An intuitionistic fuzzy Dombi–Archimedean RANCOM–AROMAN framework for AI-enabled telemedicine platform selection

Haitham Qawaqneh; Abdallah Shihadeh; Wael Mahmoud Mohammad Salameh; Walid Abdelfattah; Ikhtesham Ullah; Arif Mehmood; Jamil J. Hamja; Celine Tali Malabong

doi:10.46481/jnsps.2026.3619

Authors

Haitham Qawaqneh Department of Basic Science, Al-Zaytoonah University of Jordan, Amman 11733, Jordan
Abdallah Shihadeh Department of Mathematics, Faculty of Science, The Hashemite University, Zarqa 13133, PO box 330127, Jordan
Wael Mahmoud Mohammad Salameh Faculty of Information Technology, Abu Dhabi University, Abu Dhabi, United Arab Emirates
Walid Abdelfattah walid.abdelfattah@nbu.edu.sa Humanities and Social Research Center, Northern Border University, Arar, Saudi Arabia
Ikhtesham Ullah Department of Mathematics, Abbottabad University of Science and Technology, Havelian, Pakistan
Arif Mehmood Institute of Numerical Sciences, Gomal University, Dera Ismail Khan, Pakistan
Jamil J. Hamja Department of Mathematics, College of Mathematical Sciences, Mindanao State University–Tawi-Tawi College of Technology and Oceanography, 7500 Tawi-Tawi, Philippines
Celine Tali Malabong MSU TCTO Sitangkai Community High School, Secondary Education Department, Mindanao State University Tawi-Tawi College of Technology and Oceanography, 7500 Tawi-Tawi, Philippines

Keywords:

Array, Array, Array, Array, Array

Abstract

Artificial intelligence (AI)-enabled telemedicine platform selection involves multiple conflicting criteria and uncertain expert judgments. Conventional multi-criteria decision-making (MCDM) approaches often fail to preserve membership, non-membership, and hesitation throughout the complete decision process. Existing intuitionistic fuzzy Dombi models lack fully integrated aggregation, criterion weighting, dual-normalization fusion, and ranking mechanisms with rigorous endpoint treatment. This study develops a mathematically consistent and robust intuitionistic fuzzy Dombi--Archimedean (IFDA) decision framework that integrates IFDA--RANCOM--AROMAN to manage uncertain evaluations from criterion weighting to final ranking. IFDA--RANCOM determines criterion weights, whereas IFDA--AROMAN performs dual normalization, fuzzy fusion, benefit--cost utility construction, and alternative ranking. The framework is illustrated through the selection of six AI-enabled telemedicine platforms for rural healthcare. The obtained weights are omega = (0.36,0.28,0.20,0.12,0.04), and the baseline ranking is (A₃> A₂ > A₅ > A₁ > A₆ > A₄). RANCOM--AROMAN reproduces the same complete ranking, while other methods confirm a similar leading group. The leading alternatives remain stable under parameter variations and ( ± 15%) criterion-weight perturbations. The framework provides a reproducible uncertainty-preserving decision mechanism. The proposed model offers a rigorous and robust tool for complex fuzzy decision-making applications.

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