{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T18:30:40Z","timestamp":1774031440527,"version":"3.50.1"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T00:00:00Z","timestamp":1670889600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T00:00:00Z","timestamp":1670889600000},"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":[[2023,4]]},"DOI":"10.1007\/s00521-022-08020-8","type":"journal-article","created":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T12:04:07Z","timestamp":1670933047000},"page":"7659-7676","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Accompanying deep framework for faults in motor and gearbox with disproportion vibrational samples"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5162-2692","authenticated-orcid":false,"given":"Hanen","family":"Karamti","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maha M. A.","family":"Lashin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fadwa","family":"Alrowais","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abeer M.","family":"Mahmoud","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,13]]},"reference":[{"key":"8020_CR1","doi-asserted-by":"crossref","first-page":"1221","DOI":"10.1007\/s11831-018-9286-z","volume":"26","author":"A Choudhary","year":"2018","unstructured":"Choudhary A, Goyal D, Shimi SL, Akula A (2018) Condition monitoring and fault diagnosis of induction motors: a review. Arch Comput Methods Eng 26:1221\u20131238","journal-title":"Arch Comput Methods Eng"},{"key":"8020_CR2","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.jprocont.2015.04.006","volume":"32","author":"V Agrawal","year":"2015","unstructured":"Agrawal V, Panigrahi B, Subbarao P (2015) Review of control and fault diagnosis methods applied to coal mills. Process Control 32:138\u2013153","journal-title":"Process Control"},{"key":"8020_CR3","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1007\/s10115-012-0487-8","volume":"34","author":"V Bol\u00f3n-Canedo","year":"2013","unstructured":"Bol\u00f3n-Canedo V, S\u00e1nchez-Maro\u00f1o N, Alonso-Betanzos A (2013) A review of feature selection methods on synthetidata. Knowl Inf Syst 34:483\u2013519","journal-title":"Knowl Inf Syst"},{"key":"8020_CR4","volume":"138","author":"Y Lei","year":"2020","unstructured":"Lei Y, Yang B, Jiang X, Jia F, Li N, Nandi AK (2020) Applications of machine learning to machine fault diagnosis: a review and roadmap. Mech Syst Signal Process 138:106587","journal-title":"Mech Syst Signal Process"},{"key":"8020_CR5","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1109\/TEC.2005.847955","volume":"20","author":"S Nandi","year":"2005","unstructured":"Nandi S, Toliyat HA, Li X (2005) Condition monitoring and fault diagnosis of electrical motors\u2014a review. IEEE Trans Energy Convers 20:719\u2013729","journal-title":"IEEE Trans Energy Convers"},{"key":"8020_CR6","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1016\/j.ymssp.2015.03.002","volume":"62","author":"X Xue","year":"2015","unstructured":"Xue X, Zhou J, Xu Y, Zhu W, Li C (2015) An adaptively fast ensemble empirical mode decomposition method and its applications to rolling element bearing fault diagnosis. Mech Syst Signal Process 62:444\u2013459","journal-title":"Mech Syst Signal Process"},{"issue":"11","key":"8020_CR7","first-page":"1","volume":"10","author":"L Pan","year":"2018","unstructured":"Pan L, Zhu D, She S, Song A, Shi X, Duan S (2018) Gear fault diagnosis method based on wavelet-packet independent component analysis and support vector machine with kernel function fusion. Adv Mech Eng 10(11):1\u201310","journal-title":"Adv Mech Eng"},{"key":"8020_CR8","doi-asserted-by":"crossref","first-page":"3317","DOI":"10.1177\/0954406218805510","volume":"233","author":"M Zair","year":"2019","unstructured":"Zair M, Rahmoune C, Benazzouz D (2019) Multi-fault diagnosis of rolling bearing using fuzzy entropy of empirical mode decomposition, principal component analysis, and SOM neural network. Proc Inst Mech Eng Part C J Mech Eng Sci 233:3317\u20133328","journal-title":"Proc Inst Mech Eng Part C J Mech Eng Sci"},{"key":"8020_CR9","doi-asserted-by":"crossref","first-page":"23903","DOI":"10.3390\/s150923903","volume":"15","author":"M Cerrada","year":"2015","unstructured":"Cerrada M, Sanchez R, Cabrera D, Zurita G, Li C (2015) Multi-stage feature selection by using genetic algorithms for fault diagnosis in gearboxes based on vibration signal. Sensors 15:23903\u201323926","journal-title":"Sensors"},{"key":"8020_CR10","first-page":"620","volume":"199","author":"JX Yun","year":"2011","unstructured":"Yun JX (2011) Fault diagnosis for rolling bearing on genetic-SVM classifier. Adv Mater Res 199:620\u2013624","journal-title":"Adv Mater Res"},{"key":"8020_CR11","doi-asserted-by":"crossref","first-page":"1848","DOI":"10.1109\/ACCESS.2018.2886343","volume":"7","author":"R Huang","year":"2019","unstructured":"Huang R, Liao Y, Zhang S, Li W (2019) Deep decoupling convolutional neural network for intelligent compound fault diagnosis. IEEE Access 7:1848\u20131858","journal-title":"IEEE Access"},{"key":"8020_CR12","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1016\/j.measurement.2019.02.022","volume":"138","author":"X Zhu","year":"2019","unstructured":"Zhu X, Hou D, Zhou P, Han Z, Yuan Y, Zhou W, Yin Q (2019) Rotor fault diagnosis using a convolutional neural network with symmetrized dot pattern images. Measurement 138:526\u2013535","journal-title":"Measurement"},{"issue":"3","key":"8020_CR13","doi-asserted-by":"crossref","first-page":"591","DOI":"10.3390\/s19030591","volume":"19","author":"Z Guan","year":"2019","unstructured":"Guan Z, Liao Z, Li K, Chen P (2019) A precise diagnosis method of structural faults of rotating machinery based on combination of empirical mode decomposition, sample entropy, and deep belief network. Sensors 19(3):591","journal-title":"Sensors"},{"key":"8020_CR14","doi-asserted-by":"crossref","first-page":"3804","DOI":"10.3390\/s18113804","volume":"18","author":"W Du","year":"2018","unstructured":"Du W, Zhou J, Wang Z, Li R, Wang J (2018) Application of improved singular spectrum decomposition method for composite fault diagnosis of gear boxes. Sensors 18:3804","journal-title":"Sensors"},{"key":"8020_CR15","doi-asserted-by":"crossref","first-page":"3510","DOI":"10.3390\/s18103510","volume":"18","author":"Z Wang","year":"2018","unstructured":"Wang Z, Wang J, Du W (2018) Research on fault diagnosis of gearbox with improved vibrational mode decomposition. Sensors 18:3510","journal-title":"Sensors"},{"key":"8020_CR16","doi-asserted-by":"crossref","first-page":"45","DOI":"10.5545\/sv-jme.2016.3811","volume":"63","author":"X Chen","year":"2017","unstructured":"Chen X, Cheng G, Li H, Li Y (2017) Research of planetary gear fault diagnosis based on multi-scale fractal box dimension of ceemd and elm. Strojniski Vestnik J Mech Eng 63:45\u201355","journal-title":"Strojniski Vestnik J Mech Eng"},{"key":"8020_CR17","doi-asserted-by":"crossref","first-page":"5525","DOI":"10.1109\/TIE.2018.2868023","volume":"66","author":"X Li","year":"2018","unstructured":"Li X, Zhang W, Ding Q (2018) Cross-domain fault diagnosis of rolling element bearings using deep generative neural networks. IEEE Trans Ind Electron 66:5525\u20135534","journal-title":"IEEE Trans Ind Electron"},{"key":"8020_CR18","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.measurement.2019.01.063","volume":"138","author":"Z Xiang","year":"2019","unstructured":"Xiang Z, Zhang X, Zhang W, Xia X (2019) Fault diagnosis of rolling bearing under fluctuating speed and variable load based on tco spectrum and stacking autoencoder. Measurement 138:162\u2013174","journal-title":"Measurement"},{"key":"8020_CR19","first-page":"770","volume":"66","author":"S Ando","year":"2017","unstructured":"Ando S, Huang CY (2017) Deep oversampling framework for classifying imbalanced data. Proc Mach Learn Knowl Discov Databases 66:770\u2013785","journal-title":"Proc Mach Learn Knowl Discov Databases"},{"key":"8020_CR20","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.ins.2018.03.002","volume":"445","author":"S Garca","year":"2018","unstructured":"Garca S, Zhang ZL, Altalhi A, Alshomrani S, Herrera F (2018) Dynamic ensemble selection for multi-class imbalanced datasets. Inf Sci 445:22\u201337","journal-title":"Inf Sci"},{"key":"8020_CR21","first-page":"1","volume":"66","author":"A Radford","year":"2016","unstructured":"Radford A, Metz L, Chintala S (2016) Unsupervised representation learning with deep convolutional generative adversarial networks. Proc ICLR 66:1\u201316","journal-title":"Proc