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Розробка системи підтримки прийняття рішень для прогнозування ступеня важкості вірусних захворювань в умовах пандемії ; Development of decision-making system to forecast the severity level of viral diseases in a pandemic

Боднар, Роман Ігорович ; Bodnar, Roman Igorovych ; et al.
In: 1 Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, Tao Q, Sun Z, Xia L (2020) Correlation of chest CT and RT-PCR testing in Coronavirus Disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology. https ://doi.org/10.1148/radio l.20202 00642. 2 Duda, O., Pasichnyk, V., Kunanets, N., Antonii, R., Matsiuk, O. Multidimensional Representation of COVID-19 Data Using OLAP Information Technology. International Scientific and Technical Conference on Computer Sciences and Information Technologies, 2020, 2, pp. 277–280, 9321889. 3 Hani C, Trieu NH, Saab I, Dangeard S, Bennani S, Chassagnon G, Revel MP (2020) COVID-19 pneumonia: a review of typical CT findings and differential diagnosis. Diagn Interv Imaging. https: // doi.org/10.1016/j.diii. 4 Worldometer (2021) Daily reports of statistics about COVID-19. Roy, J.; Stewart, W.F. Prediction modeling using EHR data: Challenges, strategies, and a comparison of machine learning approaches. Med. Care 2010, 48, S106–S113. 21 Wu, C.; Rosenfeld, R.; Clermont, G. Using data-driven rules to predict mortality in severe community acquired pneumonia. PLoS ONE 2014, 9, e89053. 22 Johnson, A.E.; Pollard, T.J.; Mark, R.G. Reproducibility in critical care: A mortality prediction case study. In Proceedings of the Machine Learning for Healthcare Conference, Boston, MA, USA, 18–19 August 2017; pp. 361–376. 23 Ahmed, Faisal, et al. "An evolutionary belief rule-based clinical decision support system to predict Covid-19 severity under uncertainty." Applied Sciences 11.13 (2021): 5810. 24 Jiang, X.; Coffee, M.; Bari, A.; Wang, J.; Jiang, X.; Shi, J.; Dai, J.; Cai, J.; Zhang, T.; Wu, Z.; et al. Towards an artificial intelligence framework for data-driven prediction of coronavirus clinical severity. Comput. Mater. Contin. 2020, 63, 537–551. 25 Batista, A.d.M.; Miraglia, J.; Donato, T.; Chiavegatto Filho, A.; de Moraes Batista, A.F.; Miraglia, J.L.; Donato, T.H.R.; Chiavegatto Filho, A.D.P. COVID-19 diagnosis prediction in emergency care patients: A machine learning approach. In Hospital Israelita Albert Einstein-Big Data Analytics M; Department of Epidemiology SoPH, University of Sao Paulo: São Paulo, Brazil, 2020. 26 Schwab, P.; Schütte, A.D.; Dietz, B.; Bauer, S. predCOVID-19: A Systematic Study of Clinical Predictive Models for Coronavirus Disease 2019. arXiv 2020, arXiv:2005.08302. 27 Alakus, T.B.; Turkoglu, I. Comparison of deep learning approaches to predict COVID-19 infection. Chaos Solitons Fractals 2020, 140, 110120. 28 Yip, S.S.; Klanecek, Z.; Naganawa, S.; Kim, J.; Studen, A.; Rivetti, L.; Jeraj, R. Performance and Robustness of Machine Learning-based Radiomic COVID-19 Severity Prediction. medRxiv 2020. 29 Chen, Y.; Ouyang, L.; Bao, F.S.; Li, Q.; Han, L.; Zhu, B.; Ge, Y.; Robinson, P.; Xu, M.; Liu, J.; et al. An Interpretable Machine Learning Framework for Accurate Severe vs. Non-Severe COVID-19 Clinical Type Classification. 2020. SSRN 3638427. 