Open Access
Issue
RAIRO-Oper. Res.
Volume 59, Number 6, November-December 2025
Page(s) 3851 - 3889
DOI https://doi.org/10.1051/ro/2025147
Published online 07 January 2026
  • M. Aghamohagheghia, S.M.T. Hashemi and R. Tavakkoli-Moghaddam, Soft computing-based new interval-valued Pythagorean triangular fuzzy multi-criteria group assessment method without aggregation: application to a transport projects appraisal. Int. J. Eng. 32 (2019) 737–746. [Google Scholar]
  • M. Aghamohagheghi, S.M. Hashemi and R. Tavakkoli-Moghaddam, An advanced decision support framework to assess sustainable transport projects using a new uncertainty modeling tool: interval-valued Pythagorean trapezoidal fuzzy numbers. Iran. J. Fuzzy Syst. 18 (2021) 53–73. [Google Scholar]
  • F. Ahemad, A.Z. Khan, M.K. Mehlawat, P. Gupta and S.K. Roy, Multi-attribute group decision-making for solid waste management using interval-valued q-rung orthopair fuzzy COPRAS. RAIRO-Oper. Res. 57 (2023) 1239–1265. [Google Scholar]
  • S. Ahmad, S. Masood, N.Z. Khan, I.A. Badruddin, Ompal, A. Ahmadian, Z.A. Khan and A.H. Khan, Analysing the impact of the COVID-19 pandemic on the psychological health of people using fuzzy mcdm methods. Oper. Res. Perspect. 10 (2023) 100263. [Google Scholar]
  • H. Akdag, T. Kalaycı, S. Karagöz, H. Zülfikar and D. Giz, The evaluation of hospital service quality by fuzzy MCDM. Appl. Soft Comput. 23 (2014) 239–248. [Google Scholar]
  • M. Akram, M. Sultan, A. Adeel and M.M.A. Al-Shamiri, Pythagorean fuzzy n-soft PROMETHEE approach: a new framework for group decision making. AIMS Math. 8 (2023) 17354–17380. [Google Scholar]
  • M.M.A. Al-Shamiri, A. Farooq, M. Nabeel, G. Ali and D. Pamˇucar, Integrating topsis and electre-i methods with cubic m-polar fuzzy sets and its application to the diagnosis of psychiatric disorders. AIMS Math. 8 (2023) 11875–11915. [Google Scholar]
  • A. Alamin, M. Rahaman, K.H. Gazi, S. Alam and S.P. Mondal, Solution and analysis of coupled homogeneous linear intuitionistic fuzzy difference equation. Trans. Fuzzy Sets Syst. 5 (2025) 1–17. [Google Scholar]
  • A.H. Alamoodi, B.B. Zaidan, O.S. Albahri, S. Garfan, I.Y.Y. Ahmaro, R.T. Mohammed, A.A. Zaidan, A.R. Ismail, A.S. Albahri, F. Momani, M.S. Al-Samarraay, A.N. Jasim and R.Q. Malik, Systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions. Complex Intell. Syst. 9 (2023) 4705–4731. [Google Scholar]
  • A. Alamoodi, O. Zughoul, D. David, S. Garfan, D. Pamucar, O. Albahri, A. Albahri, S. Yussof and I.M. Sharaf, A novel evaluation framework for medical LLMS: combining fuzzy logic and MCDM for medical relation and clinical concept extraction. J. Med. Syst. 48 (2024) 81. [Google Scholar]
  • A.S. Albahri, A.A. Zaidan, H.A. AlSattar, R.A. Hamid, O.S. Albahri, S. Qahtan and A.H. Alamoodi, 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. Comput. Intell. 39 (2023) 225–257. [Google Scholar]
  • M.E. Alqaysi, A.S. Albahri and R.A. Hamid, Hybrid diagnosis models for autism patients based on medical and sociodemographic features using machine learning and multicriteria decision-making (MCDM) techniques: an evaluation and benchmarking framework. Comput. Math. Methods Med. 2022 (2022) 9410222. [Google Scholar]
  • F. Altun, R. S¸ahin and C. Güler, Multi-criteria decision making approach based on PROMETHEE with probabilistic simplified neutrosophic sets. Soft Comput. 24 (2020) 4899–4915. [Google Scholar]
