{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T20:24:13Z","timestamp":1773001453911,"version":"3.50.1"},"reference-count":79,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,12,28]],"date-time":"2025-12-28T00:00:00Z","timestamp":1766880000000},"content-version":"vor","delay-in-days":361,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100015956","name":"Special Project for Research and Development in Key areas of Guangdong Province","doi-asserted-by":"publisher","award":["2019 B010137001"],"award-info":[{"award-number":["2019 B010137001"]}],"id":[{"id":"10.13039\/501100015956","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>Deep learning is widely used in many fields, but the emergence of adversarial examples threatens the application of deep learning. Various methods have been proposed to defend against adversarial attacks. However, existing defense methods either can only detect adversarial examples without restoring their original classes or merely focus on verifying the input category and attempting to recover the classes of adversarial examples while lacking awareness of whether the input has been perturbed. To develop defense approaches that simultaneously achieve both detection and correction capabilities, a heterogeneous model combinatorial defense framework (HMCDF) is proposed for adversarial attacks in this paper. In particular, we first summarize the fundamental operations, block structures, and compositional patterns that constitute the model, while analyzing how these factors influence both the functionality and robustness of the model. According to the differences in the structure of the models, the models can be divided into isomorphic models and heterogeneous models. Then, we combine heterogeneous models to construct a heterogeneous model defense framework. Within this framework, as long as a majority of models can detect adversarial examples and restore their original labels, the voting mechanism used in the framework can determine whether the input has been perturbed, ultimately outputting legitimate labels through collective decision\u2010making. To validate the performance, we conduct extensive experiments on three public datasets: CIFAR\u201010, SVHN, and Mini\u2010ImageNet. After sufficient analysis of the simulation results, we find that our proposed method outperforms the others for the detection of adversarial attacks generated by the considered attack methods and can recover the classes of the adversarial examples.<\/jats:p>","DOI":"10.1155\/int\/7868904","type":"journal-article","created":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T06:44:56Z","timestamp":1766990696000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Heterogeneous Model Combinatorial Defense Framework (HMCDF) for Adversarial Attacks"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5631-6413","authenticated-orcid":false,"given":"Yiqin","family":"Lu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2708-3465","authenticated-orcid":false,"given":"Xiong","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhe","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongshu","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3763-458X","authenticated-orcid":false,"given":"Jiancheng","family":"Qin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,12,28]]},"reference":[{"key":"e_1_2_12_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/msp.2012.2205597"},{"key":"e_1_2_12_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.104943"},{"key":"e_1_2_12_3_2","doi-asserted-by":"crossref","unstructured":"HeK. ZhangX. RenS. andSunJ. Deep Residual Learning for Image Recognition Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 Las Vegas NV IEEE 770\u2013778 https:\/\/doi.org\/10.1109\/CVPR.2016.90 2-s2.0-84986274465.