{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T12:51:27Z","timestamp":1774702287500,"version":"3.50.1"},"reference-count":87,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.knosys.2026.115635","type":"journal-article","created":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T16:33:27Z","timestamp":1772728407000},"page":"115635","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["An advanced hybrid probabilistic neural network based on generalized hyperbolic distributions for handling non-Gaussian data"],"prefix":"10.1016","volume":"340","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5354-8674","authenticated-orcid":false,"given":"Dilpreet","family":"Kaur","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2172-0982","authenticated-orcid":false,"given":"Kavita","family":"Goyal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1266-4109","authenticated-orcid":false,"given":"Rohit","family":"Kumar Singla","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2026.115635_bib0001","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1109\/72.80210","article-title":"Probabilistic neural networks and the polynomial adaline as complementary techniques for classification","volume":"1(1)","author":"Specht","year":"1990","journal-title":"IEEE Trans. Neural Netw."},{"key":"10.1016\/j.knosys.2026.115635_bib0002","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/0893-6080(90)90049-Q","article-title":"Probabilistic neural networks","volume":"3(1)","author":"Specht","year":"1990","journal-title":"Neural Netw."},{"key":"10.1016\/j.knosys.2026.115635_bib0003","first-page":"434","article-title":"On the effectiveness of Parzen window classifier","volume":"2(3)","author":"Raudys","year":"1991","journal-title":"Informatica"},{"key":"10.1016\/j.knosys.2026.115635_bib0004","doi-asserted-by":"crossref","first-page":"1751","DOI":"10.1016\/0031-3203(96)00027-1","article-title":"On the estimation of a covariance matrix in designing Parzen classifiers","volume":"29(10)","author":"Hamamoto","year":"1996","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.knosys.2026.115635_bib0005","first-page":"242","article-title":"Filtered kernel probabilistic neural network","volume":"1962","author":"Rogers","year":"1993","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.knosys.2026.115635_bib0006","first-page":"379","article-title":"Probabilistic neural networks with rotated kernel functions","volume":"1327","author":"Galleske","year":"1997","journal-title":"Int. Conf. Artif. Neural Netw. \u2013 ICANN\u201997"},{"key":"10.1016\/j.knosys.2026.115635_bib0007","first-page":"511","article-title":"Nonlinear process monitors method based on kernel function and PNN","author":"Cuimei","year":"2007","journal-title":"Chin. Control Conf."},{"key":"10.1016\/j.knosys.2026.115635_bib0008","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.neucom.2019.01.117","article-title":"A hybrid improved kernel LDA and PNN algorithm for efficient face recognition","volume":"393","author":"Ouyanga","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.knosys.2026.115635_bib0009","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/978-3-031-57312-5_3","article-title":"Beyond the original PNN model\u2013kernel memory for modeling various neural pattern processing mechanisms","volume":"1157","author":"Hoya","year":"2024","journal-title":"Syntactic Netw.\u2013Kernel Memory Approach"},{"key":"10.1016\/j.knosys.2026.115635_bib0010","doi-asserted-by":"crossref","first-page":"507","DOI":"10.2991\/ijcis.2017.10.1.35","article-title":"A combination of models for financial crisis prediction: integrating probabilistic neural network with back-propagation based on adaptive boosting","volume":"10","author":"Wang","year":"2017","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"10.1016\/j.knosys.2026.115635_bib0011","doi-asserted-by":"crossref","first-page":"5334","DOI":"10.3390\/app9245334","article-title":"An improved probabilistic neural network model for directional prediction of a stock market index","volume":"9","author":"Chandrasekara","year":"2019","journal-title":"Appl. Sci."},{"key":"10.1016\/j.knosys.2026.115635_bib0012","doi-asserted-by":"crossref","first-page":"58","DOI":"10.3389\/fncom.2020.00058","article-title":"Cancer risk analysis based on improved probabilistic neural network","volume":"14","author":"Yang","year":"2020","journal-title":"Front. Comput. Neurosci."