  • S. Ashraf, S. Abdullah and S. Khan, Fuzzy decision support modeling for internet finance soft power evaluation based on sine trigonometric Pythagorean fuzzy information. J. Ambient Intell. Humanized Comput. 12 (2021) 3101–3119. [Google Scholar]
  • K.T. Atanassov, Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20 (1986) 87–96. [CrossRef] [Google Scholar]
  • E. Ayyildiz and A.T. Gumus, Interval-valued Pythagorean fuzzy AHP method-based supply chain performance evaluation by a new extension of scor model: Scor 4.0. Complex Intell. Syst. 7 (2021) 559–576. [Google Scholar]
  • E. Ayyildiz, A. Yildiz, A. Taskin and C. Ozkan, An interval valued Pythagorean fuzzy AHP integrated quality function deployment methodology for hazelnut production in Turkey. Expert Syst. App. 231 (2023) 120708. [Google Scholar]
  • I. Badi, M.B. Bouraima, Q. Yanjun and W. Qingping, Advancing sustainable logistics and transport systems in free trade zones: a multi-criteria decision-making approach for strategic sustainable development. Int. J. Sustain. Dev. Goals 1 (2025) 45–55. [Google Scholar]
  • R.J. Baldessarini, Psychiatric disorders. Pharmacol. Basis Ther. 391 (1980) 14–22. [Google Scholar]
  • M. Behzadian, R. Kazemzadeh, A. Albadvi and M. Aghdasi, PROMETHEE: a comprehensive literature review on methodologies and applications. Eur. J. Oper. Res. 200 (2010) 198–215. [Google Scholar]
  • J.P. Brans, R. Nadeau and M. Landry, Elaboration dinstruments daide a la decision. Methode PROMETHEE. Laide a la Decision: Nature, Instruments et perspectives Davenir (1982) 183–214. [Google Scholar]
  • E. Castrén, Neurotrophins and psychiatric disorders. Neurotrophic Factors 220 (2014) 461–479. [Google Scholar]
  • M.-H. Chang, J. J. H. Liou and H.-W. Lo, A hybrid MCDM model for evaluating strategic alliance partners in the green biopharmaceutical industry. Sustainability 11 (2019) 4065. [Google Scholar]
  • M. Chang, F.Y. Womer, X. Gong, X. Chen, L. Tang, R. Feng, S. Dong, J. Duan, Y. Chen, R. Zhang, Y. Wang, S. Ren, Y. Wang, J. Kang, Z. Yin, Y. Wei, S. Wei, X. Jiang, K. Xu, B. Cao, Y. Zhang, W. Zhang, Y. Tang, X. Zhang and F. Wang, Identifying and validating subtypes within major psychiatric disorders based on frontal-posterior functional imbalance via deep learning. Mol. Psychiatry 26 (2021) 2991–3002. [Google Scholar]
  • R. Chutia, Ordering intuitionistic fuzzy numbers by a convex combination of values and multiple of ambiguity inclusion functions with ambiguities of membership and nonmembership functions. Int. J. Intell. Syst. 36 (2021) 5785–5815. [Google Scholar]
  • W.C. Cockerham, Sociology of mental disorder. Routledge 11 (2020) 384. [Google Scholar]
  • S. Dalsgaard, E. Thorsteinsson, B.B. Trabjerg, J. Schullehner, O. Plana-Ripoll, I. Brikell, T. Wimberley, M. Thygesen, K.B. Madsen, A. Timmerman, D. Schendel, J.J. McGrath, P.B. Mortensen and C.B. Pedersen, Incidence rates and cumulative incidences of the full spectrum of diagnosed mental disorders in childhood and adolescence. JAMA Psychiatry 77 (2020) 155–164. [Google Scholar]
  • K. Debnath and S.K. Roy, Power partitioned neutral aggregation operators for t-spherical fuzzy sets: an application to h2 refuelling site selection. Expert Syst. App. 216 (2023) 119470. [Google Scholar]
  • K. Debnath and S.K. Roy, Maclaurin symmetric mean operator-based MADM approach for type-2 intuitionistic fuzzy sets, in Strategic Fuzzy Extensions and Decision-making Techniques. CRC Press (2024) 107–134. [Google Scholar]
