{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:47:11Z","timestamp":1777704431145,"version":"3.51.4"},"reference-count":25,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2020,2,18]],"date-time":"2020-02-18T00:00:00Z","timestamp":1581984000000},"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"],"published-print":{"date-parts":[[2020,5,29]]},"abstract":"<jats:p>\u00a0Digital creativity is creative expression derived from cultural creativity and information technology. In order to overcome the problem in the creative generation in the condition of fuzzy and uncertain ideas, an automatic generation method of cross-modal fuzzy creativity (AGMCFC) is proposed. In this subject, fuzzy creative data sets and learning retrieval network are constructed for the sake of extracting original creative data effectively. And the logical correlations between creative objects are acquired dynamically based on the graph neural network. Creative objects and creative styles are generated by using generative adversarial nets technology and style transfer technology, respectively. Then, the projectiles, boundary markers and location words of the creative scene objects are generated by analyzing related attributes of each entity. After adjusting the layout, creative works are automatically generated. A fuzzy creative generating environment is implemented. Experimental results show that the screened number of AGMCFC method is about twice as much as that of manual method, and the accuracy rate of AGMCFC method is improved compared with the manual method. AGMCFC method performs well at creative generation of fuzzy ideas automatically.<\/jats:p>","DOI":"10.3233\/jifs-179657","type":"journal-article","created":{"date-parts":[[2020,2,18]],"date-time":"2020-02-18T12:42:57Z","timestamp":1582029777000},"page":"5685-5696","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["An automatic generation method of cross-modal fuzzy creativity"],"prefix":"10.1177","volume":"38","author":[{"given":"Fuquan","family":"Zhang","sequence":"first","affiliation":[{"name":"Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiou","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Science and Technology Information, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chensheng","family":"Wu","sequence":"additional","affiliation":[{"name":"Beijing Institute of Science and Technology Information, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2020,2,18]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-18748"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2891105"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-018-1029-3"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-169752"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.7.1527"},{"key":"e_1_3_2_7_2","first-page":"177","article-title":"Deep learning models for EEG-based rapid serial visual presentation event classification","volume":"9","author":"Zhang F.Q.","year":"2018","unstructured":"ZhangF.Q., MaoZ.J., HuangY.F., XuL. and DingG.Y., Deep learning models for EEG-based rapid serial visual presentation event classification, Journal of Information Hiding and Multimedia Signal Processing9 (2018), 177\u2013187.","journal-title":"Journal of Information Hiding and Multimedia Signal Processing"},{"key":"e_1_3_2_8_2","first-page":"30","article-title":"Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition, Audio, Speech, and Language Processing","volume":"20","author":"Dahl G.E.","year":"2012","unstructured":"DahlG.E., YuD., DengL., et al., Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition, Audio, Speech, and Language Processing, IEEE Transactions on20 (2012), 30\u201342.","journal-title":"IEEE Transactions on"},{"key":"e_1_3_2_9_2","unstructured":"LiH.J. Research on spatial conceptual model based on natural language processing Ph.D. Dissertation Harbin Institute of Technology 2007."},{"key":"e_1_3_2_10_2","first-page":"1845","article-title":"The research and realization of the layout of objects in 3D Scene based on natural language understanding","volume":"29","author":"Li H.J.","year":"2007","unstructured":"LiH.J., LiS., ZhaoT.J., et al., The research and realization of the layout of objects in 3D Scene based on natural language understanding, Journal of Electronics & Information Technology29 (2007), 1845\u20131849.","journal-title":"Journal of Electronics & Information Technology"},{"key":"e_1_3_2_11_2","unstructured":"GoodfellowI. PougetabadieJ. MirzaM. et al. Generative adversarial nets Advances in Neural Information Processing Systems (2014) 2672\u20132680."