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_12_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"e_1_2_12_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105590"},{"key":"e_1_2_12_6_2","doi-asserted-by":"crossref","unstructured":"AndorD. AlbertiC. WeissD.et al. Globally Normalized Transition-Based Neural Networks Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) 2016 Berlin https:\/\/doi.org\/10.48550\/arXiv.1603.06042.","DOI":"10.18653\/v1\/P16-1231"},{"key":"e_1_2_12_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105210"},{"key":"e_1_2_12_8_2","doi-asserted-by":"publisher","DOI":"10.1155\/2024\/7777050"},{"key":"e_1_2_12_9_2","doi-asserted-by":"publisher","DOI":"10.1002\/aisy.202300151"},{"key":"e_1_2_12_10_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnbot.2023.1243174"},{"key":"e_1_2_12_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111925"},{"key":"e_1_2_12_12_2","unstructured":"SzegedyC. ZarembaW. SutskeverI.et al. Intriguing Properties of Neural Networks 2014 https:\/\/arxiv.org\/abs\/1312.6199."},{"key":"e_1_2_12_13_2","unstructured":"GoodfellowI. J. ShlensJ. andSzegedyC. Explaining and Harnessing Adversarial Examples 2015 https:\/\/arxiv.org\/abs\/1412.6572."},{"key":"e_1_2_12_14_2","unstructured":"TsiprasD. SanturkarS. EngstromL. TurnerA. andMadryA. Robustness May Be at Odds With Accuracy 2018 https:\/\/openreview.net\/forum?id=SyxAb30cY7%26fbclid=IwAR03sHWWIKSVJ-Vdwj914ndWzMc4d-LlGVTif7QYhwP_i4kh9dbjjv2L5k0."},{"key":"e_1_2_12_15_2","unstructured":"MetzenJ. H. GeneweinT. FischerV. andBischoffB. On Detecting Adversarial Perturbations 2017 https:\/\/openreview.net\/forum?id=SJzCSf9xg%26amp."},{"key":"e_1_2_12_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-021-10125-w"},{"key":"e_1_2_12_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.12.024"},{"key":"e_1_2_12_18_2","volume-title":"Trustworthy Cyberspace: Strategic Plan for the Federal Cyber Security Research and Development Program","author":"White House","year":"2011"},{"key":"e_1_2_12_19_2","doi-asserted-by":"crossref","unstructured":"GuoM. YangY. XuR. LiuZ. andLinD. When NAS Meets Robustness: in Search of Robust Architectures Against Adversarial Attacks Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2020 Seattle WA.","DOI":"10.1109\/CVPR42600.2020.00071"},{"key":"e_1_2_12_20_2","doi-asserted-by":"publisher","DOI":"10.1201\/9781351251389-8"},{"key":"e_1_2_12_21_2","unstructured":"MadryA. MakelovA. SchmidtL. TsiprasD. andVladuA. Towards Deep Learning Models Resistant to Adversarial Attacks 2019 https:\/\/arxiv.org\/abs\/1706.06083."},{"key":"e_1_2_12_22_2","unstructured":"AthalyeA. CarliniN. andWagnerD. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Proceedings of the 2018 International Conference on Machine Learning 2018 Stockholm."},{"key":"e_1_2_12_23_2","doi-asserted-by":"crossref","unstructured":"CarliniN.andWagnerD. Towards Evaluating the Robustness of Neural Networks Proceedings of the 2017 IEEE Symposium on Security and Privacy (SP) 2017 San Jose CA IEEE.","DOI":"10.1109\/SP.2017.49"},{"key":"e_1_2_12_24_2","unstructured":"CroceF.andHeinM. Reliable Evaluation of Adversarial Robustness With an Ensemble of Diverse Parameter-Free Attacks Proceedings of the 37th International Conference on Machine Learning 2020 Vienna."},{"key":"e_1_2_12_25_2","doi-asserted-by":"crossref","unstructured":"ChenP.-Y. ZhangH. SharmaY. YiJ. andHsiehC.-J. ZOO: Zeroth Order Optimization Based Black-Box Attacks to Deep Neural Networks Without Training Substitute Models Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security 2017 Dallas TX ACM 15\u201326 https:\/\/doi.org\/10.1145\/3128572.3140448 2-s2.0-85037345899.","DOI":"10.1145\/3128572.3140448"},{"key":"e_1_2_12_26_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58592-1_29"},{"key":"e_1_2_12_27_2","unstructured":"GuoC. GardnerJ. YouY. WilsonA. G. andWeinbergerK. Simple Black-Box Adversarial Attacks Proceedings of the 2019 International Conference on Machine Learning 2019 Long Beach CA."