  • K. Debnath, S.K. Roy, M. Deveci and H. Tomášková, Integrated MADM approach based on extended MABAC method with Aczel–Alsina generalized weighted Bonferroni mean operator. Artif. Intell. Rev. 58 (2025) 27. [Google Scholar]
  • G. Demir, Fuzzy multi-criteria decision-making based security management: risk assessment and countermeasure selection in smart cities. Knowl. Decis. Syst. App. 1 (2025) 70–91. [Google Scholar]
  • Y. Dorfeshan, A.A. Taleizadeh and M. Toloo, Assessment of risk-sharing ratio with considering budget constraint and disruption risk under a triangular Pythagorean fuzzy environment in public–private partnership projects. Expert Syst. App. 203 (2022) 117245. [Google Scholar]
  • P.A. Ejegwa, Improved composite relation for Pythagorean fuzzy sets and its application to medical diagnosis. Granular Comput. 5 (2020) 277–286. [Google Scholar]
  • D. Farooq, H.W. Iqbal, A. Farooq and M. Awais, Assessing critical road hazard factors for sustainable development in cities. Int. J. Sustain. Dev. Goals 1 (2025) 1–9. [Google Scholar]
  • E. Feldman, R. Mayou, K. Hawton, M. Ardern and E.B.O. Smith, Psychiatric disorder in medical in-patients. QJM: Int. J. Med. 63 (1987) 405–412. [Google Scholar]
  • D. Freeman, B. Sheaves, F. Waite, A.G. Harvey and P.J. Harrison, Sleep disturbance and psychiatric disorders. Lancet Psychiatry 7 (2020) 628–637. [Google Scholar]
  • F. Fregni, M.M. El-Hagrassy, K. Pacheco-Barrios, S. Carvalho, J. Leite, M. Simis, J. Brunelin, E.M. Nakamura-Palacios, P. Marangolo, G. Venkatasubramanian, D. San-Juan, W. Caumo, M. Bikson, A.R. Brunoni and N.C.W. Group, Evidence-based guidelines and secondary meta-analysis for the use of transcranial direct current stimulation in neurological and psychiatric disorders. Int. J. Neuropsychopharmacol. 24 (2021) 256–313. [Google Scholar]
  • S. Garcia-Ayllon, E. Hontoria and N. Munier, The contribution of MCDM to SUMP: the case of Spanish cities during 2006–2021. Int. J. Environ. Res. Publ. Health 19 (2022) 294. [Google Scholar]
  • S.S. Goswami and D.K. Behera, Evaluation of the best smartphone model in the market by integrating fuzzy-AHP and PROMETHEE decision-making approach. Decision 48 (2021) 71–96. [Google Scholar]
  • S.B. Guessoum, J. Lachal, R. Radjack, E. Carretier, S. Minassian, L. Benoit and M.R. Moro, Adolescent psychiatric disorders during the COVID-19 pandemic and lockdown. Psychiatry Res. 291 (2020) 113264. [CrossRef] [Google Scholar]
  • A. Guleria and R.K. Bajaj, On Pythagorean fuzzy soft matrices, operations and their applications in decision making and medical diagnosis. Soft Comput. 23 (2019) 7889–7900. [Google Scholar]
  • F.K. Gündoğdu and C. Kahraman, A novel spherical fuzzy analytic hierarchy process and its renewable energy application. Soft Comput. 24 (2020) 4607–4621. [Google Scholar]
  • M. Henderson, S.B. Harvey, S. Øverland, A. Mykletun and M. Hotopf, Work and common psychiatric disorders. J. R. Soc. Med. 104 (2011) 198–207. [Google Scholar]
  • E. Hertenstein, E. Trinca, M. Wunderlin, C.L. Schneider, M.A. Züst, K.D. Fehér, T. Su, A.V. Straten, T. Berger, C. Baglioni, A. Johann, K. Spiegelhalder, D. Riemann, B. Feige and C. Nissen, Cognitive behavioral therapy for insomnia in patients with mental disorders and comorbid insomnia: a systematic review and meta-analysis. Sleep Med. Rev. 62 (2022) 101597. [Google Scholar]
  • J. Horn, D.E. Mayer, S. Chen and E.A. Mayer, Role of diet and its effects on the gut microbiome in the pathophysiology of mental disorders. Translational Psychiatry 12 (2022) 164. [Google Scholar]
