Document Type : Research Paper


1 Department of Computer Science, University of Quebec in Montreal, 201 President Kennedy Street, Montreal, Quebec H2X 3Y7, Canada.

2 Biju Patnaik University of Technology, Chhend Colony, Rourkela, Odisha 769004, India.


The present paper proposes a new application of the prediction of human behavior using TOPSIS as an appropriate tool for data optimization. Our hypothesis was that the analysis of the candidates with this method was influenced by the change of their behavior. We found that the behavior change could occur in more than one time span when the behavior of two candidates changed simultaneously. One of the advantages of this study is that the pattern of the behavior change with time is predicted with this method. Another advantage is that the modifications in the TOPSIS algorithm have made the predictions independent from the need of changing the fuzzy membership degrees of the candidates. This is the first time that these modifications in this technique with a new application including the numerical analysis of cognitive date are reported. Our results can be used in cognitive science, experimental psychology, cognitive informatics and artificial intelligence.


Main Subjects

  1. Ruby, A. J., Aisha, B. W., & Subash, C. P. (2016). Comparison of multi criteria decision making algorithms for ranking cloud renderfarm services. Indian journal of science and technology, 9(31), 1-5. DOI: 17485/ijst/2016/v9i31/93467
  2. Balioti, V., Tzimopoulos, C., & Evangelides, C. (2018). Multi-criteria decision making using TOPSIS method under fuzzy environment. Application in spillway selection. Multidisciplinary digital publishing institute proceedings2(11), 637.
  3. Tlas, M., & Ghani, B. A. (2020). Interactive software for classification and ranking procedures based on multi-criteria decision-making algorithms. Computational ecology and software, 10(3), 64-104.
  4. Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: methods and applications. Springer Berlin, Heidelberg.
  5. Vithalani, A. A., & Vithalani, C. H. (2017). Application of combined TOPSIS and AHP method for spectrum selection in cognitive radio by channel characteristic evaluation. International journal of electronics and communication engineering10(2), 71-79.
  6. Khandan, M., Vosoughi, S., Azrah, K., Poursadeghiyan, M., & Khammar, A. (2017). Decision making models and human factors: TOPSIS and Ergonomic Behaviors (TOPSIS-EB). Management science letters7(2), 111-118.
  7. Milani, A. S., Shanian, A., & El-Lahham, C. (2008). A decision-based approach for measuring human behavioral resistance to organizational change in strategic planning. Mathematical and computer modelling48(11-12), 1765-1774.
  8. Mostafa, M. (2019). Modelling and analysing behaviours and emotions via complex user interactions. Retrieved from
  9. Trucolo, C. C., & Digiampietri, L. A. (2017). Improving trend analysis using social network features. Journal of the Brazilian computer society23(1), 1-10.
  10. Mao, N., Song, M., & Deng, S. (2016). Application of TOPSIS method in evaluating the effects of supply vane angle of a task/ambient air conditioning system on energy utilization and thermal comfort. Applied energy180, 536-545.
  11. Bulgurcu, B. K. (2012). Application of TOPSIS technique for financial performance evaluation of technology firms in Istanbul stock exchange market. Procedia-social and behavioral sciences62, 1033-1040.
  12. Dousset, B., & Gourmelon, F. (2003). Satellite multi-sensor data analysis of urban surface temperatures and landcover. ISPRS journal of photogrammetry and remote sensing58(1-2), 43-54.
  13. Javanbakht, T. (2020). Being and thinking. BouquinBec Montreal. (In Ferench).
  14. Javanbakht, T. (2016). Fuzzy logic and tree structure as category modeling tools as prototypes (Ph.D Thesis, Université du Québec À Montréal). (In Ferench). Retrieved from
  15. Lohmann, J., Herbort, O., Wagener, A., & Kiesel, A. (2008, June). Anticipation of time spans: New data from the foreperiod paradigm and the adaptation of a computational model. In Workshop on anticipatory behavior in adaptive learning systems(pp. 170-187). Springer, Berlin, Heidelberg.
