Skip to main content
Springer Nature Link
Log in

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

  • Research
  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Algorithm 1
Fig. 1

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data Availability

The data that supports the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Aguiar GA, Fernández DM, Reyes OG, Fonticiella YH. Diagnosis in children with autism spectrum disorders: their development in text comprehension. Revista de Ciencias Médicas de Pinar del Río. 2016;20(6):729–37.

    Google Scholar 

  2. Ahmmad J, Mahmood T, Chinram R, Iampan A. Some average aggregation operators based on spherical fuzzy soft sets and their applications in multi-criteria decision making. AIMS Mathematics. 2021;6(7):7798–833.

    Article  MathSciNet  Google Scholar 

  3. Akram M, Ali G. Hybrid models for decision-making based on rough Pythagorean fuzzy bipolar soft information. Granular Computing. 2020;5:1–15.

    Article  Google Scholar 

  4. Akram M, Ali G, Shabir M. A hybrid decision-making framework using rough mF bipolar soft environment. Granular Computing. 2021;6:539–55.

    Article  Google Scholar 

  5. Akram M, Zahid K, Kahraman C. Integrated outranking techniques based on spherical fuzzy information for the digitalization of transportation system. Applied Soft Computing. 2023;134:109992.

  6. Albahri AS, Zaidan AA, AlSattar HA, Hamid RA, Albahri OS, Qahtan S, Alamoodi AH. Towards physician’s experience: development of machine learning model for the diagnosis of autism spectrum disorders based on complex T-spherical fuzzy-weighted zero-inconsistency method. Computational Intelligence. 2023;39(2):225–57.

    Article  Google Scholar 

  7. Albahri AS, Hamid RA, Alzubaidi L, Homod RZ, Zidan KA, Mubark H, Shayea GG, Albahri OS, Alamoodi AH. Prioritizing complex health levels beyond autism triage using fuzzy multi-criteria decision-making. Complex and Intelligent Systems. 2024. https://doi.org/10.1007/s40747-024-01432-0.

    Article  Google Scholar 

  8. Ali G, Abidin MZU, Xin Q, Tawfiq FM. Ranking of downstream fish passage designs for a hydroelectric project under spherical fuzzy bipolar soft framework. Symmetry. 2022;14(10):2141.

    Article  Google Scholar 

  9. Ali G, Alolaiyan H, Pamučar D, Asif M, Lateef N. A novel MADM framework under q-rung orthopair fuzzy bipolar soft sets. Mathematics. 2021;9(17):2163.

    Article  Google Scholar 

  10. Ali G, Ansari MN. Multiattribute decision-making under Fermatean fuzzy bipolar soft framework. Granular Computing. 2022;7(2):337–52.

    Article  Google Scholar 

  11. Ashraf S, Abdullah S, Mahmood T, Ghani F, Mahmood T. Spherical fuzzy sets and their applications in multi-attribute decision making problems. Journal of Intelligent and Fuzzy Systems. 2019;36(3):2829–44.

    Article  Google Scholar 

  12. Atanassov KT. Intuitionistic fuzzy sets. Fuzzy Sets and Systems. 1986;20(1):87–96.

    Article  MathSciNet  Google Scholar 

  13. Babitha KV, Sunil J. Soft set relations and functions. Computers and Mathematics with Applications. 2010;60(7):1840–9.

    Article  MathSciNet  Google Scholar 

  14. Basri MAFA, Ismail WSW, Nor NK, Tohit NM, Ahmad MN, Aun NSM, Daud TIM. Validation of key components in designing a social skills training content using virtual reality for high functioning autism youth-a fuzzy Delphi method. PloS One. 2024;19(4).

  15. Cuong BC. Picture fuzzy sets-first results. part 1, seminar neuro-fuzzy systems with applications. Institute of Mathematics, Hanoi. 2013

  16. Cuong BC, Kreinovich V. Picture fuzzy sets. Journal of Computer Science and Cybernetics. 2014;30(4):409–20.

    Google Scholar 

  17. Gámez-Granados JC, Esteban A, Rodriguez-Lozano FJ, Zafra A. An algorithm based on fuzzy ordinal classification to predict students’ academic performance. it Applied Intelligence. 2023;53:27537-27559.

  18. Garg H, Arora R. A nonlinear-programming methodology for multi-attribute decision-making problem with interval-valued intuitionistic fuzzy soft sets information. Applied Intelligence. 2018;48:2031–46.

    Article  Google Scholar 

  19. Jahani H, Safaei AA. Efficient deep learning approach for diagnosis of attention-deficit/hyperactivity disorder in children based on EEG signals. Cognitive Computation. 2024. https://doi.org/10.1007/s12559-024-10302-3

  20. Gündoğdu FK, Kahraman C. Spherical fuzzy sets and spherical fuzzy TOPSIS method. Journal of Intelligent and Fuzzy Systems. 2019;36(1):337–52.

