Synergy of machine learning and the Einstein Choquet integral with LOPCOW and fuzzy measures for sustainable solid waste management

AIMS Mathematics, Journal Year: 2025, Volume and Issue: 10(1), P. 460 - 498

Published: Jan. 1, 2025

<p>Solid waste management (SWM) protects public health, the environment, and limited resources in densely populated urbanized countries such as Singapore. This work presents an advanced framework for optimizing SWM using mathematical models decision-making techniques, including circular $ q $-rung orthopair fuzzy set (C$ $-ROFS) data, combined with Choquet integral (CI) logarithmic percentage change-driven objective weighting (LOPCOW) methods, enhanced by aggregation operators (AOs) Einstein weighted averaging $-ROFECIWA) geometric $-ROFECIWG) operators. By conducting a systematic evaluation, these methods classified different alternatives to SWM, evaluating them according criteria their environmental impact, cost-effectiveness, reduction efficiency, feasibility of implementation, health safety, acceptance. The C$ $-ROFECIWA $-ROFECIWG perform better than previous approaches effective multifaceted dynamic scenarios. comparison study demonstrates that integration LOPCOW offers conclusions are more reliable sustainable. conducted Singapore successfully finds most feasible emphasizes possibility implementing environmentally sustainable practices urban environment. research practical insights policymakers need improve enhance various environments.</p>

Language: Английский

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Enhanced Multi-Attribute Ideal-Real comparative analysis with the circular intuitionistic fuzzy framework: Application to hybrid cloud services

Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 65, P. 103184 - 103184

Published: Feb. 23, 2025

Language: Английский

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AI-Driven LOPCOW-AROMAN Framework and Topological Data Analysis Using Circular Intuitionistic Fuzzy Information: Healthcare Supply Chain Innovation

Mathematics, Journal Year: 2024, Volume and Issue: 12(22), P. 3593 - 3593

Published: Nov. 16, 2024

Artificial intelligence (AI) stands out as a significant technological innovation, driving progress in diverse areas such big data analysis, supply chain management, energy efficiency, sustainable development, etc. The present study investigates how AI could contribute to the sustainability of healthcare (HSC) and managing medical needs. Medical organizations can boost logistics their tasks, reduce pharmaceutical trash, strengthen revenue projections through adoption tools. This aims provide structured evaluation AI-driven solutions for enhancing robustness, especially under conditions uncertainty complex demands. To determine investment value applications HSC current research adopted revolutionary multi-criteria decision-making (MCDM) methodology tailored sector’s unique demands, including six critical factors. In light these criteria, highly technologically advanced AI-based are examined. implementation circular intuitionistic fuzzy set (CIFS) instance discussed provides versatile expressive way describe vague uncertain information. leverages CIF topology address complexities uncover underlying structural features large dataset. At outset, we LOPCOW approach, which includes logarithmic variation assign weights whereas AROMAN method utilizes powerful two-step normalization technique rank alternatives, hence guaranteeing trustworthy accurate appraisal. A substantial degree robustness was confirmed by following comparison operators well sensitivity testing.

Language: Английский

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Emerging Trends of Biohydrogen Ecosystem on Environmental Sustainability: A Case Study

Journal of environmental chemical engineering, Journal Year: 2024, Volume and Issue: unknown, P. 115212 - 115212

Published: Dec. 1, 2024

Language: Английский

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