{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T16:15:57Z","timestamp":1778516157499,"version":"3.51.4"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:00:00Z","timestamp":1773792000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:00:00Z","timestamp":1773792000000},"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":["Soft Comput"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1007\/s00500-026-11312-z","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T10:19:15Z","timestamp":1773829155000},"page":"3631-3651","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Scientific validation of hybrid deep learning using unseen paradigm and explainability for classification of women\u2019s heart failure gene expression"],"prefix":"10.1007","volume":"30","author":[{"given":"Ekta","family":"Tiwari","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dipti","family":"Shrimankar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Krish","family":"Chaudhary","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luca","family":"Saba","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6499-396X","authenticated-orcid":false,"given":"Jasjit S.","family":"Suri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,18]]},"reference":[{"issue":"4","key":"11312_CR1","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1109\/TIM.2011.2174897","volume":"61","author":"UR Acharya","year":"2011","unstructured":"Acharya UR et al (2011) An accurate and generalized approach to plaque characterization in 346 carotid ultrasound scans. IEEE Trans Instrum Meas 61(4):1045\u20131053","journal-title":"IEEE Trans Instrum Meas"},{"key":"11312_CR2","doi-asserted-by":"publisher","first-page":"1304483","DOI":"10.3389\/frai.2024.1304483","volume":"7","author":"S Agarwal","year":"2024","unstructured":"Agarwal S et al (2024) COVLIAS 3.0: cloud-based quantized hybrid UNet3+ deep learning for COVID-19 lesion detection in lung computed tomography. Front Artif Intell 7:1304483","journal-title":"Front Artif Intell"},{"issue":"6","key":"11312_CR3","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0321761","volume":"20","author":"W Ahmad","year":"2025","unstructured":"Ahmad W, Shahzad AR, Amin MA, Bangyal WH, Alahmadi TJ, Khan SH (2025) Machine learning driven dashboard for chronic myeloid leukemia prediction using protein sequences. PLoS ONE 20(6):e0321761","journal-title":"PLoS ONE"},{"issue":"2","key":"11312_CR4","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.maturitas.2009.03.009","volume":"63","author":"F Al-Azzawi","year":"2009","unstructured":"Al-Azzawi F, Palacios S (2009) Hormonal changes during menopause. Maturitas 63(2):135\u2013137","journal-title":"Maturitas"},{"key":"11312_CR5","doi-asserted-by":"crossref","unstructured":"Alharthi M, Mahmood A (2024) xLSTMTime: long-term time series forecasting with xLSTM, vol abs\/2407.10240","DOI":"10.20944\/preprints202407.1246.v1"},{"key":"11312_CR6","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2025.3606753","author":"A Ali","year":"2025","unstructured":"Ali A, Xia Y, Zia MF, Bangyal WH, Iqbal M (2025) Trustworthy load forecasting with generative AI: a dual-attention convlstm and VAE-based approach. IEEE Trans Consum Electron. https:\/\/doi.org\/10.1109\/TCE.2025.3606753","journal-title":"IEEE Trans Consum Electron"},{"issue":"5","key":"11312_CR7","first-page":"8094","volume":"12","author":"F Angum","year":"2020","unstructured":"Angum F, Khan T, Kaler J, Siddiqui L, Hussain A (2020) The prevalence of autoimmune disorders in women: a narrative review. Cureus 12(5):8094","journal-title":"Cureus"},{"issue":"12","key":"11312_CR8","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11121890","volume":"11","author":"WH Bangyal","year":"2022","unstructured":"Bangyal WH et al (2022) Constructing domain ontology for Alzheimer disease using deep learning based approach. Electronics 11(12):1890","journal-title":"Electronics"},{"key":"11312_CR9","doi-asserted-by":"publisher","first-page":"1717913","DOI":"10.3389\/frai.2025.1717913","volume":"8","author":"WH Bangyal","year":"2025","unstructured":"Bangyal WH, Jaffar A, Alfayez AA, Ashraf