{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T12:03:41Z","timestamp":1781006621522,"version":"3.54.1"},"reference-count":58,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.knosys.2026.116118","type":"journal-article","created":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T06:24:55Z","timestamp":1777875895000},"page":"116118","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["Forward consistency learning with gated context aggregation for video anomaly detection"],"prefix":"10.1016","volume":"345","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6788-3942","authenticated-orcid":false,"given":"Jiahao","family":"Lyu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8062-2982","authenticated-orcid":false,"given":"Minghua","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuewen","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuangli","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyong","family":"Lv","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"7","key":"10.1016\/j.knosys.2026.116118_b1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3645101","article-title":"Generalized video anomaly event detection: Systematic taxonomy and comparison of deep models","volume":"56","author":"Liu","year":"2024","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.knosys.2026.116118_b2","doi-asserted-by":"crossref","unstructured":"Y. Xu, L. Zhu, Y. Yang, Mc-bench: A benchmark for multi-context visual grounding in the era of mllms, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2025, pp. 17675\u201317687.","DOI":"10.1109\/ICCV51701.2025.01642"},{"key":"10.1016\/j.knosys.2026.116118_b3","doi-asserted-by":"crossref","unstructured":"D. Gong, L. Liu, V. Le, B. Saha, M.R. Mansour, S. Venkatesh, A.v.d. Hengel, Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2019, pp. 1705\u20131714.","DOI":"10.1109\/ICCV.2019.00179"},{"key":"10.1016\/j.knosys.2026.116118_b4","doi-asserted-by":"crossref","unstructured":"H. Park, J. Noh, B. Ham, Learning memory-guided normality for anomaly detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 14372\u201314381.","DOI":"10.1109\/CVPR42600.2020.01438"},{"key":"10.1016\/j.knosys.2026.116118_b5","doi-asserted-by":"crossref","unstructured":"S. Sun, X. Gong, Hierarchical semantic contrast for scene-aware video anomaly detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 22846\u201322856.","DOI":"10.1109\/CVPR52729.2023.02188"},{"key":"10.1016\/j.knosys.2026.116118_b6","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.neucom.2023.02.027","article-title":"Video anomaly detection based on spatio-temporal relationships among objects","volume":"532","author":"Wang","year":"2023","journal-title":"Neurocomputing"},{"key":"10.1016\/j.knosys.2026.116118_b7","doi-asserted-by":"crossref","unstructured":"K. Doshi, Y. Yilmaz, Towards interpretable video anomaly detection, in: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2023, pp. 2655\u20132664.","DOI":"10.1109\/WACV56688.2023.00268"},{"key":"10.1016\/j.knosys.2026.116118_b8","doi-asserted-by":"crossref","unstructured":"W. Liu, W. Luo, D. Lian, S. Gao, Future frame prediction for anomaly detection\u2013a new baseline, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 6536\u20136545.","DOI":"10.1109\/CVPR.2018.00684"},{"key":"10.1016\/j.knosys.2026.116118_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.112010","article-title":"Bidirectional skip-frame prediction for video anomaly detection with intra-domain disparity-driven attention","volume":"170","author":"Lyu","year":"2026","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.knosys.2026.116118_b10","series-title":"Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XV 16","first-page":"329","article-title":"Clustering driven deep autoencoder for video anomaly detection","author":"Chang","year":"2020"},{"key":"10.1016\/j.knosys.2026.116118_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108336","article-title":"A cascade reconstruction model with generalization ability evaluation for anomaly detection in videos","volume":"122","author":"Zhong","year":"2022","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.knosys.2026.116118_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.127444","article-title":"DAST-net: Dense visual attention