{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:49:20Z","timestamp":1780447760135,"version":"3.54.1"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T00:00:00Z","timestamp":1742428800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T00:00:00Z","timestamp":1742428800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s00521-025-11089-6","type":"journal-article","created":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T07:21:54Z","timestamp":1742455314000},"page":"11583-11605","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing single-lead electrocardiogram arrhythmia detection with empirical mode decomposition"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1436-2278","authenticated-orcid":false,"given":"Mohamed F.","family":"Issa","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Yousry","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gergely","family":"Tuboly","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juhasz","family":"Zoltan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mazen M.","family":"Selim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed H.","family":"AbuEl-Atta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,20]]},"reference":[{"key":"11089_CR1","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.ymeth.2021.04.021","volume":"202","author":"CY Chen","year":"2022","unstructured":"Chen CY, Lin YT, Lee SJ, Tsai WC, Huang TC, Liu YH, Cheng MC, Dai CY (2022) Automated ECG classification based on 1D deep learning network. Methods 202:127\u2013135. https:\/\/doi.org\/10.1016\/j.ymeth.2021.04.021","journal-title":"Methods"},{"key":"11089_CR2","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.jelectrocard.2021.02.011","volume":"66","author":"MP Witvliet","year":"2021","unstructured":"Witvliet MP, Karregat EPM, Himmelreich JCL, de Jong JSSG, Lucassen WAM, Harskamp RE (2021) Usefulness, pitfalls and interpretation of handheld single-lead electrocardiograms. J Electrocardiol 66:33\u201337. https:\/\/doi.org\/10.1016\/j.jelectrocard.2021.02.011","journal-title":"J Electrocardiol"},{"key":"11089_CR3","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.ijcard.2021.11.039","volume":"346","author":"CM Gibson","year":"2022","unstructured":"Gibson CM, Mehta S, Ceschim MRS, Frauenfelder A, Vieira D, Botelho R, Fernandez F, Villagran C, Niklitschek S, Matheus CI, Pinto G, Vallenilla I, Lopez C, Acosta MI, Munguia A, Fitzgerald C, Mazzini J, Pisana L, Quintero S (2022) Evolution of single-lead ECG for STEMI detection using a deep learning approach. Int J Cardiol 346:47\u201352. https:\/\/doi.org\/10.1016\/j.ijcard.2021.11.039","journal-title":"Int J Cardiol"},{"key":"11089_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.17303\/jber.2019.3.101","volume":"3","author":"R Kher","year":"2019","unstructured":"Kher R (2019) Signal processing techniques for removing noise from ECG signals. Jber 3:1\u20139. https:\/\/doi.org\/10.17303\/jber.2019.3.101","journal-title":"Jber"},{"key":"11089_CR5","first-page":"96","volume":"1","author":"AO Boudraa","year":"2005","unstructured":"Boudraa AO, Cexus J-C, Saidi Z (2005) EMD-based signal noise reduction. Int J Inf Commun Eng 1:96\u201399","journal-title":"Int J Inf Commun Eng"},{"key":"11089_CR6","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1007\/s00500-021-06555-x","volume":"26","author":"AM Alqudah","year":"2022","unstructured":"Alqudah AM, Alqudah A (2022) Deep learning for single-lead ECG beat arrhythmia-type detection using novel iris spectrogram representation. Soft Comput 26:1123\u20131139. https:\/\/doi.org\/10.1007\/s00500-021-06555-x","journal-title":"Soft Comput"},{"key":"11089_CR7","unstructured":"B. Farhang-Boroujeny, B. Farhang-Boroujeny, Adaptive Filters: Theory and Applications, (1999)."