  • Z. Hua and X. Jing, A generalized shapley index-based interval-valued Pythagorean fuzzy PROMETHEE method for group decision-making. Soft Comput. 27 (2023) 6629–6652. [Google Scholar]
  • Y.-H. Huang and G.-W. Wei, TODIM method for interval-valued Pythagorean fuzzy multiple attribute decision making. Int. J. Knowl. Intell. Eng. Syst. 22 (2018) 249–259. [Google Scholar]
  • A. Hussain and M. Ali, A critical estimation of ideological and political education for sustainable development goals using an advanced decision-making model based on intuitionistic fuzzy Z-numbers. Int. J. Sustain. Dev. Goals 1 (2025) 23–44. [Google Scholar]
  • E. Ilbahar and C. Kahraman, Retail store performance measurement using a novel interval-valued Pythagorean fuzzy waspas method. J. Intell. Fuzzy Syst. 35 (2018) 3835–3846. [Google Scholar]
  • S.M.J. Jalali, M. Ahmadian, S. Ahmadian, A. Khosravi, M. Alazab and S. Nahavandi, An oppositional-Cauchy-based GSK evolutionary algorithm with a novel deep ensemble reinforcement learning strategy for COVID-19 diagnosis. Appl. Soft Comput. 111 (2021) 107675. [Google Scholar]
  • S.S. Joudar, A. Albahri and R.A. Hamid, Intelligent triage method for early diagnosis autism spectrum disorder (ASD) based on integrated fuzzy multi-criteria decision-making methods. Inf. Med. Unlocked 36 (2023) 101131. [Google Scholar]
  • K.S. Kendler, The nature of psychiatric disorders. World Psychiatry 15 (2016) 5–12. [Google Scholar]
  • K.S. Kendler, P. Zachar and C. Craver, What kinds of things are psychiatric disorders? Psychol. Med. 41 (2011) 1143–1150. [Google Scholar]
  • M.R. Khan, K. Ullah, A. Raza, Z. Ali, T. Senapati, D. Esztergár-Kiss and S. Moslem, Evaluating safety in Dublin’s bike-sharing system using the concept of intuitionistic fuzzy rough power aggregation operators. Measurement 253 (2025) 117553. [Google Scholar]
  • S.U. Khan, F. Hussain, T. Senapati, S. Hussain, Z. Ali, D. Esztergár-Kiss and S. Moslem, Analysis of computer communication networks based on evaluation of domination and double domination for interval-valued t-spherical fuzzy graphs and their applications in decision-making problems. Eng. App. Artif. Intell. 139 (2025) 109650. [Google Scholar]
  • R. Krishankumar, K.S. Ravichandran and S. AB, A new extension to PROMETHEE under intuitionistic fuzzy environment for solving supplier selection problem with linguistic preferences. Appl. Soft Comput. 60 (2017) 564–576. [Google Scholar]
  • V. Kumar, VlseKriterijumska Optimizacija I Kompromisno Resenj (VIKOR) method: MCDM approach for the medical diagnosis of vector-borne diseases. J. Comput. Cognitive Eng. 3 (2024) 240–251. [Google Scholar]
  • V. Kumar, P. Vrat and R. Shankar, MCDM model to rank the performance outcomes in the implementation of Industry 4.0. Benchmarking: Int. J. 31 (2024) 1453–1491. [Google Scholar]
  • M.V. Lakshmi and J. Dhivya, A distance measure for intuitionistic fuzzy multicriteria decision making in pattern recognition and medical diagnosis. IETE J. Res. 75 (2025) 7–16. [Google Scholar]
  • L.Z. Li and S. Wang, Prevalence and predictors of general psychiatric disorders and loneliness during COVID-19 in the United Kingdom. Psychiatry Res. 291 (2020) 113267. [Google Scholar]
  • H. Li, Y. Cao, L. Su and Q. Xia, An interval Pythagorean fuzzy multi-criteria decision making method based on similarity measures and connection numbers. Information 10 (2019) 80. [Google Scholar]
  • D.M. Low, K.H. Bentley and S.S. Ghosh, Automated assessment of psychiatric disorders using speech: a systematic review. Laryngoscope Invest. Otolaryngol. 5 (2020) 96–116. [Google Scholar]
