Labour
i.t worker
Driver
student
i.t worker
Labour
Enggineering
www.ijcsit.com
visa type
Business
Business
Labour
student
Tourist
student
Tourist
Tourist
Tourist
I.T Workers
Business
Labour
Tourist
Labour
Business
Labour
student
Tourist
Labour
Tourist
student
Business
Labour
Tourist
Business
I.T Workers
I.T Workers
Labour
Business
I.T Workers
Labour
Tourist
I.T Workers
Tourist
student
Business
Tourist
Labour
student
Tourist
student
Tourist
student
Tourist
I.T Workers
Business
Labour
Tourist
Labour
Labour
Labour
Business
Labour
Tourist
Labour
Tourist
Business
Labour
Business
Business
I.T Workers
Tourist
Labour
Labour
I.T Workers
Labour
student
I.T Workers
Labour
student
Source
Chandigarh
Banglore
Banglore
Banglore
Mumbai
Delhi
Mumbai
Delhi
Chandigarh
Delhi
Mumbai
Calcutta
chennai
Banglore
hyderabad
ahmedabad
puna
surat
patna
Mumbai
Delhi
Mumbai
Delhi
Chandigarh
Delhi
Mumbai
Calcutta
chennai
Banglore
hyderabad
ahmedabad
puna
surat
patna
Chandigarh
Banglore
Banglore
Banglore
Mumbai
Delhi
Mumbai
Mumbai
Delhi
Chandigarh
Mumbai
Delhi
Delhi
Delhi
Mumbai
Calcutta
Banglore
Banglore
Calcutta
Calcutta
Banglore
Mumbai
Delhi
Delhi
Delhi
Mumbai
Mumbai
Mumbai
Chandigarh
Chandigarh
Banglore
Banglore
Banglore
Mumbai
Delhi
Mumbai
Destination
Ireland
Ireland
Dubai
USA
Spain
UK
Spain
China
China
China
Ireland
Dubai
France
Dubai
Ireland
Dubai
USA
Switzerland
Dubai
France
UK
Ireland
Dubai
China
Ireland
Japan
USA
Dubai
Ireland
Japan
Dubai
Italy
USA
France
UK
Ireland
Switzerland
Dubai
UK
Switzerland
USA
China
UK
France
Japan
Ireland
UAE
France
UAE
UAE
UAE
Ireland
UAE
China
UAE
Switzerland
Ireland
UAE
Ireland
Ireland
USA
China
UAE
UAE
Japan
UAE
UK
USA
UAE
Norway
Praveen Rani et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (4) , 2014, 5015-5019
Name
Shweta Joon
Sonia Mittal
Ritika Ranvijay
Preeti Panchal
Vinita Mathur
Anju Joon
Ritu Saroha
Surrabh Rai
Shikha Dagar
Mahima Yadav
Sachin Tendulkar
Ravneet Ganguli
Malvika Gulati
Shwetika Karan
Ishha Bhagnani
Bhwana Balhara
Anu Gulia
Rahul Shokhanda
Rohit Aehlawat
Era David
Siya Arora
Vikram Aditya
Ravinder Gaur
Amit Saroha
Adrash Rana
Brij Singh
Vivek Mittal
Preeti Mehta
Sujata Solanki
Mohit Maan
Tarun Kumar Yadav
Manju Balhara
Gunjann Jha
Anjana
Himender
Rani
Sonam Khan
Sagar
Pradeep
Veer Singh
Nukul Shah
Niranjan
Sunil Shrama
Amanju Joon
Jyoti Ambedkar
Sunita Kumari
Rajat Johar
Parveen Dikxit
Priyanka Chwala
Amita Gulia
Kuldeep Kohli
Dheeraj Gulia
Bharat Kumar
Pramjeet Joon
Amarjeet Kaur
Saroop Gulati
Dev Bhatia
Ajay Dhereja
Aaditya Dhereja
Prem Kapoor
Ranjit
Ranjeet Kaushik
Pritam Roshan
Shaan Rao
Vishal Roshan
Shekher Shikhawat
Vishal Saroha
Jimmy Roshan
Adnan
Akshay Roshan
Age
34
44
75
75
75
25
75
36
35
70
37
60
46
38
70
49
37
37
26
48
48
29
33
33
37
69
24
65
55
42
45
40
35
60
42
58
54
33
48
25
56
47
33
43
50
72
50
39
58
60
34
50
38
51
46
72
72
75
41
41
48
45
74
78
38
27
66
50
42
65
Gender
F
F
F
F
F
F
F
M
F
F
M
F
F
F
F
F
F
M
M
F
F
M
M
M
M
M
M
F
F
M
M
F
F
F
M
F
F
M
M
M
M
M
M
M
F
F
M
M
F
F
M
M
M
M
F
M
M
M
M
M
M
M
M
M
M
M
M
M
M
M
Profession
Driver