  16. Carstensen, L. L. (2006). The influence of a sense of time on human development. Science312(5782), 1913-1915.
  17. Khandan, M., Vosoughi, S., Azrah, K., Poursadeghiyan, M., & Khammar, A. (2017). Decision making models and human factors: TOPSIS and Ergonomic Behaviors (TOPSIS-EB). Management science letters7(2), 111-118.
  18. Alharbi, M. G., & Khalifa, H. A. E. W. (2021). Enhanced fuzzy delphi method in forecasting and decision-making. Advances in fuzzy systems2021(2). DOI: 1155/2021/2459573
  19. Khalifa, H. A., & Al-Shabi, M. (2018). An interactive approach for solving fuzzy multi-objective assignment problems. Journal of advances in mathematics and computer science28(6), 1-12.
  20. Indahingwati, A., Barid, M., Wajdi, N., Susilo, D. E., Kurniasih, N., & Rahim, R. (2018). Comparison analysis of TOPSIS and fuzzy logic methods on fertilizer selection. International journal of engineering and technology7(2.3), 109-114.
  21. Jumarni, R. F., & Zamri, N. (2018). An integration of fuzzy TOPSIS and fuzzy logic for multi-criteria decision making problems. International journal of engineering and technology7(2), 102-106.
  22. Zulqarnain, R. M., Abdal, S., Maalik, A., Ali, B., Zafar, Z., Ahamad, M. I., ... & Dayan, F. (2020). Application of TOPSIS method in decision making via soft set. Biomedical journal of science and technology research24(3), 18208-18215.
  23. Dammak, F., Baccour, L., & Alimi, A. M. (2016). Crisp multi-criteria decision making methods: state of the art. International journal of computer science and information security14(8), 252-264.
  24. Borjalilu, N., Rabiei, P., & Enjoo, A. (2018). A fuzzy TOPSIS Based model for safety risk assessment of operational flight data. International journal of aerospace and mechanical engineering12(12), 1073-1080.
  25. Hajek, P., & Froelich, W. (2019). Integrating TOPSIS with interval-valued intuitionistic fuzzy cognitive maps for effective group decision making. Information sciences485, 394-412.
  26. Afsordegan, A., Sánchez, M., Agell, N., Zahedi, S., & Cremades, L. V. (2016). Decision making under uncertainty using a qualitative TOPSIS method for selecting sustainable energy alternatives. International journal of environmental science and technology13(6), 1419-1432.
  27. Khan, S. A., Alenezi, M., Agrawal, A., Kumar, R., & Khan, R. A. (2020). Evaluating performance of software durability through an integrated fuzzy-based symmetrical method of ANP and TOPSIS. Symmetry12(4), 493.
  28. Catillo, J., Martin, J. C., & Román, C. (2020). A hybrid-fuzzy TOPSIS method to analyze the consumption and buying behavior of fishery and aquaculture products (FAPs) in the EU28. British food journal.
  29. Valaskova, K., Kramarova, K., & Bartosova, V. (2015). Multi criteria models used in Slovak consumer market for business decision making. Procedia economics and finance26, 174-182.
  30. Arasteh, M. A., Shamshirband, S., & Yee, P. L. (2018). Using multi-attribute decision-making approaches in the selection of a hospital management system. Technology and health care26(2), 279-295.
  31. Mohammadian, M., Ghorbani, S., & Maleki, A. (2018). The study on consumer buying behavior satisfaction level for Persian carpet. Journal of organizational behavior research, 3, 1-11.
  32. Fan, C. K., Lee, Y. H., Lee, L. T., & Lu, W. Q. (2011). Using TOPSIS & CA evaluating intentions of consumers’ cross-buying bancassurance. Journal of service science and management4(04), 469-475.
  33. Anshu, K., & Gaur, L. (2019). E-Satisfaction estimation: a comparative analysis using AHP and intuitionistic fuzzy TOPSIS. Journal of cases on information technology (JCIT)21(2), 65-87.