},{"key":"11089_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/info10020035","volume":"10","author":"M D\u2019Aloia","year":"2019","unstructured":"D\u2019Aloia M, Longo A, Rizzi M (2019) Noisy ECG signal analysis for automatic peak detection. Inf 10:1\u201312. https:\/\/doi.org\/10.3390\/info10020035","journal-title":"Inf"},{"key":"11089_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104218","author":"Z Shamaee","year":"2023","unstructured":"Shamaee Z, Mivehchy M (2023) Dominant noise-aided EMD (DEMD): extending empirical mode decomposition for noise reduction by incorporating dominant noise and deep classification. Biomed Signal Process Control. https:\/\/doi.org\/10.1016\/j.bspc.2022.104218","journal-title":"Biomed Signal Process Control"},{"key":"11089_CR10","first-page":"104218","volume":"80","author":"SSN Jagadisha","year":"2023","unstructured":"Jagadisha SSN, Nayak B, Moksha HS, Shraddha S (2023) Detection of heart dysrhythmia using EMD. Int Res J Mod Eng Technol Sci 80:104218","journal-title":"Int Res J Mod Eng Technol Sci"},{"key":"11089_CR11","doi-asserted-by":"publisher","DOI":"10.1186\/s12938-023-01075-1","author":"AK Singh","year":"2023","unstructured":"Singh AK, Krishnan S (2023) ECG signal feature extraction trends in methods and applications. BioMed Central. https:\/\/doi.org\/10.1186\/s12938-023-01075-1","journal-title":"BioMed Central"},{"key":"11089_CR12","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3280564","author":"PG Malghan","year":"2023","unstructured":"Malghan PG, Hota MK (2023) An improved VME technique via heap based optimization algorithm and AWIT method for PLI and MA noise elimination in ECG. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2023.3280564","journal-title":"IEEE Access"},{"key":"11089_CR13","doi-asserted-by":"publisher","DOI":"10.1007\/s00034-022-02196-z","author":"I Assali","year":"2022","unstructured":"Assali I, Nouira I, Abidi A, Bedoui MH (2022) Intelligent ECG signal filtering method based on SVM algorithm, circuits. Syst Signal Process. https:\/\/doi.org\/10.1007\/s00034-022-02196-z","journal-title":"Syst Signal Process"},{"key":"11089_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116848","volume":"198","author":"GGN Geweid","year":"2022","unstructured":"Geweid GGN, Chen JDZ (2022) Automatic classification of atrial fibrillation from short single-lead ECG recordings using a hybrid approach of dual support vector machine. Expert Syst Appl 198:116848. https:\/\/doi.org\/10.1016\/j.eswa.2022.116848","journal-title":"Expert Syst Appl"},{"key":"11089_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106521","volume":"214","author":"J Park","year":"2022","unstructured":"Park J, An J, Kim J, Jung S, Gil Y, Jang Y, Lee K, Young Oh I (2022) Study on the use of standard 12-lead ECG data for rhythm-type ECG classification problems. Comput Methods Programs Biomed 214:106521. https:\/\/doi.org\/10.1016\/j.cmpb.2021.106521","journal-title":"Comput Methods Programs Biomed"},{"key":"11089_CR16","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.jelectrocard.2022.07.069","volume":"75","author":"AS Udawat","year":"2022","unstructured":"Udawat AS, Singh P (2022) An automated detection of atrial fibrillation from single-lead ECG using HRV features and machine learning. J Electrocardiol 75:70\u201381. https:\/\/doi.org\/10.1016\/j.jelectrocard.2022.07.069","journal-title":"J Electrocardiol"},{"key":"11089_CR17","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.irbm.2019.04.003","volume":"40","author":"V Gupta","year":"2019","unstructured":"Gupta V, Mittal M (2019) A Comparison of ECG signal pre-processing using FrFT. FrWT and IPCA for Improv Anal Irbm 40:145\u2013156. https:\/\/doi.org\/10.1016\/j.irbm.2019.04.003","journal-title":"FrWT and IPCA for Improv Anal Irbm"},{"key":"11089_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103125","volume":"71","author":"A Zarei","year":"2022","unstructured":"Zarei A, Beheshti H, Asl BM (2022) Detection of sleep apnea using deep neural networks and single-lead ECG signals. Biomed Signal Process Control 71:103125. https:\/\/doi.org\/10.1016\/j.bspc.2021.103125","journal-title":"Biomed