{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T14:30:33Z","timestamp":1769351433969,"version":"3.49.0"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T00:00:00Z","timestamp":1664582400000},"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":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1007\/s13042-022-01668-7","type":"journal-article","created":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T14:02:21Z","timestamp":1664632941000},"page":"861-872","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Accurate neural network classification model for schizophrenia disease based on electroencephalogram data"],"prefix":"10.1007","volume":"14","author":[{"given":"Miguel \u00c1ngel","family":"Luj\u00e1n","sequence":"first","affiliation":[]},{"given":"Jorge Mateo","family":"Sotos","sequence":"additional","affiliation":[]},{"given":"Jos\u00e9 L.","family":"Santos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2880-0678","authenticated-orcid":false,"given":"Alejandro L.","family":"Borja","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,1]]},"reference":[{"key":"1668_CR1","unstructured":"World Health Organization (WHO) (2021) Available online https:\/\/www.who.int\/. Accessed on 17 Dec 2021"},{"issue":"1","key":"1668_CR2","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.schres.2013.05.028","volume":"150","author":"R Tandon","year":"2013","unstructured":"Tandon R, Gaebel W, Barch DM et al (2013) Definition and description of schizophrenia in the DSM-5. Schizophr Res 150(1):3\u201310. https:\/\/doi.org\/10.1016\/j.schres.2013.05.028","journal-title":"Schizophr Res"},{"issue":"9690","key":"1668_CR3","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1016\/S0140-6736(09)60995-8","volume":"374","author":"J van Os","year":"2009","unstructured":"van Os J, Kapur S (2009) Schizophrenia. Lancet 374(9690):635\u2013645. https:\/\/doi.org\/10.1016\/S0140-6736(09)60995-8","journal-title":"Lancet"},{"issue":"6","key":"1668_CR4","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1016\/j.apnu.2016.02.009","volume":"30","author":"SC Cheng","year":"2016","unstructured":"Cheng SC, Schepp KG (2016) Early intervention in schizophrenia: a literature review. Arch Psychiatr Nurs 30(6):774\u2013781. https:\/\/doi.org\/10.1016\/j.apnu.2016.02.009","journal-title":"Arch Psychiatr Nurs"},{"issue":"10","key":"1668_CR5","doi-asserted-by":"publisher","first-page":"794","DOI":"10.1176\/appi.ajp.2019.19080865","volume":"176","author":"JA Lieberman","year":"2019","unstructured":"Lieberman JA, Small SA, Girgis RR (2019) Early detection and preventive intervention in schizophrenia: from fantasy to reality. Am J Psychiatry 176(10):794\u2013810. https:\/\/doi.org\/10.1176\/appi.ajp.2019.19080865","journal-title":"Am J Psychiatry"},{"issue":"36","key":"1668_CR6","doi-asserted-by":"publisher","first-page":"6451","DOI":"10.2174\/1381612811319360006","volume":"19","author":"ZU Khan","year":"2013","unstructured":"Khan ZU, Martin-Monta\u00f1ez E, Muly EC (2013) Schizophrenia: causes and treatments. Curr Pharm Des 19(36):6451\u20136461. https:\/\/doi.org\/10.2174\/1381612811319360006","journal-title":"Curr Pharm Des"},{"key":"1668_CR7","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/bs.adgen.2016.08.001","volume":"96","author":"J van de Leemput","year":"2016","unstructured":"van de Leemput J, Hess JL, Glatt SJ, Tsuang MT (2016) Genetics of schizophrenia: historical insights and prevailing evidence. Adv Genet 96:99\u2013141. https:\/\/doi.org\/10.1016\/bs.adgen.2016.08.001","journal-title":"Adv Genet"},{"issue":"3","key":"1668_CR8","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1007\/s13246-019-00779-w","volume":"42","author":"B Thilakavathi","year":"2019","unstructured":"Thilakavathi B, Shenbaga Devi S, Malaiappan M, Bhanu K (2019) EEG