30 Yao, H.; Zhang, N.; Zhang, R.; Duan, M.; Xie, T.; Pan, J.; Peng, E.; Huang, J.; Zhang, Y.; Xu, X.; et al. Severity detection for the coronavirus disease 2019 (COVID-19) patients using a machine learning model based on the blood and urine tests. Front. Cell Dev. Biol. 2020, 8, 683. 31 Onari, Mohsen Abbaspour, et al. "A medical decision support system for predicting the severity level of COVID-19." Complex & Intelligent Systems (2021): 1-15. 32 Chockalingam S, Pieters W, Teixeira A, van Gelder P (2017) Bayesian network models in cyber security: a systematic review. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 10674, pp 105–122. LNCS, Springer. https ://doi. org/10.1007/978-3-319-70290 -2_7. 33 Kahn CE, Roberts LM, Shaffer KA, Haddawy P (1997) Construction of a Bayesian network for mammographic diagnosis of breast cancer. Comput Biol Med 27(1):19–29. https ://doi.org/10.1016/ S0010 -4825(96)00039-X. 34 Pinheiro PR, De Castro AKA, Pinheiro MCD (2008) A multicriteria model applied in the diagnosis of Alzheimer’s disease: A Bayesian network. In: Proceedings—2008 IEEE 11th International Conference on Computational Science and Engineering, CSE 2008. IEEE, pp 15–22. https: //doi.org/10.1109/CSE.2008.44. 35 Özçift A, Gülten A (2013) Genetic algorithm wrapped Bayesian network feature selection applied to differential diagnosis of erythemato-squamous diseases. Digit Signal Process Rev J 23(1):230–237. https ://doi.org/10.1016/j.dsp.2012.07.008. 36 Bakhtavar E, Aghayarloo R, Yousefi S, Hewage K, Sadiq R (2019) Renewable energy based mine reclamation strategy: a hybrid fuzzy-based network analysis. J Clean Prod 230:253–263. Adhikari, N.K.; McDonald, H.; Rosas-Arellano, M.P.; Devereaux, P.J.; Beyene, J.; Sam, J.; Haynes, R.B. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: A systematic review. JAMA 2005, 293, 1223–1238. 43 Tan, C.; Huang, Y.; Shi, F.; Tan, K.; Ma, Q.; Chen, Y.; Jiang, X.; Li, X. C-reactive protein correlates with CT findings and predicts severe COVID-19 early. J. Med. Virol. 2020, 92, 856–862. 44 Yadollahpour, A.; Nourozi, J.; Mirbagheri, S.A.; Simancas-Acevedo, E.; Trejo-Macotela, F.R. Designing and implementing an ANFIS based medical decision support system to predict chronic kidney disease progression. Front. Physiol. 2018, 9, 1753. 45 Finkelstein, J.; Cheol Jeong, I. Machine learning approaches to personalize early prediction of asthma exacerbations. Ann. N. Y. Acad. Sci. 2017, 1387, 153. 46 Harjai, S.; Khatri, S.K. An intelligent clinical decision support system based on artificial neural network for early diagnosis of cardiovascular diseases in rural areas. In Proceedings of the 2019 Amity International Conference on Artificial Intelligence (AICAI), Dubai, United Arab Emirates, 4–6 February 2019; pp. 729–736. 47 Schrag, A.; Siddiqui, U.F.; Anastasiou, Z.; Weintraub, D.; Schott, J.M. Clinical variables and biomarkers in prediction of cognitive impairment in patients with newly diagnosed Parkinson’s disease: A cohort study. Lancet Neurol. 2017, 16, 66–75. 48 Anooj, P. Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules. J. King Saud Univ. Comput. Inf. Sci. 2012, 24, 27–40. 49 Kong, G.; Xu, D.L.; Body, R.; Yang, J.B.; Mackway-Jones, K.; Carley, S. A belief rule-based decision support system for clinical risk assessment of cardiac chest pain. Eur. J. Oper. Res. 2012, 219, 564–573. 