Signal Process Control"},{"key":"11089_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-021-01546-2","volume":"21","author":"C Che","year":"2021","unstructured":"Che C, Zhang P, Zhu M, Qu Y, Jin B (2021) Constrained transformer network for ECG signal processing and arrhythmia classification. BMC Med Inform Decis Mak 21:1\u201313. https:\/\/doi.org\/10.1186\/s12911-021-01546-2","journal-title":"BMC Med Inform Decis Mak"},{"key":"11089_CR20","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1007\/s40846-021-00646-8","volume":"41","author":"RV Sharan","year":"2021","unstructured":"Sharan RV, Berkovsky S, Xiong H, Coiera E (2021) End-to-end sleep apnea detection using single-lead ECG signal and 1-D residual neural networks. J Med Biol Eng 41:758\u2013766. https:\/\/doi.org\/10.1007\/s40846-021-00646-8","journal-title":"J Med Biol Eng"},{"key":"11089_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-84374-8","volume":"11","author":"K Weimann","year":"2021","unstructured":"Weimann K, Conrad TOF (2021) Transfer learning for ECG classification. Sci Rep 11:1\u201312. https:\/\/doi.org\/10.1038\/s41598-021-84374-8","journal-title":"Sci Rep"},{"key":"11089_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105884","author":"N Alamatsaz","year":"2024","unstructured":"Alamatsaz N, Tabatabaei L, Yazdchi M, Payan H, Alamatsaz N, Nasimi F (2024) A lightweight hybrid CNN-LSTM explainable model for ECG-based arrhythmia detection. Biomed Signal Process Control. https:\/\/doi.org\/10.1016\/j.bspc.2023.105884","journal-title":"Biomed Signal Process Control"},{"key":"11089_CR23","doi-asserted-by":"publisher","DOI":"10.1080\/10255842.2024.2378105","author":"N Berrahou","year":"2024","unstructured":"Berrahou N, El Alami A, Mesbah A, El Alami R, Berrahou A (2024) Arrhythmia detection in inter-patient ECG signals using entropy rate features and RR intervals with CNN architecture. Comput Methods Biomech Biomed Engin. https:\/\/doi.org\/10.1080\/10255842.2024.2378105","journal-title":"Comput Methods Biomech Biomed Engin"},{"key":"11089_CR24","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/s12530-023-09559-0","volume":"15","author":"R Anand","year":"2024","unstructured":"Anand R, Lakshmi SV, Pandey D, Pandey BK (2024) An enhanced ResNet-50 deep learning model for arrhythmia detection using electrocardiogram biomedical indicators. Evol Syst 15:83\u201397. https:\/\/doi.org\/10.1007\/s12530-023-09559-0","journal-title":"Evol Syst"},{"key":"11089_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106703","author":"N Prasanna Venkatesh","year":"2024","unstructured":"Prasanna Venkatesh N, Pradeep Kumar R, Chakravarthy Neelapu B, Pal K, Sivaraman J (2024) Automated atrial arrhythmia classification using 1D-CNN-BiLSTM: a deep network ensemble model. Biomed Signal Process Control. https:\/\/doi.org\/10.1016\/j.bspc.2024.106703","journal-title":"Biomed Signal Process Control"},{"key":"11089_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s21165290","volume":"21","author":"H Zhu","year":"2021","unstructured":"Zhu H, Zhao Y, Pan Y, Xie H, Wu F, Huan R (2021) Robust heartbeat classification for wearable single-lead ecg via extreme gradient boosting. Sensors 21:1\u201320. https:\/\/doi.org\/10.3390\/s21165290","journal-title":"Sensors"},{"key":"11089_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s21248174","volume":"21","author":"S \u015amigiel","year":"2021","unstructured":"\u015amigiel S, Pa\u0142czy\u0144ski K, Ledzi\u0144ski D (2021) Deep learning techniques in the classification of ecg signals using r-peak detection based on the ptb-xl dataset. Sensors 21:1\u201318. https:\/\/doi.org\/10.3390\/s21248174","journal-title":"Sensors"},{"key":"11089_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2023.e17974","author":"MF Issa","year":"2023","unstructured":"Issa MF, Yousry A, Tuboly G, Juhasz Z, AbuEl-Atta AH, Selim MM (2023) Heartbeat classification based on single lead-II ECG using deep