power spectrum analysis for schizophrenia during mental activity. Australas Phys Eng Sci Med 42(3):887\u2013897. https:\/\/doi.org\/10.1007\/s13246-019-00779-w","journal-title":"Australas Phys Eng Sci Med"},{"issue":"3","key":"1668_CR9","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1177\/1550059420965385","volume":"52","author":"M Arora","year":"2021","unstructured":"Arora M, Knott VJ, Labelle A, Fisher DJ (2021) Alterations of resting EEG in hallucinating and nonhallucinating schizophrenia patients. Clin EEG Neurosci 52(3):159\u2013167. https:\/\/doi.org\/10.1177\/1550059420965385","journal-title":"Clin EEG Neurosci"},{"issue":"4","key":"1668_CR10","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1177\/1550059419886686","volume":"51","author":"S Hirano","year":"2020","unstructured":"Hirano S, Spencer KM, Onitsuka T, Hirano Y (2020) Language-related neurophysiological deficits in schizophrenia. Clin EEG Neurosci 51(4):222\u2013233. https:\/\/doi.org\/10.1177\/1550059419886686","journal-title":"Clin EEG Neurosci"},{"issue":"11","key":"1668_CR11","doi-asserted-by":"publisher","first-page":"2390","DOI":"10.1109\/TNSRE.2020.3022715","volume":"28","author":"S Siuly","year":"2020","unstructured":"Siuly S, Khare SK, Bajaj V, Wang H, Zhang Y (2020) A computerized method for automatic detection of schizophrenia using EEG signals. IEEE Trans Neural Syst Rehabil Eng 28(11):2390\u20132400. https:\/\/doi.org\/10.1109\/TNSRE.2020.3022715","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"3","key":"1668_CR12","doi-asserted-by":"publisher","first-page":"R80","DOI":"10.1016\/j.cub.2018.11.052","volume":"29","author":"A Biasiucci","year":"2019","unstructured":"Biasiucci A, Franceschiello B, Murray MM (2019) Electroencephalography. Curr Biol 29(3):R80\u2013R85. https:\/\/doi.org\/10.1016\/j.cub.2018.11.052","journal-title":"Curr Biol"},{"issue":"23","key":"1668_CR13","doi-asserted-by":"publisher","first-page":"3037","DOI":"10.3390\/electronics10233037","volume":"10","author":"M\u00c1 Luj\u00e1n","year":"2021","unstructured":"Luj\u00e1n M\u00c1, Jimeno MV, Mateo Sotos J, Ricarte JJ, Borja AL (2021) A survey on EEG signal processing techniques and machine learning: applications to the neurofeedback of autobiographical memory deficits in schizophrenia. Electronics 10(23):3037. https:\/\/doi.org\/10.3390\/electronics10233037","journal-title":"Electronics"},{"key":"1668_CR14","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.neubiorev.2019.07.021","volume":"105","author":"FS de Aguiar Neto","year":"2019","unstructured":"de Aguiar Neto FS, Rosa JLG (2019) Depression biomarkers using non-invasive EEG: a review. Neurosci Biobehav Rev 105:83\u201393. https:\/\/doi.org\/10.1016\/j.neubiorev.2019.07.021","journal-title":"Neurosci Biobehav Rev"},{"issue":"4","key":"1668_CR15","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1586\/14737175.2015.1025382","volume":"15","author":"F Rosenow","year":"2015","unstructured":"Rosenow F, Klein KM, Hamer HM (2015) Non-invasive EEG evaluation in epilepsy diagnosis. Expert Rev Neurother 15(4):425\u2013444. https:\/\/doi.org\/10.1586\/14737175.2015.1025382","journal-title":"Expert Rev Neurother"},{"issue":"3","key":"1668_CR16","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ab0ab5","volume":"16","author":"A Craik","year":"2019","unstructured":"Craik A, He Y, Contreras-Vidal JL (2019) Deep learning for electroencephalogram (EEG) classification tasks: a review. J Neural Eng 16(3):031001. https:\/\/doi.org\/10.1088\/1741-2552\/ab0ab5","journal-title":"J Neural Eng"},{"key":"1668_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.117021","volume":"220","author":"LAW Gemein","year":"2020","unstructured":"Gemein LAW, Schirrmeister RT, Chrab\u0105szcz P et al (2020) Machine-learning-based diagnostics of