50 Wang, Y.M.; Yang, L.H.; Fu, Y.G.; Chang, L.L.; Chin, K.S. Dynamic rule adjustment approach for optimizing belief rule-base expert system. Knowl. Based Syst. 2016, 96, 40–60. 51 Velavan, T.P.; Meyer, C.G. Mild versus severe COVID-19: Laboratory markers. Int. J. Infect. Dis. 2020, 95, 304–307. 52 Yan, L.; Zhang, H.T.; Goncalves, J.; Xiao, Y.; Wang, M.; Guo, Y.; Sun, C.; Tang, X.; Jing, L.; Zhang, M.; et al. An interpretable mortality prediction model for COVID-19 patients. Nat. Mach. Intell. 2020, 2, 283–288. 53 Yang, J.B. Rule and utility based evidential reasoning approach for multiattribute decision analysis under uncertainties. Eur. J. Oper. Res. 2001, 131, 31–61. 54 Yang, J.B.; Liu, J.; Wang, J.; Sii, H.S.; Wang, H.W. Belief rule-base inference methodology using the evidential reasoning approach-RIMER. IEEE Trans. Syst. Man Cybern. Part Syst. Hum. 2006, 36, 266–285. 55 Hossain, M.S.; Rahaman, S.; Kor, A.L.; Andersson, K.; Pattinson, C. A belief rule based expert system for datacenter pue prediction under uncertainty. IEEE Trans. Sustain. Comput. 2017, 2, 140–153. 56 Price, K.; Storn, R.M.; Lampinen, J.A. Differential Evolution: A Practical Approach to Global Optimization; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2006. 57 Zeng L, Ge Z (2020) Improved Population-Based Incremental Learning of Bayesian Networks with partly known structure and parallel computing. Eng Appl Artif Intell 95:103920. https ://doi.org/10.1016/j.engap pai.2020.10392 0. 58 Mittal A, Kassim A (eds) (2007) Bayesian network technologies: applications and graphical models: applications and graphical models. IGI Global. 59 Friedman N, Geiger D, Goldszmidt M (1997) Bayesian network classifiers. Mach Learn 29(2–3):131–163. https ://doi. org/10.1023/A:10074 65528 199. 60 Zhang X, Mahadevan S (2020) Bayesian network modeling of accident investigation reports for aviation safety assessment. Reliab Eng Syst Saf. https ://doi.org/10.1016/j.ress.2020.10737 1. 61 Tan X, Gao X, Wang Z, He C (2020) Bidirectional heuristic search to find the optimal Bayesian network structure. Neurocomputing. https ://doi.org/10.1016/j.neuco m.2020.10.049. 62 Cooper GF, Herskovits E (1992) A Bayesian method for the induction of probabilistic networks from data. Mach Learn 9(4):309– 347. https ://doi.org/10.1007/bf009 94110. 63 Heckerman D, Geiger D, Chickering DM (1995) Learning Bayesian networks: the combination of knowledge and statistical data. Mach Learn 20(3):197–243. https: //doi.org/10.1007/bf009 94016. 64 Kalkwarf B (2017) Search parameter optimization for discrete, Bayesian, and continuous search algorithms. Naval Postgraduate School Monterey United States. 65 GeNIe (2018) The Bayesian search algorithm description by GeNIe software. https ://suppo rt.bayes fusio n.com/docs/GeNIe. 66 Kosko B (1986) Fuzzy cognitive maps. Int J Man Mach Stud 24(1):65–75. https ://doi.org/10.1016/S0020 -7373(86)80040-2. 67 Rezaee MJ, Yousefi S, Babaei M (2017) Multi-stage cognitive map for failures assessment of production processes: an extension in structure and algorithm. Neurocomputing 232:69–82. https :// doi.org/10.1016/j.neuco m.2016.10.069. 68 Rezaee MJ, Yousefi S, Valipour M, Dehdar MM (2018) Risk analysis of sequential processes in food industry integrating multistage fuzzy cognitive map and process failure mode and effects analysis. Comput Ind Eng 123:325–337. https: //doi.org/10.1016/j. cie.2018.07.012. 