  34. Chang, H. J., Chien, C. M., & Hsiao, C. Y. (2011). Equality investment strategy evaluation during the financial crisis: Using TOPSIS approach. African journal of business management5(28), 11539-11545.
  35. Yang, A., Do, B., Wang, G. L., Chang, L. Y., & Hung, F. C. (2011). Assessing competitiveness of foreign and local supermarket chains in Vietnamese market by using fuzzy TOPSIS method. E3 journal of business management and economics2(5), 209-216.
  36. Zhang, H., Gu, C. L., Gu, L. W., & Zhang, Y. (2011). The evaluation of tourism destination competitiveness by TOPSIS & information entropy–a case in the Yangtze River Delta of China. Tourism management32(2), 443-451.
  37. Yildirim, B. F., & Yildirim, S. K. (2019). The evaluation of competitiveness performance for developing eight countries by grey TOPSIS. Kırklareli üniversitesi sosyal bilimler dergisi3(2), 70-79.
  38. Qian, M., Wang, Y., Xu, W., & Deng, H. (2019). An improved TOPSIS approach for the competitiveness analysis of provincial information resource industries in China. Expert systems36(4), e12407.
  39. Han, H., & Trimi, S. (2018). A fuzzy TOPSIS method for performance evaluation of reverse logistics in social commerce platforms. Expert systems with applications103, 133-145.
  40. Bulgurcu, B. K. (2012). Application of TOPSIS technique for financial performance evaluation of technology firms in Istanbul stock exchange market. Procedia-social and behavioral sciences62, 1033-1040.
  41. Roy, S., & Das, A. (2018). Application of TOPSIS method for financial performance evaluation: a study of selected scheduled banks in Bangladesh. Journal of commerce and accounting research7(1), 24-29.
  42. Banu, A. R., & Santhiyavalli, G. A. (2019). TOPSIS approach to evaluate the financial performance of scheduled commercial banks in India. International journal of economics and research21(1), 24-33.
  43. Li, C., & Ye, C. (2014, January). Comprehensive evaluation of the operating performance for commercial banks in China based on improved TOPSIS. Proceedings of the international conference on global economy, commerce and service Science (GECSS'14) (pp. 17-21). Atlantis Press.
  44. Bozdoğan, T., Odabas, A., & Shegıwal, A. H. (2021). Analysis of financial performance of foreign banks having branches in Turkey by TOPSIS and ELECTRE methods. Alanya academic review journal, 5(2), 1049-1067.                                                                                                             
  45. Badidi, E., Atif, Y., Sheng, Q. Z., & Maheswaran, M. (2019). On personalized cloud service provisioning for mobile users using adaptive and context-aware service composition. Computing101(4), 291-318.
  46. Alharbi, A., Dong, H., Yi, X., Tari, Z., & Khalil, I. (2021). Social media identity deception detection: a survey. ACM computing surveys (CSUR)54(3), 1-35.
  47. Zhang, X., & Li, D. (2019). Research on E-commerce logistic satisfaction based on TOPSIS method. International journal of frontiers in sociology1(1). DOI: 25236/IJFS.2019.010102
  48. Fasanghari, M., Gholamy, N., Chaharsooghi, S. K., Qadami, S., & Delgosha, M. S. (2008, August). The fuzzy evaluation of E-Commerce customer satisfaction utilizing fuzzy TOPSIS. 2008 international symposium on electronic commerce and security(pp. 870-874). IEEE.                                                                                                              
  49. Sun, C. C., & Lin, G. T. (2009). Using fuzzy TOPSIS method for evaluating the competitive advantages of shopping websites. Expert systems with applications36(9), 11764-11771.
  50. Xue, D., Zhao, Q., & Guo, X. (2008, October). TOPSIS method for evaluation customer service satisfaction to fast food industry. 2008 IEEE international conference on service operations and logistics, and informatics(Vol. 1, pp. 920-925). IEEE.
