{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T16:10:31Z","timestamp":1784218231438,"version":"3.55.0"},"reference-count":54,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["QYPY2021NSFC0802"],"award-info":[{"award-number":["QYPY2021NSFC0802"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2023MF068"],"award-info":[{"award-number":["ZR2023MF068"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2021MH252"],"award-info":[{"award-number":["ZR2021MH252"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.asoc.2026.115554","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T08:03:37Z","timestamp":1780301017000},"page":"115554","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PB","title":["MAFNet: Dynamic multiscale anti-aliasing fusion network for end-to-end explainable seizure prediction"],"prefix":"10.1016","volume":"201","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6280-4905","authenticated-orcid":false,"given":"Jie","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9691-6075","authenticated-orcid":false,"given":"Yingchao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiwei","family":"Nie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1342-3374","authenticated-orcid":false,"given":"Qi","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.asoc.2026.115554_bib1","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125621","article-title":"Phase spectrogram of EEG from S-transform Enhances epileptic seizure detection","volume":"262","author":"Liu","year":"2025","journal-title":"Expert. Syst. Appl."},{"key":"10.1016\/j.asoc.2026.115554_bib2","article-title":"EEG based automated detection of seizure using machine learning approach and traditional features","volume":"251","author":"A","year":"2024","journal-title":"Expert. Syst. Appl."},{"key":"10.1016\/j.asoc.2026.115554_bib3","article-title":"Novel contrastive representation learning of epileptic electroencephalogram for seizure detection","volume":"20","author":"Wang","year":"2025","journal-title":"Cogn. Neurodyn"},{"key":"10.1016\/j.asoc.2026.115554_bib4","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.126286","article-title":"Uncertainty-guided fourier-based domain generalization for seizure prediction","volume":"268","author":"Deng","year":"2025","journal-title":"Expert. Syst. Appl."},{"key":"10.1016\/j.asoc.2026.115554_bib5","first-page":"e7","article-title":"Ensembling crowdsourced seizure prediction algorithms using long-term human intracranial EEG","volume":"61","author":"Reuben","year":"2019","journal-title":"Epilepsia"},{"key":"10.1016\/j.asoc.2026.115554_bib6","doi-asserted-by":"crossref","DOI":"10.1142\/S0129065725500340","article-title":"Tiny convolutional neural network with supervised contrastive learning for epileptic seizure prediction","volume":"35","author":"Zhang","year":"2025","journal-title":"Int. J. Neural Syst."},{"key":"10.1016\/j.asoc.2026.115554_bib7","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113703","article-title":"Blockchain-integrated multi-modal LSTM-CNN fusion for high-precision epileptic seizure detection from EEG signals","volume":"323","author":"Mokhiamar","year":"2025","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.asoc.2026.115554_bib8","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2025.117311","article-title":"Detection of cervical cancer with imbalanced class distribution based on Raman spectroscopy and novel resampling techniques","volume":"251","author":"Cao","year":"2025","journal-title":"Measurement"},{"key":"10.1016\/j.asoc.2026.115554_bib9","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2025.113104","article-title":"Mixed supervised cross-subject seizure detection with transformer and reference learning","volume":"175","author":"He","year":"2025","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115554_bib10","doi-asserted-by":"crossref","first-page":"8100","DOI":"10.1109\/JBHI.2025.3579229","article-title":"Noise-aware epileptic seizure prediction network via self-attention feature alignment","volume":"29","author":"Dong","year":"2025","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.asoc.2026.115554_bib11","doi-asserted-by":"crossref","first-page":"2731","DOI":"10.1007\/s11571-024-10109-w","article-title":"Research progress of epileptic seizure prediction methods based on EEG","volume":"18","author":"Wang","year":"2024","journal-title":"Cogn. Neurodyn"},{"key":"10.1016\/j.asoc.2026.115554_bib12","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1109\/TBDATA.2017.2675982","article-title":"An adaptive pattern learning framework to personalize online seizure prediction","volume":"7","author":"Xiao","year":"2021","journal-title":"IEEE Trans. Big Data"},{"key":"10.1016\/j.asoc.2026.115554_bib13","doi-asserted-by":"crossref","DOI":"10.1016\/j.sigpro.2021.108045","article-title":"A new blind source separation approach based on dynamical similarity and its application on epileptic seizure prediction","volume":"183","author":"Niknazar","year":"2021","journal-title":"Signal. Process."