69 Alizadeh A, Yousefi S (2019) An integrated Taguchi loss function–fuzzy cognitive map–MCGP with utility function approach for supplier selection problem. Neural Comput Appl 31(11):7595– 7614. https ://doi.org/10.1007/s0052 1-018-3591-1. 70 Abbaspour Onari M, Jahangoshai Rezaee M (2020) A fuzzy cognitive map based on Nash bargaining game for supplier selection problem: a case study on auto parts industry. Oper Res Int J. https ://doi.org/10.1007/s1235 1-020-00606. 71 Bakhtavar E, Valipour M, Yousefi S, Sadiq R, Hewage K (2020) Fuzzy cognitive maps in systems risk analysis: a comprehensive review. Complex Intell Syst. https: //doi.org/10.1007/s40747-020-00228-2. 72 Dickerson JA, Kosko B (1994) Virtual worlds as fuzzy cognitive maps. Presence Teleoperators Virtual Environ 3(2):173–189. https ://doi.org/10.1162/pres.1994.3.2.173. 73 Papageorgiou E, Stylios C, Groumpos P (2003) Fuzzy cognitive map learning based on nonlinear hebbian rule. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 2903. Springer, pp 256–268. https ://doi.org/10.1007/978-3-540-24581-0_22. 74 Papageorgiou EI, Stylios CD, Groumpos PP (2004) Active Hebbian learning algorithm to train fuzzy cognitive maps. Int J Approx Reason 37(3):219–249. https ://doi.org/10.1016/j. ijar.2004.01.001. 75 Salmeron JL, Ruiz-Celma A, Mena A (2017) Learning FCMs with multi-local and balanced memetic algorithms for forecasting industrial drying processes. Neurocomputing 232:52–57. https :// doi.org/10.1016/j.neuco m.2016.10.070. 76 Salmeron JL, Mansouri T, Moghadam MRS, Mardani A (2019) Learning fuzzy cognitive maps with modified asexual reproduction optimisation algorithm. Knowl Based Syst 163:723–735. https ://doi.org/10.1016/j.knosy s.2018.09.034. 77 Yousefi S, Jahangoshai Rezaee M, Moradi A (2020) Causal effect analysis of logistics processes risks in manufacturing industries using sequential multi-stage fuzzy cognitive map: a case study. Int J Comput Integr Manuf 33(10–11):1055–1075. https ://doi. org/10.1080/09511 92X.2020.17476 41. 78 National Health Commission (2020) Diagnosis and treatment protocol for novel coronavirus pneumonia (Trial Version 7). Chin Med J (Engl) 133(9):1087–1095. 79 Abbaspour Onari M, Yousefi S, Jahangoshai Rezaee M (2020) Risk assessment in discrete production processes considering uncertainty and reliability: Z-number multi-stage fuzzy cognitive map with fuzzy learning algorithm. Artif Intell Rev. https :// doi.org/10.1007/s1046 2-020-09883-w. 80 kaggle. Боднар Р. І. Розробка системи підтримки прийняття рішень для прогнозування ступеня важкості вірусних захворювань в умовах пандемії : кваліфікаційна робота освітнього рівня „Магістр“ „124 – системний аналіз“ / Р. І. Боднар. – Тернопіль : ТНТУ, 2021. – 70 с.;
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Titel:
Розробка системи підтримки прийняття рішень для прогнозування ступеня важкості вірусних захворювань в умовах пандемії ; Development of decision-making system to forecast the severity level of viral diseases in a pandemic
Autor/in / Beteiligte Person: Боднар, Роман Ігорович ; Bodnar, Roman Igorovych ; Пасічник, Володимир Володимирович ; Микитишин, Андрій Григорович ; ТНТУ ім. І. Пулюя, Факультет комп’ютерно-інформаційних систем і програмної інженерії, Кафедра комп’ютерних наук, м. Тернопіль, Україна