  51. Rajiv, B., & Salunkhe, M. (2018). Courier partner selection for e-commerce business using TOPSIS method. IOSR journal of mechanical and civil engineering, 33-38.
  52. Anisseh, M., & Yusuf, R. M. (2011). Developing a fuzzy TOPSIS model in multiple attribute group decision making. Scientific research and essays, 6(5), 1046-1052.
  53. Baky, I. A., & Abo-Sinna, M. A. (2013). TOPSIS for bi-level MODM problems. Applied mathematical modelling37(3), 1004-1015.
  54. Salehi, S., Amiri, M., Ghahramani, P., & Abedini, M. (2018, September). A novel integrated AHP-TOPSIS model to deal with big data in group decision making. Proceedings of the international conference on industrial engineering and operations management(pp. 1043-1053). Washington DC, USA.
  55. Vinay, S., Aithal, S., & Sudhakara, G. (2012). Integrating TOPSIS and AHP into GORE decision support system. International journal of computer applications56(17), 46-53.
  56. Reynoso-Meza, G., Alves Ribeiro, V. H., & Carreño-Alvarado, E. P. (2017). A comparison of preference handling techniques in multi-objective optimisation for water distribution systems. Water9(12), 996.
  57. Abou-El-Enien, T. H. (2015). An Interactive decomposition algorithm for two-level large scale linear multiobjective optimization problems with stochastic parameters using TOPSIS method. International journal of engineering research and applications5(4), 61-76.
  58. Javanbakht, T., Laurent, S., Stanicki, D., & David, E. (2020). Related physicochemical, rheological, and dielectric properties of nanocomposites of superparamagnetic iron oxide nanoparticles with polyethyleneglycol. Journal of applied polymer science137(3), 48280.                           
  59. Ghane-Motlagh, B., Javanbakht, T., Shoghi, F., Wilkinson, K. J., Martel, R., & Sawan, M. (2016). Physicochemical properties of peptide-coated microelectrode arrays and their in vitro effects on neuroblast cells. Materials science and engineering: c68, 642-650.
  60. Javanbakht, T., Bérard, A., & Tavares, J. R. (2016). Polyethylene glycol and poly (vinyl alcohol) hydrogels treated with photo-initiated chemical vapor deposition. Canadian journal of chemistry94(9), 744-750.
  61. Javanbakht, T., Raphael, W., & Tavares, J. R. (2016). Physicochemical properties of cellulose nanocrystals treated by photo‐initiated chemical vapour deposition (PICVD). The Canadian journal of chemical engineering94(6), 1135-1139.
  62. Javanbakht, T., Laurent, S., Stanicki, D., Raphael, W., & Tavares, J. R. (2015). Charge effect of superparamagnetic iron oxide nanoparticles on their surface functionalization by photo-initiated chemical vapour deposition. Journal of nanoparticle research17(12), 1-11.
  63. Djavanbakht, T., Carrier, V., Andre, J. M., Barchewitz, R., & Troussel, P. (2000). Effects of thermal heating on the performance of Mo/Si, Mo/C and Ni/C multilayer mirrors for soft X-radiation. The journal of physics IV, 10, 281-287. (In Ferench).
  64. Javanbakht, T., Hadian, H., & Wilkinson, K. J. (2020). Comparative study of physicochemical properties and antibiofilm activity of graphene oxide nanoribbons. Journal of engineering sciences, 7(1), C1-C8.
  65. Javanbakht, T., Ghane-Motlagh, B., & Sawan, M. (2020). Comparative study of antibiofilm activity and physicochemical properties of microelectrode arrays. Microelectronic engineering229, 111305.
  66. Javanbakht, T., & David, E. (2020). Rheological and physical properties of a nanocomposite of graphene oxide nanoribbons with polyvinyl alcohol. Journal of thermoplastic composite materials, 0892705720912767.