},{"key":"10.1016\/j.knosys.2026.115635_bib0013","first-page":"68","article-title":"SAPNN: self-adaptive probabilistic neural network for medical diagnosis","volume":"27(1)","author":"Xiong","year":"2024","journal-title":"Int. J. Comput. Sci. Eng."},{"key":"10.1016\/j.knosys.2026.115635_bib0014","doi-asserted-by":"crossref","DOI":"10.1109\/TIM.2021.3079558","article-title":"Application of a novel PNN evaluation algorithm to a greenhouse monitoring system","volume":"70","author":"Guan","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.knosys.2026.115635_bib0015","doi-asserted-by":"crossref","first-page":"1194","DOI":"10.1166\/jctn.2021.9400","article-title":"Fuzzy C-means (FCM) clustering with probabilistic neural network (PNN) model for detection and classification of rice plant diseases in internet of things-cloud centric precision agriculture","volume":"18(4)","author":"Sindhu","year":"2021","journal-title":"J. Comput. Theor. Nanosci."},{"key":"10.1016\/j.knosys.2026.115635_bib0016","doi-asserted-by":"crossref","DOI":"10.1016\/j.oceaneng.2020.107193","article-title":"Data mining and application of ship impact spectrum acceleration based on PNN neural network","volume":"203","author":"Guo","year":"2020","journal-title":"Ocean Eng."},{"key":"10.1016\/j.knosys.2026.115635_bib0017","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.aei.2019.01.001","article-title":"BA-PNN-based methods for power transformer fault diagnosis","volume":"39","author":"Yang","year":"2019","journal-title":"Adv. Eng. Inf."},{"key":"10.1016\/j.knosys.2026.115635_bib0018","doi-asserted-by":"crossref","first-page":"474","DOI":"10.3390\/pr11020474","article-title":"Application of improved PNN in transformer fault diagnosis","volume":"11(2)","author":"Zhang","year":"2023","journal-title":"processes"},{"key":"10.1016\/j.knosys.2026.115635_bib0019","first-page":"161","article-title":"Early diagnosis of Alzheimer\u2019s disease from MRI images using PNN","author":"Mathew","year":"2018","journal-title":"Int. CET Conf. Control Commun. Comput."},{"key":"10.1016\/j.knosys.2026.115635_bib0020","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1007\/s00521-019-04475-4","article-title":"Bat algorithm-based weighted Laplacian probabilistic neural network","volume":"32","author":"Naik","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.knosys.2026.115635_bib0021","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.csda.2013.06.022","article-title":"Robust mixture regression model fitting by Laplace distribution","volume":"71","author":"Song","year":"2014","journal-title":"Comput. Stat. Data Anal."},{"key":"10.1016\/j.knosys.2026.115635_bib0022","first-page":"128","article-title":"Some Bayes\u2019 estimators for laplace distribution under different loss functions","volume":"22(3)","author":"Huda","year":"2014","journal-title":"J. Babylon Univ."},{"key":"10.1016\/j.knosys.2026.115635_bib0023","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s11682-018-9831-2","article-title":"Alzheimer disease detection from structural MR images using FCM based weighted probabilistic neural network","volume":"13","author":"Duraisamy","year":"2019","journal-title":"Brain Imaging Behav."},{"key":"10.1016\/j.knosys.2026.115635_bib0024","first-page":"9","article-title":"Classification of brain tissues using multiwavelet transformation and probabilistic neural network","volume":"7(9)","author":"Ramakrishna","year":"2006","journal-title":"Int. J. Simul. Syst. Sci. Technol."},{"key":"10.1016\/j.knosys.2026.115635_bib0025","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1007\/s10614-023-10457-5","article-title":"A review of generalized hyperbolic distributions","volume":"64","author":"Jiang1","year":"2024","journal-title":"Comput. Econ."},{"key":"10.1016\/j.knosys.2026.115635_bib0026","first-page":"151","article-title":"Hyperbolic distributions and distributions on hyperbolae","volume":"5(3)","author":"Barndorff-Nielsen","year":"1978","journal-title":"Scand. J. Stat."},{"key":"10.1016\/j.knosys.2026.115635_bib0027","article-title":"The physics of blown sand and desert dunes","author":"Bagnold","year":"1941","journal-title":"Methuen"},{"key":"10.1016\/j.knosys.2026.115635_bib0028","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1080\/01621459.1923.10502116","article-title":"First and second laws of error","volume":"18(143)","author":"Wilson","year":"1923","journal-title":"J. Am. Stat. Assoc."