{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T13:56:40Z","timestamp":1780667800534,"version":"3.54.1"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T00:00:00Z","timestamp":1620172800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T00:00:00Z","timestamp":1620172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Fuzzy Syst."],"published-print":{"date-parts":[[2021,10]]},"DOI":"10.1007\/s40815-021-01076-z","type":"journal-article","created":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T14:04:09Z","timestamp":1620223449000},"page":"1955-1971","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["A Novel Optimization Algorithm: Cascaded Adaptive Neuro-Fuzzy Inference System"],"prefix":"10.1007","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3783-7184","authenticated-orcid":false,"given":"Namal","family":"Rathnayake","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tuan Linh","family":"Dang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yukinobu","family":"Hoshino","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,5,5]]},"reference":[{"key":"1076_CR1","doi-asserted-by":"crossref","unstructured":"Karatekin, T., Sancak, S., Celik, G., Topcuoglu, S., Karatekin, G., Kirci, P., Okatan, A.: Interpretable machine learning in healthcare through generalized additive model with pairwise interactions (ga2m): Predicting severe retinopathy of prematurity. In: 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML), pp. 61\u201366. IEEE (2019)","DOI":"10.1109\/Deep-ML.2019.00020"},{"key":"1076_CR2","unstructured":"Tan, Y., Zhang, G.J.: The application of machine learning algorithm in underwriting process. In: 2005 International Conference on Machine Learning and Cybernetics, vol.\u00a06, pp. 3523\u20133527. IEEE (2005)"},{"key":"1076_CR3","doi-asserted-by":"crossref","unstructured":"Pahwa, K., Agarwal, N.: Stock market analysis using supervised machine learning. In: 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), pp. 197\u2013200. IEEE (2019)","DOI":"10.1109\/COMITCon.2019.8862225"},{"key":"1076_CR4","doi-asserted-by":"publisher","DOI":"10.1002\/9780470496916","volume-title":"Metaheuristics: from design to implementation","author":"EG Talbi","year":"2009","unstructured":"Talbi, E.G.: Metaheuristics: from design to implementation, vol. 74. Wiley, New York (2009)"},{"key":"1076_CR5","doi-asserted-by":"crossref","unstructured":"Yang, X.S., Chien, S.F., Ting, T.O.: Computational intelligence and metaheuristic algorithms with applications (2014)","DOI":"10.1155\/2014\/425853"},{"key":"1076_CR6","volume-title":"The nature of statistical learning theory","author":"V Vapnik","year":"2013","unstructured":"Vapnik, V.: The nature of statistical learning theory. Springer, Berlin (2013)"},{"issue":"17","key":"1076_CR7","doi-asserted-by":"publisher","first-page":"1330","DOI":"10.1002\/tal.1033","volume":"22","author":"AH Gandomi","year":"2013","unstructured":"Gandomi, A.H., Talatahari, S., Yang, X.S., Deb, S.: Design optimization of truss structures using cuckoo search algorithm. Struct. Des. Tall Special Build. 22(17), 1330\u20131349 (2013)","journal-title":"Struct. Des. Tall Special Build."