• Nov 29, 2022 News!IJCTE Vol. 14, No. 1-No. 3 have been indexed by SCOPUS.   [Click]
  • Aug 08, 2022 News![International Journal of Computer Theory and Engineering] Accepted for Coverage in Scopus   [Click]
  • Feb 01, 2023 News!IJCTE Vol.15, No.1 has been published.   [Click]
General Information
    • ISSN: 1793-8201 (Print)
    • Abbreviated Title: Int. J. Comput. Theory Eng.
    • Frequency: Quarterly
    • DOI: 10.7763/IJCTE
    • Editor-in-Chief: Prof. Mehmet Sahinoglu
    • Associate Editor-in-Chief: Assoc. Prof. Alberto Arteta
    • Executive Editor: Ms. Mia Hu
    • Abstracting/Indexing: Scopus (Since 2022), INSPEC (IET), CNKI,  Google Scholar, EBSCO, etc.
    • E-mail: ijcte@iacsitp.com
Editor-in-chief
Prof. Mehmet Sahinoglu
Faculty at Computer Science Department, Troy University, USA
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.

International Journal of Computer Theory and Engineering (IJCTE) is an international academic open access journal which gains a foothold in Singapore, Asia and opens to the world. It aims to promote the integration of computer theory and engineering. The focus is to publish papers on state-of-the-art computer theory and engineering. Submitted papers will be reviewed by technical committees of the journal and association. The audience includes researchers, managers and operators for computer theory and engineering as well as designers and developers.
 
All submitted articles should report original, previously unpublished research results, experimental or theoretical, and will be peer-reviewed. Articles submitted to the journal should meet these criteria and must not be under consideration for publication elsewhere. Manuscripts should follow the style of the journal and are subject to both review and editing.

Important Notice: IJCTE will only accept new submissions through online submission system.
Featured Article

Feature Selection by ModifiedBoostARoota and Classification by CatBoost Model
on High D
imensional Heart Disease Datasets

Anuradha. P and Vasantha Kalyani David


Abstract—As heart disease is the leading cause of mortality worldwide, early detection
and prevention of the disease would reduce the mortality rate. Various Machine Learning
Algorithms are employed in the classification and prediction of diseases. For accurate
prediction, Feature Selection algorithms are employed to choose features that have a
significant association with the disease or target variable.
.....
   [Read More]


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