Defence Services
Driver
Labour
Delhi Police
student
Defence Services
Labour
Politics
i.t worker
Bussiness
Labour
Govt. sector
Labour
Salesman
Labour
Human Resource
Salesman
Defence Services
Bussiness
Enggineering
Banking
Driver
Driver
Banking
i.t worker
Enggineering
Salesman
i.t worker
i.t worker
Driver
student
i.t worker
i.t worker
Enggineering
Banking
Defence Services
Driver
Labour
Delhi Police
student
Defence Services
Labour
Real Estate
i.t worker
Bussiness
Labour
Govt. sector
Labour
Driver
Labour
Labour
Salesman
Defence Services
teacher
Enggineering
Banking
Driver
Defence Services
Banking
Real Estate
Enggineering
Salesman
Human Resource
i.t worker
Driver
student
Defence Services
teacher
Enggineering
www.ijcsit.com
visa type
Labour
Tourist
Labour
Labour
Tourist
student
Tourist
Labour
Business
I.T Workers
Business
Labour
Tourist
Labour
Labour
Labour
Tourist
Labour
Tourist
Business
Tourist
Business
Labour
Labour
Business
I.T Workers
Business
Labour
I.T Workers
I.T Workers
Labour
student
I.T Workers
I.T Workers
Business
Business
Tourist
Labour
Labour
Tourist
student
Tourist
Labour
Business
I.T Workers
Business
Labour
Tourist
Labour
Labour
Labour
Labour
Labour
Tourist
Tourist
student
Business
Labour
Tourist
Business
Business
student
Labour
Tourist
I.T Workers
Labour
student
Tourist
Tourist
Business
Source
Delhi
Chandigarh
Delhi
Mumbai
Delhi
Chandigarh
Mumbai
Delhi
Delhi
Delhi
Mumbai
Calcutta
Banglore
Banglore
Calcutta
Calcutta
Banglore
Mumbai
Delhi
Delhi
Delhi
Mumbai
Mumbai
Mumbai
Chandigarh
Chandigarh
Banglore
Banglore
Banglore
Mumbai
Delhi
Mumbai
Delhi
Chandigarh
Delhi
Mumbai
Calcutta
chennai
Banglore
hyderabad
ahmedabad
puna
surat
patna
Mumbai
Chandigarh
Chandigarh
Banglore
Banglore
Banglore
Mumbai
Delhi
Mumbai
Delhi
Chandigarh
Delhi
Mumbai
Calcutta
chennai
Banglore
hyderabad
ahmedabad
puna
surat
patna
Mumbai
Delhi
Mumbai
Delhi
Chandigarh
Destination
UAE
France
UAE
UAE
Switzerland
New Zealand
China
UAE
Ireland
USA
Ireland
UAE
France
UAE
UAE
UAE
France
UAE
Switzerland
Ireland
Spain
Ireland
UAE
Soudi Arab
Ireland
USA
Ireland
Soudi Arab
USA
USA
Soudi Arab
New Zealand
USA
USA
Ireland
Ireland
Spain
Soudi Arab
Soudi Arab
Italy
USA
Spain
Soudi Arab
Ireland
USA
Ireland
Soudi Arab
Italy
Soudi Arab
Soudi Arab
Soudi Arab
Dubai
Dubai
France
Spain
UK
Ireland
Dubai
Italy
Ireland
Ireland
UK
Dubai
Switzerland
USA
Dubai
UK
China
France
Ireland
Praveen Rani et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (4) , 2014, 5015-5019
Name
RK Dahiya
Mukul Maan
Arshat
Sachi Roshan
Priyanshu Roshan
Neil Nitin Mukesh
Roshan Shokeen
Bipin
Mahende Rana
Harpal Gulia
Ajeet Gurawaliya
Rajkumar Rana
Tina Verma
Twinkle
Rajiv Dahiya
Harman Chopra
Aryaman Kaif
Abhiman
Daksh Gulati
Tanish Roy
Sonu Vohra
Tansh Wadhwa
Rishab Roshan
Anil Kohla
Dharmender
Prakash
Shivam Yadav
Dharmender
Drisha
Dia
Shikha
Kamakshi
Pooja
Renu
Amit Pundhir
Manisha
Tanmay
Gouri
Mallika
Amrita
Shraman
Atif
Pooja Garg
Sunidh Gaur
Sunishi Kumar
Shreya
Tohit Chauhan
Arsh
Akshat
Moni
Sumita
Kumkum Wadhwa
Bani Gulia
Bablu
Ramnju
Sumitra Devi