  • Z. Ma, J. Zhao, Y. Li, D. Chen, T. Wang, Z. Zhang, Z. Chen, Q. Yu, J. Jiang, F. Fan and X. Liu, Mental health problems and correlates among 746 217 college students during the coronavirus disease 2019 outbreak in China. Epidemiol. Psychiatric Sci. 29 (2020) e181. [Google Scholar]
  • C. Macharis, J. Springael, K.D. Brucker and A. Verbeke, Promethee and AHP: the design of operational synergies in multicriteria analysis: strengthening PROMETHEE with ideas of AHP. Eur. J. Oper. Res. 153 (2004) 307–317. [Google Scholar]
  • S. Mandal, K.H. Gazi, S. Salahshour, S.P. Mondal, P. Bhattacharya and A.K. Saha, Application of interval valued intuitionistic fuzzy uncertain MCDM methodology for Ph.D supervisor selection problem. Results Control Optim. 15 (2024) 100411. [Google Scholar]
  • R. Mayou and K. Hawton, Psychiatric disorder in the general hospital. Br. J. Psychiatry 149 (1986) 172–190. [Google Scholar]
  • M. Mengi and D. Malhotra, A systematic literature review on traditional to artificial intelligence based socio-behavioral disorders diagnosis in India: challenges and future perspectives. Appl. Soft Comput. 129 (2022) 109633. [Google Scholar]
  • M.U. Molla, B.C. Giri and P. Biswas, Extended PROMETHEE method with Pythagorean fuzzy sets for medical diagnosis problems. Soft Comput. 25 (2021) 4503–4512. [Google Scholar]
  • A. Möllmann, N. Heinrichs and A. Herwig, A conceptual framework on body representations and their relevance for mental disorders. Front. Psychol. 14 (2024) 1231640. [Google Scholar]
  • N.C. Momen, O. Plana-Ripoll, E. Agerbo, M.E. Benros, A.D. Børglum, M.K. Christensen, S. Dalsgaard, L. Degenhardt, P. de Jonge, J.C.P. Debost and M. Fenger-Grøn, Association between mental disorders and subsequent medical conditions. New England J. Med. 382 (2020) 1721–1731. [Google Scholar]
  • A.F. Momena, S. Mandal, K.H. Gazi, B.C. Giri and S.P. Mondal, Prediagnosis of disease based on symptoms by generalized dual hesitant hexagonal fuzzy multi-criteria decision-making techniques. Systems 11 (2023) 231. [Google Scholar]
  • A.F. Momena, K.H. Gazi, M. Rahaman, A. Sobczak, S. Salahshour, S.P. Mondal and A. Ghosh, Ranking and challenges of supply chain companies using MCDM methodology. Logistics 8 (2024) 1–32. [Google Scholar]
  • A.F. Momena, K.H. Gazi and S.P. Mondal, Multi-criteria decision analysis for sustainable medicinal supply chain problems with adaptability and challenges issues. Logistics 9 (2025) 1–32. [Google Scholar]
  • A. Mondal, S.K. Roy and M. Deveci, Regret-based domination and prospect-based scoring in three-way decision making using q-rung orthopair fuzzy Mahalanobis distance. Artif. Intell. Rev. 56 (2023) 2311–2348. [Google Scholar]
  • S. Moslem, A novel parsimonious spherical fuzzy analytic hierarchy process for sustainable urban transport solutions. Eng. App. Artif. Intell. 128 (2024) 107447. [Google Scholar]
  • S. Moslem, Evaluating commuters’ travel mode choice using the z-number extension of parsimonious best worst method. Appl. Soft Comput. 173 (2025) 112918. [Google Scholar]
  • N. Mullins, J. Kang, A.I. Campos, J.R. Coleman, A.C. Edwards, H. Galfalvy, D.F. Levey, A. Lori, A. Shabalin, A. Starnawska and M.H. Su, Dissecting the shared genetic architecture of suicide attempt, psychiatric disorders and known risk factors. Biol. Psychiatry 91 (2022) 313–327. [Google Scholar]