    Article  Google Scholar 

  21. Kahraman C, Gündogdu FK. Decision making with spherical fuzzy sets. Studies in fuzziness and soft computing. 2021;392. https://doi.org/10.1007/978-3-030-45461-6.

  22. Komorowski J, Pawlak Z, Polkowski L, Skowron A. Rough sets: a tutorial. Rough fuzzy hybridization: A new trend in decision-making; 1999. p. 3–98.

    Google Scholar 

  23. Kshirsagar PP, Surve AR, Pujari SD. Identification of autism spectrum disorder using machine learning and deep learning techniques. In Intelligent Solutions for Cognitive Disorders 2024 (pp. 87-98). IGI Global.

  24. Levy SE, DS M, Schultz RT. Autism. Lancet. 2009;374(9701):1627-1638.

  25. Mahmood T, Ullah K, Khan Q, Jan N. An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets. Neural Computing and Applications. 2019;31:7041–53.

    Article  Google Scholar 

  26. Maji PK, Biswas R, Roy AR. Soft set theory. Computers and Mathematics with Applications. 2003;45(4–5):555–62.

    Article  MathSciNet  Google Scholar 

  27. Malik N, Shabir M. Rough fuzzy bipolar soft sets and application in decision-making problems. Soft Computing. 2019;23:1603–14.

    Article  Google Scholar 

  28. Mardani A, Hooker RE, Ozkul S, Yifan S, Nilashi M, Sabzi HZ, Fei GC. Application of decision making and fuzzy sets theory to evaluate the healthcare and medical problems: a review of three decades of research with recent developments. Expert Systems with Applications. 2019;137:202–31.

    Article  Google Scholar 

  29. Molodtsov D. Soft set theory-first results. Computers and Mathematics with Applications. 1999;37(4–5):9–31.

    MathSciNet  Google Scholar 

  30. Mythili MS, Shanavas AM. A study on autism spectrum disorders using classification techniques. International Journal of Soft Computing and Engineering. 2014;4(5):88–91.

    Google Scholar 

  31. Naz M, Shabir M. On fuzzy bipolar soft sets, their algebraic structures and applications. Journal of Intelligent and Fuzzy Systems. 2014;26(4):1645–56.

    Article  MathSciNet  Google Scholar 

  32. Paik B, Mondal SK. Scoring rule and its application in intuitionistic fuzzy parameterized soft set-based decision-making problem. Journal of Ambient Intelligence and Humanized Computing. 2023;14(10):14209–24.

    Article  Google Scholar 

  33. Pawlak Z, Grzymala-Busse J, Slowinski R, Ziarko W. Rough sets. Communications of the ACM. 1995;38(11):88–95.

    Article  Google Scholar 

  34. Pawlak Z. Rough sets. International journal of computer and information sciences. 1982;11:341–56.

    Article  MathSciNet  Google Scholar 

  35. Perveen PAF, Sunil JJ, Babitha KV, Garg H. Spherical fuzzy soft sets and its applications in decision-making problems. Journal of Intelligent and Fuzzy Systems. 2019;37(6):8237–50.

  36. Randolph-Gips M, Srinivasan P. Modeling autism: a systems biology approach. Journal of Clinical Bioinformatics. 2012;2:1–15.

    Article  Google Scholar 

  37. Salgado LNR, Argilagos MER, Garrido AS, Herrera ARV, Al-Subhi SHS. Model for the diagnosis of autism based on neutrosophic cognitive maps. Neutrosophic Sets and Systems. 2021;44:125–32.

    Google Scholar 

  38. Shabir M, Naz M. On bipolar soft sets. arXiv preprint arXiv:1303.1344. 2013

  39. World Health Organization “Autism Spectrum Disorders,” April 2, 2021. Available: https://www.who.int/news-room/fact-sheets/detail/autism-spectrum-disorders. Access 22 May 2021.

  40. Yager RR. Pythagorean fuzzy subsets. 2013 joint IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS). 2013;57-61.

  41. Zadeh LA. Fuzzy sets. Information and Control. 1965;8(3):338–53.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics, Division of Science & Technology, University of Education, Lahore, Pakistan

    Ghous Ali, Nimra Lateef & Tehseen Abbas

  2. Department of Special Education, Division of Education, University of Education, Lahore, Pakistan

    Muhammad Usman Zia

Authors
  1. Ghous Ali
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Nimra Lateef
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Muhammad Usman Zia
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Tehseen Abbas
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Ghous Ali: methodology, writing—original draft. Nimra Lateef: conceptualization, writing—original draft. Muhammad Usman Zia: conceptualization, formal analysis. Tehseen Abbas: writing—review and editing.

Corresponding author

Correspondence to Ghous Ali.

Ethics declarations

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of Interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ali, G., Lateef, N., Zia, M.U. et al. A Novel Cognitive Rough Approach for Severity Analysis of Autistic Children Using Spherical Fuzzy Bipolar Soft Sets. Cogn Comput 16, 3260–3285 (2024). https://doi.org/10.1007/s12559-024-10349-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-024-10349-2

Keywords