},{"key":"e_1_2_12_28_2","article-title":"Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-Box Attacks","volume":"32","author":"Guo Y.","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_12_29_2","unstructured":"LiY. LiL. WangL. ZhangT. andGongB. NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks Proceedings of the 2019 International Conference on Machine Learning 2019 Long Beach CA."},{"key":"e_1_2_12_30_2","unstructured":"IlyasA. EngstromL. AthalyeA. andLinJ. Black-Box Adversarial Attacks With Limited Queries and Information Proceedings of the 2018 International Conference on Machine Learning 2018 Stockholm."},{"key":"e_1_2_12_31_2","unstructured":"ShafahiA. NajibiM. GhiasiA.et al. Adversarial Training for Free! 2019 https:\/\/arxiv.org\/abs\/1904.12843."},{"key":"e_1_2_12_32_2","unstructured":"SongC. HeK. WangL. andHopcroftJ. E. Improving the Generalization of Adversarial Training With Domain Adaptation 2018 https:\/\/openreview.net\/forum?id=SyfIfnC5Ym."},{"key":"e_1_2_12_33_2","unstructured":"Tram\u00e8rF. KurakinA. PapernotN. GoodfellowI. BonehD. andMcDanielP. Ensemble Adversarial Training: Attacks and Defenses 2020 https:\/\/arxiv.org\/abs\/1705.07204."},{"key":"e_1_2_12_34_2","unstructured":"WangY. ZouD. YiJ. BaileyJ. MaX. andGuQ. Improving Adversarial Robustness Requires Revisiting Misclassified Examples Proceedings of the 2019 International Conference on Learning Representations 2019 New Orleans LA."},{"key":"e_1_2_12_35_2","unstructured":"LaidlawC. SinglaS. andFeiziS. Perceptual Adversarial Robustness: Defense Against Unseen Threat Models 2021 https:\/\/arxiv.org\/abs\/2006.12655."},{"key":"e_1_2_12_36_2","doi-asserted-by":"crossref","unstructured":"VivekB. S.andVenkatesh BabuR. Single-Step Adversarial Training With Dropout Scheduling Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020 Seattle WA https:\/\/doi.org\/10.1109\/CVPR42600.2020.00103.","DOI":"10.1109\/CVPR42600.2020.00103"},{"key":"e_1_2_12_37_2","unstructured":"SongC. HeK. LinJ. WangL. andHopcroftJ. E. Robust Local Features for Improving the Generalization of Adversarial Training 2020 https:\/\/arxiv.org\/abs\/1909.10147."},{"key":"e_1_2_12_38_2","doi-asserted-by":"crossref","unstructured":"ZhengH. ZhangZ. GuJ. LeeH. andPrakashA. Efficient Adversarial Training With Transferable Adversarial Examples Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2020 Seattle WA.","DOI":"10.1109\/CVPR42600.2020.00126"},{"key":"e_1_2_12_39_2","first-page":"4218","article-title":"Improving Robustness Using Generated Data","volume":"34","author":"Gowal S.","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_12_40_2","first-page":"8270","article-title":"Adversarial Distributional Training for Robust Deep Learning","volume":"33","author":"Dong Y.","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_12_41_2","doi-asserted-by":"crossref","unstructured":"JiaX. ZhangY. WuB. MaK. WangJ. andCaoX. LAS-AT: Adversarial Training With Learnable Attack Strategy Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2022 New Orleans LA.","DOI":"10.1109\/CVPR52688.2022.01304"},{"key":"e_1_2_12_42_2","unstructured":"MaX. LiB. WangY.et al. Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality 2018 https:\/\/arxiv.org\/abs\/1801.02613."},{"key":"e_1_2_12_43_2","unstructured":"LeeK. LeeK. LeeH. andShinJ. A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks 2018 https:\/\/arxiv.org\/abs\/1807.03888."},{"key":"e_1_2_12_44_2","doi-asserted-by":"crossref","unstructured":"ChenK. ChenY. ZhouH.et al. Adversarial Examples Detection Beyond Image Space Proceedings of the ICASSP 2021-2021 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP) 2021 Toronto 3850\u20133854.","DOI":"10.1109\/ICASSP39728.2021.9414008"},{"key":"e_1_2_12_45_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110388"},{"key":"e_1_2_12_46_2","unstructured":"SamangoueiP. KabkabM. andChellappaR. Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models 2018 https:\/\/arxiv.org\/abs\/1805.06605."