{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T18:55:15Z","timestamp":1764960915674,"version":"3.46.0"},"reference-count":48,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12471482","12031003","12271117","12071369"],"award-info":[{"award-number":["12471482","12031003","12271117","12071369"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shaanxi Fundamental Science Research Project for Mathematics and Physics","award":["22JSZ008"],"award-info":[{"award-number":["22JSZ008"]}]},{"name":"Cross-Key Special Project of Mathematical Tianyuan Fund of National Natural Science Foundation of China","award":["12326612"],"award-info":[{"award-number":["12326612"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1109\/tnnls.2025.3596244","type":"journal-article","created":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T18:16:43Z","timestamp":1755627403000},"page":"20009-20023","source":"Crossref","is-referenced-by-count":0,"title":["Bidirectional Multiscale Efficient Dilated Convolutional Recurrent Neural Network Improved by Swarm Intelligence Optimization"],"prefix":"10.1109","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1017-3496","authenticated-orcid":false,"given":"Qinwei","family":"Fan","sequence":"first","affiliation":[{"name":"School of Mathematics and Information Science, Guangzhou University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8173-2864","authenticated-orcid":false,"given":"Shuai","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xidian University, Xi&#x2019;an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6622-534X","authenticated-orcid":false,"given":"Jacek M.","family":"Zurada","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9610-846X","authenticated-orcid":false,"given":"Tingwen","family":"Huang","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3381-8525","authenticated-orcid":false,"given":"Xiaolong","family":"Qin","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Hangzhou Normal University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9547-2585","authenticated-orcid":false,"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Medical Big Data Research Center, Northwest University, Xi&#x2019;an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3327386"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108012"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3335355"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3326140"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3329466"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3160696"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3293131"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2023.3316289"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110419"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2025.126464"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.108322"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2772336"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3335267"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3338091"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2024.107524"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2021.100863"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TTE.2023.3291501"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2025.101874"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2024.117428"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2021.3060833"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2024.111362"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2024.132583"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.icheatmasstransfer.2024.108537"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2024.122624"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107515"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3105384"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2021.3093519"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2024.3351918"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2025.134751"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1080\/21642583.2019.1708830"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.75"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2024.3469960"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.105885"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119293"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2024.101811"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2024.101836"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106752"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-022-10218-0"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.128032"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3319989"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210006"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2025.3525502"},{"key":"ref44","first-page":"1","article-title":"ITransformer: Inverted transformers are effective for time series forecasting","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Liu"},{"key":"ref45","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Wu"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3294064"},{"key":"ref47","first-page":"1","article-title":"TimesNet: Temporal 2D-variation modeling for general time series analysis","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Wu"},{"key":"ref48","first-page":"1","article-title":"MICN: Multi-scale local and global context modeling for long-term series forecasting","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Wang"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/5962385\/11272992\/11130363.pdf?arnumber=11130363","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T18:39:50Z","timestamp":1764959990000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11130363\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":48,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2025.3596244","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"type":"print","value":"2162-237X"},{"type":"electronic","value":"2162-2388"}],"subject":[],"published":{"date-parts":[[2025,12]]}}}