},{"key":"10.1016\/j.knosys.2026.115635_bib0029","first-page":"1","article-title":"The probable error of a mean","volume":"6(1)","author":"Gosset","year":"1908","journal-title":"Biometrika"},{"key":"10.1016\/j.knosys.2026.115635_bib0030","first-page":"275","article-title":"The generalized hyperbolic skew student\u2019s t-Distribution","volume":"4(2)","author":"Aas","year":"2006","journal-title":"J. Financ. Econom."},{"key":"10.1016\/j.knosys.2026.115635_bib0031","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1061\/(ASCE)1084-0699(1999)4:3(189)","article-title":"Halphen distribution system. I: mathematical and statistical properties","volume":"4(3)","author":"Perreault","year":"1999","journal-title":"J. Hydrol. Eng."},{"key":"10.1016\/j.knosys.2026.115635_bib0032","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1109\/72.668893","article-title":"Supervised texture classification using a probabilistic neural network and constraint satisfaction model","volume":"9(3)","author":"Raghu","year":"1998","journal-title":"IEEE Trans. Neural Netw."},{"key":"10.1016\/j.knosys.2026.115635_bib0033","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1023\/A:1008981510081","article-title":"Robust mixture modelling using the t distribution","volume":"10","author":"Peel","year":"2000","journal-title":"Stat. Comput."},{"key":"10.1016\/j.knosys.2026.115635_bib0034","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1007\/s00180-008-0129-5","article-title":"Computationally efficient learning of multivariate t mixture models with missing information","volume":"24","author":"Lin","year":"2009","journal-title":"Comput. Stat."},{"key":"10.1016\/j.knosys.2026.115635_bib0035","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.csda.2018.08.016","article-title":"Mixtures of generalized hyperbolic distributions and mixtures of skew-t distributions for model-based clustering with incomplete data","volume":"130","author":"Wei","year":"2019","journal-title":"Comput. Stat. Data Anal."},{"key":"10.1016\/j.knosys.2026.115635_bib0036","first-page":"1","article-title":"Generalized hyperbolic distributions and Brazilian data","volume":"52","author":"Fajardo","year":"2002","journal-title":"Banco Central do Brasil"},{"key":"10.1016\/j.knosys.2026.115635_bib0037","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2014\/263465","article-title":"A comparison of generalized hyperbolic distribution models for equity returns","volume":"2014","author":"Socgnia","year":"2014","journal-title":"J. Appl. Math."},{"key":"10.1016\/j.knosys.2026.115635_bib0038","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.physa.2015.09.021","article-title":"Highly flexible distributions to fit multiple frequency financial returns","volume":"442","author":"BenSa\u00efda","year":"2016","journal-title":"Physica A"},{"key":"10.1016\/j.knosys.2026.115635_bib0039","first-page":"1","article-title":"ARMA-GARCH model with fractional generalized hyperbolic innovations","volume":"8(48)","author":"Kim","year":"2022","journal-title":"Financ. Innovation"},{"key":"10.1016\/j.knosys.2026.115635_bib0040","first-page":"111","article-title":"Generalized hyperbolic distributions and Brazilian data","volume":"16","author":"Predota","year":"2005","journal-title":"J. Appl. Math."},{"key":"10.1016\/j.knosys.2026.115635_bib0041","first-page":"1763","article-title":"The distribution of commodity futures: a test of the generalized hyperbolic process","volume":"56(15)","author":"Pal","year":"2023","journal-title":"Appl. Econ."},{"key":"10.1016\/j.knosys.2026.115635_bib0042","first-page":"319","article-title":"Generalized hyperbolic distributions and value-at-risk estimation for the South African mining index","volume":"13(2)","author":"Huang","year":"2014","journal-title":"Int. Bus Econ. Res. J."},{"key":"10.1016\/j.knosys.2026.115635_bib0043","first-page":"199","article-title":"Portfolio optimization under the generalized hyperbolic distribution: optimal allocation, performance and tail behavior","volume":"21(2)","author":"Birge","year":"2021","journal-title":"PLoS ONE"},{"key":"10.1016\/j.knosys.2026.115635_bib0044","first-page":"161","article-title":"The statistical analysis of the log-return series of the Chinese stock prices: an application of the generalized hyperbolic distributions","volume":"2","author":"Li","year":"2007","journal-title":"J. Data Anal."