EEG pathology. Neuroimage 220:117021. https:\/\/doi.org\/10.1016\/j.neuroimage.2020.117021","journal-title":"Neuroimage"},{"issue":"7","key":"1668_CR18","doi-asserted-by":"publisher","first-page":"1515","DOI":"10.1007\/s11517-020-02176-y","volume":"58","author":"M Zheng","year":"2020","unstructured":"Zheng M, Yang B, Xie Y (2020) EEG classification across sessions and across subjects through transfer learning in motor imagery-based brain-machine interface system. Med Biol Eng Comput 58(7):1515\u20131528. https:\/\/doi.org\/10.1007\/s11517-020-02176-y","journal-title":"Med Biol Eng Comput"},{"key":"1668_CR19","doi-asserted-by":"publisher","first-page":"3040","DOI":"10.1109\/EMBC44109.2020.9175344","volume":"2020","author":"C Ju","year":"2020","unstructured":"Ju C, Gao D, Mane R, Tan B, Liu Y, Guan C (2020) Federated transfer learning for EEG signal classification. Annu Int Conf IEEE Eng Med Biol Soc 2020:3040\u20133045. https:\/\/doi.org\/10.1109\/EMBC44109.2020.9175344","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"key":"1668_CR20","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/978-1-62703-748-8_7","volume":"1107","author":"Y Ba\u015ftanlar","year":"2014","unstructured":"Ba\u015ftanlar Y, Ozuysal M (2014) Introduction to machine learning. Methods Mol Biol 1107:105\u2013128. https:\/\/doi.org\/10.1007\/978-1-62703-748-8_7","journal-title":"Methods Mol Biol"},{"key":"1668_CR21","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2022.3168357","author":"M Tanveer","year":"2022","unstructured":"Tanveer M, Jangir J, Ganaie MA, Beheshti I, Tabish M, Chhabra N (2022) Diagnosis of schizophrenia: a comprehensive evaluation. IEEE J Biomed Health Inform. https:\/\/doi.org\/10.1109\/JBHI.2022.3168357","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"1668_CR22","doi-asserted-by":"publisher","DOI":"10.1002\/mpr.1818","volume":"29","author":"YT Jo","year":"2020","unstructured":"Jo YT, Joo SW, Shon SH, Kim H, Kim Y, Lee J (2020) Diagnosing schizophrenia with network analysis and a machine learning method. Int J Methods Psychiatr Res 29(1):e1818. https:\/\/doi.org\/10.1002\/mpr.1818","journal-title":"Int J Methods Psychiatr Res"},{"issue":"7","key":"1668_CR23","doi-asserted-by":"publisher","first-page":"2517","DOI":"10.3390\/s22072517","volume":"22","author":"AS G\u00f3ngora","year":"2022","unstructured":"G\u00f3ngora AS, Marques G, Agarwal D, De la Torre D\u00edez I, Franco-Mart\u00edn M (2022) Comparison of machine learning algorithms in the prediction of hospitalized patients with schizophrenia. Sensors (Basel) 22(7):2517. https:\/\/doi.org\/10.3390\/s22072517","journal-title":"Sensors (Basel)"},{"key":"1668_CR24","doi-asserted-by":"crossref","unstructured":"Almutairi MM, Alhamad N, Alyami A, Alshobbar Z, Alfayez H, Al-Akkas N, Alhiyafi JA, Olatunji SO (2019) Preemptive diagnosis of schizophrenia disease using computational intelligence techniques. In: Proceedings of the 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS), Riyadh, Saudi Arabia; pp. 1\u20136","DOI":"10.1109\/CAIS.2019.8769513"},{"key":"1668_CR25","doi-asserted-by":"crossref","unstructured":"Khan SI, Islam A, Hossen A, Zahangir TI, Hoque ASML (2018) Supporting the treatment of mental diseases using data mining. In: Proceedings of the 2018 International Conference on Innovations in Science, Engineering and Technology (ICISET), Chittagong, Bangladesh, pp. 339\u2013344","DOI":"10.1109\/ICISET.2018.8745591"},{"issue":"Suppl 3","key":"1668_CR26","first-page":"261","volume":"31","author":"A Vacca","year":"2019","unstructured":"Vacca A, Longo R, Mencar C (2019) Identification and evaluation of cognitive deficits in schizophrenia using \u201cMachine learning.