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All Journal Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Pawan Kumar Singh
Department of Community Medical, Autonomous State Medical College, Kanpur Dehat 209101, Utter Pardesh
Computer Science & IT Engineering Mechanical Engineering Transportation

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Performance evaluation of the fast forward quantum optimization algorithm in digital image clustering Sanjeev Kumar Singh; Pawan Kumar Singh
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 2 (2026)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v3i2.46206

Abstract

The primary objective of clustering in image analysis is to establish a meaningful correspondence between image features and clusters. This process is instrumental in extracting higher-level semantic information from digital images. In this study, we propose a novel image clustering approach that integrates the fast forward quantum optimization algorithm (FFQOA) with the K-means clustering (KMC) algorithm, forming a hybrid method referred to as FFQOA + KMC. The FFQOA + KMC initiates clustering based on the grayscale values of images using KMC and then refines the clustering outcome through FFQOA to achieve optimal segmentation. Subsequently, FFQOA + KMC is applied to several benchmark grayscale images, with results compared to those from alternative clustering techniques. Experimental findings confirm the robustness and superiority of FFQOA + KMC through both visual inspections and statistical metrics
A Concise Comparative Analysis of Ambiguous Set Theory in Relation to Fuzzy, Intuitionistic Fuzzy, and Neutrosophic Sets Pawan Kumar Singh
Vokasi UNESA Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 3 (2026): (In Progress)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In recent times, effectively measuring uncertainty in data with fuzzy characteristics has become a notable challenge. To tackle this issue, researchers have extensively investigated several extensions of fuzzy logic, including fuzzy set (FS), intuitionistic fuzzy set (IFS), and neutrosophic set (NS). However, a fundamental issue persists in these models: the computation of the complement of truth or falsity becomes problematic in the presence of indeterminacy. In contrast to the indeterminacy described in neutrosophic set theory, some real-world situations can exhibit conditions that are entirely true, partially true, or partially false. To better model such nuanced situations, the theory of ambiguous set (AS) has been recently introduced. This study introduces comparative study of ambiguous set theory with respect to FS, IFS and NS.
Co-Authors Sanjeev Kumar Singh