},{"key":"10.1016\/j.knosys.2026.115635_bib0045","first-page":"7","article-title":"Generalized hyperbolic distributions: empirical evidence on bucharest stock exchange","volume":"7(1)","author":"Baciu","year":"2015","journal-title":"Rev. Finance Banking"},{"key":"10.1016\/j.knosys.2026.115635_bib0046","first-page":"1567","article-title":"Analytic option pricing and risk measures under a regime-switching generalized hyperbolic model with an application to equity-linked insurance","volume":"10","author":"Wang","year":"2017","journal-title":"Physica A"},{"key":"10.1016\/j.knosys.2026.115635_bib0047","doi-asserted-by":"crossref","DOI":"10.1016\/j.physa.2019.04.136","article-title":"The generalised hyperbolic distribution and its subclass in the analysis of a new era of cryptocurrencies: ethereum and its financial risk","volume":"526","author":"Zhang","year":"2019","journal-title":"Physica A"},{"key":"10.1016\/j.knosys.2026.115635_bib0048","article-title":"Statistical analysis of bitcoin during explosive behavior periods","volume":"14(3)","author":"N\u00fa\u00f1ez","year":"2019","journal-title":"PLoS ONE"},{"key":"10.1016\/j.knosys.2026.115635_bib0049","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.ins.2019.04.016","article-title":"A robust classification framework with mixture correntropy","volume":"491","author":"Wang","year":"2019","journal-title":"Inf. Sci."},{"key":"10.1016\/j.knosys.2026.115635_bib0050","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ejor.2006.08.043","article-title":"Bankruptcy prediction in banks and firms via statistical and intelligent techniques - a review","volume":"180(1)","author":"Kumar","year":"2007","journal-title":"Eur. J. Oper. Res."},{"key":"10.1016\/j.knosys.2026.115635_bib0051","doi-asserted-by":"crossref","first-page":"3355","DOI":"10.1016\/j.eswa.2008.01.003","article-title":"Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: a comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey","volume":"36(2)","author":"Boyacioglu","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2026.115635_bib0052","doi-asserted-by":"crossref","first-page":"2353","DOI":"10.1016\/j.eswa.2013.09.033","article-title":"An improved boosting based on feature selection for corporate bankruptcy prediction","volume":"41(5)","author":"Wang","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2026.115635_bib0053","first-page":"81","article-title":"Comparing the bank failure prediction performance of neural networks and support vector machines: the Turkish case","volume":"26(3)","author":"Ecer","year":"2013","journal-title":"Econ. Res."},{"key":"10.1016\/j.knosys.2026.115635_bib0054","doi-asserted-by":"crossref","first-page":"1366","DOI":"10.1007\/s11142-017-9407-1","article-title":"Corporate bankruptcy prediction: a high dimensional analysis","volume":"22","author":"Jones","year":"2017","journal-title":"Rev. Accounting Stud."},{"key":"10.1016\/j.knosys.2026.115635_bib0055","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.cbrev.2022.08.002","article-title":"Bootstrap-DEA management efficiency and early prediction of bank failure: evidence from 2008-2009 U.S. bank failures","volume":"22","author":"Samad","year":"2022","journal-title":"Central Bank Rev."},{"key":"10.1016\/j.knosys.2026.115635_bib0056","doi-asserted-by":"crossref","first-page":"3161","DOI":"10.1007\/s10614-023-10537-6","article-title":"Forecasting bank failure in the U.S.: A cost-sensitive approach","volume":"64","author":"Ekinci","year":"2024","journal-title":"Comput. Econ."},{"key":"10.1016\/j.knosys.2026.115635_bib0057","doi-asserted-by":"crossref","first-page":"2633","DOI":"10.1016\/j.eswa.2008.01.053","article-title":"Effects of feature construction on classification performance: an empirical study in bank failure prediction","volume":"36","author":"Zhao","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2026.115635_bib0058","doi-asserted-by":"crossref","DOI":"10.1080\/23322039.2020.1729569","article-title":"Failure prediction of Indian Banks using SMOTE, Lasso regression, bagging and boosting","volume":"8","author":"Shrivastava","year":"2020","journal-title":"Cogent Econ. Finance"},{"key":"10.1016\/j.knosys.2026.115635_bib0059","doi-asserted-by":"crossref","DOI":"10.1016\/j.ribaf.2022.101644","article-title":"EU-27 bank failure prediction with C5.0 decision trees and deep learning neural networks","volume":"61","author":"Krist\u00f3f","year":"2022","journal-title":"Res. Int. Business Finance"},{"key":"10.1016\/j.knosys.2026.115635_bib0060","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1016\/j.ijforecast.2019.11.005","article-title":"Predicting bank insolvencies using machine learning techniques","volume":"36","author":"Petropoulos","year":"2012","journal-title":"Int. J. Forecast."