},{"issue":"10","key":"1076_CR8","doi-asserted-by":"publisher","first-page":"2356","DOI":"10.1177\/1077546314546682","volume":"22","author":"B Wang","year":"2016","unstructured":"Wang, B., Xue, J., Chen, D.: Takagi\u2013Sugeno fuzzy control for a wide class of fractional-order chaotic systems with uncertain parameters via linear matrix inequality. J. Vibrat. Control 22(10), 2356\u20132369 (2016)","journal-title":"J. Vibrat. Control"},{"key":"1076_CR9","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1016\/j.ymssp.2014.04.021","volume":"50","author":"SD Nguyen","year":"2015","unstructured":"Nguyen, S.D., Nguyen, Q.H., Choi, S.B.: Hybrid clustering based fuzzy structure for vibration control-part 1: a novel algorithm for building neuro-fuzzy system. Mech. Syst. Signal Process. 50, 510\u2013525 (2015)","journal-title":"Mech. Syst. Signal Process."},{"key":"1076_CR10","doi-asserted-by":"crossref","unstructured":"Bandara, R.N., Gaspe, S.: Fuzzy logic controller design for an unmanned aerial vehicle. In: 2016 IEEE International Conference on Information and Automation for Sustainability (ICIAfS), pp. 1\u20135. IEEE (2016)","DOI":"10.1109\/ICIAFS.2016.7946544"},{"key":"1076_CR11","doi-asserted-by":"crossref","unstructured":"Ratnayake, R., De\u00a0Silva, T., Rodrigo, C.: A comparison of fuzzy logic controller and pid controller for differential drive wall-following mobile robot. In: 2019 14th Conference on Industrial and Information Systems (ICIIS), pp. 523\u2013528. IEEE (2019)","DOI":"10.1109\/ICIIS47346.2019.9063333"},{"issue":"1","key":"1076_CR12","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1109\/TFUZZ.2004.839659","volume":"13","author":"M Panella","year":"2005","unstructured":"Panella, M., Gallo, A.S.: An input\u2013output clustering approach to the synthesis of anfis networks. IEEE Trans. Fuzzy Syst. 13(1), 69\u201381 (2005)","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"9","key":"1076_CR13","first-page":"1103","volume":"232","author":"SD Nguyen","year":"2018","unstructured":"Nguyen, S.D., Lam, B.D., Nguyen, Q.H., Choi, S.B.: A fuzzy-based dynamic inversion controller with application to vibration control of vehicle suspension system subjected to uncertainties. Proc. Inst. Mech. Eng. I 232(9), 1103\u20131119 (2018)","journal-title":"Proc. Inst. Mech. Eng. I"},{"key":"1076_CR14","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.asoc.2016.11.016","volume":"53","author":"SD Nguyen","year":"2017","unstructured":"Nguyen, S.D., Nguyen, Q.H., Seo, T.I.: Anfis deriving from jointed input-output data space and applying in smart-damper identification. Appl. Soft Comput. 53, 45\u201360 (2017)","journal-title":"Appl. Soft Comput."},{"issue":"6","key":"1076_CR15","first-page":"1075","volume":"36","author":"H Khodadadi","year":"2019","unstructured":"Khodadadi, H., Ghadiri, H.: Fuzzy logic self-tuning pid controller design for ball mill grinding circuits using an improved disturbance observer. Mining Metall. Explor. 36(6), 1075\u20131090 (2019)","journal-title":"Mining Metall. Explor."},{"key":"1076_CR16","doi-asserted-by":"crossref","unstructured":"Li, H., Guo, C., Yang, S.X., Jin, H.: Hybrid model of wt and anfis and its application on time series prediction of ship roll motion. In: The Proceedings of the Multiconference on\u201c Computational Engineering in Systems Applications\u201d, vol.\u00a01, pp. 333\u2013337. IEEE (2006)","DOI":"10.1109\/CESA.2006.4281673"},{"key":"1076_CR17","unstructured":"Chen, D.W., Zhang, J.P.: Time series prediction based on ensemble anfis. In: 2005 International Conference on Machine Learning and Cybernetics, vol.