augmented spatio-temporal network for unsupervised video anomaly detection","author":"Kommanduri","year":"2024","journal-title":"Neurocomputing"},{"key":"10.1016\/j.knosys.2026.116118_b13","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.neucom.2023.03.008","article-title":"PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies","volume":"534","author":"Astrid","year":"2023","journal-title":"Neurocomputing"},{"key":"10.1016\/j.knosys.2026.116118_b14","series-title":"Subject information extraction for novelty detection with domain shifts","author":"Qu","year":"2025"},{"key":"10.1016\/j.knosys.2026.116118_b15","first-page":"938","article-title":"Appearance-motion memory consistency network for video anomaly detection","volume":"vol. 35","author":"Cai","year":"2021"},{"key":"10.1016\/j.knosys.2026.116118_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108232","article-title":"Spatiotemporal consistency-enhanced network for video anomaly detection","volume":"121","author":"Hao","year":"2022","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.knosys.2026.116118_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125581","article-title":"Rethinking prediction-based video anomaly detection from local\u2013global normality perspective","volume":"262","author":"Zhao","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2026.116118_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110877","article-title":"Fast video anomaly detection via context-aware shortcut exploration and abnormal feature distance learning","volume":"157","author":"Park","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.knosys.2026.116118_b19","article-title":"Moba: Motion memory-augmented deblurring AutoEncoder for video anomaly detection","author":"Lyu","year":"2025","journal-title":"Knowl.-Based Syst.","ISSN":"https:\/\/id.crossref.org\/issn\/0950-7051","issn-type":"print"},{"key":"10.1016\/j.knosys.2026.116118_b20","series-title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","first-page":"2063","article-title":"Drafting and revision: advancing high-fidelity video inpainting","author":"Wu","year":"2025"},{"key":"10.1016\/j.knosys.2026.116118_b21","series-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.knosys.2026.116118_b22","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"11","key":"10.1016\/j.knosys.2026.116118_b23","doi-asserted-by":"crossref","first-page":"9389","DOI":"10.1109\/TNNLS.2022.3159538","article-title":"Self-supervised attentive generative adversarial networks for video anomaly detection","volume":"34","author":"Huang","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.knosys.2026.116118_b24","doi-asserted-by":"crossref","unstructured":"Z. Yang, J. Liu, Z. Wu, P. Wu, X. Liu, Video Event Restoration Based on Keyframes for Video Anomaly Detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 14592\u201314601.","DOI":"10.1109\/CVPR52729.2023.01402"},{"key":"10.1016\/j.knosys.2026.116118_b25","series-title":"2022 IEEE International Conference on Multimedia and Expo","first-page":"1","article-title":"Object-guided and motion-refined attention network for video anomaly detection","author":"Zhou","year":"2022"},{"issue":"3","key":"10.1016\/j.knosys.2026.116118_b26","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1109\/TNNLS.2020.3039899","article-title":"Anomaly detection with bidirectional consistency in videos","volume":"33","author":"Fang","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.knosys.2026.116118_b27","doi-asserted-by":"crossref","first-page":"4106","DOI":"10.1109\/TMM.2020.3037538","article-title":"Multi-encoder towards effective anomaly detection in videos","volume":"23","author":"Fang","year":"2021","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.knosys.2026.116118_b28","doi-asserted-by":"crossref","unstructured":"R. Morais, V. Le, T. Tran, B. Saha, M. Mansour, S. Venkatesh, Learning regularity in skeleton trajectories for anomaly detection in videos, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 11996\u201312004.","DOI":"10.1109\/CVPR.2019.01227"},{"key":"10.1016\/j.knosys.2026.116118_b29","series-title":"2022 IEEE International Conference on Data Mining","first-page":"1215","article-title":"Making reconstruction-based method great again for video anomaly detection","author":"Wang","year":"2022"},{"key":"10.1016\/j.knosys.2026.116118_b30","doi-asserted-by":"crossref","unstructured":"T.