},{"key":"10.1016\/j.asoc.2026.115554_bib14","doi-asserted-by":"crossref","first-page":"2711","DOI":"10.1109\/TNSRE.2020.3035836","article-title":"Seizure prediction using directed transfer function and convolution neural network on intracranial EEG","volume":"28","author":"Wang","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.asoc.2026.115554_bib15","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106855","article-title":"CNN-Informer: a hybrid deep learning model for seizure detection on long-term EEG","volume":"181","author":"Li","year":"2025","journal-title":"Neural Netw."},{"key":"10.1016\/j.asoc.2026.115554_bib16","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106267","article-title":"Cosine convolutional neural network and its application for seizure detection","volume":"174","author":"Liu","year":"2024","journal-title":"Neural Netw."},{"key":"10.1016\/j.asoc.2026.115554_bib17","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106401","article-title":"EEG-based seizure prediction via hybrid vision transformer and data uncertainty learning","volume":"123","author":"Deng","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.asoc.2026.115554_bib18","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.129203","article-title":"ARNN: attentive recurrent neural network for multi-channel EEG signals to identify epileptic seizures","volume":"620","author":"Rukhsar","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.asoc.2026.115554_bib19","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107826","article-title":"Implementation of a non-linear SVM classification for seizure EEG signal analysis on FPGA","volume":"131","author":"Shanmugam","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.asoc.2026.115554_bib20","article-title":"A multi-scale information fusion approach for brain network construction in epileptic EEG analysis","volume":"661","author":"Ren","year":"2025","journal-title":"Physica A Statistical Mechanics Applications"},{"key":"10.1016\/j.asoc.2026.115554_bib21","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2023.107856","article-title":"Lightweight convolution transformer for cross-patient seizure detection in multi-channel EEG signals","volume":"242","author":"Rukhsar","year":"2023","journal-title":"Comput. Methods Prog. Biomed."},{"key":"10.1016\/j.asoc.2026.115554_bib22","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1109\/TNSRE.2023.3346955","article-title":"B2-ViT Net: broad vision transformer network with broad attention for seizure prediction","volume":"32","author":"Shi","year":"2024","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.asoc.2026.115554_bib23","doi-asserted-by":"crossref","first-page":"4266","DOI":"10.1109\/TNSRE.2023.3321414","article-title":"Dynamic multi-graph convolution-based channel-weighted transformer feature fusion network for epileptic seizure prediction","volume":"31","author":"Wang","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"10.1016\/j.asoc.2026.115554_bib24","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.107075","article-title":"EEG-based patient-specific seizure prediction based on spatial\u2013temporal hypergraph attention transformer","volume":"100","author":"Dong","year":"2025","journal-title":"Biomed. Signal. Process. Control."},{"key":"10.1016\/j.asoc.2026.115554_bib25","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2022.111948","article-title":"EEG-based seizure prediction via Transformer guided CNN","volume":"203","author":"Li","year":"2022","journal-title":"Measurement"},{"key":"10.1016\/j.asoc.2026.115554_bib26","doi-asserted-by":"crossref","first-page":"21123","DOI":"10.1109\/JSEN.2024.3401771","article-title":"Hybrid LSTM\u2013transformer model for the prediction of epileptic seizure using scalp EEG","volume":"24","author":"Xia","year":"2024","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.asoc.2026.115554_bib27","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108565","article-title":"Parallel dual-branch fusion network for epileptic seizure prediction","volume":"176","author":"Ma","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.asoc.2026.115554_bib28","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.bspc.2015.05.007","article-title":"Noise robustness analysis of sparse representation based classification method for non-stationary EEG signal classification","volume":"21","author":"Shin","year":"2015","journal-title":"Biomed. Signal. Process. Control."