ICLR"},{"key":"8020_CR22","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.eswa.2017.09.030","volume":"91","author":"G Douzas","year":"2018","unstructured":"Douzas G, Bacao F (2018) Effective data generation for imbalanced learning using conditional generative adversarial networks. Expert Syst Appl 91:464\u2013471","journal-title":"Expert Syst Appl"},{"key":"8020_CR23","doi-asserted-by":"crossref","unstructured":"Mullick SS, Datta S, Das S (2019) Generative adversarial minority oversampling. In: Processing of IEEE\/CVF international conference on computer vision (ICCV), pp 1695\u20131704","DOI":"10.1109\/ICCV.2019.00178"},{"key":"8020_CR24","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1155\/2017\/8617315","volume":"2017","author":"L Eren","year":"2017","unstructured":"Eren L (2017) Bearing fault detection by one-dimensional convolutional neural networks. Math Probl Eng 2017:9","journal-title":"Math Probl Eng"},{"key":"8020_CR25","doi-asserted-by":"crossref","first-page":"3196","DOI":"10.1109\/TIE.2018.2844805","volume":"66","author":"G Jiang","year":"2019","unstructured":"Jiang G, He H, Yan J, Xie P (2019) Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox. IEEE Trans Ind Electron 66:3196\u20133207","journal-title":"IEEE Trans Ind Electron"},{"issue":"2","key":"8020_CR26","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.engappai.2011.09.010","volume":"25","author":"KC Gryllias","year":"2012","unstructured":"Gryllias KC, Antoniadis IA (2012) A support vector machine approach based on physical model training for rolling element bearing fault detection in industrial environments. Eng Appl Artif Intell 25(2):326\u2013344","journal-title":"Eng Appl Artif Intell"},{"key":"8020_CR27","doi-asserted-by":"crossref","first-page":"2589","DOI":"10.1016\/j.ymssp.2011.02.017","volume":"25","author":"Z Li","year":"2011","unstructured":"Li Z, Yan X, Yuan C, Peng Z, Li L (2011) Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method. Mech Syst Signal Process 25:2589\u20132607","journal-title":"Mech Syst Signal Process"},{"key":"8020_CR28","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1016\/j.asoc.2013.01.006","volume":"13","author":"V Janakiraman","year":"2013","unstructured":"Janakiraman V, Nguyen X, Assanis D (2013) Nonlinear identification of a gasoline HCCI engine using neural networks coupled with principal component analysis. Appl Soft Comput 13:2375\u20132389","journal-title":"Appl Soft Comput"},{"key":"8020_CR29","doi-asserted-by":"crossref","first-page":"6719","DOI":"10.1007\/s00500-018-3256-0","volume":"22","author":"DK Appana","year":"2018","unstructured":"Appana DK, Prosvirin A, Kim J-M (2018) Reliable fault diagnosis of bearings with varying rotational speeds using envelope spectrum and convolution neural networks. Soft Comput 22:6719\u20136729","journal-title":"Soft Comput"},{"key":"8020_CR30","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.3390\/s18051429","volume":"18","author":"S Guo","year":"2018","unstructured":"Guo S, Yang T, Gao W, Zhang C (2018) A novel fault diagnosis method for rotating machinery based on a convolutional neural network. Sensors 18:1429","journal-title":"Sensors"},{"key":"8020_CR31","doi-asserted-by":"crossref","unstructured":"Pandhare V, Singh J, Lee J (2019) Convolutional neural network based rolling-element bearing fault diagnosis for naturally occurring and progressing defects using time-frequency domain features. In: Proceedings of the prognostics and system health management conference, pp 320\u2013326","DOI":"10.1109\/PHM-Paris.2019.00061"},{"key":"8020_CR32","doi-asserted-by":"crossref","first-page":"2391","DOI":"10.1109\/TIM.2017.2698738","volume":"66","author":"G Jiang","year":"2017","unstructured":"Jiang G, He H, Xie P, Tang Y (2017) Stacked multilevel-denoising autoencoders: a new representation learning approach for wind turbine gearbox fault diagnosis. IEEE Trans Instrum Meas 66:2391\u20132402","journal-title":"IEEE Trans Instrum Meas"},{"key":"8020_CR33","first-page":"10","volume":"2018","author":"G Liu","year":"2018","unstructured":"Liu G, Bao H, Han B (2018) A stacked autoencoder-based deep neural network for achieving gearbox fault diagnosis. Math Problems Eng 2018:10","journal-title":"Math Problems Eng"},{"key":"8020_CR34","first-page":"2672","volume":"27","author":"I