learning. Heliyon. https:\/\/doi.org\/10.1016\/j.heliyon.2023.e17974","journal-title":"Heliyon"},{"key":"11089_CR29","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/s13748-021-00243-5","volume":"10","author":"R Siouda","year":"2021","unstructured":"Siouda R, Nemissi M, Seridi H (2021) ECG beat classification using neural classifier based on deep autoencoder and decomposition techniques. Prog Artif Intell 10:333\u2013347. https:\/\/doi.org\/10.1007\/s13748-021-00243-5","journal-title":"Prog Artif Intell"},{"key":"11089_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114809","author":"Z Zhou","year":"2021","unstructured":"Zhou Z, Zhai X, Tin C (2021) Fully automatic electrocardiogram classification system based on generative adversarial network with auxiliary classifier. Expert Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2021.114809","journal-title":"Expert Syst Appl"},{"key":"11089_CR31","unstructured":"Maciej Serda FG, Becker M, Cleary RM, Team, Holtermann HD, The, Agenda N, Science P, Sk SKR, Hinnebusch R, Hinnebusch A, Rabinovich I, Olmert Y, Uld DQGLQ, Ri WKHUV, Lq WKH, Frxqwu E, Zklfk LV, Edvhg RQ, Wkh FG, Becker N, Aboueldahab R, Khalaf LR, De Elvira T, Zintl R, Hinnebusch M, Karimi SM, Mousavi Shafaee DO, O \u2019driscoll S, Watts J, Kavanagh B, Frederick T, Norlen A, O\u2019Mahony P, Voorhies T, Szayna N, Spalding MO, Jackson M, Morelli B, Satpathy B, Muniapan M, Dass P, Katsamunska Y, Pamuk A, Stahn E, Commission, TED Piccone MK, Annan S, Djankov M, Reynal-Querol M, Couttenier R, Soubeyran P, Vym E, Prague, World Bank, Bodea C, Sambanis N, Florea A, Florea A, Karimi M, Mousavi Shafaee SM, Spalding N, Sambanis N,  \n                  \n                \n                  \n                , PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals., Circulation. 101 (2000) E215-220."},{"key":"11089_CR32","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.cvdhj.2020.07.002","volume":"1","author":"N Saghir","year":"2020","unstructured":"Saghir N, Aggarwal A, Soneji N, Valencia V, Rodgers G, Kurian T (2020) A comparison of manual electrocardiographic interval and waveform analysis in lead 1 of 12-lead ECG and Apple Watch ECG: A validation study. Cardiovasc Digit Heal J 1:30\u201336. https:\/\/doi.org\/10.1016\/j.cvdhj.2020.07.002","journal-title":"Cardiovasc Digit Heal J"},{"key":"11089_CR33","doi-asserted-by":"publisher","first-page":"52","DOI":"10.3390\/technologies9030052","volume":"9","author":"MM Ahsan","year":"2021","unstructured":"Ahsan MM, Mahmud MAP, Saha PK, Gupta KD, Siddique Z (2021) Effect of data scaling methods on machine learning algorithms and model performance. Technologies 9:52. https:\/\/doi.org\/10.3390\/technologies9030052","journal-title":"Technologies"},{"key":"11089_CR34","doi-asserted-by":"publisher","first-page":"3809","DOI":"10.1109\/EMBC.2016.7591558","volume":"2016","author":"HA Mahamat","year":"2016","unstructured":"Mahamat HA, Jacquir S, Khalil C, Laurent G, Binczak S (2016) Wolff-Parkinson-White (WPW) syndrome: the detection of delta wave in an electrocardiogram (ECG). Proc Annu Int Conf IEEE Eng Med Biol Soc EMBS 2016:3809\u20133812. https:\/\/doi.org\/10.1109\/EMBC.2016.7591558","journal-title":"Proc Annu Int Conf IEEE Eng Med Biol Soc EMBS"},{"key":"11089_CR35","doi-asserted-by":"publisher","first-page":"5369","DOI":"10.1007\/s00034-018-0819-3","volume":"37","author":"R Wang","year":"2018","unstructured":"Wang R, Sun S, Guo X, Yan D (2018) EMD threshold denoising algorithm based on variance estimation, circuits. Syst Signal Process 37:5369\u20135388. https:\/\/doi.org\/10.1007\/s00034-018-0819-3","journal-title":"Syst Signal Process"},{"key":"11089_CR36","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1098\/rspa.1998.0193","volume":"454","author":"NE Huang","year":"1996","unstructured":"Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Yen N, Tung CC, Liu HH (1996) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc A 454:903\u2013995","journal-title":"Proc