{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T09:15:51Z","timestamp":1742807751432,"version":"3.37.3"},"reference-count":58,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100012964","name":"Enseignement Sup\u00e9rieur et de la Recherche Scientifique","doi-asserted-by":"publisher","award":["LR11ES48"],"id":[{"id":"10.13039\/100012964","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1016\/j.neucom.2024.128251","type":"journal-article","created":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T03:07:12Z","timestamp":1722395232000},"page":"128251","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":4,"special_numbering":"C","title":["A deep learning based interval type-2 fuzzy approach for image retrieval systems"],"prefix":"10.1016","volume":"603","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1967-9434","authenticated-orcid":false,"given":"Yosr","family":"Ghozzi","sequence":"first","affiliation":[]},{"given":"Tarek M.","family":"Hamdani","sequence":"additional","affiliation":[]},{"given":"Hani","family":"Hagras","sequence":"additional","affiliation":[]},{"given":"Khmaies","family":"Ouahada","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1101-2360","authenticated-orcid":false,"given":"Habib","family":"Chabchoub","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0642-3384","authenticated-orcid":false,"given":"Adel M.","family":"Alimi","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2024.128251_b1","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.inffus.2018.11.004","article-title":"Content based image retrieval using image features information fusion","volume":"51","author":"Ahmed","year":"2019","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.neucom.2024.128251_b2","article-title":"Interval type-2 beta fuzzy near sets approach to content-based image retrieval","author":"Yosr","year":"2021","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10.1016\/j.neucom.2024.128251_b3","first-page":"1","article-title":"A review on recent advances in content-based image retrieval used in image search engine","author":"Bhoir","year":"2021","journal-title":"Libr. Philos. Pract."},{"year":"2017","series-title":"Recent advance in content-based image retrieval: A literature survey","author":"Zhou","key":"10.1016\/j.neucom.2024.128251_b4"},{"key":"10.1016\/j.neucom.2024.128251_b5","doi-asserted-by":"crossref","DOI":"10.1155\/2019\/9658350","article-title":"Content-based image retrieval and feature extraction: a comprehensive review","volume":"2019","author":"Latif","year":"2019","journal-title":"Math. Probl. Eng."},{"issue":"7","key":"10.1016\/j.neucom.2024.128251_b6","first-page":"3","article-title":"Tiny imagenet visual recognition challenge","volume":"7","author":"Le","year":"2015","journal-title":"CS 231N"},{"key":"10.1016\/j.neucom.2024.128251_b7","first-page":"1","article-title":"State of the art content based image retrieval techniques using deep learning: a survey","author":"Kapoor","year":"2021","journal-title":"Multimedia Tools Appl."},{"issue":"5","key":"10.1016\/j.neucom.2024.128251_b8","doi-asserted-by":"crossref","first-page":"1224","DOI":"10.1109\/TPAMI.2017.2709749","article-title":"SIFT meets CNN: A decade survey of instance retrieval","volume":"40","author":"Zheng","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"10.1016\/j.neucom.2024.128251_b9","doi-asserted-by":"crossref","first-page":"4187","DOI":"10.1007\/s10586-018-1731-0","article-title":"Content based image retrieval using deep learning process","volume":"22","author":"Saritha","year":"2019","journal-title":"Cluster Comput."},{"issue":"7","key":"10.1016\/j.neucom.2024.128251_b10","doi-asserted-by":"crossref","first-page":"5261","DOI":"10.1007\/s10462-020-09820-x","article-title":"Deep hashing for multi-label image retrieval: a survey","volume":"53","author":"Rodrigues","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.neucom.2024.128251_b11","series-title":"ICEIS (1)","first-page":"165","article-title":"Hybrid shallow learning and deep learning for feature extraction and image retrieval","author":"Karamti","year":"2020"},{"key":"10.1016\/j.neucom.2024.128251_b12","article-title":"Self-supervised visual feature learning with deep neural networks: A survey","author":"Jing","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neucom.2024.128251_b13","first-page":"1","article-title":"Categorizing white blood cells by utilizing deep features of proposed 4B-AdditionNet-based CNN network with ant colony optimization","author":"Shahzad","year":"2021","journal-title":"Complex Intell. Syst."