Goodfellow","year":"2014","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial networks. Adv Neural Inf Process Syst 27:2672\u20132680","journal-title":"Adv Neural Inf Process Syst"},{"key":"8020_CR35","doi-asserted-by":"crossref","first-page":"9515","DOI":"10.1109\/ACCESS.2018.2890693","volume":"7","author":"W Mao","year":"2019","unstructured":"Mao W, Liu Y, Ding L, Li Y (2019) Imbalanced fault diagnosis of rolling bearing based on generative adversarial network: a comparative study. IEEE Access 7:9515\u20139530","journal-title":"IEEE Access"},{"key":"8020_CR36","doi-asserted-by":"crossref","unstructured":"Lee YO, Jo J, Hwang J (2017) Application of deep neural network and generative adversarial network to industrial maintenance: a case study of induction motor fault detection. In: Proceedings of the IEEE international conference big data, pp 3248\u20133253","DOI":"10.1109\/BigData.2017.8258307"},{"key":"8020_CR37","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1016\/j.neucom.2018.07.034","volume":"315","author":"H Liu","year":"2018","unstructured":"Liu H, Zhou J, Xu Y, Zheng Y, Peng X, Jiang W (2018) Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks. Neurocomputing 315:412\u2013424","journal-title":"Neurocomputing"},{"key":"8020_CR38","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1016\/j.knosys.2018.12.019","volume":"165","author":"T Han","year":"2019","unstructured":"Han T, Liu C, Yang W, Jiang D (2019) A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults. Knowl Based Syst 165:474\u2013487","journal-title":"Knowl Based Syst"},{"issue":"2","key":"8020_CR39","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1784\/204764217821144278","volume":"7","author":"M Frini","year":"2017","unstructured":"Frini M, Soualhi A, Badaoui M, Marrakchi G (2017) Gear fault detection using the geometric properties of electrical currents in three-phase induction motor-based systems. Condition Monit 7(2):47\u201352","journal-title":"Condition Monit"},{"key":"8020_CR40","doi-asserted-by":"crossref","unstructured":"JF Lea Jr, L Rowlan (2019) Use of beam pumps to deliquefy gas wells, gas well deliquification, 3rd ed. sciencDirect, Elsevier","DOI":"10.1016\/B978-0-12-815897-5.00010-X"},{"key":"8020_CR41","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1016\/j.ymssp.2017.05.024","volume":"98","author":"X Liang","year":"2018","unstructured":"Liang X, Zuo MJ, Feng Z (2018) Dynamic modeling of gearbox faults: a review. Mech Syst Signal Process 98:852\u2013876","journal-title":"Mech Syst Signal Process"},{"issue":"11","key":"8020_CR42","volume":"753","author":"F Schwack","year":"2016","unstructured":"Schwack F, Stammler M, Poll G, Reuter A (2016) Comparison of life calculations for oscillating bearings considering individual pitch control in wind turbines. J Phys Conf Ser 753(11):112013","journal-title":"J Phys Conf Ser"},{"key":"8020_CR43","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1007\/s00542-016-2839-x","volume":"22","author":"B Seo","year":"2016","unstructured":"Seo B, Sung S, Kang K, Song J, Jang G (2016) Unbalanced magnetic force and cogging torque of PM motors due to the interaction between PM magnetization and stator eccentricity. Micro Syst Technol 22:1249\u20131255","journal-title":"Micro Syst Technol"},{"issue":"8","key":"8020_CR44","first-page":"3103","volume":"25","author":"C Fan","year":"2011","unstructured":"Fan C, Syu J, Pan M, Tsao W (2011) Study of start-up vibration response for oil whirl, oil whip and dry whip. Mech Syst Signal Process 25(8):3103\u201331115","journal-title":"Mech Syst Signal Process"},{"key":"8020_CR45","first-page":"14","volume":"66","author":"S Singhal","year":"2009","unstructured":"Singhal S, Mistry R (2009) Oil whirl rotor dynamic instability phenomenon-diagnosis and cure in large induction motor. Proc Ind Appl Soc 66:14\u201316","journal-title":"Proc Ind Appl Soc"},{"key":"8020_CR46","doi-asserted-by":"crossref","unstructured":"P Dang, L Do, N Vo, T Ngo, H Le (2019) Identification of unbalance in rotating machinery using vibration analyse solution. In: 4th International conference on mechatronics and electrical systems, p 841","DOI":"10.1088\/1757-899X\/841\/1\/012011"},{"issue":"4","key":"8020_CR47","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1109\/TEC.2016.2583473","volume":"31","author":"J