โก Quick Summary
This study introduces a novel approach using complex picture fuzzy information measures (CPF-IM) to enhance the TOPSIS-based decision-making framework for speech matching and sports training feature recognition. The proposed model demonstrates superior accuracy and reliability in handling uncertainties and cyclical behaviors in these domains.
๐ Key Details
- ๐ Evaluation Method: Real-world scenario with four domain experts
- ๐งฉ Criteria: Five speaker profiles rated under ten relevant criteria
- โ๏ธ Technology: TOPSIS with CPF-IM
- ๐ Performance: Achieves consistent alternative rankings
๐ Key Takeaways
- ๐ Novel CPF-IM enhances decision-making frameworks in AI and sports science.
- ๐ก TOPSIS is improved by incorporating complex picture fuzzy sets.
- ๐ฉโ๐ฌ Real-world application involved expert ratings of speaker profiles.
- ๐ Results indicate superior accuracy compared to traditional methods.
- ๐ค New similarity and distance measures were developed for better evaluation.
- ๐ Research establishes a new decision framework for handling uncertain data.
- ๐ Addresses cyclical patterns and evaluation hesitations effectively.
๐ Background
In the realms of artificial intelligence and sports science, the need for robust decision-making frameworks is paramount. Traditional multi-criteria decision-making (MCDM) approaches often struggle with the inherent uncertainties and interactions present in these fields. This study aims to bridge that gap by introducing a more sophisticated method that can accurately assess complex scenarios.
๐๏ธ Study
The research involved a comprehensive evaluation where four domain experts rated five different speaker profiles based on ten relevant criteria. By employing the TOPSIS method enhanced with complex picture fuzzy information measures, the study sought to improve the accuracy of decision-making in both speech matching and sports training contexts.
๐ Results
The findings revealed that the proposed CPF-TOPSIS approach consistently outperformed other techniques in terms of accuracy and reliability. The model effectively detected interdependent relationships between acoustic and biomechanical parameters, leading to more informed decision-making outcomes.
๐ Impact and Implications
This research has significant implications for both speech science and sports training. By accurately handling uncertain data and cyclical patterns, the proposed framework can enhance expert assessments, ultimately leading to better training outcomes and improved performance in sports. The integration of such advanced methodologies could pave the way for future innovations in these fields.
๐ฎ Conclusion
The study highlights the transformative potential of incorporating complex picture fuzzy information measures into decision-making frameworks. By enhancing the TOPSIS method, researchers can now tackle uncertainties and improve the accuracy of assessments in speech and sports sciences. This breakthrough opens up exciting avenues for further research and application in various domains.
๐ฌ Your comments
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TOPSIS driven complex picture fuzzy approach for speech matching and sports training feature recognition.
Abstract
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.
Author: [‘Emam W’, ‘Amin M’, ‘Imran R’, ‘Nazeer MS’, ‘Ullah K’, ‘Ali Z’]
Journal: Sci Rep
Citation: Emam W, et al. TOPSIS driven complex picture fuzzy approach for speech matching and sports training feature recognition. TOPSIS driven complex picture fuzzy approach for speech matching and sports training feature recognition. 2025; 15:34136. doi: 10.1038/s41598-025-03572-w