\u201d Psychiatr Danub 31(Suppl 3):261\u2013264","journal-title":"Psychiatr Danub"},{"issue":"10","key":"1668_CR27","doi-asserted-by":"publisher","first-page":"4389","DOI":"10.1109\/TNNLS.2019.2952000","volume":"31","author":"DPP Mesquita","year":"2020","unstructured":"Mesquita DPP, Freitas LA, Gomes JPP, Mattos CLC (2020) LS-SVR as a Bayesian RBF network. IEEE Trans Neural Netw Learn Syst 31(10):4389\u20134393. https:\/\/doi.org\/10.1109\/TNNLS.2019.2952000","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"3","key":"1668_CR28","doi-asserted-by":"publisher","first-page":"368504211026111","DOI":"10.1177\/00368504211026111","volume":"104","author":"H Wen","year":"2021","unstructured":"Wen H, Yan T, Liu Z, Chen D (2021) Integrated neural network model with pre-RBF kernels. Sci Prog 104(3):368504211026111. https:\/\/doi.org\/10.1177\/00368504211026111","journal-title":"Sci Prog"},{"issue":"1","key":"1668_CR29","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1109\/TITB.2006.888702","volume":"12","author":"I Maglogiannis","year":"2008","unstructured":"Maglogiannis I, Sarimveis H, Kiranoudis CT, Chatziioannou AA, Oikonomou N, Aidinis V (2008) Radial basis function neural networks classification for the recognition of idiopathic pulmonary fibrosis in microscopic images. IEEE Trans Inf Technol Biomed 12(1):42\u201354. https:\/\/doi.org\/10.1109\/TITB.2006.888702","journal-title":"IEEE Trans Inf Technol Biomed"},{"key":"1668_CR30","volume-title":"Data mining: concepts and techniques","author":"J Han","year":"2011","unstructured":"Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier, Amsterdam"},{"issue":"5","key":"1668_CR31","doi-asserted-by":"publisher","first-page":"2601","DOI":"10.1109\/TCYB.2019.2907002","volume":"51","author":"A Gupta","year":"2021","unstructured":"Gupta A, Datta S, Das S (2021) Fuzzy clustering to identify clusters at different levels of fuzziness: an evolutionary multiobjective optimization approach. IEEE Trans Cybern 51(5):2601\u20132611. https:\/\/doi.org\/10.1109\/TCYB.2019.2907002","journal-title":"IEEE Trans Cybern"},{"issue":"1","key":"1668_CR32","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1109\/TCYB.2020.2980794","volume":"52","author":"Y Mi","year":"2022","unstructured":"Mi Y, Shi Y, Li J, Liu W, Yan M (2022) Fuzzy-based concept learning method: exploiting data with fuzzy conceptual clustering. IEEE Trans Cybern 52(1):582\u2013593. https:\/\/doi.org\/10.1109\/TCYB.2020.2980794","journal-title":"IEEE Trans Cybern"},{"key":"1668_CR33","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.neunet.2018.07.012","volume":"108","author":"M Kusy","year":"2018","unstructured":"Kusy M (2018) Fuzzy c-means-based architecture reduction of a probabilistic neural network. Neural Netw 108:20\u201332. https:\/\/doi.org\/10.1016\/j.neunet.2018.07.012","journal-title":"Neural Netw"},{"key":"1668_CR34","doi-asserted-by":"publisher","first-page":"519","DOI":"10.3233\/THC-150989","volume":"23","author":"Y Wu","year":"2015","unstructured":"Wu Y, Duan H, Du S (2015) Multiple fuzzy c-means clustering algorithm in medical diagnosis. Technol Health Care 23:519\u2013527. https:\/\/doi.org\/10.3233\/THC-150989","journal-title":"Technol Health Care"},{"issue":"8","key":"1668_CR35","doi-asserted-by":"publisher","first-page":"917","DOI":"10.2174\/1573405616666210104111218","volume":"17","author":"G Latif","year":"2021","unstructured":"Latif G, Alghazo J, Sibai FN, Iskandar DNFA, Khan AH (2021) Recent advancements in fuzzy c-means based techniques for brain MRI segmentation. Curr Med Imaging 17(8):917\u2013930. https:\/\/doi.org\/10.2174\/1573405616666210104111218","journal-title":"Curr Med Imaging"},{"issue":"3","key":"1668_CR36","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0248737","volume":"16","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Han J (2021) Differential privacy fuzzy C-means clustering algorithm based on