},{"key":"10.1016\/j.asoc.2026.115554_bib29","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.112178","article-title":"Optimal segmentation of non-linear and non-stationary time series based on fractal dimension and Poincare section and its application in solving EEG-signal","volume":"166","author":"Mahdi","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115554_bib30","unstructured":"T. Kim, J. Kim, Y. Tae, C. Park, J.-H. Choi, J. ChooReversible instance normalization for accurate time-series forecasting against distribution shift Int. Conf. Learn. Represent.2021."},{"key":"10.1016\/j.asoc.2026.115554_bib31","doi-asserted-by":"crossref","unstructured":"X. Ma, X. Li, L. Fang, T. Zhao, C. Zhang, U-mixer: An unet-mixer architecture with stationarity correction for time series forecasting, in: Proceedings of the AAAI conference on artificial intelligence, 2024, pp. 14255-14262.","DOI":"10.1609\/aaai.v38i13.29337"},{"key":"10.1016\/j.asoc.2026.115554_bib32","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106447","article-title":"Deep learning based automatic seizure prediction with EEG time-frequency representation","volume":"95","author":"Dong","year":"2024","journal-title":"Biomed. Signal. Process. Control."},{"key":"10.1016\/j.asoc.2026.115554_bib33","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.117733","article-title":"Personalized attention-based EEG channel selection for epileptic seizure prediction","volume":"206","author":"Affes","year":"2022","journal-title":"Expert. Syst. Appl."},{"key":"10.1016\/j.asoc.2026.115554_bib34","unstructured":"T. Kim, J. Kim, Y. Tae, C. Park, J. Choi, J. Choo, Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift, in: International Conference on Learning Representations, 2022."},{"key":"10.1016\/j.asoc.2026.115554_bib35","doi-asserted-by":"crossref","DOI":"10.1142\/S0129065725500807","article-title":"Lightweight seizure prediction model based on kernel-enhanced global temporal attention","volume":"36","author":"Zhai","year":"2026","journal-title":"Int. J. Neural Syst."},{"key":"10.1016\/j.asoc.2026.115554_bib36","doi-asserted-by":"crossref","DOI":"10.1142\/S0129065726500103","article-title":"Explainable end-to-end seizure prediction via dynamic multiscale cross-band fusion filter network","volume":"36","author":"Wang","year":"2026","journal-title":"Int. J. Neural Syst."},{"key":"10.1016\/j.asoc.2026.115554_bib37","doi-asserted-by":"crossref","first-page":"4158","DOI":"10.3390\/app12094158","article-title":"Seizure prediction based on transformer using scalp electroencephalogram","volume":"12","author":"Yan","year":"2022","journal-title":"Appl. Sci."},{"key":"10.1016\/j.asoc.2026.115554_bib38","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.109028","article-title":"Continual learning for seizure prediction via memory projection strategy","volume":"181","author":"Shi","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.asoc.2026.115554_bib39","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1109\/JBHI.2021.3100297","article-title":"Geometric deep learning for subject independent epileptic seizure prediction using scalp EEG signals","volume":"26","author":"Dissanayake","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.asoc.2026.115554_bib40","doi-asserted-by":"crossref","unstructured":"A. Burrello, L. Cavigelli, K. Schindler, L. Benini, A. Rahimi, Laelaps: An Energy-Efficient Seizure Detection Algorithm from Long-term Human iEEG Recordings without False Alarms, in: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2019, pp. 752-757.","DOI":"10.23919\/DATE.2019.8715186"},{"key":"10.1016\/j.asoc.2026.115554_bib41","doi-asserted-by":"crossref","first-page":"6557","DOI":"10.1109\/JBHI.2024.3438829","article-title":"Combination of channel reordering strategy and dual CNN-LSTM for epileptic seizure prediction using three iEEG datasets","volume":"28","author":"Wang","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.asoc.2026.115554_bib42","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2022.112278","article-title":"Spatio-temporal MLP network for seizure prediction using EEG signals","volume":"206","author":"Li","year":"2023","journal-title":"Measurement"},{"key":"10.1016\/j.asoc.2026.115554_bib43","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.108665","article-title":"End-to-end model for automatic seizure detection using supervised contrastive learning","volume":"133","author":"Li","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.asoc.2026.115554_bib44","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.104519","article-title":"CNN-based classification of epileptic states for seizure prediction using combined temporal and spectral features","volume":"82","author":"Assali","year":"2023","journal-title":"Biomed. Signal. Process. Control."