A, Alazmi M, Hussain M (2025) Gender based Alzheimer\u2019s detection using ResNet-50 and binary dragonfly algorithm on neuroimaging. Front Artif Intell 8:1717913","journal-title":"Front Artif Intell"},{"issue":"9","key":"11312_CR10","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1007\/s10462-024-10873-5","volume":"57","author":"A Bathula","year":"2024","unstructured":"Bathula A et al (2024) Blockchain, artificial intelligence, and healthcare: the tripod of future\u2014a narrative review. Artif Intell Rev 57(9):238","journal-title":"Artif Intell Rev"},{"key":"11312_CR11","first-page":"107547","volume":"37","author":"M Beck","year":"2024","unstructured":"Beck M et al (2024) xlstm: extended long short-term memory. Adv Neural Inf Process Syst 37:107547\u2013107603","journal-title":"Adv Neural Inf Process Syst"},{"issue":"17","key":"11312_CR12","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics14171894","volume":"14","author":"M Bhagawati","year":"2024","unstructured":"Bhagawati M et al (2024) Cardiovascular disease risk stratification using hybrid deep learning paradigm: first of its kind on Canadian trial data. Diagnostics 14(17):1894","journal-title":"Diagnostics"},{"key":"11312_CR13","doi-asserted-by":"crossref","unstructured":"Bissoto A, Valle E, Avila S (2021) Gan-based data augmentation and anonymization for skin-lesion analysis: a critical review. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1847\u20131856","DOI":"10.1109\/CVPRW53098.2021.00204"},{"key":"11312_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2024.102437","volume":"117","author":"M Biswas","year":"2024","unstructured":"Biswas M et al (2024) MultiNet 2.0: a lightweight attention-based deep learning network for stenosis measurement in carotid ultrasound scans and cardiovascular risk assessment. Comput Med Imaging Graph 117:102437","journal-title":"Comput Med Imaging Graph"},{"key":"11312_CR15","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357","journal-title":"J Artif Intell Res"},{"issue":"1","key":"11312_CR16","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1186\/s12911-020-1023-5","volume":"20","author":"D Chicco","year":"2020","unstructured":"Chicco D, Jurman G (2020) Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Med Inform Decis Mak 20(1):16","journal-title":"BMC Med Inform Decis Mak"},{"key":"11312_CR17","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2019.00265","volume":"10","author":"MK Desai","year":"2019","unstructured":"Desai MK, Brinton RD (2019) Autoimmune disease in women: endocrine transition and risk across the lifespan. Front Endocrinol 10:265","journal-title":"Front Endocrinol"},{"issue":"11","key":"11312_CR18","doi-asserted-by":"publisher","first-page":"1954","DOI":"10.3390\/diagnostics13111954","volume":"13","author":"AK Dubey","year":"2023","unstructured":"Dubey AK et al (2023) Ensemble deep learning derived from transfer learning for classification of COVID-19 patients on hybrid deep-learning-based lung segmentation: a data augmentation and balancing framework. Diagnostics 13(11):1954","journal-title":"Diagnostics"},{"key":"11312_CR19","doi-asserted-by":"crossref","unstructured":"Fan X, Tao C, Zhao J (2024) Advanced stock price prediction with xLSTM-based models: improving long-term forecasting. In: 2024 11th international conference on soft computing & machine intelligence (ISCMI), pp 117\u2013123","DOI":"10.1109\/ISCMI63661.2024.10851608"},{"issue":"3","key":"11312_CR20","doi-asserted-by":"publisher","first-page":"1775","DOI":"10.3892\/mmr.2020.11247","volume":"22","author":"H-Z Fang","year":"2020","unstructured":"Fang H-Z, Hu D-L, Li Q, Tu S (2020) Risk gene identification and support vector machine learning to construct an early diagnosis model of myocardial infarction. Mol Med Rep 22(3):1775\u20131782","journal-title":"Mol Med Rep"},{"issue":"14","key":"11312_CR21","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics14141534","volume":"14","author":"S Gupta","year":"2024","unstructured":"Gupta S et al (2024) Four transformer-based deep learning classifiers embedded with an attention U-Net-based lung segmenter and layer-wise relevance