  • M. Munir, N. Kausar and S.I. Khan, Generalized fuzzy sets and their applications in purchase satisfaction, personnel posting, and disease diagnosis. Soft Comput. 27 (2023) 3907–3920. [Google Scholar]
  • K. Naeem, M. Riaz and F. Karaaslan, A mathematical approach to medical diagnosis via Pythagorean fuzzy soft TOPSIS, VIKOR and generalized aggregation operators. Complex Intell. Syst. 7 (2021) 2783–2795. [Google Scholar]
  • K. Nemani, C. Li, M. Olfson, E.M. Blessing, N. Razavian, J. Chen, E. Petkova and D.C. Goff, Association of psychiatric disorders with mortality among patients with COVID-19. JAMA Psychiatry 78 (2021) 380–386. [Google Scholar]
  • L. Oubahman, S. Duleba and D. Esztergár-Kiss, Analyzing university students’ mode choice preferences by using a hybrid AHP group-PROMETHEE model: evidence from Budapest city. Eur. Transp. Res. Rev. 16 (2024) 8. [Google Scholar]
  • D. Pamucar and Ö.F. Görçün, Evaluation of the European container ports using a new hybrid fuzzy LBWA-CoCoSo’B techniques. Expert Syst. App. 203 (2022) 117463. [Google Scholar]
  • O. Parkash and R. Kumar, Modified fuzzy divergence measure and its applications to medical diagnosis and MCDM. Risk Decis. Anal. 6 (2017) 231–237. [Google Scholar]
  • R. Peijia, X. Zeshui, L. Huchang and Z. Xiao-Jun, A thermodynamic method of intuitionistic fuzzy MCDM to assist the hierarchical medical system in China. Inf. Sci. 420 (2017) 490–504. [Google Scholar]
  • A. Perez-Aguilar, M. Ortiz-Barrios, P. Pancardo and F. Orrante-Weber-Burque, A hybrid fuzzy MCDM approach to identify the intervention priority level of COVID-19 patients in the emergency department: a case study. Int. Conf. Human-Comput. Interaction 14029 (2023) 284–297. [Google Scholar]
  • S. Ping Wan, W. Chang Zou, L. Gen Zhong and J. Ying Dong, Some new information measures for hesitant fuzzy PROMETHEE method and application to green supplier selection. Soft Comput. 24 (2020) 9179–9203. [CrossRef] [Google Scholar]
  • K. Rahman, S. Abdullah and M.S.A. Khan, Some interval-valued Pythagorean fuzzy Einstein weighted averaging aggregation operators and their application to group decision making. J. Intell. Syst. 29 (2018) 393–408. [Google Scholar]
  • M. Rahaman, D. Chalishajar, K.H. Gazi, S. Alam, S. Salahshour and S.P. Mondal, Fractional calculus for type 2 interval-valued functions. Fractal Fract. 9 (2025) 102. [Google Scholar]
  • P. Ren, Z. Xu and X. Gou, Pythagorean fuzzy TODIM approach to multi-criteria decision making. Appl. Soft Comput. 42 (2016) 246–259. [Google Scholar]
  • J. Roy, A. Ranjan and A. Debnath, An extended multi attributive border approximation area comparison using interval type-2 trapezoidal fuzzy numbers. Preprint arXiv:1607.01254v3 (2016). [Google Scholar]
  • T.L. Saaty, How to make a decision: the analytic hierarchy process. Eur. J. Oper. Res. 48 (1990) 9–26. [CrossRef] [Google Scholar]
  • W.M. Sabry and A. Vohra, Role of Islam in the management of psychiatric disorders. Indian J. Psychiatry 55 (2013) S205–S214. [Google Scholar]
  • M. Safaei, E.A. Sundararajan, S. Asadi, M. Nilashi, M.J.A. Aziz, M.S. Saravanan, M. Abdelhaq and R. Alsaqour, A hybrid MCDM approach based on fuzzy-logic and dematel to evaluate adult obesity. Int. J. Environ. Res. Publ. Health 19 (2022) 15432. [Google Scholar]
  • M.M. Sati, B. Joshi, T. Pal, N. Kumar, A. Singh and S. Goyal, Ambiguous fuzzy Einstein geometric operator: utilizing to analyze power generation techniques, in 2024 2nd International Conference on Disruptive Technologies (ICDT) (2024) 1536–1541. [Google Scholar]