\u00a06, pp. 3552\u20133556. IEEE (2005)"},{"key":"1076_CR18","doi-asserted-by":"crossref","unstructured":"Muhammad, A., Nazaruddin, Y.Y., Siregar, P.I.: Design of nonlinear adaptive-predictive control system with anfis modeling for urea plant reactor unit. In: 2019 6th International Conference on Instrumentation, Control, and Automation (ICA), pp. 147\u2013152. IEEE (2019)","DOI":"10.1109\/ICA.2019.8916735"},{"key":"1076_CR19","doi-asserted-by":"crossref","unstructured":"Davanipour, M., Zekri, M., Sheikholeslam, F.: The preference of fuzzy wavelet neural network to anfis in identification of nonlinear dynamic plants with fast local variation. In: 2010 18th Iranian Conference on Electrical Engineering, pp. 605\u2013609. IEEE (2010)","DOI":"10.1109\/IRANIANCEE.2010.5506998"},{"key":"1076_CR20","unstructured":"de\u00a0Martins, J., de\u00a0Ara\u00fajo, F.: Nonlinear system identification based on modified anfis. In: Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics (ICINCO), Colmar, France, pp. 21\u201323 (2015)"},{"issue":"3","key":"1076_CR21","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1109\/21.256541","volume":"23","author":"JS Jang","year":"1993","unstructured":"Jang, J.S.: Anfis: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665\u2013685 (1993)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"1076_CR22","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.asoc.2013.10.014","volume":"15","author":"S Kar","year":"2014","unstructured":"Kar, S., Das, S., Ghosh, P.K.: Applications of neuro fuzzy systems: a brief review and future outline. Appl. Soft Comput. 15, 243\u2013259 (2014)","journal-title":"Appl. Soft Comput."},{"key":"1076_CR23","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1016\/j.molliq.2016.10.050","volume":"224","author":"A Barati-Harooni","year":"2016","unstructured":"Barati-Harooni, A., Najafi-Marghmaleki, A., Mohammadi, A.H.: Anfis modeling of ionic liquids densities. J. Mol. Liq. 224, 965\u2013975 (2016)","journal-title":"J. Mol. Liq."},{"key":"1076_CR24","doi-asserted-by":"publisher","first-page":"1266","DOI":"10.1016\/j.molliq.2016.10.112","volume":"224","author":"A Tatar","year":"2016","unstructured":"Tatar, A., Barati-Harooni, A., Najafi-Marghmaleki, A., Norouzi-Farimani, B., Mohammadi, A.H.: Predictive model based on anfis for estimation of thermal conductivity of carbon dioxide. J. Mol. Liq. 224, 1266\u20131274 (2016)","journal-title":"J. Mol. Liq."},{"issue":"3","key":"1076_CR25","doi-asserted-by":"publisher","first-page":"732","DOI":"10.1016\/j.cie.2009.01.019","volume":"57","author":"O Taylan","year":"2009","unstructured":"Taylan, O., Karag\u00f6zo\u011flu, B.: An adaptive neuro-fuzzy model for prediction of student\u2019s academic performance. Comput. Ind. Eng. 57(3), 732\u2013741 (2009)","journal-title":"Comput. Ind. Eng."},{"key":"1076_CR26","doi-asserted-by":"crossref","unstructured":"Peymanfar, A., Khoei, A., Hadidi, K.: A new anfis based learning algorithm for cmos neuro-fuzzy controllers. In: 2007 14th IEEE International Conference on Electronics, Circuits and Systems, pp. 890\u2013893. IEEE (2007)","DOI":"10.1109\/ICECS.2007.4511134"},{"key":"1076_CR27","first-page":"6","volume":"28","author":"P Dziwi\u0144ski","year":"2019","unstructured":"Dziwi\u0144ski, P., Bartczuk, \u0141.: A new hybrid particle swarm optimization and genetic algorithm method controlled by fuzzy logic. IEEE Trans. Fuzzy Sys. 28, 6 (2019)","journal-title":"IEEE Trans. Fuzzy Sys."},{"issue":"4","key":"1076_CR28","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.1007\/s40815-018-0449-8","volume":"20","author":"T Liu","year":"2018","unstructured":"Liu, T., Zhang, W., McLean, P., Ueland, M., Forbes, S.L., Su, S.W.: Electronic nose-based odor classification using genetic algorithms and fuzzy support vector machines. Int. J. Fuzzy Syst. 20(4), 1309\u20131320 (2018)","journal-title":"Int. J. Fuzzy Syst."