Preeti Kohli
Meenu Mittal
Anurag
Bipasha Basu
Virat Kohla
Ishant
Shilpa Kalia
Viraj Rana
Shelesh Dhoni
Sohaan
Rani Dahiya
Salmaan Khurshid
Mithoon
Ashish Gulia
Age
22
31
45
34
48
48
54
23
65
48
65
70
70
36
50
55
55
26
41
53
32
58
45
65
52
73
53
66
60
29
75
75
52
68
29
31
68
70
58
58
29
49
33
32
34
33
58
48
60
60
60
60
60
60
75
39
39
28
38
27
27
57
55
63
36
36
36
36
36
24
Gender
M
M
M
F
M
M
M
M
M
M
M
M
F
F
M
M
M
M
M
M
M
M
M
M
M
M
F
M
F
F
F
F
F
F
M
F
M
F
F
F
M
M
F
M
F
F
M
M
M
F
F
F
F
M
M
F
F
F
M
F
M
M
F
M
M
M
F
M
M
M
Profession
Banking
Driver
Defence Services
Banking
i.t worker
Enggineering
Salesman
Labour
i.t worker
Driver
student
Driver
Labour
Delhi Police
student
Defence Services
Real Estate
Driver
i.t worker
Human Resource
Labour
Govt. sector
Labour
Salesman
Labour
Real Estate
Salesman
Driver
Human Resource
i.t worker
teacher
Enggineering
Banking
Defence Services
Driver
Bussiness
Delhi Police
student
Defence Services
Bussiness
Politics
i.t worker
Bussiness
Labour
Govt. sector
Labour
Salesman
Labour
i.t worker
Salesman
Defence Services
i.t worker
Enggineering
Banking
Driver
Real Estate
Banking
i.t worker
Human Resource
Salesman
Bussiness
i.t worker
Enggineering
student
i.t worker
Labour
Enggineering
Driver
Defence Services
Driver
www.ijcsit.com
visa type
Business
Labour
Tourist
Business
I.T Workers
Business
Tourist
Labour
I.T Workers
Labour
student
Labour
Labour
Tourist
student
Tourist
Business
Labour
I.T Workers
Tourist
Labour
Tourist
Labour
Labour
Labour
Business
Business
Labour
Tourist
I.T Workers
Tourist
student
Business
Tourist
Labour
student
Tourist
student
Tourist
student
Tourist
I.T Workers
Business
Labour
Tourist
Labour
Business
Labour
student
student
Tourist
student
student
Business
Labour
student
Business
I.T Workers
student
Business
Business
I.T Workers
student
student
student
Labour
Tourist
Labour
Tourist
Labour
Source
Delhi
Mumbai
Calcutta
chennai
Banglore
hyderabad
ahmedabad
puna
surat
patna
Chandigarh
Banglore
Banglore
Banglore
Mumbai
Delhi
Mumbai
Mumbai
Delhi
Chandigarh
Mumbai
Delhi
Delhi
Delhi
Mumbai
Calcutta
Banglore
Banglore
Calcutta
Calcutta
Banglore
Mumbai
Delhi
Delhi
Delhi
Mumbai
Mumbai
Mumbai
Chandigarh
Chandigarh
Banglore
Banglore
Banglore
Mumbai
Delhi
Mumbai
Delhi
Chandigarh
Delhi
Mumbai
Calcutta
chennai
Banglore
hyderabad
ahmedabad
puna
surat
patna
Mumbai
Chandigarh
Chandigarh
Banglore
Banglore
Banglore
Mumbai
Delhi
Mumbai
Delhi
Chandigarh
Delhi
Destination
Ireland
Dubai
Malaysia
Ireland
USA
Ireland
Italy
Dubai
USA
Dubai
UK
Dubai
Dubai
Italy
UK
France
Ireland
Dubai
USA
Malaysia
Dubai
Spain
Dubai
Dubai
Dubai
Ireland
Hong Kong
Dubai
France
USA
China
USA
Hong Kong
Spain
Dubai
USA
Malaysia
USA
France
USA
China
USA
Hong Kong
Dubai
France
Dubai
Hong Kong
Dubai
UK
UK
Italy
UK
UK
Hong Kong
Dubai
UK
Hong Kong
USA
UK
Hong Kong
Hong Kong
USA
UK
UK
USA
Soudi Arab
Spain
Dubai
Spain
Soudi Arab
Praveen Rani et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (4) , 2014, 5015-5019
Name
Amit Shokken
Ajay Arora
Neeru Dagar
Komal Dabass
Neeru Malik
Nitu Anyil
Karamveer Drall
