Analysis of computer communication networks based on evaluation of domination and double domination for interval-valued T-spherical fuzzy graphs and their applications in decision-making problems

IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Sami Ullah Khan , Fiaz Hussain , Tapan Senapati , Shoukat Hussain , Zeeshan Ali , Domokos Esztergár-Kiss , Sarbast Moslem
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引用次数: 0

Abstract

This research introduces the Interval-Valued T-Spherical Fuzzy Graph (IVTSFG), a novel extension of fuzzy graph theory designed to address imprecision in decision-making processes, network analysis, and Computer Communication Networks (CCNs). Integrating four types of membership degrees-membership, non-membership, abstinence, and hesitancy-the IVTSFG framework significantly enhances the ability to model and analyze complex systems with uncertain data. The study explores the theories of domination and double domination within the context of IVTSFGs, presenting new methods for evaluating network resilience and optimization. Key findings include the development of innovative techniques for applying domination and double domination in IVTSFGs, demonstrating improved performance in managing CCNs. Comparative analysis with existing fuzzy graph models highlights the advantages of IVTSFGs, particularly in capturing nuanced relationships within network structures. The research provides practical examples and empirical comparisons, showcasing the framework's effectiveness in various decision-making scenarios.
基于区间值 T 球形模糊图的支配和双重支配评价的计算机通信网络分析及其在决策问题中的应用
本研究介绍了区间值 T 球形模糊图(IVTSFG),它是模糊图理论的一种新扩展,旨在解决决策过程、网络分析和计算机通信网络(CCN)中的不确定性问题。IVTSFG 框架集成了四种成员度--成员度、非成员度、弃权度和犹豫度,大大提高了对具有不确定数据的复杂系统进行建模和分析的能力。该研究在 IVTSFG 的背景下探讨了支配和双重支配理论,提出了评估网络弹性和优化的新方法。研究的主要发现包括开发了在 IVTSFGs 中应用支配和双重支配的创新技术,从而提高了 CCNs 的管理性能。与现有模糊图模型的对比分析凸显了 IVTSFG 的优势,尤其是在捕捉网络结构中的细微关系方面。研究提供了实际案例和经验比较,展示了该框架在各种决策场景中的有效性。
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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