A family of similarity measures for q‐rung orthopair fuzzy sets and their applications to multiple criteria decision making

Analyzing the causes of sports injuries in college sports activities and research on the recovery strategy using an intelligent approach

Sports injuries have become a significant concern in modern athletics, particularly among college athletes. These injuries impact athletes' physical health and affect their mental well-being and academic performance. This article explores the various causes of sports injuries in college athletics, shedding light on the high risks involved. After an in-depth analysis, it is evident that factors such as intense training pressure, environmental conditions, psychological challenges, stress, and anxiety contribute significantly to the risk of injuries. This article presents a cutting-edge recovery strategy for intelligent medical treatment to address these challenges. The approach leverages a fuzzy information-based algorithm grounded in similarity measures for spherical fuzzy sets. The proposed method aims to enhance the diagnosis of injuries, design effective rehabilitation programs, and facilitate informed decisions about athletes' return to play. This approach offers valuable insights into improving athletes' health and performance by evaluating similarity measures for spherical fuzzy sets and factoring in injury risks and recovery timelines. The findings demonstrate that integrating traditional medical techniques with advanced algorithmic strategies can significantly reduce recovery time, reduce re-injury risk, and create a more supportive environment for college athletes. The developed assessment algorithm assessed four recovery plans in this study based on four crucial factors. The recovery plan scores are 0.6449, 0.4121, 0.7185, and 0.8215 respectively. The score of the fourth plan is the highest; hence, it is considered the most suitable recovery plan for the athlete.

Vector Similarity Measures of Dual Hesitant Fuzzy Linguistic Term Sets and Their Applications

The dual hesitant fuzzy linguistic term set (DHFLTS) is defined by two functions that express the grade of membership and the grade of non-membership using a set of linguistic terms. In the present work, we first quote an example to point out that the existing complement operation of DHFLTS is on the wrong track. Meanwhile, we redefine this operation to fill the holes in the existing ones. Next, the notion of information energy under a dual hesitant fuzzy linguistic background is provided in order to build the criteria weight determination model. To further facilitate the theory of DHFLTS, we propose two vector similarity measures, i.e., Jaccard and Dice similarity measures, and their weighted forms for DHFLTS. In addition, we pioneer some generalized similarity measures of DHFLTSs and indicate that the Dice similarity measures are particular instances of the generalized similarity measures for some parameter values. Afterward, the similarity measures-based model with unknown weight information under the background of dual hesitant fuzzy linguistic environment is constructed. Lastly, an illustrated example is included to validate the method’s application, along with sensitivity analysis and comparative analysis, demonstrating the practicality and validity of its results.

A New Correlation Coefficient Based on T-Spherical Fuzzy Information with Its Applications in Medical Diagnosis and Pattern Recognition

The T-Spherical fuzzy set (TSFS) is the most generalized form among the introduced fuzzy frameworks. It obtains maximum information from real-life phenomena due to its maximum range. Consequently, TSFS is a very useful structure for dealing with information uncertainties, especially when human opinion is involved. The correlation coefficient (CC) is a valuable tool, possessing symmetry, to determine the similarity degree between objects under uncertainties. This research aims to develop a new CC for TSFS to overcome the drawbacks of existing methods. The proposed CCs are generalized, flexible, and can handle uncertain situations where information has more than one aspect. In addition, the proposed CCs provide decision-makers independence in establishing their opinion. Based on some remarks, the usefulness of the new CC is reviewed, and its generalizability is evaluated. Moreover, the developed new CC is applied to pattern recognition for investment decisions and medical diagnosis of real-life problems to observe their effectiveness and applicability. Finally, the validity of the presented CC is tested by comparing it with the results of the previously developed CC.

Extended many-item similarity indices for sets of nucleotide and protein sequences

Quantification of similarities between protein sequences or DNA/RNA strands is a (sub-)task that is ubiquitously present in bioinformatics workflows, and is usually accomplished by pairwise comparisons of sequences, utilizing simple (e.g. percent identity) or more intricate concepts (e.g. substitution scoring matrices). Complex tasks (such as clustering) rely on a large number of pairwise comparisons under the hood, instead of a direct quantification of set similarities. Based on our recently introduced framework that enables multiple comparisons of binary molecular fingerprints (i.e., direct calculation of the similarity of fingerprint sets), here we introduce novel symmetric similarity indices for analogous calculations on sets of character sequences with more than two (t) possible items (e.g. DNA/RNA sequences with t = 4, or protein sequences with t = 20). The features of these new indices are studied in detail with analysis of variance (ANOVA), and demonstrated with three case studies of protein/DNA sequences with varying degrees of similarity (or evolutionary proximity). The Python code for the extended many-item similarity indices is publicly available at: https://github.com/ramirandaq/tn_Comparisons.