propagation-based heatmaps for COVID-19 X-ray scans. Diagnostics 14(14):1534","journal-title":"Diagnostics"},{"key":"11312_CR22","unstructured":"Hsu H, Lachenbruch PA (2014) Paired t test. In: Wiley StatsRef: statistics reference online"},{"key":"11312_CR23","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2022.813438","volume":"13","author":"D Huang","year":"2022","unstructured":"Huang D, Zheng S, Liu Z, Zhu K, Zhi H, Ma G (2022) Machine learning revealed ferroptosis features and a novel ferroptosis-based classification for diagnosis in acute myocardial infarction. Front Genet 13:813438","journal-title":"Front Genet"},{"issue":"12","key":"11312_CR24","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11122257","volume":"11","author":"PK Jain","year":"2021","unstructured":"Jain PK et al (2021) Unseen artificial intelligence\u2014deep learning paradigm for segmentation of low atherosclerotic plaque in carotid ultrasound: a multicenter cardiovascular study. Diagnostics (Basel) 11(12):2257","journal-title":"Diagnostics (Basel)"},{"key":"11312_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106017","volume":"149","author":"PK Jain","year":"2022","unstructured":"Jain PK, Sharma N, Kalra MK, Johri A, Saba L, Suri JS (2022) Far wall plaque segmentation and area measurement in common and internal carotid artery ultrasound using U-series architectures: an unseen artificial intelligence paradigm for stroke risk assessment. Comput Biol Med 149:106017","journal-title":"Comput Biol Med"},{"issue":"5","key":"11312_CR26","doi-asserted-by":"publisher","first-page":"420","DOI":"10.21037\/cdt.2019.09.03","volume":"9","author":"A Jamthikar","year":"2019","unstructured":"Jamthikar A et al (2019) A low-cost machine learning-based cardiovascular\/stroke risk assessment system: integration of conventional factors with image phenotypes. Cardiovasc Diagn Ther 9(5):420","journal-title":"Cardiovasc Diagn Ther"},{"key":"11312_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104043","volume":"126","author":"AD Jamthikar","year":"2020","unstructured":"Jamthikar AD et al (2020a) Artificial intelligence framework for predictive cardiovascular and stroke risk assessment models: a narrative review of integrated approaches using carotid ultrasound. Comput Biol Med 126:104043","journal-title":"Comput Biol Med"},{"key":"11312_CR28","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/B978-0-12-819445-4.00015-1","volume-title":"Cognitive informatics, computer modelling, and cognitive science","author":"A Jamthikar","year":"2020","unstructured":"Jamthikar A et al (2020b) A special report on changing trends in preventive stroke\/cardiovascular risk assessment via B-mode ultrasonography. Cognitive informatics, computer modelling, and cognitive science. pp 291\u2013318"},{"issue":"4","key":"11312_CR29","doi-asserted-by":"publisher","first-page":"919","DOI":"10.21037\/cdt.2020.01.07","volume":"10","author":"A Jamthikar","year":"2020","unstructured":"Jamthikar A et al (2020c) Cardiovascular\/stroke risk predictive calculators: a comparison between statistical and machine learning models. Cardiovasc Diagn Ther 10(4):919","journal-title":"Cardiovasc Diagn Ther"},{"issue":"12","key":"11312_CR30","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1007\/s10916-020-01675-7","volume":"44","author":"AD Jamthikar","year":"2020","unstructured":"Jamthikar AD et al (2020d) Low-cost office-based cardiovascular risk stratification using machine learning and focused carotid ultrasound in an Asian-Indian cohort. J Med Syst 44(12):208","journal-title":"J Med Syst"},{"issue":"4","key":"11312_CR31","doi-asserted-by":"publisher","first-page":"1171","DOI":"10.1007\/s10554-020-02099-7","volume":"37","author":"AD Jamthikar","year":"2021","unstructured":"Jamthikar AD et al (2021) Multiclass machine learning vs. conventional calculators for stroke\/CVD risk assessment using carotid plaque predictors with coronary angiography scores as gold standard: a 500 participants study. Int J Cardiovasc Imaging 37(4):1171\u20131187","journal-title":"Int J Cardiovasc Imaging"},{"issue":"1","key":"11312_CR32","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1186\/s12872-024-03907-x","volume":"24","author":"N