Periodicity: Bi Annual.
Impact Factor:
SJIF:5.079 & GIF:0.416
Submission:Any Time
Publisher: IIR Groups
Language: English
Review Process:
Double Blinded

Paper Template
Copyright Form
Subscription Form
web counter
web counter

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

IJCOA provides online manuscript tracking system.

Every issue of Journal of IJCOA 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

IJCOA 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 IJCOA website. For complete procedure, contact us at

SCIA Journal Metrics


Stability of Indexed Microarray and Text Data

T.Velmurugan,S.Deepa Lakshmi

Published in:   Vol. 8 Issue 1 Date of Publication:   December 2019
Page(s):   34-38 Publisher:   Integrated Intelligent Research (IIR)
DOI:   10.20894/IJCOA. SAI : 2017SCIA316F0923

The common challenge for machine learning and data mining tasks is the curse of High Dimensionality. Feature selection reduces the dimensionality by selecting the relevant and optimal features from the huge dataset. In this research work, a clustering and genetic algorithm based feature selection (CLUST-GA-FS) is proposed that has three stages namely irrelevant feature removal, redundant feature removal, and optimal feature generation. The performance of the feature selection algorithms are analyzed using the parameters like classification accuracy, precision, recall and error rate. Recently, an increasing attention is given to the stability of feature selection algorithms which is an indicator that requires that similar subsets of features are selected every time the algorithm is executed on the same dataset. This work analyzes the stability of the algorithm on four publicly available dataset using stability measurements Average normal hamming distance(ANHD), Dices coefficient, Tanimoto distance, Jaccards index and Kuncheva index.