Yazar "Farid, Hafiz Muhammad Athar" seçeneğine göre listele

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    Benchmarking of industrial wastewater treatment processes using a complex probabilistic hesitant fuzzy soft Schweizer-Sklar prioritized-based framework
    (Elsevier, 2024) Saqib, Muhammad; Ashraf, Shahzaib; Farid, Hafiz Muhammad Athar; Simic, Vladimir; Kousar, Muneeba
    The objective of this research work is to effectively handle the intricate and uncertain nature of decisionmaking in the context of industrial wastewater treatment. The prioritization of wastewater treatment is of utmost importance in order to save the environment, promote human health, ensure adherence to legal regulations, conserve resources, and foster overall sustainability. The demand for efficient and sustainable wastewater treatment processes is increasing globally, but selecting appropriate treatment technologies for industrial effluents is challenging due to its complex nature. Our study aims to provide a systematic framework for evaluating and selecting treatments based on efficacy, affordability, and environmental impact. Aggregation operators are one of the fundamental ideas in the framework of information fusion for addressing reallife problems. Numerous scholars have made significant contributions to the development of aggregation operators specifically designed for multi -parameter decision -making (MPDM) whenever circumstances are characterized by uncertainty. Regrettably, the current operators can only be employed within strict limits and constraints. Therefore, this research formulates novel prioritized aggregation operators that alleviate the restrictive constraints associated with the current operators. After this, we present a new methodology that combines the Schweizer-Sklar prioritized aggregation operators with a complex probabilistic hesitant fuzzy soft framework. For this purpose, we develop the averaging aggregation operators and geometric aggregation operators. Following that, we proceed to examine the theorems and properties associated with them by providing rigorous proofs. Then, we develop a novel decision -making technique with a numerical example based on the wastewater treatment process. Through this novel technique, the best process is the activated sludge process. We perform the validity tests to evaluate the feasibility and effectiveness of MPDM techniques.
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    Selection of internet of things-enabled sustainable real-time monitoring strategies for manufacturing processes using a disc spherical fuzzy schweizer–sklar aggregation model
    (Elsevier ltd, 2025) Ashraf, Shahzaib; Naeem, Muhammad; Iqbal, Wania; Farid, Hafiz Muhammad Athar; Shakeel, Hafiz Muhammad; Simic, Vladimir; Tırkolaee, Erfan Babaee
    The emergence of the Internet of Things (IoT) for monitoring in real-time is geared towards sustainable energy consumption practices by taking control over energy loss. The promising potential of current IoT real-time monitoring systems paves the way for future developments in monitoring devices with eco-friendly sensing capabilities. As a result, the creation of effective IoT real-time monitoring devices targeted at decreasing energy loss becomes crucial. This modeling procedure falls under the realm of multiple-attribute group decision-making (MAGDM), aiming to integrate the Schweizer-Sklar (SS) in-norm and in-conorm within the disc spherical fuzzy (D-SF) framework. The objective is to enhance the flexibility of D-SF in dealing with intricate and uncertain data. The core focus of this research is on deriving SS in-norm and in-conorm for D-SF data, consequently introducing innovative aggregation operators. The article offers the fundamental D-SF operations using SS aggregation operators in a systematic manner, with thorough theorem justifications. A new MAGDM tool is presented, created simply to manage ambiguous and imprecise data utilizing the suggested operators. Our model is specifically designed to tackle the critical issue of reducing energy loss in IoT real-time monitoring systems. The research not only focuses on model accuracy but also emphasizes its effectiveness in solving this pressing problem, demonstrating significant advancements in sustainable energy practices. Moreover, the proposed aggregation operators are subjected to a comparative analysis. This comprehensive comparison not only enhances the operators' efficacy but also underscores their relevance in real-world decision-making scenarios.