},{"key":"10.1016\/j.knosys.2026.115635_bib0061","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11156-014-0492-y","article-title":"On the prediction of financial distress in developed and emerging markets: does the choice of accounting and market information matter? A comparison of UK and Indian Firms","volume":"47","author":"Charalambakis","year":"2016","journal-title":"Rev. Quant. Finance Accounting"},{"key":"10.1016\/j.knosys.2026.115635_bib0062","doi-asserted-by":"crossref","first-page":"1100","DOI":"10.1111\/jbfa.12338","article-title":"Abnormal trading behavior of specific types of shareholders before US firm bankruptcy and its implications for firm bankruptcy prediction","volume":"45","author":"Cheng","year":"2018","journal-title":"J. Bus. Finance Accounting"},{"key":"10.1016\/j.knosys.2026.115635_bib0063","doi-asserted-by":"crossref","first-page":"32","DOI":"10.58496\/BJM\/2025\/005","article-title":"Enhanced parameter estimation for the modified gompertz-makeham model in nonhomogeneous poisson processes using modified likelihood and swarm intelligence approaches","volume":"2025","author":"Hussain","year":"2025","journal-title":"Babylonian J. Math."},{"key":"10.1016\/j.knosys.2026.115635_bib0064","doi-asserted-by":"crossref","DOI":"10.1016\/j.amc.2021.126738","article-title":"An adaptive wavelet optimized finite difference B-spline polynomial chaos method for random partial differential equations","volume":"415","author":"Kaur","year":"2022","journal-title":"App. Math. Comp."},{"key":"10.1016\/j.knosys.2026.115635_bib0065","doi-asserted-by":"crossref","first-page":"8","DOI":"10.70470\/KHWARIZMIA\/2024\/003","article-title":"Advanced composite materials for sustainable construction: innovations in civil engineering applications","volume":"2024","author":"Hussein","year":"2024","journal-title":"Khwarizmia"},{"key":"10.1016\/j.knosys.2026.115635_bib0066","doi-asserted-by":"crossref","first-page":"146","DOI":"10.58496\/BJAI\/2024\/016","article-title":"Advancing arabic handwritten digit recognition with AI-enhanced neural network architectures","volume":"2024","author":"Qasim","year":"2024","journal-title":"Babylonian J. Artif. Intell."},{"key":"10.1016\/j.knosys.2026.115635_bib0067","doi-asserted-by":"crossref","first-page":"106","DOI":"10.58496\/BJML\/2025\/009","article-title":"An analytical comparison of transfer learning techniques for brain tumor detection and classification","volume":"2025","author":"Almaiah","year":"2025","journal-title":"Babylonian J. Mach. Learn."},{"key":"10.1016\/j.knosys.2026.115635_bib0068","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1007\/s11156-023-01158-z","article-title":"Bank failure prediction: corporate governance and financial indicators","volume":"61","author":"Alzayed","year":"2023","journal-title":"Rev. Quant. Finance Accounting"},{"key":"10.1016\/j.knosys.2026.115635_bib0069","doi-asserted-by":"crossref","first-page":"52","DOI":"10.3390\/risks8020052","article-title":"Bankruptcy prediction and stress quantification using support vector machine: evidence from indian banks","volume":"8(2)","author":"Shrivastav","year":"2020","journal-title":"risks"},{"key":"10.1016\/j.knosys.2026.115635_bib0070","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1108\/JFEP-02-2013-0006","article-title":"Bank structure and failure during the financial crisis","volume":"5(3)","author":"Lu","year":"2013","journal-title":"J. Financ. Econ. Policy"},{"key":"10.1016\/j.knosys.2026.115635_bib0071","first-page":"299","article-title":"Bankruptcy prediction using decision tree","author":"Aoki","year":"2004","journal-title":"Appl. Econophys."},{"key":"10.1016\/j.knosys.2026.115635_bib0072","first-page":"349","article-title":"Bank failure prediction: a comparison of machine learning approaches","volume":"1","author":"Manthoulis","year":"2021","journal-title":"Financ. Risk Manage. Model."},{"key":"10.1016\/j.knosys.2026.115635_bib0073","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eswa.2005.01.004","article-title":"A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms","volume":"29(1)","author":"Lee","year":"2005","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2026.115635_bib0074","doi-asserted-by":"crossref","first-page":"135","DOI":"10.58496\/BJN\/2024\/014","article-title":"Survey on neural networks in networking: applications and advancements","volume":"2024","author":"Ayad","year":"2024","journal-title":"Babylonian J. Netw."},{"key":"10.1016\/j.knosys.2026.115635_bib0075","doi-asserted-by":"crossref","first-page":"132","DOI":"10.3390\/jrfm17040132","article-title":"Artificial intelligence techniques for bankruptcy prediction of tunisian companies: an application of machine learning and deep learning-based models","volume":"17(4)","author":"Hamdi","year":"2024","journal-title":"J. Risk Financ. Manage."