Speech matching and sports training feature recognition have become increasingly significant in artificial intelligence and sports science, necessitating robust decision-making frameworks to address inherent uncertainty, hesitation, and cyclic behaviors in these domains. Current approaches to multi-criteria decision-making (MCDM) often fail to address uncertainties and interactions adequately. To overcome these limitations, this paper proposes the incorporation of complex picture fuzzy information measures (CPF-IM) to boost the accuracy of TOPSIS-based decision-making. Particularly, novel similarity measures (SMs) and distance measures (DMs) have been developed to cover real and imaginary components assigned to membership degree (MD), abstinence degree (AD), and non-membership degree (NDM) within a complex picture fuzzy set (CPFS). The evaluation method employs a real-world scenario in which four domain experts rated five speaker profiles under ten relevant criteria. Result outcomes indicate that the proposed model achieves consistent alternative rankings by detecting the interdependent relationships between acoustic and biomechanical parameters. The proposed CPF-TOPSIS approach surpasses other techniques in terms of accuracy and reliability, as evidenced by the results of comparative studies. The research establishes a new decision framework for speech and sports sciences, which enhances expert assessment decisions by accurately handling uncertain data, cyclical patterns, and evaluation hesitations.

This study focuses on understanding the bioconvection in Jeffery–Hamel (JH) flow, which has valuable applications in areas like converging dies, hydrology, and the automotive industry, which make it a topic of practical importance. This research aims to explore Homogeneous–Heterogeneous (HH) chemical reactions in a magnetic Darcy–Forchheimer model with bioconvection in convergent/divergent channels. To analyze the role of porosity, the Darcy–Forchheimer law is applied. The main system of equations is simplified through similarity transformation into ordinary differential equations solved numerically with the help of the NDSolve technique. The results, compared with previous studies for validation, are presented through graphs and tables. The study reveals that in divergent channels, the velocity decreases with higher solid volume fractions, while in convergent channels, it increases. Furthermore, various physical parameters, such as the Eckert number and porosity parameter, increase skin friction in divergent channels but decrease it in convergent channels. These findings suggest that the parameters investigated in this study can effectively enhance homogeneous reactions, providing valuable insights for practical applications.

In this study, we investigate the coupled system of fractal fractional differential equations (FFDEs) from an existence and stability point of view. The area related to coupled FFDEs is significant because its permitting us to analyze and predict the relationships between several variables throughout the real-world phenomena. Coupled systems have numerous applications in different fields, such as modeling of brain activities and spreads of disease in biology and medicine, modeling of mechanical systems, electrical circuits in engineering, and modeling of population dynamics, predator-prey models in ecology and financial mathematical in economics. Furthermore, the afore mentioned area is significant for environmental science in pollution control, climate change modeling, artificial intelligence, and control systems in technology. We analysis a coupled system and make more informed choices in a variety of fields through coupled system of FFDEs. Keeping these informative aspects of coupled systems in mind, this article aims to explore the qualitative analysis such as existence, uniqueness, and stability for the solution of underlying coupled systems of FFDEs. The tools of functional analysis (FA) and fixed point theory (FPT) have been applied to deduce our required results. We have used the Banach contraction principle (BCP) and the Krasnoselskii fixed point theorem (KFPT) to demonstrate the conditions for existence and uniqueness of a solutions (EUS). Additionally, the results related to stability have demonstrated by using Ulam's concept. Subsequently, a suitable example is given to illustrates the validity of this work.

Personalized learning (PL) achievement in secondary schools is often questioned because it does not always lead to improved student performance, as it has consistently lacked strong data-driven models that can effectively address the uncertainty in student data. Although teachers tailor the lessons to meet the requirements of individual students, it is always challenging to measure if these efforts improve learning. This is because student preferences, strengths, and weaknesses generate a significant amount of unclear and complex data. To manage these complexities, our study introduced an innovative and novel decision-making approach to determining the effectiveness of personalized learning, utilizing the proposed Circular Intuitionistic Fuzzy Aczel–Alsina Bonferroni means (CIFAABM) aggregation operator (AO). This approach enables us to combine multiple data sources, which reveals the impact of PL on individuals, captures the interrelation between various factors, and provides a more comprehensive and precise way to assess student progress. In this paper, we integrate the Bonferroni Mean (BM) AO by using the Aczel–Alsina (A-A) operational rules within the circular context, which provides a more realistic way to evaluate these assessments. The proposed approach demonstrates its practical application in optimizing personalized learning paths and enhancing student engagement by accurately assessing these complex criteria. Its effectiveness is validated through comparative analysis with existing methods. The results validate the use of our model, which impacts the accuracy of PL evaluations for educators in a scalable and empirical decision support framework.

An advanced filtration technique for reusing greywater is a critical and essential procedure for solving water scarcity and meeting the rising demand for clean water in densely populated and urban areas. Greywater or wastewater from washing machines, sinks, and baths can be safely reused for non-potable purposes such as toilet flushing and irrigation when properly treated. The major problems lie in the variable quality of greywater, which may contain pathogens, organic matter, oil, and detergents, making treatment difficult. Traditional filtration models often fail to simultaneously and consistently meet safety standards; therefore, we designed a circular intuitionistic symmetry point of criterion technique with a circular intuitionistic fuzzy rank-sum model for evaluating a multi-attributive ideal real comparative assessment (MAIRCA) model based on Sugeno–Weber aggregation operators. In this study, we found that the most valuable and essential indicators for valuing the advanced filtration techniques are the benefits of greywater, like membrane filtration, activated carbon filtration, advanced oxidation processes, constructed wetlands, and ultraviolet disinfection. To this end, we present some numerical examples of performance evaluation and compare the proposed ranking framework with existing methods to demonstrate the supremacy of the proposed method.