A Novel Cognitive Rough Approach for Severity Analysis of Autistic Children Using Spherical Fuzzy Bipolar Soft Sets

• Published in: Cognitive computation Vol. 16; no. 6; pp. 3260 - 3285
• Main Authors: Ali, Ghous, Lateef, Nimra, Zia, Muhammad Usman, Abbas, Tehseen
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-11-2024; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Assessments; Autism; Complexity; Computation by Abstract Devices; Computational Biology/Bioinformatics; Computer Science; Decision making; Deep learning; Environmental management; Fuzzy sets; Pathogenesis; Performance evaluation; Qualitative analysis; Securities markets; Spherical fuzzy bipolar soft sets; Multiple attribute decision-making; Algorithm; Rough sets; Autism spectrum disorder
• ISSN: 1866-9956; 1866-9964
• DOI: 10.1007/s12559-024-10349-2

Autism spectrum disorders (ASDs) pose complex challenges, characterized by atypical behaviors, sensory sensitivities, and difficulties in social interaction. Despite extensive research, their exact causes remain elusive, indicating a multifactorial interplay of genetic, environmental, and neurological factors. This complexity calls for innovative approaches to ASD understanding and management. Motivated by the need to address the nuanced and uncertain nature of ASD-related data, in this study, we introduce a novel hybrid model called rough spherical fuzzy bipolar soft sets (RSFBSSs) by integrating rough sets, spherical fuzzy sets, and bipolar soft sets, which accommodates imprecision inherent in clinical assessments. We build upon foundational concepts of RSFBSS theory, developing a comprehensive algorithm for uncertain multiple attribute decision-making (MADM). Leveraging this framework, we aim to assess ASD symptom severity in pediatric populations, considering diverse contributing factors to ASD pathogenesis. The RSFBSSs offer advantages over existing methodologies, providing a robust framework for handling complex ASD data. The algorithmic framework facilitates accurate and individualized assessments of ASD symptomatology. To validate our model’s efficacy, we conduct a comparative analysis with preexisting hybrid models, employing quantitative metrics and qualitative evaluations. Through this comprehensive evaluation, we demonstrate the superior performance and versatility of RSFBSSs, offering promising avenues for advancing ASD management.