  • K.W. Scangos, M.W. State, A.H. Miller, J.T. Baker and L.M. Williams, New and emerging approaches to treat psychiatric disorders. Nat. Med. 29 (2023) 317–333. [Google Scholar]
  • M. Shakeel, S. Abdullah, M. Shahzad and N. Siddiqui, Geometric aggregation operators with interval-valued Pythagorean trapezoidal fuzzy numbers based on Einstein operations and their application in group decision making. Int. J. Mach. Learn. Cybern. 10 (2019) 2867–2886. [Google Scholar]
  • M. Shakeel, S. Abdullah, M.S.A. Khan and K. Rahman, Averaging aggregation operators with interval Pythagorean trapezoidal fuzzy numbers and their application to group decision making. Punjab Univ. J. Math. 50 (2020). [Google Scholar]
  • Z. S¸im¸sir, H. Koç, T. Seki and M.D. Griffiths, The relationship between fear of COVID-19 and mental health problems: a meta-analysis. Death Stud. 46 (2022) 515–523. [Google Scholar]
  • P. Singh, K.H. Gazi, M. Rahaman, T. Basuri and S.P. Mondal, Solution strategy and associated results for fuzzy mellin transformation. Franklin Open 7 (2024) 100112. [Google Scholar]
  • M. Solmi, J. Radua, M. Olivola, E. Croce, L. Soardo, G.S. de Pablo, J.I. Shin, J.B. Kirkbride, P. Jones, J.H. Kim, J.Y. Kim, A.F. Carvalho, M.V. Seeman, C.U. Correll and P. Fusar-Poli, Age at onset of mental disorders worldwide: large-scale meta-analysis of 192 epidemiological studies. Mol. Psychiatry 27 (2022) 281–295. [Google Scholar]
  • K.F. Sotiropoulou, A.P. Vavatsikos and P.N. Botsaris, A hybrid AHP-PROMETHEE II onshore wind farms multicriteria suitability analysis using KNN and SVM regression models in Northeastern Greece. Renewable Energy 221 (2024) 119795. [Google Scholar]
  • A. Srivastava and P.K. Mishra, Energy efficient clustering using modified PROMETHEE-II and AHP approach in wireless sensor networks. Multimedia Tools App. 82 (2023) 47049–47080. [Google Scholar]
  • M.J.P. Staab, C.J. Datto, R.M. Weinrieb, P. Gariti, M. Rynn and D.L. Evans, Detection and diagnosis of psychiatric disorders in primary medical care settings. Med. Clin. North Am. 85 (2001) 579–596. [Google Scholar]
  • D.J. Stein, S.J. Shoptaw, D.V. Vigo, C. Lund, P. Cuijpers, J. Bantjes, N. Sartorius and M. Maj, Psychiatric diagnosis and treatment in the 21st century: paradigm shifts versus incremental integration. World Psychiatry 21 (2022) 393–414. [Google Scholar]
  • J. Sun, Q.-X. Dong, S.-W. Wang, Y.-B. Zheng, X.-X. Liu, T.-S. Lu, K. Yuan, J. Shi, B. Hu, L. Lu and Y. Han, Artificial intelligence in psychiatry research, diagnosis and therapy. Asian J. Psychiatry 87 (2023) 103705. [Google Scholar]
  • M.H. Teicher, J.B. Gordon and C.B. Nemeroff, Recognizing the importance of childhood maltreatment as a critical factor in psychiatric diagnoses, treatment, research, prevention and education. Mol. Psychiatry 27 (2022) 1331–1338. [Google Scholar]
  • J. Torkzadeh, S.N.S. Shahzadi, T. Allahviranloo and M. Shahriari, An interval-valued Pythagorean fuzzy group AHP-PROMETHEE approach for organizational behavior assessment and ranking in higher education of Iran considering environmental criteria. Soft Comput. (2023) 1–16. [Google Scholar]
  • M. Touqeer, R. Umer and M.I. Ali, A chance-constraint programming model with interval-valued Pythagorean fuzzy constraints. J. Intell. Fuzzy Syst. 40 (2021) 11183–11199. [Google Scholar]
  • G. van de Kaa, J. Rezaei, L. Kamp and A. de Winter, Photovoltaic technology selection: a fuzzy MCDM approach. Renewable Sustain. Energy Rev. 32 (2014) 662–670. [Google Scholar]