},{"key":"10.1016\/j.neucom.2024.128251_b14","article-title":"A decade survey of content based image retrieval using deep learning","author":"Dubey","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.neucom.2024.128251_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.114940","article-title":"Deep convolutional features for image retrieval","volume":"177","author":"Gkelios","year":"2021","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"10.1016\/j.neucom.2024.128251_b16","doi-asserted-by":"crossref","first-page":"47","DOI":"10.3233\/IDA-184411","article-title":"Detailed investigation of deep features with sparse representation and dimensionality reduction in cbir: A comparative study","volume":"24","author":"Tarawneh","year":"2020","journal-title":"Intell. Data Anal."},{"key":"10.1016\/j.neucom.2024.128251_b17","series-title":"2020 IEEE International Conference on Fuzzy Systems","first-page":"1","article-title":"Hybrid deep learning type-2 fuzzy logic systems for explainable AI","author":"Chimatapu","year":"2020"},{"issue":"1","key":"10.1016\/j.neucom.2024.128251_b18","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1109\/TFUZZ.2020.2984991","article-title":"Deep fuzzy hashing network for efficient image retrieval","volume":"29","author":"Lu","year":"2020","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"7","key":"10.1016\/j.neucom.2024.128251_b19","doi-asserted-by":"crossref","first-page":"2208","DOI":"10.1007\/s10489-019-01625-y","article-title":"FRWCAE: joint faster-RCNN and wasserstein convolutional auto-encoder for instance retrieval","volume":"50","author":"Zhang","year":"2020","journal-title":"Appl. Intell."},{"issue":"2","key":"10.1016\/j.neucom.2024.128251_b20","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1007\/s10462-018-9636-0","article-title":"Scene analysis and search using local features and support vector machine for effective content-based image retrieval","volume":"52","author":"Sharif","year":"2019","journal-title":"Artif. Intell. Rev."},{"issue":"5","key":"10.1016\/j.neucom.2024.128251_b21","doi-asserted-by":"crossref","DOI":"10.1117\/1.JEI.29.5.053012","article-title":"Effective image retrieval method of natural images in a large database using fuzzy class membership","volume":"29","author":"Kale","year":"2020","journal-title":"J. Electron. Imaging"},{"key":"10.1016\/j.neucom.2024.128251_b22","doi-asserted-by":"crossref","first-page":"41934","DOI":"10.1109\/ACCESS.2021.3063545","article-title":"Maximum response deep learning using Markov, retinal & primitive patch binding with GoogLeNet & VGG-19 for large image retrieval","volume":"9","author":"Ahmed","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.neucom.2024.128251_b23","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1016\/j.matpr.2021.04.326","article-title":"Experimental evaluation of unsupervised image retrieval application using hybrid feature extraction by integrating deep learning and handcrafted techniques","volume":"81","author":"Devulapalli","year":"2023","journal-title":"Mater. Today Proc."},{"key":"10.1016\/j.neucom.2024.128251_b24","first-page":"1","article-title":"Improve the efficiency of handcrafted features in image retrieval by adding selected feature generating layers of deep convolutional neural networks","author":"Shamsipour","year":"2024","journal-title":"Signal Image Video Process."},{"issue":"3","key":"10.1016\/j.neucom.2024.128251_b25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3470568","article-title":"CBIR using features derived by deep learning","volume":"2","author":"Maji","year":"2021","journal-title":"ACM\/IMS Trans. Data Sci. (TDS)"},{"key":"10.1016\/j.neucom.2024.128251_b26","article-title":"A new method for image classification and image retrieval using convolutional neural networks","author":"Giveki","year":"2021","journal-title":"Concurr. Comput.: Pract. Exper."