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Modern Approach in Pattern Recognition Using Circular Fermatean Fuzzy Similarity Measure for Decision Making with Practical Applications

The circular Fermatean fuzzy (CFF) set is an advancement of the Fermatean fuzzy (FF) set and the interval-valued Fermatean fuzzy (IVFF) set which deals with uncertainty. The CFF set is represented as a circle of radius ranging from 0 to 2 with the center at the degree of association (DA) and degree of nonassociation (DNA). If multiple people are involved in making decisions, the CFF set, as an alternative to the FF and IVFF sets, can deal with ambiguity more effectively by encircling the decision values within a circle rather than taking an average. Using algorithms, a pattern can be observed computationally or visually. Machine learning algorithm utilizes pattern recognition as an instrument for identifying patterns and also similarity measure (SM) is a beneficial pattern recognition tool used to classify items, discover variations, and make future predictions for decision making. In this work, we introduce the CFF cosine and Dice similarity measures (CFFDMs and CFFSMs), and their properties are studied. Unlike traditional approaches of decision making, which emphasize a single number, the proposed CFFSMs observe the pattern over the circular region to help in dealing with uncertainty more effectively. We introduce an innovative decision-making method in the FF setting. Available bank loans and applicants’ eligibility levels are represented as CFF set using their FF criteria and are taken as loan patterns and customer eligibility patterns. The loan is allocated to the applicant by measuring the CFFCSM and CFFDSM between the two patterns. Also, laptops are suggested to the customers by measuring the similarity between specification pattern and requirement pattern. The correctness and consistency of the proposed models are ensured by comparison analysis and graphical simulations of the input and similarity CFFNs.

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Unraveling the impact of nanopollution on plant metabolism and ecosystem dynamics

Nanopollution (NPOs), a burgeoning consequence of the widespread use of nanoparticles (NPs) across diverse industrial and consumer domains, has emerged as a critical environmental issue. While extensive research has scrutinized the repercussions of NPs pollution on ecosystems and human health, scant attention has been directed towards unraveling its implications for plant life. This comprehensive review aims to bridge this gap by delving into the nuanced interplay between NPOs and plant metabolism, encompassing both primary and secondary processes. Our exploration encompasses an in-depth analysis of the intricate mechanisms governing the interaction between plants and NPs. This involves a thorough examination of how physicochemical properties such as size, shape, and surface characteristics influence the uptake and translocation of NPs within plant tissues. The impact of NPOs on primary metabolic processes, including photosynthesis, respiration, nutrient uptake, and water transport. Additionally, this study explored the multifaceted alterations in secondary metabolism, shedding light on the synthesis and modulation of secondary metabolites in response to NPs exposure. In assessing the consequences of NPOs for plant life, we scrutinize the potential implications for plant growth, development, and environmental interactions. The intricate relationships revealed in this review underscore the need for a holistic understanding of the plant-NPs dynamics. As NPs become increasingly prevalent in ecosystems, this investigation establishes a fundamental guide that underscores the importance of additional research to shape sustainable environmental management strategies and address the extensive effects of NPs on the development of plant life and environmental interactions.

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