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During the implementation of the RBFLN and MLFFN-based QSPR models, the networks which are associated with the minimum weighted average AIC (Akaike\u2019s information criterion) and BIC (Bayesian information criterion) scores are trained by using a hybrid scheme combining the cuckoo search and Levenberg-Marquardt algorithm. Our results show that the RBFLN-based QSPR model outperforms the other ones in terms of the external validation metrics. The study also reveals that it may have a promising potential to study the relationship between various measurement\/experimental data or processing elements in a hybrid way of artificial intelligence modelling.<\/jats:p>","DOI":"10.3233\/jcm-200033","type":"journal-article","created":{"date-parts":[[2020,8,11]],"date-time":"2020-08-11T17:00:02Z","timestamp":1597165202000},"page":"1341-1356","source":"Crossref","is-referenced-by-count":1,"title":["QSPR prediction of polymers\u2019 solubility parameters by radial basis functional link net"],"prefix":"10.66113","volume":"20","author":[{"given":"Dilek \u0130mren","family":"Ko\u00e7","sequence":"first","affiliation":[{"name":"Chemical Engineering Department, Faculty of Engineering, Cumhuriyet University, Sivas, 58140, Turkey"}]},{"given":"Mehmet Levent","family":"Ko\u00e7","sequence":"additional","affiliation":[{"name":"Civil Engineering Department, Faculty of Engineering, Cumhuriyet University, Sivas, 58140, 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