{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T11:40:20Z","timestamp":1773747620225,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"29","license":[{"start":{"date-parts":[[2023,8,19]],"date-time":"2023-08-19T00:00:00Z","timestamp":1692403200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,19]],"date-time":"2023-08-19T00:00:00Z","timestamp":1692403200000},"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":[[2023,10]]},"DOI":"10.1007\/s00521-023-08836-y","type":"journal-article","created":{"date-parts":[[2023,8,19]],"date-time":"2023-08-19T03:20:36Z","timestamp":1692415236000},"page":"21663-21673","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep learning-based risk reduction approach using novel banking parameters on a standardized dataset"],"prefix":"10.1007","volume":"35","author":[{"given":"Hamed","family":"Haddadi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seyed Naser","family":"Razavi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amin","family":"Babazadeh Sangar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,19]]},"reference":[{"issue":"1","key":"8836_CR1","doi-asserted-by":"publisher","first-page":"9","DOI":"10.52810\/TPRIS.2021.100009","volume":"1","author":"X Ning","year":"2021","unstructured":"Ning X, Wang Y, Tian W, Liu L, Cai W (2021) A biomimetic covering learning method based on principle of homology continuity. ASP Trans Pattern Recognit Intell Syst 1(1):9\u201316","journal-title":"ASP Trans Pattern Recognit Intell Syst"},{"issue":"5","key":"8836_CR2","doi-asserted-by":"publisher","first-page":"2575","DOI":"10.1109\/TIP.2018.2806229","volume":"27","author":"X Ning","year":"2018","unstructured":"Ning X, Li W, Tang B, He H (2018) BULDP: biomimetic uncorrelated locality discriminant projection for feature extraction in face recognition. IEEE Trans Image Process 27(5):2575\u20132586","journal-title":"IEEE Trans Image Process"},{"key":"8836_CR3","doi-asserted-by":"publisher","first-page":"107402","DOI":"10.1016\/j.apacoust.2020.107402","volume":"168","author":"Z Mousavi","year":"2020","unstructured":"Mousavi Z, Ettefagh MM, Sadeghi MH, Razavi SN (2020) Developing deep neural network for damage detection of beam-like structures using dynamic response based on FE model and real healthy state. Appl Acoust 168:107402","journal-title":"Appl Acoust"},{"issue":"1","key":"8836_CR4","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.slast.2021.10.011","volume":"27","author":"Z Mousavi","year":"2022","unstructured":"Mousavi Z, Shahini N, Sheykhivand S, Mojtahedi S, Arshadi A (2022) COVID-19 detection using chest X-ray images based on a developed deep neural network. SLAS Technol 27(1):63\u201375","journal-title":"SLAS Technol"},{"key":"8836_CR5","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.econmod.2019.04.003","volume":"84","author":"M Zori\u010d\u00e1k","year":"2020","unstructured":"Zori\u010d\u00e1k M, Gnip P, Drot\u00e1r P, Gazda V (2020) Bankruptcy prediction for small-and medium-sized companies using severely imbalanced datasets. Econ Model 84:165\u2013176","journal-title":"Econ Model"},{"issue":"1","key":"8836_CR6","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1080\/08839514.2019.1691849","volume":"34","author":"M Soui","year":"2020","unstructured":"Soui M, Smiti S, Mkaouer MW, Ejbali R (2020) Bankruptcy prediction using stacked auto-encoders. Appl Artif Intell 34(1):80\u2013100","journal-title":"Appl Artif Intell"},{"key":"8836_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.9734\/ajpas\/2019\/v3i430100","volume":"3","author":"DM Obare","year":"2019","unstructured":"Obare DM, Njoroge GG, Muraya MM (2019) Analysis of individual loan defaults using logit under supervised machine learning approach. Asian J Probab Stat 3:1\u201312","journal-title":"Asian J Probab Stat"},{"issue":"11\u201312","key":"8836_CR8","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.1016\/S0305-0548(99)00148-3","volume":"27","author":"L Motiwalla","year":"2000","unstructured":"Motiwalla L, Wahab M (2000) Predictable variation and profitable trading of US equities: a trading simulation using neural networks. Comput Oper Res 27(11\u201312):1111\u20131129","journal-title":"Comput Oper Res"},{"key":"8836_CR9","doi-asserted-by":"crossref","unstructured":"Metawa N, Elhoseny M, Hassan MK, Hassanien AE (2016) Loan portfolio optimization using genetic algorithm: a case of credit constraints. In: 2016 12th international computer engineering conference (ICENCO), 2016. IEEE, pp 59\u201364","DOI":"10.1109\/ICENCO.2016.7856446"},{"key":"8836_CR10","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.eswa.2017.03.021","volume":"80","author":"N Metawa","year":"2017","unstructured":"Metawa N, Hassan MK, Elhoseny M (2017) Genetic algorithm based model for optimizing bank lending decisions. Expert Syst Appl 80:75\u201382","journal-title":"Expert Syst Appl"},{"key":"8836_CR11","doi-asserted-by":"crossref","unstructured":"Lopes RG, Carvalho RN, Ladeira M, Carvalho RS (2016) Predicting recovery of credit operations on a Brazilian bank. In: 2016 15th IEEE international conference on machine learning and applications (ICMLA), 2016. IEEE, pp 780\u2013784","DOI":"10.1109\/ICMLA.2016.0139"},{"key":"8836_CR12","first-page":"176","volume":"5","author":"M Khanbabaei","year":"2013","unstructured":"Khanbabaei M (2013) The use of genetic algorithm, clustering and feature selection techniques in construction of decision tree models for credit scoring. Int J Manag Inf Technol (IJMIT) 5:176\u2013181","journal-title":"Int J Manag Inf Technol (IJMIT)"},{"key":"8836_CR13","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1016\/j.eswa.2019.02.033","volume":"128","author":"W Bao","year":"2019","unstructured":"Bao W, Lianju N, Yue K (2019) Integration of unsupervised and supervised machine learning algorithms for credit risk assessment. Expert Syst Appl 128:301\u2013315","journal-title":"Expert Syst Appl"},{"issue":"6","key":"8836_CR14","doi-asserted-by":"publisher","first-page":"e12217","DOI":"10.1111\/exsy.12217","volume":"34","author":"S Dahiya","year":"2017","unstructured":"Dahiya S, Handa S, Singh N (2017) A feature selection enabled hybrid-bagging algorithm for credit risk evaluation. Expert Syst 34(6):e12217","journal-title":"Expert Syst"},{"key":"8836_CR15","unstructured":"Petropoulos A, Siakoulis V, Stavroulakis E, Klamargias A (2019) A robust machine learning approach for credit risk analysis of large loan level datasets using deep learning and extreme gradient boosting. In: IFC bulletins chapters, vol 49, 2019"},{"key":"8836_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.eswa.2016.12.020","volume":"73","author":"J Abell\u00e1n","year":"2017","unstructured":"Abell\u00e1n J, Castellano JG (2017) A comparative study on base classifiers in ensemble methods for credit scoring. Expert Syst Appl 73:1\u201310","journal-title":"Expert Syst Appl"},{"key":"8836_CR17","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.eswa.2017.05.050","volume":"86","author":"A Bequ\u00e9","year":"2017","unstructured":"Bequ\u00e9 A, Lessmann S (2017) Extreme learning machines for credit scoring: an empirical evaluation. Expert Syst Appl 86:42\u201353","journal-title":"Expert Syst Appl"},{"key":"8836_CR18","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.eswa.2017.10.022","volume":"93","author":"Y Xia","year":"2018","unstructured":"Xia Y, Liu C, Da B, Xie F (2018) A novel heterogeneous ensemble credit scoring model based on bstacking approach. Expert Syst Appl 93:182\u2013199","journal-title":"Expert Syst Appl"},{"key":"8836_CR19","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1016\/j.engappai.2016.12.002","volume":"65","author":"C Luo","year":"2017","unstructured":"Luo C, Wu D, Wu DJ (2017) A deep learning approach for credit scoring using credit default swaps. Eng Appl Artif Intell 65:465\u2013470","journal-title":"Eng Appl Artif Intell"},{"key":"8836_CR20","doi-asserted-by":"crossref","unstructured":"Shoumo SZH, Dhruba MIM, Hossain S, Ghani NH, Arif H, Islam S (2019) Application of machine learning in credit risk assessment: a prelude to smart banking. In: TENCON 2019\u20132019 IEEE region 10 conference (TENCON), 2019. IEEE, pp 2023\u20132028","DOI":"10.1109\/TENCON.2019.8929527"},{"key":"8836_CR21","doi-asserted-by":"crossref","unstructured":"Li J, Han P, Ren X, Hu J, Chen L, Shang S (2021) Sequence labeling with meta-learning. IEEE Trans Knowl Data Eng","DOI":"10.1109\/TKDE.2021.3118469"},{"issue":"9","key":"8836_CR22","doi-asserted-by":"publisher","first-page":"3819","DOI":"10.1109\/TNNLS.2020.3015912","volume":"32","author":"J Li","year":"2020","unstructured":"Li J, Shang S, Chen L (2020) Domain generalization for named entity boundary detection via metalearning. IEEE Trans Neural Netw Learn Syst 32(9):3819\u20133830","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"4","key":"8836_CR23","doi-asserted-by":"publisher","first-page":"1790","DOI":"10.1109\/TKDE.2020.2981329","volume":"33","author":"J Li","year":"2020","unstructured":"Li J, Sun A, Ma Y (2020) Neural named entity boundary detection. IEEE Trans Knowl Data Eng 33(4):1790\u20131795","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"8836_CR24","doi-asserted-by":"crossref","unstructured":"Gribonval R, Kutyniok G, Nielsen M, Voigtlaender F (2021) Approximation spaces of deep neural networks. Construct Approx 1\u2013109","DOI":"10.1007\/s00365-021-09543-4"},{"key":"8836_CR25","doi-asserted-by":"crossref","unstructured":"Benz P, Zhang C, Karjauv A, Kweon IS (2021) Revisiting batch normalization for improving corruption robustness. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, 2021, pp 494\u2013503","DOI":"10.1109\/WACV48630.2021.00054"},{"key":"8836_CR26","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1016\/j.ins.2020.08.075","volume":"544","author":"C Han","year":"2021","unstructured":"Han C, Lei Y, Xie Y, Zhou D, Gong M (2021) Learning smooth representations with generalized softmax for unsupervised domain adaptation. Inf Sci 544:415\u2013426","journal-title":"Inf Sci"},{"key":"8836_CR27","unstructured":"Powers V (2020) Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv preprint arXiv:2010.16061, 2020"},{"key":"8836_CR28","doi-asserted-by":"publisher","first-page":"139332","DOI":"10.1109\/ACCESS.2020.3011882","volume":"8","author":"S Sheykhivand","year":"2020","unstructured":"Sheykhivand S, Mousavi Z, Rezaii TY, Farzamnia A (2020) Recognizing emotions evoked by music using CNN-LSTM networks on EEG signals. IEEE Access 8:139332\u2013139345","journal-title":"IEEE Access"},{"issue":"4","key":"8836_CR29","first-page":"313","volume":"11","author":"S Sheykhivand","year":"2018","unstructured":"Sheykhivand S, Rezaii TY, Mousavi Z, Meshgini S (2018) Automatic stage scoring of single-channel sleep EEG using discrete wavelet transform and a hybrid model of simulated annealing algorithm and neural network. Iran J Biomed Eng 11(4):313\u2013325","journal-title":"Iran J Biomed Eng"},{"key":"8836_CR30","doi-asserted-by":"publisher","DOI":"10.1177\/147592172093261","author":"Z Mousavi","year":"2019","unstructured":"Mousavi Z, Varahram S, Ettefagh MM, Sadeghi MH, Razavi SN (2020) Deep neural networks-based damage detection using vibration signals of finite element model and real intact state: an evaluation via a lab-scale offshore jacket structure. Struct Health Monit. https:\/\/doi.org\/10.1177\/147592172093261","journal-title":"Struct Health Monit"},{"key":"8836_CR31","doi-asserted-by":"publisher","first-page":"108312","DOI":"10.1016\/j.jneumeth.2019.108312","volume":"324","author":"Z Mousavi","year":"2019","unstructured":"Mousavi Z, Rezaii TY, Sheykhivand S, Farzamnia A, Razavi S (2019) Deep convolutional neural network for classification of sleep stages from single-channel EEG signals. J Neurosci Methods 324:108312","journal-title":"J Neurosci Methods"},{"key":"8836_CR32","doi-asserted-by":"crossref","unstructured":"Ha V-S, Nguyen H-N (2016) Credit scoring with a feature selection approach based deep learning. In: MATEC web of conferences, 2016, vol 54: EDP Sciences, p 05004","DOI":"10.1051\/matecconf\/20165405004"},{"key":"8836_CR33","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.knosys.2016.04.013","volume":"104","author":"M Ala'raj","year":"2016","unstructured":"Ala\u2019raj M, Abbod MF (2016) Classifiers consensus system approach for credit scoring. Knowl Based Syst 104:89\u2013105","journal-title":"Knowl Based Syst"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08836-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08836-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08836-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T14:10:26Z","timestamp":1694873426000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08836-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,19]]},"references-count":33,"journal-issue":{"issue":"29","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["8836"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08836-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,19]]},"assertion":[{"value":"28 November 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2023","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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare the following financial interests\/personal relationships which may be considered as potential competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}