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In the 21st century, global waste challenges worsen in developing nations relying on manual sorting. This improper waste disposal poses significant threats to human health and the environment, necessitating the adoption of Artificial Intelligence-Based Solid Waste Segregation Technology (AIBSWST). In this context, the advanced Frank t-norm captures nuanced relationships in fuzzy logic, crucial in scenarios where fuzzy set order matters. Building on these principles, the complex q-rung picture fuzzy set (Cq-RPFS) becomes instrumental in representing decision-makers preferences in a two-dimensional manner, enhancing the handling of vague information in real-world scenarios. Expanding on these foundational principles, the paper introduces innovative Frank operations grounded in Frank t-norms within the context of Cq-RPFS. Leveraging these operations, the paper proposes four robust aggregation operators (AOs) under Cq-RPFS: complex q-rung picture fuzzy Frank weighted average (Cq-PFFWA), complex q-rung picture fuzzy Frank weighted geometric (Cq-PFFWG), complex q-rung picture fuzzy Frank ordered weighted average (Cq-PFFWOA), and complex q-rung picture fuzzy Frank ordered weighted geometric (Cq-PFFWOG). These AOs exhibit essential properties such as idempotency, monotonicity, and boundedness. A Multi-Criteria Decision-Making (MCDM) method based on the proposed AOs is suggested to validate these strategies. A real-life case study on India's adoption of AIBSWST serves as a practical application, with thorough analyses, including sensitivity, comparative, and superiority assessments, evaluating the performance of the approaches. A thoughtful discussion of the pros and cons of the proposed AOs accompanies the analysis, emphasizing the significance of the approach in ensuring the cleanliness and health of developing nations. [ABSTRACT FROM AUTHOR]

In the 21st century, global waste challenges worsen in developing nations relying on manual sorting. This improper waste disposal poses significant threats to human health and the environment, necessitating the adoption of Artificial Intelligence-Based Solid Waste Segregation Technology (AIBSWST). In this context, the advanced Frank t-norm captures nuanced relationships in fuzzy logic, crucial in scenarios where fuzzy set order matters. Building on these principles, the complex q-rung picture fuzzy set (Cq-RPFS) becomes instrumental in representing decision-makers preferences in a two-dimensional manner, enhancing the handling of vague information in real-world scenarios. Expanding on these foundational principles, the paper introduces innovative Frank operations grounded in Frank t-norms within the context of Cq-RPFS. Leveraging these operations, the paper proposes four robust aggregation operators (AOs) under Cq-RPFS: complex q-rung picture fuzzy Frank weighted average (Cq-PFFWA), complex q-rung picture fuzzy Frank weighted geometric (Cq-PFFWG), complex q-rung picture fuzzy Frank ordered weighted average (Cq-PFFWOA), and complex q-rung picture fuzzy Frank ordered weighted geometric (Cq-PFFWOG). These AOs exhibit essential properties such as idempotency, monotonicity, and boundedness. A Multi-Criteria Decision-Making (MCDM) method based on the proposed AOs is suggested to validate these strategies. A real-life case study on India's adoption of AIBSWST serves as a practical application, with thorough analyses, including sensitivity, comparative, and superiority assessments, evaluating the performance of the approaches. A thoughtful discussion of the pros and cons of the proposed AOs accompanies the analysis, emphasizing the significance of the approach in ensuring the cleanliness and health of developing nations. [ABSTRACT FROM AUTHOR]