{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:21:09Z","timestamp":1777706469752,"version":"3.51.4"},"reference-count":39,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T00:00:00Z","timestamp":1711065600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems: Applications in Engineering and Technology"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:p>This paper presents an approach incorporating fuzzy logic techniques inside a convolutional neural network to manage uncertainty present in the multiple data sources that the model handles when training. The implementation considers the use of information and filters in the fuzzy spectrum, as well as the creation of a new layer to replace the traditional convolution layer with a fuzzy convolutional layer. The aim is to design artificial intelligence algorithms that combine the potential of deep convolutional neural networks and fuzzy logic to create robust systems that allow modeling the uncertainty present in the sources of data and that are applied to classification problems. The fuzzification process is developed using three membership functions, including the Triangular, Gaussian, and S functions. The work was tested in databases oriented to traffic signs, due to the complexity of the different circumstances and factors in which a traffic sign can be found.<\/jats:p>","DOI":"10.3233\/jifs-219369","type":"journal-article","created":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T11:59:19Z","timestamp":1711108759000},"page":"515-525","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Fuzzy convolutional neural network model applied to classification problems"],"prefix":"10.1177","volume":"50","author":[{"given":"Claudia I.","family":"Gonzalez","sequence":"first","affiliation":[{"name":"Tijuana Institute of Technology\/TecNM","place":["Mexico"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cesar","family":"Torres","sequence":"additional","affiliation":[{"name":"Tijuana Institute of Technology\/TecNM","place":["Mexico"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2024,3,22]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"e_1_3_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2021.3062899"},{"issue":"3","key":"e_1_3_2_4_1","article-title":"Fuzzy Distribution Sets","volume":"26","author":"Batyrshin I.Z.","year":"2022","unstructured":"BatyrshinI.Z., Fuzzy Distribution Sets, Computacion y Sistemas26(3) (2022)\u2013.","journal-title":"Computacion y Sistemas"},{"issue":"6","key":"e_1_3_2_5_1","first-page":"6895","article-title":"Soft computing and advances in intelligent systems","volume":"43","author":"Batyrshin I.Z.","year":"2022","unstructured":"BatyrshinI.Z.GomideF.KreinovichV.ShahbazovaS., Soft computing and advances in intelligent systems, & Fuzzy Systems43(6) (2022), 6895\u20136896.","journal-title":"& Fuzzy Systems"},{"key":"e_1_3_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2021.3079503"},{"key":"e_1_3_2_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40815-021-01124-8"},{"issue":"17","key":"e_1_3_2_8_1","article-title":"Decision-making in a fuzzy environment","volume":"4","author":"Bellman R.E.","year":"1970","unstructured":"BellmanR.E.ZadehL.A., Decision-making in a fuzzy environment, Management Science4(17) (1970), B144\u2013B164.","journal-title":"Management Science"},{"key":"e_1_3_2_9_1","doi-asserted-by":"crossref","unstructured":"PierrardR.PoliJ.Hudelot.C.Learning fuzzy relations and properties for explainable artificial intelligence. 