Jin","year":"2024","unstructured":"Jin N, Rong J, Chen X, Huang L, Ma H (2024) Exploring T-cell exhaustion features in acute myocardial infarction for a novel diagnostic model and new therapeutic targets by bio-informatics and machine learning. BMC Cardiovasc Disord 24(1):272","journal-title":"BMC Cardiovasc Disord"},{"key":"11312_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106018","volume":"150","author":"AM Johri","year":"2022","unstructured":"Johri AM et al (2022) Deep learning artificial intelligence framework for multiclass coronary artery disease prediction using combination of conventional risk factors, carotid ultrasound, and intraplaque neovascularization. Comput Biol Med 150:106018","journal-title":"Comput Biol Med"},{"key":"11312_CR34","doi-asserted-by":"crossref","unstructured":"Kasula VK, Tumma C, Konda B (2025) A comprehensive review of artificial intelligence models for lifetime value optimization. In: 2025 2nd international conference on research methodologies in knowledge management, artificial intelligence and telecommunication engineering (RMKMATE). IEEE, pp 1\u20135","DOI":"10.1109\/RMKMATE64874.2025.11042517"},{"issue":"2","key":"11312_CR35","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1111\/echo.14242","volume":"36","author":"NN Khanna","year":"2019","unstructured":"Khanna NN et al (2019a) Nonlinear model for the carotid artery disease 10\u2010year risk prediction by fusing conventional cardiovascular factors to carotid ultrasound image phenotypes: a Japanese diabetes cohort study. Echocardiography 36(2):345\u2013361","journal-title":"Echocardiography"},{"issue":"2","key":"11312_CR36","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1007\/s11883-019-0766-x","volume":"21","author":"NN Khanna","year":"2019","unstructured":"Khanna NN et al (2019b) Rheumatoid arthritis: atherosclerosis imaging and cardiovascular risk assessment using machine and deep learning\u2013based tissue characterization. Curr Atheroscler Rep 21(2):7","journal-title":"Curr Atheroscler Rep"},{"issue":"12","key":"11312_CR37","doi-asserted-by":"publisher","first-page":"2493","DOI":"10.3390\/healthcare10122493","volume":"10","author":"NN Khanna","year":"2022","unstructured":"Khanna NN et al (2022) Economics of artificial intelligence in healthcare: diagnosis vs. treatment. Healthcare 10(12):2493","journal-title":"Healthcare"},{"issue":"Supplement_1","key":"11312_CR38","doi-asserted-by":"publisher","first-page":"i111","DOI":"10.1093\/bioinformatics\/btad239","volume":"39","author":"A Lacan","year":"2023","unstructured":"Lacan A, Sebag M, Hanczar B (2023) GAN-based data augmentation for transcriptomics: survey and comparative assessment. Bioinformatics 39(Supplement_1):i111\u2013i120","journal-title":"Bioinformatics"},{"issue":"1","key":"11312_CR39","doi-asserted-by":"publisher","DOI":"10.1186\/s12920-025-02135-2","volume":"18","author":"M Li","year":"2025","unstructured":"Li M, Yang L, Wang Y, Zhang L (2025) Comprehensive analysis of diagnostic biomarkers related to histone acetylation in acute myocardial infarction. BMC Med Genomics 18(1):75","journal-title":"BMC Med Genomics"},{"issue":"1","key":"11312_CR40","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1186\/s12859-022-04689-9","volume":"23","author":"S Liu","year":"2022","unstructured":"Liu S, Yao W (2022) Prediction of lung cancer using gene expression and deep learning with KL divergence gene selection. BMC Bioinform 23(1):175","journal-title":"BMC Bioinform"},{"key":"11312_CR41","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.cmpb.2019.04.008","volume":"176","author":"M Maniruzzaman","year":"2019","unstructured":"Maniruzzaman M et al (2019) Statistical characterization and classification of colon microarray gene expression data using multiple machine learning paradigms. Comput Methods Programs Biomed 176:173\u2013193","journal-title":"Comput Methods Programs Biomed"},{"key":"11312_CR42","first-page":"1","volume-title":"The Corsini encyclopedia of psychology","author":"PE McKnight","year":"2010","unstructured":"McKnight PE, Najab J (2010) Mann\u2010Whitney U test. The Corsini encyclopedia of psychology. pp 1\u20131"},{"key":"11312_CR43","doi-asserted-by":"crossref","unstructured":"Nasrin S, Alom MZ, Khan S, Taha TM (2024) Deep learning-based explainable approaches for RNA-seq gene expression data analysis. In: Applications of machine learning 2024, vol 13138. SPIE, p 1313804","DOI":"10.1117\/12.3030851"},{"issue":"5","key":"11312_CR44","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1186\/ar2825","volume":"11","author":"JE Oliver","year":"2009","unstructured":"Oliver JE, Silman AJ (2009) Why are women predisposed to autoimmune rheumatic diseases? Arthritis Res Ther 11(5):252","journal-title":"Arthritis Res Ther"},{"issue":"1","key":"11312_CR45","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12010166","volume":"12","author":"S Paul","year":"2022","unstructured":"Paul S et al (2022) Bias investigation in artificial intelligence systems for early detection of Parkinson\u2019s disease: a narrative review. Diagnostics 12(1):166","journal-title":"Diagnostics"},{"issue":"1","key":"11312_CR46","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1186\/s12872-022-02481-4","volume":"22","author":"W Peng","year":"2022","unstructured":"Peng W, Sun Y, Zhang L (2022) Construction of genetic classification model for coronary atherosclerosis heart disease using three machine learning methods. BMC Cardiovasc Disord 22(1):42","journal-title":"BMC Cardiovasc Disord"},{"key":"11312_CR47","doi-asserted-by":"crossref","unstructured":"Phan B-C, Ma T, Nguyen H-H, Do T-N (2025) BOLIMES: Boruta and LIME optiMized fEature selection for gene expression classification. arXiv:2502.13080","DOI":"10.1007\/978-3-032-00972-2_28"},{"key":"11312_CR48","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1109\/TPAMI.2009.187","volume":"32","author":"JD Rodr\u00edguez","year":"2010","unstructured":"Rodr\u00edguez JD, Mart\u00ednez AP, Lozano JA (2010) Sensitivity analysis of k-fold cross validation in prediction error estimation. IEEE Trans Pattern Anal Mach Intell 32:569\u2013575","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11312_CR49","doi-asserted-by":"publisher","unstructured":"Ruby U, Yendapalli V (2020) Binary cross entropy with deep learning technique for image classification. Int J Adv Trends Comput Sci Eng 9(10). https:\/\/doi.org\/10.30534\/ijatcse\/2020\/175942020","DOI":"10.30534\/ijatcse\/2020\/175942020"},{"issue":"4","key":"11312_CR50","doi-asserted-by":"publisher","DOI":"10.3390\/app12041850","volume":"12","author":"L Rukhsar","year":"2022","unstructured":"Rukhsar L, Bangyal WH, Khan MSA, Ibrahim AAA, Nisar K, Rawat DB (2022) Analyzing RNA-seq gene expression data using deep learning approaches for cancer classification. Appl Sci 12(4):1850","journal-title":"Appl Sci"},{"issue":"12","key":"11312_CR51","doi-asserted-by":"publisher","DOI":"10.31083\/j.rcm2512463","volume":"25","author":"L Saba","year":"2024","unstructured":"Saba L et al (2024a) An artificial intelligence-based non-invasive approach for cardiovascular disease risk stratification in obstructive sleep apnea patients: a narrative review. Rev Cardiovasc Med 25(12):463","journal-title":"Rev Cardiovasc Med"},{"issue":"5","key":"11312_CR52","doi-asserted-by":"publisher","DOI":"10.31083\/j.rcm2505184","volume":"25","author":"L Saba","year":"2024","unstructured":"Saba L et al (2024b) UltraAIGenomics: Artificial intelligence-based cardiovascular disease risk assessment by fusion of ultrasound-based radiomics and genomics features for preventive, personalized and precision medicine: a narrative review. Rev Cardiovasc Med 25(5):184","journal-title":"Rev Cardiovasc Med"},{"issue":"1","key":"11312_CR53","doi-asserted-by":"publisher","first-page":"51","DOI":"10.4103\/abr.abr_383_21","volume":"12","author":"S Saeedbakhsh","year":"2023","unstructured":"Saeedbakhsh S, Sattari M, Mohammadi M, Najafian J, Mohammadi F (2023) Diagnosis of coronary artery disease based on machine learning algorithms support vector machine, artificial neural network, and random forest. Adv Biomed Res 12(1):51","journal-title":"Adv Biomed