},{"issue":"6","key":"1076_CR29","doi-asserted-by":"publisher","first-page":"1938","DOI":"10.1007\/s40815-018-0478-3","volume":"20","author":"H Kalia","year":"2018","unstructured":"Kalia, H., Dehuri, S., Ghosh, A., Cho, S.B.: Surrogate-assisted multi-objective genetic algorithms for fuzzy rule-based classification. Int. J. Fuzzy Syst. 20(6), 1938\u20131955 (2018)","journal-title":"Int. J. Fuzzy Syst."},{"key":"1076_CR30","doi-asserted-by":"publisher","first-page":"1694","DOI":"10.1007\/s40815-020-00849-2","volume":"22","author":"C Zhang","year":"2020","unstructured":"Zhang, C.: Classification rule mining algorithm combining intuitionistic fuzzy rough sets and genetic algorithm. Int. J. Fuzzy Syst. 22, 1694 (2020)","journal-title":"Int. J. Fuzzy Syst."},{"issue":"8","key":"1076_CR31","doi-asserted-by":"publisher","first-page":"2524","DOI":"10.1007\/s40815-019-00735-6","volume":"21","author":"TL Le","year":"2019","unstructured":"Le, T.L., Huynh, T.T., Lin, C.M.: Self-evolving interval type-2 wavelet cerebellar model articulation control design for uncertain nonlinear systems using pso. Int. J. Fuzzy Syst. 21(8), 2524\u20132541 (2019)","journal-title":"Int. J. Fuzzy Syst."},{"issue":"1","key":"1076_CR32","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/s40815-017-0331-0","volume":"20","author":"MF Zarandi","year":"2018","unstructured":"Zarandi, M.F., Dorry, F.: A hybrid fuzzy pso algorithm for solving steelmaking-continuous casting scheduling problem. Int. J. Fuzzy Syst. 20(1), 219\u2013235 (2018)","journal-title":"Int. J. Fuzzy Syst."},{"issue":"5","key":"1076_CR33","doi-asserted-by":"publisher","first-page":"1362","DOI":"10.1007\/s40815-017-0301-6","volume":"19","author":"CM Lin","year":"2017","unstructured":"Lin, C.M., Le, T.L.: Pso-self-organizing interval type-2 fuzzy neural network for antilock braking systems. Int. J. Fuzzy Syst. 19(5), 1362\u20131374 (2017)","journal-title":"Int. J. Fuzzy Syst."},{"issue":"5","key":"1076_CR34","doi-asserted-by":"publisher","first-page":"1685","DOI":"10.1007\/s40815-018-0453-z","volume":"20","author":"CC Chen","year":"2018","unstructured":"Chen, C.C.: Optimization of zero-order tsk-type fuzzy system using enhanced particle swarm optimizer with dynamic mutation and special initialization. Int. J. Fuzzy Syst. 20(5), 1685\u20131698 (2018)","journal-title":"Int. J. Fuzzy Syst."},{"issue":"1","key":"1076_CR35","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1007\/s40815-017-0302-5","volume":"20","author":"E Zakeri","year":"2018","unstructured":"Zakeri, E., Moezi, S.A., Eghtesad, M.: Tracking control of ball on sphere system using tuned fuzzy sliding mode controller based on artificial bee colony algorithm. Int. J. Fuzzy Syst. 20(1), 295\u2013308 (2018)","journal-title":"Int. J. Fuzzy Syst."},{"issue":"1","key":"1076_CR36","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1007\/s40815-015-0009-4","volume":"17","author":"E Sasikala","year":"2015","unstructured":"Sasikala, E., Rengarajan, N.: An intelligent technique to detect jamming attack in wireless sensor networks (wsns). Int. J. Fuzzy Syst. 17(1), 76\u201383 (2015)","journal-title":"Int. J. Fuzzy Syst."},{"issue":"2","key":"1076_CR37","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1007\/s40815-016-0205-x","volume":"19","author":"CH Kuo","year":"2017","unstructured":"Kuo, C.H., Kuo, Y.C., Chou, H.C., Lin, Y.T.: P300-based brain-computer interface with latency estimation using abc-based interval type-2 fuzzy logic system. Int. J. Fuzzy Syst. 19(2), 529\u2013541 (2017)","journal-title":"Int. J. Fuzzy Syst."