CERTIFICATE

IMPACT FACTOR 2021

Subject Area

  • Life Sciences / Biology
  • Architecture / Building Management
  • Asian Studies
  • Business & Management
  • Chemistry
  • Computer Science
  • Economics & Finance
  • Engineering / Acoustics
  • Environmental Science
  • Agricultural Sciences
  • Pharmaceutical Sciences
  • General Sciences
  • Materials Science
  • Mathematics
  • Medicine
  • Nanotechnology & Nanoscience
  • Nonlinear Science
  • Chaos & Dynamical Systems
  • Physics
  • Social Sciences & Humanities

Why Us? >>

  • Open Access
  • Peer Reviewed
  • Rapid Publication
  • Life time hosting
  • Free promotion service
  • Free indexing service
  • More citations
  • Search engine friendly

Artificial neural network approach for nonlinear principal components analysis

Author: 
Canan Demir and Sıddık Keskin
Subject Area: 
Health Sciences
Abstract: 

Background: Nonlinear Principal Component Analysis (NLPCA) is one of the explanatory dimension reduction techniques and presents numerical and graphical results for variable sets including linear or nonlinear relationships. In Nonlinear Principal Component Analysis, categorical and ordinal variables, as well as numerical variables, can be included in the analysis. Linearity assumption for observed variables is not required for Nonlinear Principal Component Analysis. In this study, an artificial neural network approach for NLPCA is explained and applied. Methods: The hypothyroid data with 19 variables from 422 patients were used in the application. In order to see the difference of NLPCA from PCA, the results obtained using Principal Component Analysis (PCA) together with NLPCA were interpreted by presenting in tables and graphics. Results: The first two principal components explained 95.65% of the total variance in the NLPCA, while they explained 90.08% of the total variance in the PCA. Conclusion: In the analysis conducted by reducing the number of observations, it was seen that NLPCA has a high explanation rate compared to PCA, regardless of the number of observations. This can be attributed to NLPCA's ability to identify nonlinear relationships. Accordingly, it can be said that NLPCA gives effective results in the analysis made with both numerical and categorical variables.

PDF file: 

CALL FOR PAPERS

 

ONLINE PAYPAL PAYMENT

IJMCE RECOMMENDATION

Advantages of IJCR

  • Rapid Publishing
  • Professional publishing practices
  • Indexing in leading database
  • High level of citation
  • High Qualitiy reader base
  • High level author suport

Plagiarism Detection

IJCR is following an instant policy on rejection those received papers with plagiarism rate of more than 20%. So, All of authors and contributors must check their papers before submission to making assurance of following our anti-plagiarism policies.

 

EDITORIAL BOARD

CHUDE NKIRU PATRICIA
Nigeria
Dr. Swamy KRM
India
Dr. Abdul Hannan A.M.S
Saudi Arabia.
Luai Farhan Zghair
Iraq
Hasan Ali Abed Al-Zu’bi
Jordanian
Fredrick OJIJA
Tanzanian
Firuza M. Tursunkhodjaeva
Uzbekistan
Faraz Ahmed Farooqi
Saudi Arabia
Eric Randy Reyes Politud
Philippines
Elsadig Gasoom FadelAlla Elbashir
Sudan
Eapen, Asha Sarah
United State
Dr.Arun Kumar A
India
Dr. Zafar Iqbal
Pakistan
Dr. SHAHERA S.PATEL
India
Dr. Ruchika Khanna
India
Dr. Recep TAS
Turkey
Dr. Rasha Ali Eldeeb
Egypt
Dr. Pralhad Kanhaiyalal Rahangdale
India
DR. PATRICK D. CERNA
Philippines
Dr. Nicolas Padilla- Raygoza
Mexico
Dr. Mustafa Y. G. Younis
Libiya
Dr. Muhammad shoaib Ahmedani
Saudi Arabia
DR. MUHAMMAD ISMAIL MOHMAND
United State
DR. MAHESH SHIVAJI CHAVAN
India
DR. M. ARUNA
India
Dr. Lim Gee Nee
Malaysia
Dr. Jatinder Pal Singh Chawla
India
DR. IRAM BOKHARI
Pakistan
Dr. FARHAT NAZ RAHMAN
Pakistan
Dr. Devendra kumar Gupta
India
Dr. ASHWANI KUMAR DUBEY
India
Dr. Ali Seidi
Iran
Dr. Achmad Choerudin
Indonesia
Dr Ashok Kumar Verma
India
Thi Mong Diep NGUYEN
France
Dr. Muhammad Akram
Pakistan
Dr. Imran Azad
Oman
Dr. Meenakshi Malik
India
Aseel Hadi Hamzah
Iraq
Anam Bhatti
Malaysia
Md. Amir Hossain
Bangladesh
Ahmet İPEKÇİ
Turkey
Mirzadi Gohari
Iran