2018 IEEE International Conference on Fuzzy Systems (FUZZIEEE) IEEE Rio de Janeiro Brazil (2018).","DOI":"10.1109\/FUZZ-IEEE.2018.8491538"},{"key":"e_1_3_2_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/0165-0114(78)90030-1"},{"key":"e_1_3_2_11_1","doi-asserted-by":"crossref","unstructured":"TakagiT.Sugeno.M.Fuzzy identification of systems and its applications to modeling and control Readings in Fuzzy Sets for Intelligent Systems (1993) 387\u2013403.","DOI":"10.1016\/B978-1-4832-1450-4.50045-6"},{"key":"e_1_3_2_12_1","doi-asserted-by":"crossref","unstructured":"KorshunovaK.P.A convolutional fuzzy neural network for image classification in 2018 3rd Russian-Pacific Conference on Computer Technology and Applications (RPC) Vladivostok Russia 2018.","DOI":"10.1109\/RPC.2018.8482211"},{"key":"e_1_3_2_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40815-019-00764-1"},{"issue":"6","key":"e_1_3_2_14_1","first-page":"6025","article-title":"A fuzzy convolutional neural network for text sentiment analysis","volume":"35","author":"Nguyen T.-L.","year":"2018","unstructured":"NguyenT.-L.KavuriS.LeeM., A fuzzy convolutional neural network for text sentiment analysis, Special Section: Green and Human Information Technology35(6) (2018), 6025\u20136034.","journal-title":"Special Section: Green and Human Information Technology"},{"key":"e_1_3_2_15_1","doi-asserted-by":"publisher","DOI":"10.3390\/app122412937"},{"key":"e_1_3_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2020.3024023"},{"key":"e_1_3_2_17_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics12102281"},{"key":"e_1_3_2_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.06.010"},{"key":"e_1_3_2_19_1","first-page":"269","article-title":"Smile detection using convolutional neural network and fuzzy logic","volume":"36","author":"Jamal K.M.","year":"2020","unstructured":"JamalK.M.DiwanS.A.AbdulhusseinZ.A., Smile detection using convolutional neural network and fuzzy logic, Journal of Information Science and Engineering36 (2020), 269\u2013278.","journal-title":"Journal of Information Science and Engineering"},{"key":"e_1_3_2_20_1","first-page":"1407","article-title":"Interpretable deep convolutional fuzzy classifier","volume":"28","author":"Yeganejou M.","year":"2020","unstructured":"YeganejouM.DickS.MillerJ., Interpretable deep convolutional fuzzy classifier, IEEE Transactions on Fuzzy Systems28 (2020), 1407\u20131419.","journal-title":"IEEE Transactions on Fuzzy Systems"},{"key":"e_1_3_2_21_1","first-page":"1242","article-title":"Lip image segmentation based on a fuzzy convolutional neural network","volume":"28","author":"Guan C.","year":"2020","unstructured":"GuanC.WangS.LiewW.C., Lip image segmentation based on a fuzzy convolutional neural network, IEEE Transactions on Fuzzy Systems28 (2020), 1242\u20131251.","journal-title":"IEEE Transactions on Fuzzy Systems"},{"key":"e_1_3_2_22_1","first-page":"1356","article-title":"Fuzzy multilayer clustering and fuzzy label regularization for unsupervised person reidentification","volume":"28","author":"Zhang Z.","year":"2020","unstructured":"ZhangZ.HuangM.LiuS., et al. Fuzzy multilayer clustering and fuzzy label regularization for unsupervised person reidentification, IEEE Transactions on Fuzzy Systems28 (2020), 1356\u20131368.","journal-title":"IEEE Transactions on Fuzzy Systems"},{"key":"e_1_3_2_23_1","first-page":"1252","article-title":"A fuzzy deep neural network with sparse autoencoder for emotional intention understanding in human-robot interaction","volume":"28","author":"Chen L.","year":"2020","unstructured":"ChenL.SuW.WuM., et al. A fuzzy deep neural network with sparse autoencoder for emotional intention understanding in human-robot interaction, IEEE Transactions on Fuzzy Systems28 (2020), 1252\u20131264.","journal-title":"IEEE Transactions on Fuzzy Systems"},{"key":"e_1_3_2_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijhydene.2020.03.035"},{"key":"e_1_3_2_25_1","doi-asserted-by":"crossref","unstructured":"YamunadeviM.M.RanjaniS.S.Efficient segmentation of the lung carcinoma by adaptive fuzzy\u2013GLCM (AFGLCM) with deep learning based classification Journal of Ambient Intelligence and Humanized Computing 2020.","DOI":"10.1007\/s12652-020-01874-7"},{"key":"e_1_3_2_26_1","first-page":"4379","article-title":"Hybrid recommendation system for heart disease diagnosis based on multiple kernel learning with adaptive neuro-fuzzy inference