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Quelle: 1 Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, Tao Q, Sun Z, Xia L (2020) Correlation of chest CT and RT-PCR testing in Coronavirus Disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology. https ://doi.org/10.1148/radio l.20202 00642. 2 Duda, O., Pasichnyk, V., Kunanets, N., Antonii, R., Matsiuk, O. Multidimensional Representation of COVID-19 Data Using OLAP Information Technology. International Scientific and Technical Conference on Computer Sciences and Information Technologies, 2020, 2, pp. 277–280, 9321889. 3 Hani C, Trieu NH, Saab I, Dangeard S, Bennani S, Chassagnon G, Revel MP (2020) COVID-19 pneumonia: a review of typical CT findings and differential diagnosis. Diagn Interv Imaging. https: // doi.org/10.1016/j.diii. 4 Worldometer (2021) Daily reports of statistics about COVID-19. Roy, J.; Stewart, W.F. Prediction modeling using EHR data: Challenges, strategies, and a comparison of machine learning approaches. Med. Care 2010, 48, S106–S113. 21 Wu, C.; Rosenfeld, R.; Clermont, G. Using data-driven rules to predict mortality in severe community acquired pneumonia. PLoS ONE 2014, 9, e89053. 22 Johnson, A.E.; Pollard, T.J.; Mark, R.G. Reproducibility in critical care: A mortality prediction case study. In Proceedings of the Machine Learning for Healthcare Conference, Boston, MA, USA, 18–19 August 2017; pp. 361–376. 23 Ahmed, Faisal, et al. "An evolutionary belief rule-based clinical decision support system to predict Covid-19 severity under uncertainty." Applied Sciences 11.13 (2021): 5810. 24 Jiang, X.; Coffee, M.; Bari, A.; Wang, J.; Jiang, X.; Shi, J.; Dai, J.; Cai, J.; Zhang, T.; Wu, Z.; et al. Towards an artificial intelligence framework for data-driven prediction of coronavirus clinical severity. Comput. Mater. Contin. 2020, 63, 537–551. 25 Batista, A.d.M.; Miraglia, J.; Donato, T.; Chiavegatto Filho, A.; de Moraes Batista, A.F.; Miraglia, J.L.; Donato, T.H.R.; Chiavegatto Filho, A.D.P. COVID-19 diagnosis prediction in emergency care patients: A machine learning approach. In Hospital Israelita Albert Einstein-Big Data Analytics M; Department of Epidemiology SoPH, University of Sao Paulo: São Paulo, Brazil, 2020. 26 Schwab, P.; Schütte, A.D.; Dietz, B.; Bauer, S. predCOVID-19: A Systematic Study of Clinical Predictive Models for Coronavirus Disease 2019. arXiv 2020, arXiv:2005.08302. 27 Alakus, T.B.; Turkoglu, I. Comparison of deep learning approaches to predict COVID-19 infection. Chaos Solitons Fractals 2020, 140, 110120. 28 Yip, S.S.; Klanecek, Z.; Naganawa, S.; Kim, J.; Studen, A.; Rivetti, L.; Jeraj, R. Performance and Robustness of Machine Learning-based Radiomic COVID-19 Severity Prediction. medRxiv 2020. 29 Chen, Y.; Ouyang, L.; Bao, F.S.; Li, Q.; Han, L.; Zhu, B.; Ge, Y.; Robinson, P.; Xu, M.; Liu, J.; et al. An Interpretable Machine Learning Framework for Accurate Severe vs. Non-Severe COVID-19 Clinical Type Classification. 2020. SSRN 3638427. 30 Yao, H.; Zhang, N.; Zhang, R.; Duan, M.; Xie, T.; Pan, J.; Peng, E.; Huang, J.; Zhang, Y.; Xu, X.; et al. Severity detection for the coronavirus disease 2019 (COVID-19) patients using a machine learning model based on the blood and urine tests. Front. Cell Dev. Biol. 2020, 8, 683. 