  • F.X. Vollenweider and K.H. Preller, Psychedelic drugs: neurobiology and potential for treatment of psychiatric disorders. Nat. Rev. Neurosci. 21 (2020) 611–624. [Google Scholar]
  • T. Wang, L. Zhang, B. Huang and X. Zhou, Three-way conflict analysis based on interval-valued Pythagorean fuzzy sets and prospect theory. Artif. Intell. Rev. 56 (2023) 6061–6099. [Google Scholar]
  • Y. Wang, W. Wang, Z. Wang, M. Deveci, S.K. Roy and S. Kadry, Selection of sustainable food suppliers using the Pythagorean fuzzy CRITIC-MARCOS method. Inf. Sci. 664 (2024) 120326. [Google Scholar]
  • W. Wu, Probabilistic linguistic PROMETHEE I and II methods for evaluation of the reform scheme of postgraduate innovation and entrepreneurship education talent training mode under the big data environment. Math. Prob. Eng. 2022 (2022) 8341052. [Google Scholar]
  • Y.-X. Xuea, J.-X. Youa, X.-D. Lai and H.-C. Liua, An interval-valued intuitionistic fuzzy MABAC approach for material selection with incomplete weight information. Expert Syst. App. 38 (2016) 703–713. [Google Scholar]
  • R.R. Yager, Pythagorean fuzzy subsets. 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (2013) 57–61. [Google Scholar]
  • J. Ye and T.-Y. Chen, Pythagorean fuzzy sets combined with the PROMETHEE method for the selection of cotton woven fabric. J. Nat. Fibers 19 (2022) 12447–12461. [Google Scholar]
  • T. Yu Chen, A novel PROMETHEE-based outranking approach for multiple criteria decision analysis with Pythagorean fuzzy information. IEEE Access 6 (2018) 54495–54506. [Google Scholar]
  • C. Yua, Y. Shaoa, K. Wang and L. Zhang, A group decision making sustainable supplier selection approach using extended TOPSIS under interval-valued Pythagorean fuzzy environment. Expert Syst. App. 121 (2019) 1–17. [Google Scholar]
  • L.A. Zadeh, Fuzzy sets. Inf. Control 8 (1965) 338–353. [Google Scholar]
  • M.H.F. Zarandi, S. Soltanzadeh, A. Mohammadi and O. Castillo, Designing a general type-2 fuzzy expert system for diagnosis of depression. Appl. Soft Comput. 80 (2019) 329–341. [Google Scholar]
  • E.K. Zavadskas, R. Bausys, A. Kaklauskas and S. Raslanas, Hedonic shopping rent valuation by one-to-one neuromarketing and neutrosophic PROMETHEE method. Appl. Soft Comput. 85 (2019) 105832. [Google Scholar]
  • S. Zeng, J. Chen and X. Li, A hybrid method for Pythagorean fuzzy multiple-criteria decision making. Int. J. Inf. Technol. Decis. Making 15 (2016) 403–422. [Google Scholar]
  • M. Zhang, T. Zheng, W. Zheng and L. Zhou, Interval-valued Pythagorean hesitant fuzzy set and its application to multiattribute group decision-making. Complexity 2–20 (2020) 1–26. [Google Scholar]
  • Y. Zhang, H. Zhang, X. Ma and Q. Di, Mental health problems during the COVID-19 pandemics and the mitigation effects of exercise: a longitudinal study of college students in China. Int. J. Environ. Res. Publ. Health 17 (2020) 3722. [Google Scholar]
  • G. Zheng, N. Zhu, Z. Tian, Y. Chen and B. Sun, Application of a trapezoidal fuzzy AHP method for work safety evaluation and early warning rating of hot and humid environments. Saf. Sci. 50 (2012) 228–239. [Google Scholar]
  • Y. Zhou, X. Zhang, Y. Chen, X. Xu and M. Li, A water-land-energy-carbon nexus evaluation of agricultural sustainability under multiple uncertainties: the application of a multi-attribute group decision method determined by an interval-valued intuitionistic fuzzy set. Expert Syst. App. 242 (2024) 122833. [Google Scholar]

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