Res"},{"issue":"6","key":"11312_CR54","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1007\/s42979-025-04209-5","volume":"6","author":"GS Sajja","year":"2025","unstructured":"Sajja GS, Meesala MK, Addula SR, Ravipati P (2025) Optimizing retail supply chain sales forecasting with a mayfly algorithm-enhanced bidirectional gated recurrent unit. SN Comput Sci 6(6):737","journal-title":"SN Comput Sci"},{"key":"11312_CR55","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-025-10901-8","author":"Y Sharma","year":"2025","unstructured":"Sharma Y et al (2025) StockAI 3.0: ensemble fusion paradigms using novel gating mechanism in long short-term memory architectures for forecasting sentiment-based stock trends. Soft Comput. https:\/\/doi.org\/10.1007\/s00500-025-10901-8","journal-title":"Soft Comput"},{"key":"11312_CR56","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1002\/9780470994955.ch2","volume-title":"Heart disease in pregnancy","author":"CK Silversides","year":"2007","unstructured":"Silversides CK, Colman JM (2007) Physiological changes in pregnancy. Heart disease in pregnancy. pp 6\u201317"},{"issue":"12","key":"11312_CR57","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13122092","volume":"13","author":"J Singh","year":"2023","unstructured":"Singh J, Singh N, Fouda MM, Saba L, Suri JS (2023) Attention-enabled ensemble deep learning models and their validation for depression detection: a domain adoption paradigm. Diagnostics 13(12):2092","journal-title":"Diagnostics"},{"issue":"1","key":"11312_CR58","doi-asserted-by":"publisher","first-page":"7154","DOI":"10.1038\/s41598-024-56786-9","volume":"14","author":"J Singh","year":"2024","unstructured":"Singh J et al (2024) GeneAI 3.0: powerful, novel, generalized hybrid and ensemble deep learning frameworks for miRNA species classification of stationary patterns from nucleotides. Sci Rep 14(1):7154","journal-title":"Sci Rep"},{"key":"11312_CR59","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2022.831179","volume":"13","author":"SR St. Pierre","year":"2022","unstructured":"St. Pierre SR, Peirlinck M, Kuhl E (2022) Sex matters: a comprehensive comparison of female and male hearts. Front Physiol 13:831179","journal-title":"Front Physiol"},{"issue":"12","key":"11312_CR60","doi-asserted-by":"publisher","first-page":"2367","DOI":"10.3390\/diagnostics11122367","volume":"11","author":"JS Suri","year":"2021","unstructured":"Suri JS et al (2021) COVLIAS 1.0 vs. MedSeg: artificial intelligence-based comparative study for automated COVID-19 computed tomography lung segmentation in Italian and Croatian cohorts. Diagnostics 11(12):2367","journal-title":"Diagnostics"},{"key":"11312_CR61","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.105204","volume":"142","author":"JS Suri","year":"2022","unstructured":"Suri JS et al (2022a) Understanding the bias in machine learning systems for cardiovascular disease risk assessment: the first of its kind review. Comput Biol Med 142:105204","journal-title":"Comput Biol Med"},{"issue":"10","key":"11312_CR62","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1007\/s10916-022-01850-y","volume":"46","author":"JS Suri","year":"2022","unstructured":"Suri JS et al (2022b) Multicenter study on COVID-19 lung computed tomography segmentation with varying glass ground opacities using unseen deep learning artificial intelligence paradigms: COVLIAS 1.0 validation. J Med Syst 46(10):62","journal-title":"J Med Syst"},{"issue":"1","key":"11312_CR63","doi-asserted-by":"publisher","first-page":"111","DOI":"10.3390\/cancers11010111","volume":"11","author":"GS Tandel","year":"2019","unstructured":"Tandel GS et al (2019) A review on a deep learning perspective in brain cancer classification. Cancers 11(1):111","journal-title":"Cancers"},{"key":"11312_CR64","doi-asserted-by":"publisher","first-page":"105639","DOI":"10.1016\/j.compbiomed.2022.105639","volume":"147","author":"JS Teji","year":"2022","unstructured":"Teji JS, Jain S, Gupta SK, Suri JS (2022) NeoAI 1.0: machine learning-based paradigm for prediction of neonatal and infant risk of death. Comput Biol Med 147:105639","journal-title":"Comput Biol