31 Onari, Mohsen Abbaspour, et al. "A medical decision support system for predicting the severity level of COVID-19." Complex & Intelligent Systems (2021): 1-15. 32 Chockalingam S, Pieters W, Teixeira A, van Gelder P (2017) Bayesian network models in cyber security: a systematic review. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 10674, pp 105–122. LNCS, Springer. https ://doi. org/10.1007/978-3-319-70290 -2_7. 33 Kahn CE, Roberts LM, Shaffer KA, Haddawy P (1997) Construction of a Bayesian network for mammographic diagnosis of breast cancer. Comput Biol Med 27(1):19–29. https ://doi.org/10.1016/ S0010 -4825(96)00039-X. 34 Pinheiro PR, De Castro AKA, Pinheiro MCD (2008) A multicriteria model applied in the diagnosis of Alzheimer’s disease: A Bayesian network. In: Proceedings—2008 IEEE 11th International Conference on Computational Science and Engineering, CSE 2008. IEEE, pp 15–22. https: //doi.org/10.1109/CSE.2008.44. 35 Özçift A, Gülten A (2013) Genetic algorithm wrapped Bayesian network feature selection applied to differential diagnosis of erythemato-squamous diseases. Digit Signal Process Rev J 23(1):230–237. https ://doi.org/10.1016/j.dsp.2012.07.008. 36 Bakhtavar E, Aghayarloo R, Yousefi S, Hewage K, Sadiq R (2019) Renewable energy based mine reclamation strategy: a hybrid fuzzy-based network analysis. J Clean Prod 230:253–263. Adhikari, N.K.; McDonald, H.; Rosas-Arellano, M.P.; Devereaux, P.J.; Beyene, J.; Sam, J.; Haynes, R.B. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: A systematic review. JAMA 2005, 293, 1223–1238. 43 Tan, C.; Huang, Y.; Shi, F.; Tan, K.; Ma, Q.; Chen, Y.; Jiang, X.; Li, X. C-reactive protein correlates with CT findings and predicts severe COVID-19 early. J. Med. Virol. 2020, 92, 856–862. 44 Yadollahpour, A.; Nourozi, J.; Mirbagheri, S.A.; Simancas-Acevedo, E.; Trejo-Macotela, F.R. Designing and implementing an ANFIS based medical decision support system to predict chronic kidney disease progression. Front. Physiol. 2018, 9, 1753. 45 Finkelstein, J.; Cheol Jeong, I. Machine learning approaches to personalize early prediction of asthma exacerbations. Ann. N. Y. Acad. Sci. 2017, 1387, 153. 46 Harjai, S.; Khatri, S.K. An intelligent clinical decision support system based on artificial neural network for early diagnosis of cardiovascular diseases in rural areas. In Proceedings of the 2019 Amity International Conference on Artificial Intelligence (AICAI), Dubai, United Arab Emirates, 4–6 February 2019; pp. 729–736. 47 Schrag, A.; Siddiqui, U.F.; Anastasiou, Z.; Weintraub, D.; Schott, J.M. Clinical variables and biomarkers in prediction of cognitive impairment in patients with newly diagnosed Parkinson’s disease: A cohort study. Lancet Neurol. 2017, 16, 66–75. 48 Anooj, P. Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules. J. King Saud Univ. Comput. Inf. Sci. 2012, 24, 27–40. 49 Kong, G.; Xu, D.L.; Body, R.; Yang, J.B.; Mackway-Jones, K.; Carley, S. A belief rule-based decision support system for clinical risk assessment of cardiac chest pain. Eur. J. Oper. Res. 2012, 219, 564–573. 50 Wang, Y.M.; Yang, L.H.; Fu, Y.G.; Chang, L.L.; Chin, K.S. Dynamic rule adjustment approach for optimizing belief rule-base expert system. Knowl. Based Syst. 2016, 96, 40–60. 51 Velavan, T.P.; Meyer, C.G. Mild versus severe COVID-19: Laboratory markers. Int. J. Infect. Dis. 2020, 95, 304–307. 