},{"key":"e_1_3_2_12_2","first-page":"150","article-title":"Prediction model of ammunition consumption based on fuzzy logic theory","volume":"40","author":"Li J.H.","year":"2019","unstructured":"LiJ.H., HuangT., YuH.M. and ZhangM.L., Prediction model of ammunition consumption based on fuzzy logic theory, Journal of Ordnance Equipment Engineering40 (2019), 150\u2013153.","journal-title":"Journal of Ordnance Equipment Engineering"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","unstructured":"WuJ.M.-T. LinJ.C.-W. and TamrakarA. High-utility itemset mining with effective pruning strategies ACM Transactions on Knowledge Discovery from Data (2019). https:\/\/doi.org\/10.1145\/3363571","DOI":"10.1145\/3363571"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2019.2903289"},{"key":"e_1_3_2_15_2","first-page":"1717","article-title":"Alpha-fraction first strategy for hierarchical wireless sensor networks","volume":"19","author":"Pan J.-S.","year":"2018","unstructured":"PanJ.-S., KongL., SungT.-W., TsaiP.-W. and SnaselV., Alpha-fraction first strategy for hierarchical wireless sensor networks, Journal of Internet Technology19 (2018), 1717\u20131726.","journal-title":"Journal of Internet Technology"},{"key":"e_1_3_2_16_2","first-page":"58","article-title":"Overview of graph neural network","volume":"23","author":"Wang J.","year":"2019","unstructured":"WangJ., Overview of graph neural network, Modern Computer23 (2019), 58\u201362.","journal-title":"Modern Computer"},{"key":"e_1_3_2_17_2","unstructured":"WuJ. ZhangC. XueT. et al. Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling arXiv:1610.07584v2 [cs.CV] 4 Jan 2017."},{"key":"e_1_3_2_18_2","doi-asserted-by":"crossref","unstructured":"LeeK.C. DCSE (Digital Creativity Simulation Engine) Digital Creativity Model and Its Relationship with Corporate Performance Springer International Publishing 2016.","DOI":"10.1007\/978-3-319-39991-1_4"},{"key":"e_1_3_2_19_2","doi-asserted-by":"crossref","unstructured":"GatysL.A. EckerA.S. BethgeM. Image style transfer using convolutional neural networks in: Computer Vision and Pattern Recognition IEEE Computer Society Press 2016 pp. 2414-2423.","DOI":"10.1109\/CVPR.2016.265"},{"key":"e_1_3_2_20_2","first-page":"1083","article-title":"Fuzzy control strategy of double motor for screw pile machine","volume":"36","author":"Wu L.Z.","year":"2019","unstructured":"WuL.Z., MaF. and LiaoZ., Fuzzy control strategy of double motor for screw pile machine, Journal of Mechanical & Electrical Engineering36 (2019), 1083\u20131088.","journal-title":"Journal of Mechanical & Electrical Engineering"},{"key":"e_1_3_2_21_2","unstructured":"BattagliaP.W. HamrickJ.B. BapstV. et al. Relational inductive biases deep learning and graph networks arXiv:1806.01261v3 [cs.LG] 17 Oct 2018."},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1080\/01969729508927498"},{"key":"e_1_3_2_23_2","doi-asserted-by":"crossref","unstructured":"LinT.-L. ChuangC.-H. ChenS.-L. and et al. An efficient image processing methodology based on fuzzy decision for dental shade matching Journal of Intelligent and Fuzzy Systems36 (2019) 1133\u20131142.","DOI":"10.3233\/JIFS-169887"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1080\/02533839.2018.1537807"},{"key":"e_1_3_2_25_2","unstructured":"WangX. Automatic spatial layout of named entity in natural sentence Master Dissertation Harbin Institute of Technology 2014."},{"key":"e_1_3_2_26_2","unstructured":"JingY. YangY. FengZ. et al. Neural style transfer: a review arXiv:1705.04058v6 [cs.CV] 17 Jun 2018."}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-179657","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-179657","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-179657","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:41:12Z","timestamp":1777455672000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-179657"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,18]]},"references-count":25,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2020,5,29]]}},"alternative-id":["10.3233\/JIFS-179657"],"URL":"https:\/\/doi.org\/10.3233\/jifs-179657","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,18]]}}}