R Soc A"},{"key":"11089_CR37","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1109\/MSP.2013.2267931","volume":"30","author":"DP Mandic","year":"2013","unstructured":"Mandic DP, Ur Rehman N, Wu Z, Huang NE (2013) Empirical mode decomposition-based time-frequency analysis of multivariate signals: the power of adaptive data analysis. IEEE Signal Process Mag 30:74\u201386. https:\/\/doi.org\/10.1109\/MSP.2013.2267931","journal-title":"IEEE Signal Process Mag"},{"key":"11089_CR38","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1109\/LSP.2003.821662","volume":"11","author":"P Flandrin","year":"2004","unstructured":"Flandrin P, Rilling G, Gon\u00e7alv\u00e9s P (2004) Empirical mode decomposition as a filter bank. IEEE Signal Process Lett 11:112\u2013114. https:\/\/doi.org\/10.1109\/LSP.2003.821662","journal-title":"IEEE Signal Process Lett"},{"key":"11089_CR39","doi-asserted-by":"publisher","unstructured":"Mohammed R, Rawashdeh J, Abdullah M, (2020) Machine Learning with Oversampling and Undersampling Techniques: Overview Study and Experimental Results. In: 2020 11th international conference on information and communication systems 2020 pp. 243 248 https:\/\/doi.org\/10.1109\/ICICS49469.2020.239556","DOI":"10.1109\/ICICS49469.2020.239556"},{"key":"11089_CR40","first-page":"565","volume":"36","author":"HE Ollmann","year":"2022","unstructured":"Ollmann HE (2022) How to choose a good book. J Read 36:565\u2013567","journal-title":"J Read"},{"key":"11089_CR41","first-page":"7694","volume":"31","author":"J Bjorck","year":"2018","unstructured":"Bjorck J, Gomes C, Selman B, Weinberger KQ (2018) Understanding batch normalization. Adv Neural Inf Process Syst 31:7694\u20137705","journal-title":"Adv Neural Inf Process Syst"},{"key":"11089_CR42","first-page":"310","volume":"4","author":"S Siddharth","year":"2020","unstructured":"Siddharth S, Simone S, Anidhya A (2020) Activation functions in neural networks. Int J Eng Appl Sci Technol 4:310\u2013316","journal-title":"Int J Eng Appl Sci Technol"},{"key":"11089_CR43","unstructured":"P. Belagatti, Understanding the Softmax Activation Function: A Comprehensive Guide, SingleStore. (2024)."},{"key":"11089_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5121\/ijdkp.2015.5201","volume":"5","author":"M Hossin","year":"2015","unstructured":"Hossin M, Bin Sulaiman, MN (2015) A review on evaluation metrics for data classification evaluations. Int J Data Min Knowl Manag Process 5:1\u201311","journal-title":"Int J Data Min Knowl Manag Process"},{"key":"11089_CR45","unstructured":"A. Bhandari, AUC-ROC Curve in Machine Learning Clearly Explained, (2020)."},{"key":"11089_CR46","first-page":"8","volume":"3","author":"G Rilling","year":"2003","unstructured":"Rilling G, Flandrin P, Goncalves P (2003) On empirical mode decomposition and its algorithms. IEEE-EURASIP Work Nonlinear Signal Image Process 3:8\u201311","journal-title":"IEEE-EURASIP Work Nonlinear Signal Image Process"},{"key":"11089_CR47","unstructured":"D. Laszuk, EMD, (2023). https:\/\/pyemd.readthedocs.io\/en\/latest\/emd.html."}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11089-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11089-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11089-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T09:17:35Z","timestamp":1748683055000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11089-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,20]]},"references-count":47,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["11089"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11089-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,20]]},"assertion":[{"value":"5 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors state that they do not have any known conflicting financial interests or personal relationships that could have potentially influenced the findings presented in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}