},{"issue":"4","key":"1076_CR38","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1109\/MCI.2006.329691","volume":"1","author":"M Dorigo","year":"2006","unstructured":"Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28\u201339 (2006)","journal-title":"IEEE Comput. Intell. Mag."},{"key":"1076_CR39","doi-asserted-by":"crossref","unstructured":"Zakaria, Z., Rahman, T., Hassan, E.: Economic load dispatch via an improved bacterial foraging optimization. In: 2014 IEEE 8th International Power Engineering and Optimization Conference (PEOCO2014), pp. 380\u2013385. IEEE (2014)","DOI":"10.1109\/PEOCO.2014.6814458"},{"issue":"02","key":"1076_CR40","doi-asserted-by":"publisher","first-page":"1350007","DOI":"10.1142\/S1469026813500077","volume":"12","author":"M Orouskhani","year":"2013","unstructured":"Orouskhani, M., Mansouri, M., Orouskhani, Y., Teshnehlab, M.: A hybrid method of modified cat swarm optimization and gradient descent algorithm for training anfis. Int. J. Comput. Intell. Appl. 12(02), 1350007 (2013)","journal-title":"Int. J. Comput. Intell. Appl."},{"key":"1076_CR41","doi-asserted-by":"crossref","unstructured":"Suchetha, N., Nikhil, A., Hrudya, P.: Comparing the wrapper feature selection evaluators on twitter sentiment classification. In: 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), pp. 1\u20136. IEEE (2019)","DOI":"10.1109\/ICCIDS.2019.8862033"},{"key":"1076_CR42","doi-asserted-by":"crossref","unstructured":"\u00c7atalkaya, M.B., Kal\u0131ps\u0131z, O., Akta\u015f, M.S., Turgut, U.O.: Data feature selection methods on distributed big data processing platforms. In: 2018 3rd International Conference on Computer Science and Engineering (UBMK), pp. 133\u2013138. IEEE (2018)","DOI":"10.1109\/UBMK.2018.8566451"},{"issue":"5","key":"1076_CR43","doi-asserted-by":"publisher","first-page":"1613","DOI":"10.1007\/s40815-019-00645-7","volume":"21","author":"LQ Li","year":"2019","unstructured":"Li, L.Q., Wang, X.L., Liu, Z.X., Xie, W.X.: A novel intuitionistic fuzzy clustering algorithm based on feature selection for multiple object tracking. Int. J. Fuzzy Syst. 21(5), 1613\u20131628 (2019)","journal-title":"Int. J. Fuzzy Syst."},{"issue":"2","key":"1076_CR44","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1007\/s40815-018-0562-8","volume":"21","author":"TC Ahn","year":"2019","unstructured":"Ahn, T.C., Roh, S.B., Kim, Y.S., Wang, J.: Prototypes reduction and feature selection based on fuzzy boundary area for nearest neighbor classifiers. Int. J. Fuzzy Syst. 21(2), 639\u2013654 (2019)","journal-title":"Int. J. Fuzzy Syst."},{"key":"1076_CR45","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.neucom.2018.02.049","volume":"290","author":"HG Han","year":"2018","unstructured":"Han, H.G., Chen, Z.Y., Liu, H.X., Qiao, J.F.: A self-organizing interval type-2 fuzzy-neural-network for modeling nonlinear systems. Neurocomputing 290, 196\u2013207 (2018)","journal-title":"Neurocomputing"},{"issue":"3","key":"1076_CR46","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1109\/TNNLS.2016.2522401","volume":"28","author":"Y Deng","year":"2016","unstructured":"Deng, Y., Bao, F., Kong, Y., Ren, Z., Dai, Q.: Deep direct reinforcement learning for financial signal representation and trading. IEEE Trans. Neural Netw. Learn. Syst. 28(3), 653\u2013664 (2016)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"1076_CR47","doi-asserted-by":"crossref","unstructured":"Salleh, M.N.M., Talpur, N., Hussain, K.: Adaptive neuro-fuzzy inference system: Overview, strengths, limitations, and solutions. In: International Conference on Data Mining and Big Data, pp. 527\u2013535. Springer, Berlin (2017)","DOI":"10.1007\/978-3-319-61845-6_52"},{"issue":"15","key":"1076_CR48","doi-asserted-by":"publisher","first-page":"1359","DOI":"10.1080\/10916466.2016.1202975","volume":"34","author":"A Baghban","year":"2016","unstructured":"Baghban, A.: Application of the anfis strategy to estimate vaporization enthalpies of petroleum fractions and pure hydrocarbons. Petrol. Sci. Technol. 