  67. Javanbakht, T., & Sokolowski, W. (2015). Thiol-ene/acrylate systems for biomedical shape-memory polymers. In shape memory polymers for biomedical applications(pp. 157-166). Woodhead Publishing.                                                                                                                                                                                                                 
  68. Javanbakht, T., Laurent, S., Stanicki, D., & Frenette, M. (2020). Correlation between physicochemical properties of superparamagnetic iron oxide nanoparticles and their reactivity with hydrogen peroxide. Canadian journal of chemistry98(10), 601-608.
  69. Maldonado-Bascón, S., Acevedo-Rodríguez, F. J., Montoya-Andúgar, F., & Gil-Jiménez, P. (2018, February). Low cost robot for indoor cognitive disorder people orientation. 2018 IEEE international conference on industrial technology (ICIT)(pp. 1769-1774). IEEE.
  70. Stogl, D., Armbruster, O., Mende, M., Hein, B., Wang, X., & Meyer, P. (2019). Robot-based training for people with mild cognitive impairment. IEEE robotics and automation letters4(2), 1916-1923.
  71. Andriella, A., Alenyà, G., Hernández-Farigola, J., & Torras, C. (2018). Deciding the different robot roles for patient cognitive training. International journal of human-computer studies117, 20-29.
  72. Dolan, R. J. (2002). Emotion, cognition, and behavior. Science298(5596), 1191-1194.
  73. Hayes, S. C., & Wilson, K. G. (1995). The role of cognition in complex human behavior: a contextualistic perspective. Journal of behavior therapy and experimental psychiatry26(3), 241-248.
  74. Reis, H. T., & Collins, W. A. (2004). Relationships, human behavior, and psychological science. Current directions in psychological science13(6), 233-237.
  75. Griffiths, T. L., Abbott, J. T., & Hsu, A. S. (2016). Exploring human cognition using large image databases. Topics in cognitive science8(3), 569-588.
  76. Al-Shawaf, L., Conroy-Beam, D., Asao, K., & Buss, D. M. (2016). Human emotions: an evolutionary psychological perspective. Emotion review8(2), 173-186.
  77. Köppe, C., Held, M., & Schütz, A. (2019). Improving emotion perception and emotion regulation through a web-based emotional intelligence training (WEIT) program for future leaders. International journal of emotional education11(2), 17-32.
  78. Schreuder, E., Van Erp, J., Toet, A., & Kallen, V. L. (2016). Emotional responses to multisensory environmental stimuli: a conceptual framework and literature review. Sage open6(1), 2158244016630591.
  79. Zamani-Sabzi, H., King, J. P., Gard, C. C., & Abudu, S. (2016). Statistical and analytical comparison of multi-criteria decision-making techniques under fuzzy environment. Operations research perspectives3, 92-117.
  80. Vommi, V. (2017). TOPSIS with statistical distances: a new approach to MADM. Decision science letters6(1), 49-66.
  81. Simanavičienė, R., & Cibulskaitė, J. (2015). Statistical analysis of the reliability of a decision obtained by the TOPSIS method. Lithuanian journal of statistics54(1), 110-118.
  82. Simanavičienė, R., & Petraitytė, V. (2016). Sensitivity analysis of the TOPSIS method in respect of initial data distributions. Lithuanian journal of statistics55(1), 45-51.
  83. Demirel, T., & Tüzün, S. (2016). Analyzing and comparing the districts of Istanbul using TOPSIS. International journal of arts & sciences9(1), 339-348.
  84. Nayeb, A., Jabari, S., & Yousefi Nejad Attari, M. (2015). A combination of factor analysis and combined approach techniques (AHP-TOPSIS) for ranking criteria and evaluating the factors affecting brand. Technical journal of engineering and applied sciences, 4(4), 349-358. DOI: 13140/2.1.4227.1524
  85. Holota, T., Holienčinová, M., Kotus, M., & Chrastina, J. (2017). The use of TOPSIS method in the manufacturing process of clutch plate of agricultural machinery. Agronomy research15(1), 155-161.