DOI:10.20894/IJCNES.
Periodicity: Bi Annual.
Impact Factor:
SJIF:5.217
Submission:Any Time
Publisher:IIR Groups
Language:English
Review Process:
Double Blinded

News and Updates

Author can submit their paper through online submission. Click here

Paper Submission -> Blind Peer Review Process -> Acceptance -> Publication.

On an average time is 3 to 5 days from submission to first decision of manuscripts.

Double blind review and Plagiarism report ensure the originality

IJCNES provides online manuscript tracking system.

Every issue of Journal of IJCNES is available online from volume 1 issue 1 to the latest published issue with month and year.

Paper Submission:
Any Time
Review process:
One to Two week
Journal Publication:
June / December

IJCNES special issue invites the papers from the NATIONAL CONFERENCE, INTERNATIONAL CONFERENCE, SEMINAR conducted by colleges, university, etc. The Group of paper will accept with some concession and will publish in IJCNES website. For complete procedure, contact us at admin@iirgroups.org

Paper Template
Copyright Form
Subscription Form
web counter
web counter
Published in:   Vol. 5 Issue 2 Date of Publication:   December 2016

A Modern Approach of Discovering Friends in Social Networks Based on Friend Matching Graph

P.Abirami,B. Moohambigai

Page(s):   47-49 ISSN:   2278-2397
DOI:   10.20894/IJCNES.103.005.002.005 Publisher:   Integrated Intelligent Research (IIR)

SNS is a platform to build social networks or social relations among people based on their social graph It is not satisfied to user�s preference on friend selection in real life. Mean while in prosposed system,we recommend friend by used semantic-based (or)user based on their lifestyle. By taking merits of sensor-rich smartphones, Friend matching graph find out something life styles of people from user-centric sensor data, action to achieve something the similarity of life styles between users, and to advise someone to users if their life styles have most similarity. An extra ordinary quality by data mining, A user�s daily life documents are extracted by using the Hierarchical dirichlet algorithm . Past a certain point ,a similar metric to measure the similarity of life styles between users and enumerate the recommend user�s the action of one object coming forcibly into contact with another. When receiving a request ,It returns a list of social network user with highest recommendation scores to the query user. At last, Friend matching graph combine with another to form a whole the feedback mechanism to further improve the recommendation user accurate. We implemented on the Android-based smartphones, and its performance on both small-scale experiments and large-scale simulations. The results show that the recommendations defect reflect the preferences of users in choosing friends in social network.