52 Yan, L.; Zhang, H.T.; Goncalves, J.; Xiao, Y.; Wang, M.; Guo, Y.; Sun, C.; Tang, X.; Jing, L.; Zhang, M.; et al. An interpretable mortality prediction model for COVID-19 patients. Nat. Mach. Intell. 2020, 2, 283–288. 53 Yang, J.B. Rule and utility based evidential reasoning approach for multiattribute decision analysis under uncertainties. Eur. J. Oper. Res. 2001, 131, 31–61. 54 Yang, J.B.; Liu, J.; Wang, J.; Sii, H.S.; Wang, H.W. Belief rule-base inference methodology using the evidential reasoning approach-RIMER. IEEE Trans. Syst. Man Cybern. Part Syst. Hum. 2006, 36, 266–285. 55 Hossain, M.S.; Rahaman, S.; Kor, A.L.; Andersson, K.; Pattinson, C. A belief rule based expert system for datacenter pue prediction under uncertainty. IEEE Trans. Sustain. Comput. 2017, 2, 140–153. 56 Price, K.; Storn, R.M.; Lampinen, J.A. Differential Evolution: A Practical Approach to Global Optimization; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2006. 57 Zeng L, Ge Z (2020) Improved Population-Based Incremental Learning of Bayesian Networks with partly known structure and parallel computing. Eng Appl Artif Intell 95:103920. https ://doi.org/10.1016/j.engap pai.2020.10392 0. 58 Mittal A, Kassim A (eds) (2007) Bayesian network technologies: applications and graphical models: applications and graphical models. IGI Global. 59 Friedman N, Geiger D, Goldszmidt M (1997) Bayesian network classifiers. Mach Learn 29(2–3):131–163. https ://doi. org/10.1023/A:10074 65528 199. 60 Zhang X, Mahadevan S (2020) Bayesian network modeling of accident investigation reports for aviation safety assessment. Reliab Eng Syst Saf. https ://doi.org/10.1016/j.ress.2020.10737 1. 61 Tan X, Gao X, Wang Z, He C (2020) Bidirectional heuristic search to find the optimal Bayesian network structure. Neurocomputing. https ://doi.org/10.1016/j.neuco m.2020.10.049. 62 Cooper GF, Herskovits E (1992) A Bayesian method for the induction of probabilistic networks from data. Mach Learn 9(4):309– 347. https ://doi.org/10.1007/bf009 94110. 63 Heckerman D, Geiger D, Chickering DM (1995) Learning Bayesian networks: the combination of knowledge and statistical data. Mach Learn 20(3):197–243. https: //doi.org/10.1007/bf009 94016. 64 Kalkwarf B (2017) Search parameter optimization for discrete, Bayesian, and continuous search algorithms. Naval Postgraduate School Monterey United States. 65 GeNIe (2018) The Bayesian search algorithm description by GeNIe software. https ://suppo rt.bayes fusio n.com/docs/GeNIe. 66 Kosko B (1986) Fuzzy cognitive maps. Int J Man Mach Stud 24(1):65–75. https ://doi.org/10.1016/S0020 -7373(86)80040-2. 67 Rezaee MJ, Yousefi S, Babaei M (2017) Multi-stage cognitive map for failures assessment of production processes: an extension in structure and algorithm. Neurocomputing 232:69–82. https :// doi.org/10.1016/j.neuco m.2016.10.069. 68 Rezaee MJ, Yousefi S, Valipour M, Dehdar MM (2018) Risk analysis of sequential processes in food industry integrating multistage fuzzy cognitive map and process failure mode and effects analysis. Comput Ind Eng 123:325–337. https: //doi.org/10.1016/j. cie.2018.07.012. 69 Alizadeh A, Yousefi S (2019) An integrated Taguchi loss function–fuzzy cognitive map–MCGP with utility function approach for supplier selection problem. Neural Comput Appl 31(11):7595– 7614. https ://doi.org/10.1007/s0052 1-018-3591-1. 