},{"key":"10.1016\/j.asoc.2026.115554_bib45","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.105276","article-title":"Cluster-based phase space density feature in multichannel scalp EEG for seizure prediction by deep learning","volume":"86","author":"Feizbakhsh","year":"2023","journal-title":"Biomed. Signal. Process. Control."},{"key":"10.1016\/j.asoc.2026.115554_bib46","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108510","article-title":"EEG power spectra parameterization and adaptive channel selection towards semi-supervised seizure prediction","volume":"175","author":"Li","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.asoc.2026.115554_bib47","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1109\/LSP.2024.3400037","article-title":"Dynamic functional connectivity neural network for epileptic seizure prediction using multi-channel EEG signal","volume":"31","author":"Xu","year":"2024","journal-title":"IEEE Signal. Process. Lett."},{"key":"10.1016\/j.asoc.2026.115554_bib48","first-page":"1","article-title":"Dual-cross tri-level routing transformer based metric learning network for epileptic seizure prediction using a single-channel iEEG","author":"Wang","year":"2025","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.asoc.2026.115554_bib49","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110757","article-title":"Long short-term memory and kolmogorov arnold network theorem for epileptic seizure prediction","volume":"154","author":"Hasan","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.asoc.2026.115554_bib50","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.108628","article-title":"A multi-feature fusion model with temporal convolution and vision transformer for epileptic seizure prediction","volume":"112","author":"Li","year":"2026","journal-title":"Biomed. Signal. Process. Control."},{"key":"10.1016\/j.asoc.2026.115554_bib51","doi-asserted-by":"crossref","first-page":"8059","DOI":"10.1109\/JBHI.2025.3584861","article-title":"An EEG-based seizure prediction model encoding brain network temporal dynamics","volume":"29","author":"Liao","year":"2025","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.asoc.2026.115554_bib52","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.109370","article-title":"Epileptic seizure prediction based on a residual capsule network with multidimensional electroencephalography feature fusion","volume":"114","author":"Xi","year":"2026","journal-title":"Biomed. Signal. Process. Control."},{"key":"10.1016\/j.asoc.2026.115554_bib53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2026.3662893","article-title":"Lightweight and interpretable channel selection for EEG measurement systems in epileptic seizure prediction","volume":"75","author":"Lou","year":"2026","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.asoc.2026.115554_bib54","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103513","article-title":"TSSF-CapsNet: capsule network with temporal-spectral-spatial fusion for EEG-based seizure prediction","volume":"125","author":"Sun","year":"2026","journal-title":"Inf. Fusion."}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494626010021?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494626010021?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T15:55:41Z","timestamp":1784217341000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1568494626010021"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":54,"alternative-id":["S1568494626010021"],"URL":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115554","relation":{},"ISSN":["1568-4946"],"issn-type":[{"value":"1568-4946","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"MAFNet: Dynamic multiscale anti-aliasing fusion network for end-to-end explainable seizure prediction","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115554","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 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":"115554"}}