Med"},{"issue":"1","key":"11312_CR65","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1007\/s00296-024-05756-5","volume":"45","author":"E Tiwari","year":"2025","unstructured":"Tiwari E et al (2025) Artificial intelligence-based cardiovascular\/stroke risk stratification in women affected by autoimmune disorders: a narrative survey. Rheumatol Int 45(1):14","journal-title":"Rheumatol Int"},{"key":"11312_CR66","doi-asserted-by":"publisher","DOI":"10.1016\/j.jrras.2024.101080","author":"S \u00dcnalan","year":"2024","unstructured":"\u00dcnalan S, G\u00fcnay O, Akkurt I, Gunoglu K, Tekin HO (2024) A comparative study on breast cancer classification with stratified shuffle split and K-fold cross validation via ensembled machine learning. J Radiat Res Appl Sci. https:\/\/doi.org\/10.1016\/j.jrras.2024.101080","journal-title":"J Radiat Res Appl Sci"},{"key":"11312_CR67","doi-asserted-by":"crossref","unstructured":"Waddar R, Rathod V, Netravati H, Chikkamath S, Nirmala SR, Budihal SV (2024) A CNN-based stutter detection using MFCC features with binary cross-entropy loss function. In: 2024 IEEE international conference on contemporary computing and communications (InC4), vol 1, pp 1\u20136","DOI":"10.1109\/InC460750.2024.10649122"},{"key":"11312_CR68","first-page":"1","volume-title":"Wiley encyclopedia of clinical trials","author":"RF Woolson","year":"2007","unstructured":"Woolson RF (2007) Wilcoxon signed\u2010rank test. Wiley encyclopedia of clinical trials. pp 1\u20133"},{"issue":"1","key":"11312_CR69","volume":"2020","author":"H Xie","year":"2020","unstructured":"Xie H, Zha E, Zhang Y (2020) Identification of featured metabolism\u2010related genes in patients with acute myocardial infarction. Dis Markers 2020(1):8880004","journal-title":"Dis Markers"},{"key":"11312_CR70","unstructured":"Xu L, Skoularidou M, Cuesta-Infante A, Veeramachaneni K (2019) Modeling tabular data using conditional gan. In: Advances in neural information processing systems, vol 32"},{"key":"11312_CR71","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106619","volume":"154","author":"FH Yagin","year":"2023","unstructured":"Yagin FH et al (2023) Explainable artificial intelligence model for identifying COVID-19 gene biomarkers. Comput Biol Med 154:106619","journal-title":"Comput Biol Med"},{"key":"11312_CR72","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2021.663509","volume":"8","author":"C Yifan","year":"2021","unstructured":"Yifan C, Jianfeng S, Jun P (2021) Development and validation of a random forest diagnostic model of acute myocardial infarction based on ferroptosis-related genes in circulating endothelial cells. Front Cardiovasc Med 8:663509","journal-title":"Front Cardiovasc Med"},{"issue":"1","key":"11312_CR73","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1186\/s12967-024-05415-0","volume":"22","author":"Y Zhu","year":"2024","unstructured":"Zhu Y, Chen Y, Zu Y (2024) Leveraging a neutrophil-derived PCD signature to predict and stratify patients with acute myocardial infarction: from AI prediction to biological interpretation. J Transl Med 22(1):612","journal-title":"J Transl Med"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-026-11312-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-026-11312-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-026-11312-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T16:03:19Z","timestamp":1778515399000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-026-11312-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,18]]},"references-count":73,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["11312"],"URL":"https:\/\/doi.org\/10.1007\/s00500-026-11312-z","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,18]]},"assertion":[{"value":"21 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 2026","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 author(s) declared no potential conflicts concerning this article\u2019s research, authorship, and\/or publication.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study did not involve experiments on human participants or animals. Therefore, ethical approval was not required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}