34(15), 1359\u20131366 (2016)","journal-title":"Petrol. Sci. Technol."},{"issue":"3","key":"1076_CR49","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1007\/s11769-019-1036-0","volume":"29","author":"L Ye","year":"2019","unstructured":"Ye, L., Ou, X.: Spatial\u2013temporal analysis of daily air quality index in the Yangtze river delta region of china during 2014 and 2016. Chin. Geogr. Sci. 29(3), 382\u2013393 (2019)","journal-title":"Chin. Geogr. Sci."},{"key":"1076_CR50","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1109\/TSMC.1985.6313399","volume":"1","author":"T Takagi","year":"1985","unstructured":"Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 1, 116\u2013132 (1985)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"issue":"10","key":"1076_CR51","doi-asserted-by":"publisher","first-page":"965","DOI":"10.3390\/math7100965","volume":"7","author":"S Shamshirband","year":"2019","unstructured":"Shamshirband, S., Hadipoor, M., Baghban, A., Mosavi, A., Bukor, J., V\u00e1rkonyi-K\u00f3czy, A.R.: Developing an anfis-pso model to predict mercury emissions in combustion flue gases. Mathematics 7(10), 965 (2019)","journal-title":"Mathematics"},{"key":"1076_CR52","unstructured":"Asuncion, A., Newman, D.: Uci machine learning repository (2007)"},{"key":"1076_CR53","doi-asserted-by":"crossref","unstructured":"Khan, T.A., Zain-Ul-Abideen, K., Ling, S.H.: A modified particle swarm optimization algorithm used for feature selection of uci biomedical data sets. In: 60th International Scientific Conference on Information Technology and Management Science of Riga Technical University, ITMS 2019-Proceedings (2019)","DOI":"10.1109\/ITMS47855.2019.8940760"},{"issue":"21","key":"1076_CR54","doi-asserted-by":"publisher","first-page":"3628","DOI":"10.3390\/ma12213628","volume":"12","author":"IM Alarifi","year":"2019","unstructured":"Alarifi, I.M., Nguyen, H.M., Naderi Bakhtiyari, A., Asadi, A.: Feasibility of anfis-pso and anfis-ga models in predicting thermophysical properties of al2o3-mwcnt\/oil hybrid nanofluid. Materials 12(21), 3628 (2019)","journal-title":"Materials"}],"container-title":["International Journal of Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40815-021-01076-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40815-021-01076-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40815-021-01076-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T22:09:00Z","timestamp":1634767740000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40815-021-01076-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,5]]},"references-count":54,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2021,10]]}},"alternative-id":["1076"],"URL":"https:\/\/doi.org\/10.1007\/s40815-021-01076-z","relation":{},"ISSN":["1562-2479","2199-3211"],"issn-type":[{"value":"1562-2479","type":"print"},{"value":"2199-3211","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,5]]},"assertion":[{"value":"27 August 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 February 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}