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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)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v3i3.46080

Abstract

Uncertainty, vagueness, indeterminacy, and ambiguity are fundamental challenges in representing information arising from complex real-world environments, particularly when human perception and decision-making involve indistinct boundaries between truth and falsity. This article presents a conceptual review and comparative analysis of Ambiguous Set (AS) theory in relation to Fuzzy Set (FS), Intuitionistic Fuzzy Set (IFS), and Neutrosophic Set (NS). The objective is to clarify the structural and representational characteristics of AS and identify situations in which its formulation may provide additional flexibility for uncertainty modeling. The comparison considers the basic components, mathematical constraints, relationships among parameters, interpretation of uncertainty, and capability to represent human perception. FS represents uncertainty through a single membership degree, whereas IFS incorporates membership, non-membership, and hesitation. NS further introduces independently characterized truth, indeterminacy, and falsity components. In contrast, AS employs four interrelated components: true membership, false membership, true-ambiguous membership, and false-ambiguous membership, allowing partially true and partially false conditions to be explicitly represented. The reviewed literature indicates that AS has been explored in image processing, multi-criteria decision-making, ambiguous logic, mathematical modeling, complex analysis, and time-series applications. However, its additional parameters may increase computational and interpretational complexity. Therefore, AS should not be regarded as universally superior to FS, IFS, or NS; its suitability depends on the characteristics of the uncertainty and application context. This review provides a balanced assessment of the strengths, limitations, and potential applications of AS while identifying future research opportunities in machine learning, hybrid decision-making, temporal ambiguity modeling, cognitive artificial intelligence, optimization, and formal mathematical development.
Co-Authors Sanjeev Kumar Singh