},{"issue":"12","key":"10.1016\/j.neucom.2024.128251_b27","article-title":"Analysis of content based image retrieval using deep feature extraction and similarity matching","volume":"13","author":"Mathews","year":"2022","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"issue":"4","key":"10.1016\/j.neucom.2024.128251_b28","doi-asserted-by":"crossref","first-page":"2977","DOI":"10.3233\/JIFS-181503","article-title":"Towards a general theory of similarity and association measures: similarity, dissimilarity and correlation functions","volume":"36","author":"Batyrshin","year":"2019","journal-title":"J. Intell. Fuzzy Systems"},{"issue":"3","key":"10.1016\/j.neucom.2024.128251_b29","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis. (IJCV)"},{"year":"2016","series-title":"An analysis of deep neural network models for practical applications","author":"Canziani","key":"10.1016\/j.neucom.2024.128251_b30"},{"year":"1999","series-title":"Data Preparation for Data Mining","author":"Pyle","key":"10.1016\/j.neucom.2024.128251_b31"},{"issue":"3","key":"10.1016\/j.neucom.2024.128251_b32","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1109\/23.589532","article-title":"Importance of input data normalization for the application of neural networks to complex industrial problems","volume":"44","author":"Sola","year":"1997","journal-title":"IEEE Trans. Nucl. Sci."},{"key":"10.1016\/j.neucom.2024.128251_b33","first-page":"225","article-title":"Beta fuzzy logic systems: Approximation properties in the MIMO case","volume":"13","author":"Alimi","year":"2003","journal-title":"Int. J. Appl. Math. Comput. Sci."},{"issue":"2","key":"10.1016\/j.neucom.2024.128251_b34","doi-asserted-by":"crossref","first-page":"56","DOI":"10.38094\/jastt1224","article-title":"A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction","volume":"1","author":"Zebari","year":"2020","journal-title":"J. Appl. Sci. Technol. Trends"},{"key":"10.1016\/j.neucom.2024.128251_b35","doi-asserted-by":"crossref","DOI":"10.1002\/0470013192.bsa501","article-title":"Principal component analysis","author":"Jolliffe","year":"2005","journal-title":"Encyclopedia Stat. Behav. Sci."},{"key":"10.1016\/j.neucom.2024.128251_b36","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.neucom.2015.08.104","article-title":"Auto-encoder based dimensionality reduction","volume":"184","author":"Wang","year":"2016","journal-title":"Neurocomputing"},{"issue":"7","key":"10.1016\/j.neucom.2024.128251_b37","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"10.1016\/j.neucom.2024.128251_b38","series-title":"Machine Learning, Big Data, and IoT for Medical Informatics","first-page":"389","article-title":"Chapter 22 - a review of deep learning models for medical diagnosis","author":"Kunapuli","year":"2021"},{"issue":"6","key":"10.1016\/j.neucom.2024.128251_b39","doi-asserted-by":"crossref","first-page":"2412","DOI":"10.1021\/ci060149f","article-title":"Melting point prediction employing k-nearest neighbor algorithms and genetic parameter optimization","volume":"46","author":"Nigsch","year":"2006","journal-title":"J. Chem. Inf. Model."},{"issue":"8","key":"10.1016\/j.neucom.2024.128251_b40","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1016\/j.ins.2008.12.010","article-title":"A comparative study of ranking methods, similarity measures and uncertainty measures for interval type-2 fuzzy sets","volume":"179","author":"Wu","year":"2009","journal-title":"Inform. Sci."