gaussian kernel function. PLoS ONE 16(3):e0248737. https:\/\/doi.org\/10.1371\/journal.pone.0248737","journal-title":"PLoS ONE"},{"issue":"5","key":"1668_CR37","doi-asserted-by":"publisher","first-page":"921","DOI":"10.3233\/THC-202619","volume":"29","author":"MA Li","year":"2021","unstructured":"Li MA, Wang RT, Wei LN (2021) Fuzzy support vector machine with joint optimization of genetic algorithm and fuzzy c-means. Technol Health Care 29(5):921\u2013937. https:\/\/doi.org\/10.3233\/THC-202619","journal-title":"Technol Health Care"},{"issue":"4","key":"1668_CR38","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1016\/j.rcc.2005.08.004","volume":"11","author":"JA Munday","year":"2005","unstructured":"Munday JA (2005) Instrumentation and electrode placement. Respir Care Clin N Am 11(4):605. https:\/\/doi.org\/10.1016\/j.rcc.2005.08.004","journal-title":"Respir Care Clin N Am"},{"key":"1668_CR39","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/B978-0-444-64032-1.00004-7","volume":"160","author":"RC Burgess","year":"2019","unstructured":"Burgess RC (2019) Filtering of neurophysiologic signals. Handb Clin Neurol 160:51\u201365. https:\/\/doi.org\/10.1016\/B978-0-444-64032-1.00004-7","journal-title":"Handb Clin Neurol"},{"issue":"5","key":"1668_CR40","doi-asserted-by":"publisher","first-page":"346","DOI":"10.2345\/0899-8205-54.5.346","volume":"54","author":"DR Alkhorshid","year":"2020","unstructured":"Alkhorshid DR, Molaeezadeh SF, Alkhorshid MR (2020) Analysis: electroencephalography acquisition system: analog design. Biomed Instrum Technol 54(5):346\u2013351. https:\/\/doi.org\/10.2345\/0899-8205-54.5.346","journal-title":"Biomed Instrum Technol"},{"key":"1668_CR41","unstructured":"Brain Vision. Available online https:\/\/brainvision.com. Accessed on 12 Jan 2022"},{"issue":"5","key":"1668_CR42","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/S1470-2045(19)30149-4","volume":"20","author":"KY Ngiam","year":"2019","unstructured":"Ngiam KY, Khor IW (2019) Big data and machine learning algorithms for health-care delivery. Lancet Oncol 20(5):262\u2013273. https:\/\/doi.org\/10.1016\/S1470-2045(19)30149-4","journal-title":"Lancet Oncol"},{"issue":"4","key":"1668_CR43","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.jormas.2021.04.003","volume":"122","author":"Q Hennocq","year":"2021","unstructured":"Hennocq Q, Khonsari RH, Beno\u00eet V, Rio M, Garcelon N (2021) Computational diagnostic methods on 2D photographs: a review of the literature. J Stomatol Oral Maxillofac Surg 122(4):71\u201375. https:\/\/doi.org\/10.1016\/j.jormas.2021.04.003","journal-title":"J Stomatol Oral Maxillofac Surg"},{"issue":"9","key":"1668_CR44","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1038\/s41584-020-0461-x","volume":"16","author":"DH Solomon","year":"2020","unstructured":"Solomon DH, Rudin RS (2020) Digital health technologies: opportunities and challenges in rheumatology. Nat Rev Rheumatol 16(9):525\u2013535. https:\/\/doi.org\/10.1038\/s41584-020-0461-x","journal-title":"Nat Rev Rheumatol"},{"issue":"6","key":"1668_CR45","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1111\/joim.12822","volume":"284","author":"GS Handelman","year":"2018","unstructured":"Handelman GS, Kok HK, Chandra RV, Razavi AH, Lee MJ, Asadi H (2018) eDoctor: machine learning and the future of medicine. J Intern Med 284(6):603\u2013619. https:\/\/doi.org\/10.1111\/joim.12822","journal-title":"J Intern Med"},{"issue":"1","key":"1668_CR46","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1007\/s12038-020-00114-6","volume":"45","author":"M Sreepadmanabh","year":"2020","unstructured":"Sreepadmanabh M, Sahu AK, Chande A (2020) COVID-19: advances in diagnostic tools, treatment strategies, and vaccine development. J Biosci 45(1):148. https:\/\/doi.org\/10.1007\/s12038-020-00114-6","journal-title":"J