70 Abbaspour Onari M, Jahangoshai Rezaee M (2020) A fuzzy cognitive map based on Nash bargaining game for supplier selection problem: a case study on auto parts industry. Oper Res Int J. https ://doi.org/10.1007/s1235 1-020-00606. 71 Bakhtavar E, Valipour M, Yousefi S, Sadiq R, Hewage K (2020) Fuzzy cognitive maps in systems risk analysis: a comprehensive review. Complex Intell Syst. https: //doi.org/10.1007/s40747-020-00228-2. 72 Dickerson JA, Kosko B (1994) Virtual worlds as fuzzy cognitive maps. Presence Teleoperators Virtual Environ 3(2):173–189. https ://doi.org/10.1162/pres.1994.3.2.173. 73 Papageorgiou E, Stylios C, Groumpos P (2003) Fuzzy cognitive map learning based on nonlinear hebbian rule. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 2903. Springer, pp 256–268. https ://doi.org/10.1007/978-3-540-24581-0_22. 74 Papageorgiou EI, Stylios CD, Groumpos PP (2004) Active Hebbian learning algorithm to train fuzzy cognitive maps. Int J Approx Reason 37(3):219–249. https ://doi.org/10.1016/j. ijar.2004.01.001. 75 Salmeron JL, Ruiz-Celma A, Mena A (2017) Learning FCMs with multi-local and balanced memetic algorithms for forecasting industrial drying processes. Neurocomputing 232:52–57. https :// doi.org/10.1016/j.neuco m.2016.10.070. 76 Salmeron JL, Mansouri T, Moghadam MRS, Mardani A (2019) Learning fuzzy cognitive maps with modified asexual reproduction optimisation algorithm. Knowl Based Syst 163:723–735. https ://doi.org/10.1016/j.knosy s.2018.09.034. 77 Yousefi S, Jahangoshai Rezaee M, Moradi A (2020) Causal effect analysis of logistics processes risks in manufacturing industries using sequential multi-stage fuzzy cognitive map: a case study. Int J Comput Integr Manuf 33(10–11):1055–1075. https ://doi. org/10.1080/09511 92X.2020.17476 41. 78 National Health Commission (2020) Diagnosis and treatment protocol for novel coronavirus pneumonia (Trial Version 7). Chin Med J (Engl) 133(9):1087–1095. 79 Abbaspour Onari M, Yousefi S, Jahangoshai Rezaee M (2020) Risk assessment in discrete production processes considering uncertainty and reliability: Z-number multi-stage fuzzy cognitive map with fuzzy learning algorithm. Artif Intell Rev. https :// doi.org/10.1007/s1046 2-020-09883-w. 80 kaggle. Боднар Р. І. Розробка системи підтримки прийняття рішень для прогнозування ступеня важкості вірусних захворювань в умовах пандемії : кваліфікаційна робота освітнього рівня „Магістр“ „124 – системний аналіз“ / Р. І. Боднар. – Тернопіль : ТНТУ, 2021. – 70 с.;
Veröffentlichung: 2021
Medientyp: Hochschulschrift
DOI: 10.1148/radio
Schlagwort:
  • ТНТУ ім. І.Пулюя
  • ФІС
  • м. Тернопіль
  • Україна
  • UA
  • експертна система
  • expert system
  • когнітивна карта
  • cognitive map
  • машинне навчання
  • machine learning
  • оптимізація
  • optimization
  • система підтримки прийняття рішень
  • decision support system
  • рівень тяжкості
  • severity level
  • COVID-19
  • 004.9
  • Subject Geographic: ТНТУ ім. І.Пулюя ФІС м. Тернопіль Україна UA
Sonstiges:
  • Nachgewiesen in: BASE
  • Sprachen: Ukrainian
  • Collection: Ternopil Ivan Pul’uj National Technical University: ELARTU
  • Document Type: master thesis
  • Language: Ukrainian
  • Rights: © Боднар Роман Ігорович, 2021

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