},{"key":"10.1016\/j.neucom.2024.128251_b41","doi-asserted-by":"crossref","DOI":"10.1016\/j.aiopen.2021.08.002","article-title":"Pre-trained models: Past, present and future","author":"Han","year":"2021","journal-title":"AI Open"},{"year":"2014","series-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","key":"10.1016\/j.neucom.2024.128251_b42"},{"year":"2017","series-title":"Learning transferable architectures for scalable image recognition","author":"Zoph","key":"10.1016\/j.neucom.2024.128251_b43"},{"year":"2015","series-title":"Rethinking the inception architecture for computer vision","author":"Szegedy","key":"10.1016\/j.neucom.2024.128251_b44"},{"year":"2016","series-title":"Inception-v4, inception-ResNet and the impact of residual connections on learning","author":"Szegedy","key":"10.1016\/j.neucom.2024.128251_b45"},{"year":"2016","series-title":"Xception: Deep learning with depthwise separable convolutions","author":"Chollet","key":"10.1016\/j.neucom.2024.128251_b46"},{"year":"2016","series-title":"Densely connected convolutional networks","author":"Huang","key":"10.1016\/j.neucom.2024.128251_b47"},{"year":"2017","series-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","key":"10.1016\/j.neucom.2024.128251_b48"},{"issue":"5500","key":"10.1016\/j.neucom.2024.128251_b49","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","article-title":"Nonlinear dimensionality reduction by locally linear embedding","volume":"290","author":"Roweis","year":"2000","journal-title":"Science"},{"issue":"3","key":"10.1016\/j.neucom.2024.128251_b50","doi-asserted-by":"crossref","first-page":"421","DOI":"10.5755\/j01.itc.49.3.25918","article-title":"Unsupervised text feature learning via deep variational auto-encoder","volume":"49","author":"Liu","year":"2020","journal-title":"Inf. Technol. Control"},{"issue":"9","key":"10.1016\/j.neucom.2024.128251_b51","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1109\/34.955109","article-title":"Simplicity: Semantics-sensitive integrated matching for picture libraries","volume":"23","author":"Wang","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neucom.2024.128251_b52","doi-asserted-by":"crossref","first-page":"53511","DOI":"10.1109\/ACCESS.2020.2981288","article-title":"Pseudo labels and soft multi-part corresponding similarity for unsupervised deep hashing","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"year":"2019","series-title":"Keras examples","author":"Training ResNet on CIFAR-10 using keras","key":"10.1016\/j.neucom.2024.128251_b53"},{"issue":"7","key":"10.1016\/j.neucom.2024.128251_b54","first-page":"1407","article-title":"Interpretable deep convolutional fuzzy classifier","volume":"28","author":"Yeganejou","year":"2019","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"3","key":"10.1016\/j.neucom.2024.128251_b55","first-page":"1577","article-title":"A deep learning content-based image retrieval approach using cloud computing","volume":"29","author":"Sayed","year":"2023","journal-title":"Int. Res. J. Eng. Technol. (IRJET)"},{"issue":"3","key":"10.1016\/j.neucom.2024.128251_b56","article-title":"An efficient content-based image retrieval system using kNN and fuzzy mathematical algorithm","volume":"124","author":"Wang","year":"2020","journal-title":"CMES Comput. Model. Eng. Sci."},{"key":"10.1016\/j.neucom.2024.128251_b57","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1007\/s13042-016-0597-9","article-title":"A new fusion approach for content based image retrieval with color histogram and local directional pattern","volume":"9","author":"Zhou","year":"2018","journal-title":"Int. J. Mach. Learn. Cybern."},{"issue":"3","key":"10.1016\/j.neucom.2024.128251_b58","first-page":"72","article-title":"Dual phase CBIR model using hybrid feature extraction and manhattan distance measure","volume":"14","author":"Kenchappa","year":"2021","journal-title":"Int. J. Intell. Eng. Syst."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231224010221?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231224010221?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T20:36:04Z","timestamp":1724445364000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231224010221"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10]]},"references-count":58,"alternative-id":["S0925231224010221"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2024.128251","relation":{},"ISSN":["0925-2312"],"issn-type":[{"type":"print","value":"0925-2312"}],"subject":[],"published":{"date-parts":[[2024,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A deep learning based interval type-2 fuzzy approach for image retrieval systems","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2024.128251","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"128251"}}