-N. Nguyen, J. Meunier, Anomaly detection in video sequence with appearance-motion correspondence, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2019, pp. 1273\u20131283.","DOI":"10.1109\/ICCV.2019.00136"},{"key":"10.1016\/j.knosys.2026.116118_b31","series-title":"2017 IEEE International Conference on Image Processing","first-page":"1577","article-title":"Abnormal event detection in videos using generative adversarial nets","author":"Ravanbakhsh","year":"2017"},{"key":"10.1016\/j.knosys.2026.116118_b32","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.107969","article-title":"NM-GAN: Noise-modulated generative adversarial network for video anomaly detection","volume":"116","author":"Chen","year":"2021","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.knosys.2026.116118_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.cviu.2022.103416","article-title":"Detecting abnormality with separated foreground and background: Mutual generative adversarial networks for video abnormal event detection","volume":"219","author":"Zhang","year":"2022","journal-title":"Comput. Vis. Image Underst."},{"key":"10.1016\/j.knosys.2026.116118_b34","doi-asserted-by":"crossref","unstructured":"Z. Wu, K. Chen, K. Li, H. Fan, Y. Yang, BVINet: Unlocking blind video inpainting with zero annotations, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2025, pp. 14017\u201314027.","DOI":"10.1109\/ICCV51701.2025.01301"},{"key":"10.1016\/j.knosys.2026.116118_b35","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1016\/j.neucom.2021.05.112","article-title":"Improving video anomaly detection performance by mining useful data from unseen video frames","volume":"462","author":"Wu","year":"2021","journal-title":"Neurocomputing"},{"issue":"11","key":"10.1016\/j.knosys.2026.116118_b36","doi-asserted-by":"crossref","first-page":"7505","DOI":"10.1109\/TPAMI.2021.3129349","article-title":"Future frame prediction network for video anomaly detection","volume":"44","author":"Luo","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.knosys.2026.116118_b37","series-title":"ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"1","article-title":"A video anomaly detection framework based on appearance-motion semantics representation consistency","author":"Huang","year":"2023"},{"issue":"12","key":"10.1016\/j.knosys.2026.116118_b38","doi-asserted-by":"crossref","first-page":"8285","DOI":"10.1109\/TCSVT.2022.3190539","article-title":"Bidirectional spatio-temporal feature learning with multiscale evaluation for video anomaly detection","volume":"32","author":"Zhong","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.knosys.2026.116118_b39","doi-asserted-by":"crossref","first-page":"2395","DOI":"10.1109\/TIP.2019.2948286","article-title":"BMAN: Bidirectional multi-scale aggregation networks for abnormal event detection","volume":"29","author":"Lee","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.knosys.2026.116118_b40","doi-asserted-by":"crossref","DOI":"10.1016\/j.cviu.2024.104074","article-title":"Lightning fast video anomaly detection via multi-scale adversarial distillation","volume":"247","author":"Croitoru","year":"2024","journal-title":"Comput. Vis. Image Underst."},{"key":"10.1016\/j.knosys.2026.116118_b41","doi-asserted-by":"crossref","unstructured":"Z. Deng, D. Chen, S. Deng, Prior Knowledge Guided Network for Video Anomaly Detection, in: Proceedings of the 5th ACM International Conference on Multimedia in Asia, 2023, pp. 1\u20137.","DOI":"10.1145\/3595916.3626421"},{"key":"10.1016\/j.knosys.2026.116118_b42","doi-asserted-by":"crossref","unstructured":"J. Lyu, M. Zhao, J. Hu, X. Huang, Y. Chen, S. Du, VADMamba: Exploring State Space Models for Fast Video Anomaly Detection, in: 2025 IEEE International Conference on Multimedia and Expo, ICME, 2025, pp. 1\u20136.","DOI":"10.1109\/ICME59968.2025.11209020"},{"key":"10.1016\/j.knosys.2026.116118_b43","series-title":"Gated attention for large language models: Non-linearity, sparsity, and attention-sink-free","author":"Qiu","year":"2025"},{"key":"10.1016\/j.knosys.2026.116118_b44","doi-asserted-by":"crossref","unstructured":"Z. Wu, C. Sun, H. Xuan, Y. Yan, Deep stereo video