},{"key":"e_1_2_12_47_2","doi-asserted-by":"crossref","unstructured":"LiuZ. LiuQ. LiuT.et al. Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2019 Long Beach CA IEEE.","DOI":"10.1109\/CVPR.2019.00095"},{"key":"e_1_2_12_48_2","unstructured":"DasN. ShanbhogueM. ChenS.-T.et al. Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning With JPEG Compression 2017 https:\/\/arxiv.org\/abs\/1705.02900."},{"key":"e_1_2_12_49_2","doi-asserted-by":"crossref","unstructured":"RaffE. SylvesterJ. ForsythS. andMcLeanM. Barrage of Random Transforms for Adversarially Robust Defense Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2019 Long Beach CA.","DOI":"10.1109\/CVPR.2019.00669"},{"key":"e_1_2_12_50_2","doi-asserted-by":"crossref","unstructured":"GuptaP.andRahtuE. CIIDefence: Defeating Adversarial Attacks by Fusing Class-Specific Image Inpainting and Image Denoising Proceedings of the IEEE\/CVF International Conference on Computer Vision 2019 Seoul.","DOI":"10.1109\/ICCV.2019.00681"},{"key":"e_1_2_12_51_2","doi-asserted-by":"crossref","unstructured":"AkhtarN. LiuJ. andMianA. Defense Against Universal Adversarial Perturbations Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2018 Salt Lake City UT.","DOI":"10.1109\/CVPR.2018.00357"},{"key":"e_1_2_12_52_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106176"},{"key":"e_1_2_12_53_2","unstructured":"NieW. GuoB. HuangY. XiaoC. VahdatA. andAnandkumarA. Diffusion Models for Adversarial Purification 2022 https:\/\/arxiv.org\/abs\/2205.07460."},{"key":"e_1_2_12_54_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32430-8_28"},{"key":"e_1_2_12_55_2","doi-asserted-by":"crossref","unstructured":"RoyA. ChhabraA. KamhouaC. A. andMohapatraP. A Moving Target Defense Against Adversarial Machine Learning Proceedings of the 4th ACM\/IEEE Symposium on Edge Computing 2019 Arlington VA ACM 383\u2013388 https:\/\/doi.org\/10.1145\/3318216.3363338.","DOI":"10.1145\/3318216.3363338"},{"key":"e_1_2_12_56_2","doi-asserted-by":"crossref","unstructured":"SongQ. YanZ. andTanR. Moving Target Defense for Embedded Deep Visual Sensing Against Adversarial Examples Proceedings of the 17th Conference on Embedded Networked Sensor Systems 2019 New York NY ACM 124\u2013137 https:\/\/doi.org\/10.1145\/3356250.3360025.","DOI":"10.1145\/3356250.3360025"},{"key":"e_1_2_12_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/3469032"},{"key":"e_1_2_12_58_2","doi-asserted-by":"publisher","DOI":"10.1145\/3517806"},{"key":"e_1_2_12_59_2","doi-asserted-by":"crossref","unstructured":"LiY. ZhouQ. LiS. andLiB. wAdvMTD: A Mitigation to White-Box Adversarial Examples Using Heterogeneous Models and Moving Target Defense Proceedings of the 2023 3rd Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS) 2023 Shenyang IEEE.","DOI":"10.1109\/ACCTCS58815.2023.00115"},{"key":"e_1_2_12_60_2","first-page":"5545","article-title":"Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks","author":"Huang H.","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_12_61_2","doi-asserted-by":"crossref","unstructured":"SuD. ZhangH. ChenH. YiJ. ChenP.-Y. andGaoY. Is Robustness the Cost of Accuracy? A Comprehensive Study on the Robustness of 18 Deep Image Classification Models Proceedings of the 2018 European Conference on Computer Vision (ECCV) 2018 Munich.","DOI":"10.1007\/978-3-030-01258-8_39"},{"key":"e_1_2_12_62_2","doi-asserted-by":"crossref","unstructured":"XieC. WuY. MaatenL. YuilleA. L. andHeK. Feature Denoising for Improving Adversarial Robustness Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2019 Long Beach CA https:\/\/doi.org\/10.1109\/cvpr.2019.00059.","DOI":"10.1109\/CVPR.2019.00059"},{"key":"e_1_2_12_63_2","unstructured":"XiaoC. ZhongP. andZhengC. Enhancing Adversarial Defense by K-Winners-Take-All Proceedings of the 2020 International Conference on Learning Representations 2020 Addis Ababa."