system, &","volume":"77","author":"Manogaran G.","year":"2018","unstructured":"ManogaranG.VaratharajanR.PriyanM.K., Hybrid recommendation system for heart disease diagnosis based on multiple kernel learning with adaptive neuro-fuzzy inference system, &, Applications77 (2018), 4379\u20134399.","journal-title":"Applications"},{"key":"e_1_3_2_27_1","doi-asserted-by":"publisher","unstructured":"YangP.WangD.DuX.L. et al. Evolutionary DBN for the customers\u2019 sentiment classification with incremental rules Industrial Conference on Data Mining. Springer 2018. doi:10.1007\/978-3-319-95786-9_9.","DOI":"10.1007\/978-3-319-95786-9_9"},{"key":"e_1_3_2_28_1","doi-asserted-by":"publisher","unstructured":"SinghS.P.KumarA.DarbariH. et al. Extract reordering rules of sentence structure using neuro-fuzzy machine learning system International Conference on Smart Technologies for Smart Nation IEEE 2017. doi:10.1109\/SmartTechCon.2017.8358364.","DOI":"10.1109\/SmartTechCon.2017.8358364"},{"key":"e_1_3_2_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-23281-8_16"},{"key":"e_1_3_2_30_1","doi-asserted-by":"publisher","unstructured":"ZhuX.RehmanK.U.WangB. et al. Modern soft-sensing modeling methods for fermentation processes Sensors 2020. doi:10.3390\/s20061771.","DOI":"10.3390\/s20061771"},{"key":"e_1_3_2_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejcon.2019.06.009"},{"key":"e_1_3_2_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfranklin.2019.05.006"},{"key":"e_1_3_2_33_1","first-page":"1492","article-title":"Online deep fuzzy learning for control of nonlinear systems using expert knowledge","volume":"28","author":"Sarabakha A.","year":"2020","unstructured":"SarabakhaA.KayacanE., Online deep fuzzy learning for control of nonlinear systems using expert knowledge, IEEE Transactions on Fuzzy Systems28 (2020), 1492\u20131503.","journal-title":"IEEE Transactions on Fuzzy Systems"},{"key":"e_1_3_2_34_1","doi-asserted-by":"publisher","unstructured":"BelloS.A.YuS.S.WangC.Review: deep learning on 3D point clouds Remote Sensing 2020. doi:10.3390\/rs12111729.","DOI":"10.3390\/rs12111729"},{"key":"e_1_3_2_35_1","doi-asserted-by":"publisher","DOI":"10.1080\/10798587.2017.1329245"},{"key":"e_1_3_2_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.02.034"},{"key":"e_1_3_2_37_1","unstructured":"AbadiM.BarhamP.ChenJ.ChenZ.DavisA.DeanJ.TensorFlow: A system for large-scale in 12th USENIX Symposium on Operating Systems Design Savannah 2016."},{"key":"e_1_3_2_38_1","doi-asserted-by":"crossref","unstructured":"StallkampJ.SchlipsingM.SalmenJ.IgelC.The GermanTraffic Sign Recognition Benchmark:Amulti-class classification competition in Proceedings of the IEEE International Joint Conference on Neural Network San Jose 1453\u20131460.","DOI":"10.1109\/IJCNN.2011.6033395"},{"key":"e_1_3_2_39_1","doi-asserted-by":"crossref","unstructured":"ZhangY.WangZ.QiY.LiuJ.YangJ.CTSD: A dataset for traffic sign recognition in complex real-world images in 2018 IEEE Visual Communications and Image Processing (VCIP) Taichung 2018.","DOI":"10.1109\/VCIP.2018.8698666"},{"key":"e_1_3_2_40_1","unstructured":"GlorotX.BengioY.Understanding the difficulty of training deep feedforward neural networks in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics Sardinia 2010."}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems: Applications in Engineering and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-219369","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-219369","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-219369","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:46:54Z","timestamp":1777456014000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-219369"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,22]]},"references-count":39,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["10.3233\/JIFS-219369"],"URL":"https:\/\/doi.org\/10.3233\/jifs-219369","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,22]]}}}