inpainting, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 5693\u20135702.","DOI":"10.1109\/CVPR52729.2023.00551"},{"issue":"8","key":"10.1016\/j.knosys.2026.116118_b45","doi-asserted-by":"crossref","first-page":"10055","DOI":"10.1109\/TPAMI.2023.3262578","article-title":"Local-global context aware transformer for language-guided video segmentation","volume":"45","author":"Liang","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"10.1016\/j.knosys.2026.116118_b46","doi-asserted-by":"crossref","first-page":"3631","DOI":"10.1109\/TNNLS.2024.3355166","article-title":"GT-HAD: Gated transformer for hyperspectral anomaly detection","volume":"36","author":"Lian","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.knosys.2026.116118_b47","first-page":"1","article-title":"Dual-branch learning with prior information for surface anomaly detection","volume":"72","author":"Wang","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"3","key":"10.1016\/j.knosys.2026.116118_b48","doi-asserted-by":"crossref","first-page":"3240","DOI":"10.1007\/s10489-022-03613-1","article-title":"Attention-based residual autoencoder for video anomaly detection","volume":"53","author":"Le","year":"2023","journal-title":"Appl. Intell."},{"key":"10.1016\/j.knosys.2026.116118_b49","series-title":"2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","first-page":"1975","article-title":"Anomaly detection in crowded scenes","author":"Mahadevan","year":"2010"},{"key":"10.1016\/j.knosys.2026.116118_b50","doi-asserted-by":"crossref","unstructured":"C. Lu, J. Shi, J. Jia, Abnormal event detection at 150 fps in matlab, in: Proceedings of the IEEE International Conference on Computer Vision, 2013, pp. 2720\u20132727.","DOI":"10.1109\/ICCV.2013.338"},{"key":"10.1016\/j.knosys.2026.116118_b51","doi-asserted-by":"crossref","unstructured":"W. Luo, W. Liu, S. Gao, A revisit of sparse coding based anomaly detection in stacked rnn framework, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 341\u2013349.","DOI":"10.1109\/ICCV.2017.45"},{"key":"10.1016\/j.knosys.2026.116118_b52","series-title":"European Conference on Computer Vision","first-page":"404","article-title":"Dynamic local aggregation network with adaptive clusterer for anomaly detection","author":"Yang","year":"2022"},{"key":"10.1016\/j.knosys.2026.116118_b53","article-title":"Hybrid attention and motion constraint for anomaly detection in crowded scenes","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.knosys.2026.116118_b54","series-title":"ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"1","article-title":"Spatial-temporal graph convolutional network boosted flow-frame prediction for video anomaly detection","author":"Cheng","year":"2023"},{"key":"10.1016\/j.knosys.2026.116118_b55","series-title":"ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"1","article-title":"Memorizing normality to detect anomaly: Memory-augmented deep autoencoder video anomaly detection","author":"Liu","year":"2023"},{"key":"10.1016\/j.knosys.2026.116118_b56","unstructured":"C. Huang, J. Wen, C. Liu, Y. Liu, Long short-term dynamic prototype alignment learning for video anomaly detection, in: Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024, pp. 866\u2013874."},{"key":"10.1016\/j.knosys.2026.116118_b57","article-title":"Multi-branch GAN-based abnormal events detection via context learning in surveillance videos","author":"Li","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.knosys.2026.116118_b58","series-title":"A2seek: Towards reasoning-centric benchmark for aerial anomaly understanding","author":"Mo","year":"2025"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126008440?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126008440?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T11:31:13Z","timestamp":1781004673000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126008440"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":58,"alternative-id":["S0950705126008440"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116118","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Forward consistency learning with gated context aggregation for video anomaly detection","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116118","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"116118"}}