Biosci"},{"issue":"1","key":"1668_CR47","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1097\/SLA.0000000000002693","volume":"268","author":"DA Hashimoto","year":"2018","unstructured":"Hashimoto DA, Rosman G, Rus D, Meireles OR (2018) Artificial intelligence in surgery: promises and perils. Ann Surg 268(1):70\u201376. https:\/\/doi.org\/10.1097\/SLA.0000000000002693","journal-title":"Ann Surg"},{"issue":"5\u20136","key":"1668_CR48","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1016\/S0893-6080(03)00086-8","volume":"16","author":"D Casasent","year":"2003","unstructured":"Casasent D, Chen XW (2003) Radial basis function neural networks for nonlinear Fisher discrimination and Neyman-Pearson classification. Neural Netw 16(5\u20136):529\u2013535. https:\/\/doi.org\/10.1016\/S0893-6080(03)00086-8","journal-title":"Neural Netw"},{"issue":"4","key":"1668_CR49","doi-asserted-by":"publisher","first-page":"219","DOI":"10.4103\/jmss.JMSS_69_19","volume":"10","author":"J Ostadieh","year":"2020","unstructured":"Ostadieh J, Amirani MC, Valizadeh M (2020) Enhancing obstructive apnea disease detection using dual-tree complex wavelet transform-based features and the hybrid \u201ck-means, recursive least-squares\u201d learning for the radial basis function network. J Med Signals Sens 10(4):219\u2013227. https:\/\/doi.org\/10.4103\/jmss.JMSS_69_19","journal-title":"J Med Signals Sens"},{"issue":"8","key":"1668_CR50","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TPAMI.2019.2906594","volume":"42","author":"Q Que","year":"2020","unstructured":"Que Q, Belkin M (2020) Back to the future: radial basis function network revisited. IEEE Trans Pattern Anal Mach Intell 42(8):1856\u20131867. https:\/\/doi.org\/10.1109\/TPAMI.2019.2906594","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"1668_CR51","doi-asserted-by":"publisher","first-page":"1839","DOI":"10.1093\/genetics\/154.4.1839","volume":"154","author":"HF Utz","year":"2000","unstructured":"Utz HF, Melchinger AE, Sch\u00f6n CC (2000) Bias and sampling error of the estimated proportion of genotypic variance explained by quantitative trait loci determined from experimental data in maize using cross validation and validation with independent samples. Genetics 154(3):1839\u20131849","journal-title":"Genetics"},{"key":"1668_CR52","unstructured":"Matlab toolbox (Matlab 2021a) The Mathworks Inc., Natick, MA, US"},{"key":"1668_CR53","unstructured":"Meyes R, Lu M, de Puiseau CW, Meisen T (2019) Ablation studies in artificial neural networks, pp 1\u201319, arXiv: 1901.08644"},{"key":"1668_CR54","volume-title":"A friendly introduction to analysis","author":"WAJ Kosmala","year":"2004","unstructured":"Kosmala WAJ (2004) A friendly introduction to analysis. Prentice Hall, New Jersey"},{"key":"1668_CR55","volume-title":"Quantum computation and quantum information","author":"M Nielsen","year":"2011","unstructured":"Nielsen M, Chuang I (2011) Quantum computation and quantum information, 10th edn. Cambridge University Press, Cambridge","edition":"10"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01668-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-022-01668-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01668-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T03:48:28Z","timestamp":1677037708000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-022-01668-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,1]]},"references-count":55,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["1668"],"URL":"https:\/\/doi.org\/10.1007\/s13042-022-01668-7","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,1]]},"assertion":[{"value":"5 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 October 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}