Jung","year":"2016","unstructured":"Jung J, Lee SB, Lim C, Cho C, Kim K (2016) Electrical monitoring of mechanical looseness for induction motors with sleeve bearings. IEEE Trans Energy Convers 31(4):1377\u20131386","journal-title":"IEEE Trans Energy Convers"},{"key":"8020_CR48","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s11668-014-9785-7","volume":"14","author":"A Verma","year":"2014","unstructured":"Verma A, Sarangi S, Kolekar MH (2014) Experimental investigation of misalignment effects on rotor shaft vibration and on stator current signature. Fail Anal Prev 14:125\u2013138","journal-title":"Fail Anal Prev"},{"key":"8020_CR49","first-page":"20","volume":"66","author":"L Patidar","year":"2012","unstructured":"Patidar L, Rao KU (2012) Soft foot and motor problem solved through predictive maintenance approach. Proc Recent Trends Eng Sci 66:20\u201321","journal-title":"Proc Recent Trends Eng Sci"},{"key":"8020_CR50","doi-asserted-by":"crossref","first-page":"2153","DOI":"10.1590\/1679-78254231","volume":"14","author":"S Majid","year":"2017","unstructured":"Majid S, Nikravesh Y, Goudarzi MD (2017) A review paper on looseness detection methods in bolted structures. Latin Am J Solids Struct 14:2153\u20132176","journal-title":"Latin Am J Solids Struct"},{"key":"8020_CR51","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.ymssp.2018.12.032","volume":"122","author":"Z Qiao","year":"2019","unstructured":"Qiao Z, Lei Y, Li N (2019) Applications of stochastic resonance to machinery fault detection: a review and tutorial. Mech Syst Signal Process 122:502\u2013536","journal-title":"Mech Syst Signal Process"},{"key":"8020_CR52","first-page":"227","volume":"66","author":"T Plante","year":"2015","unstructured":"Plante T, Nejadpak A, Yang CX (2015) Faults detection and failures prediction using vibration analysis. Proc IEEE Autotestcon 66:227\u2013231","journal-title":"Proc IEEE Autotestcon"},{"key":"8020_CR53","first-page":"66","volume":"2018","author":"M Sohaib","year":"2018","unstructured":"Sohaib M, Kim J-M (2018) Reliable fault diagnosis of rotary machine bearings using a stacked sparse autoencoder-based deep neural network. Shock Vib 2018:66","journal-title":"Shock Vib"},{"key":"8020_CR54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2020.3043959","volume":"70","author":"AG Nath","year":"2021","unstructured":"Nath AG, Sharma A, Udmale SS, Singh SK (2021) An early classification approach for improving structural rotor fault diagnosis. IEEE Trans Instrum Meas 70:1\u201313","journal-title":"IEEE Trans Instrum Meas"},{"key":"8020_CR55","doi-asserted-by":"crossref","unstructured":"Sun N, Mo X, Wei T, Zhang D, Luo W (2020) The effectiveness of noise in data augmentation for fine-grained image classification. In: ACPR 2019. Lecture notes in computer science, vol 12046. Springer, Cham, pp 779\u2013792","DOI":"10.1007\/978-3-030-41404-7_55"},{"key":"8020_CR56","first-page":"1","volume":"66","author":"A Helwan","year":"2017","unstructured":"Helwan A, Ozsahin DU (2017) Sliding window based machine learning system for the left ventricle localization in MR cardiac images. Appl Comput Intell Soft Comput 66:1\u20139","journal-title":"Appl Comput Intell Soft Comput"},{"key":"8020_CR57","doi-asserted-by":"crossref","DOI":"10.1016\/j.jneumeth.2020.108885","volume":"346","author":"E Lashgari","year":"2020","unstructured":"Lashgari E, Liang D, Maoz U (2020) Data augmentation for deep-learning-based electroencephalography. J Neurosci Methods 346:108885","journal-title":"J Neurosci Methods"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-08020-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-08020-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-08020-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T01:44:52Z","timestamp":1679881492000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-08020-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,13]]},"references-count":57,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["8020"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-08020-8","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,13]]},"assertion":[{"value":"19 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2022","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 declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}