},{"key":"e_1_2_12_64_2","first-page":"1633","article-title":"On Adaptive Attacks to Adversarial Example Defenses","volume":"33","author":"Tram\u00e8r F.","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_12_65_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2020.103154"},{"key":"e_1_2_12_66_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.123085"},{"key":"e_1_2_12_67_2","unstructured":"HuangG. LiuZ. MaatenL. andWeinbergerK. Q. Densely Connected Convolutional Networks 2018 https:\/\/arxiv.org\/abs\/1608.06993."},{"key":"e_1_2_12_68_2","doi-asserted-by":"crossref","unstructured":"SzegedyC. LiuW. JiaY.et al. Going Deeper With Convolutions Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015 Boston MA IEEE 1\u20139 https:\/\/doi.org\/10.1109\/CVPR.2015.7298594 2-s2.0-84937522268.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_2_12_69_2","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_2_12_70_2","unstructured":"HintonG. VinyalsO. andDeanJ. Distilling the Knowledge in a Neural Network 2015 https:\/\/arxiv.org\/abs\/1503.02531."},{"key":"e_1_2_12_71_2","article-title":"Generative Adversarial Nets","author":"Goodfellow I. J.","year":"2014","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_12_72_2","unstructured":"SimonyanK.andZissermanA. Very Deep Convolutional Networks for Large-Scale Image Recognition 2015 https:\/\/arxiv.org\/abs\/1409.1556."},{"key":"e_1_2_12_73_2","unstructured":"KrizhevskyA.andHintonG. Learning Multiple Layers of Features From Tiny Images 2009 https:\/\/www.cs.utoronto.ca\/~kriz\/learning-features-2009-TR.pdf."},{"key":"e_1_2_12_74_2","unstructured":"NetzerY. WangT. CoatesA. BissaccoA. WuB. andNgA. Y. Reading Digits in Natural Images With Unsupervised Feature Learning Proceedings of the 2011 NIPS Workshop on Deep Learning and Unsupervised Feature Learning 2011 Granada."},{"key":"e_1_2_12_75_2","article-title":"Matching Networks for One Shot Learning","volume":"29","author":"Vinyals O.","year":"2016","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_12_76_2","doi-asserted-by":"crossref","unstructured":"LiuZ. MaoH. WuC.-Y. FeichtenhoferC. DarrellT. andXieS. A ConvNet for the 2020s 2022 https:\/\/arxiv.org\/abs\/2201.03545.","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"e_1_2_12_77_2","doi-asserted-by":"crossref","unstructured":"MaN. ZhangX. ZhengH.-T. andSunJ. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design Proceedings of the 2018 European Conference on Computer Vision (ECCV) 2018 Munich.","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"e_1_2_12_78_2","unstructured":"TanM.andLeQ. V. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks 2020 https:\/\/arxiv.org\/abs\/1905.11946."},{"key":"e_1_2_12_79_2","doi-asserted-by":"crossref","unstructured":"SandlerM. HowardA. ZhuM. ZhmoginovA. andChenL.-C. Mobilenetv2: Inverted Residuals and Linear Bottlenecks Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition 2018 Salt Lake City UT https:\/\/doi.org\/10.1109\/cvpr.2018.00474 2-s2.0-85062799511.","DOI":"10.1109\/CVPR.2018.00474"}],"container-title":["International Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/int\/7868904","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1155\/int\/7868904","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/int\/7868904","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T18:00:51Z","timestamp":1772992851000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/int\/7868904"}},"subtitle":[],"editor":[{"given":"Richard","family":"Murray","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":79,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1155\/int\/7868904"],"URL":"https:\/\/doi.org\/10.1155\/int\/7868904","archive":["Portico"],"relation":{},"ISSN":["0884-8173","1098-111X"],"issn-type":[{"value":"0884-8173","type":"print"},{"value":"1098-111X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"2025-03-25","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-20","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"7868904"}}