},{"key":"10.1016\/j.knosys.2026.115635_bib0076","first-page":"489","article-title":"The rise of artificial intelligence and robots in the 4th industrial revolution: implications for future South African job creation","volume":"15(4)","author":"Rapanyane","year":"2020","journal-title":"J. Acad. Soc. Sci."},{"key":"10.1016\/j.knosys.2026.115635_bib0077","first-page":"281","article-title":"Characteristics of consulting firms associated with the diffusion of big data analytics","volume":"28(4)","author":"Oyewo","year":"2020","journal-title":"J. Asian Bus. Econ. Stud."},{"key":"10.1016\/j.knosys.2026.115635_bib0078","doi-asserted-by":"crossref","first-page":"121","DOI":"10.58496\/BJML\/2024\/012","article-title":"A fuzzy wavelet neural network (FWNN) and hybrid optimization machine learning technique for traffic flow prediction","volume":"2024","author":"Balasubramani","year":"2024","journal-title":"Babylonian J. Mach. Learn."},{"key":"10.1016\/j.knosys.2026.115635_bib0079","doi-asserted-by":"crossref","first-page":"26","DOI":"10.3390\/jrfm18010026","article-title":"Challenges of artificial intelligence for the prevention and identification of bankruptcy risk in financial institutions: a systematic review","volume":"18(1)","author":"V\u00e1squez-Serpa","year":"2025","journal-title":"J. Risk Financ. Manage."},{"key":"10.1016\/j.knosys.2026.115635_bib0080","first-page":"22","article-title":"Bankruptcy prediction of Indian banks using advanced analytics","volume":"32(4)","author":"Oberoi","year":"2023","journal-title":"Econ. Stud."},{"key":"10.1016\/j.knosys.2026.115635_bib0081","first-page":"401","article-title":"Exponentially decreasing distributions for the logarithm of particle size","volume":"353","author":"Barndorff-Nielsen","year":"1977","journal-title":"Proc. R. Soc. A Math. Phys. Sci."},{"key":"10.1016\/j.knosys.2026.115635_bib0082","unstructured":"Federal Financial Institutions Examination Council, Download Bulk Data \u2013 FFIEC Central Data Repository\u2019s Public Data Distribution, 2000-2024, (https:\/\/cdr.ffiec.gov\/public\/PWS\/DownloadBulkData.aspx). Accessed: 27 January 2025."},{"key":"10.1016\/j.knosys.2026.115635_bib0083","article-title":"The lambert way to gaussianize heavy-tailed data with the inverse of Tukey\u2019s h transformation as a special case","volume":"2015(1)","author":"Goerg","year":"2015","journal-title":"Sci. World J."},{"key":"10.1016\/j.knosys.2026.115635_bib0084","doi-asserted-by":"crossref","first-page":"2312","DOI":"10.1080\/02664763.2019.1630372","article-title":"Ordered quantile normalization: a semiparametric transformation built for the cross-validation era","volume":"47(13-15)","author":"Peterson","year":"2020","journal-title":"J. Appl. Stat."},{"key":"10.1016\/j.knosys.2026.115635_bib0085","doi-asserted-by":"crossref","first-page":"2164","DOI":"10.3390\/app8112164","article-title":"Stability prediction model of roadway surrounding rock based on concept lattice reduction and a symmetric alpha stable distribution probability neural network","volume":"8(11)","author":"Liu","year":"2018","journal-title":"Appl. Sci."},{"key":"10.1016\/j.knosys.2026.115635_bib0086","article-title":"Skew-probabilistic neural networks for learning from imbalanced data","volume":"165","author":"Naika","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.knosys.2026.115635_bib0087","article-title":"Bayesian optimization with optuna for enhanced soil nutrient prediction: a comparative study with genetic algorithm and particle swarm optimization","volume":"12","author":"Dada","year":"2025","journal-title":"Smart Agric. Technol."}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126003758?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126003758?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T12:11:17Z","timestamp":1774699877000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126003758"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":87,"alternative-id":["S0950705126003758"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.115635","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"An advanced hybrid probabilistic neural network based on generalized hyperbolic distributions for handling non-Gaussian data","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.115635","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":"115635"}}