Sangeeta Drall
Roop Malik
Sanjay Kadain
Adresh Srivastav
Abhishek Srivastav
Amitab
Rajnikant
Shruti Hassan
Raja Hassan
Altaf Raza
Alfaz
Kiran Kumar
Sahil Arora
Shrad
Kamlesh Kumari
Lalita
Vickey Pundhir
Pankaj Pandey
Anil Pandey
Aditya Pancholi
Udit Narayan
Ahem Narayan
Arjun Narwal
Nagarjun
Angel
Ramesh Dahiya
Ramesh Dhankhar
Amanju
Anuj Gupta
Deepak Dhankher
Deepa Dhankher
Pankaj Udas
Shakti
Pankaj Kumar
Akshyat Kundra
Tanshil
Rahil Modi
Ranbir Singh
Asin
Vidya Balan
Preeneta
Priyankaa Chopra
Kareena Kapoor
Karan Johar
Gopi
Ahem Modi
Rashi
Jassraj
Badhshah
Deep Money
Rekha Sharma
Sushank Sharma
Rohit
Arun
Age
48
27
74
50
50
48
32
32
32
32
32
58
64
28
60
48
64
58
45
45
70
30
53
40
66
46
34
43
56
60
66
30
30
45
65
66
65
50
60
56
50
46
52
34
34
32
72
72
50
60
60
60
39
39
48
55
47
57
24
23
23
Gender
M
M
F
F
F
F
M
F
M
M
M
M
M
M
F
M
M
M
M
M
M
F
F
M
M
M
M
M
M
M
M
F
M
M
M
M
M
F
M
M
M
M
M
M
M
F
F
F
F
F
M
F
M
F
M
M
M
F
M
M
M
Profession
Labour
Delhi Police
student
Defence Services
Bussiness
Politics
i.t worker
Bussiness
Labour
Govt. sector
Labour
Salesman
Labour
Human Resource
Salesman
Defence Services
Banking
Enggineering
Banking
Driver
Driver
Banking
i.t worker
Enggineering
Salesman
Banking
i.t worker
Driver
student
i.t worker
Labour
Enggineering
Banking
Defence Services
Driver
Labour
Delhi Police
student
Defence Services
Banking
Real Estate
i.t worker
Bussiness
Labour
Govt. sector
Labour
House Maker
Labour
Bussiness
Salesman
Defence Services
Labour
Enggineering
Banking
Driver
Defence Services
Banking
teacher
student
student
student
www.ijcsit.com
visa type
Labour
Tourist
student
Tourist
Business
Tourist
I.T Workers
Business
Labour
Tourist
Labour
Tourist
Labour
Tourist
Business
Business
Business
student
Business
Labour
Labour
Business
student
I.T Workers
Labour
Business
I.T Workers
Labour
student
I.T Workers
Labour
student
Business
Tourist
Labour
Labour
Tourist
student
Tourist
Business
Business
I.T Workers
Business
Labour
Tourist
Labour
Labour
Labour
Business
Labour
Tourist
Labour
student
Business
Labour
Tourist
Business
Tourist
student
student
student
Source
Mumbai
Calcutta
chennai
Banglore
hyderabad
ahmedabad
puna
surat
patna
Mumbai
Delhi
Chandigarh
Mumbai
Delhi
Delhi
Delhi
Mumbai
Calcutta
Banglore
Banglore
Calcutta
Calcutta
Banglore
Mumbai
Delhi
Delhi
Delhi
Mumbai
Mumbai
Mumbai
Chandigarh
Chandigarh
Banglore
Banglore
Banglore
Mumbai
Delhi
Mumbai
Delhi
Chandigarh
Delhi
Mumbai
Calcutta
chennai
Banglore
hyderabad
ahmedabad
puna
surat
patna
Mumbai
Chandigarh
Chandigarh
Banglore
Banglore
Banglore
Mumbai
Delhi
Mumbai
Delhi
Mumbai
Destination
Dubai
France
USA
Malaysia
Hong Kong
France
USA
Hong Kong
Soudi Arab
Malaysia
Dubai
Malaysia
Soudi Arab
Spain
Ireland
Ireland
Ireland
UK
New Zealand
Dubai
Soudi Arab
New Zealand
UK
USA
Dubai
New Zealand
USA
Soudi Arab
UK
USA
Dubai
UK
New Zealand
Italy
Soudi Arab
Dubai
France
UK
France
New Zealand
New Zealand
USA
New Zealand
Soudi Arab
Switzerland
Dubai
Soudi Arab
Dubai
New Zealand
Soudi Arab
Spain
UAE
USA
New Zealand
UAE
Italy
New Zealand
Spain
USA
USA
USA