{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T18:13:56Z","timestamp":1774030436677,"version":"3.50.1"},"reference-count":173,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T00:00:00Z","timestamp":1740528000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation (NSF)","award":["2321939"],"award-info":[{"award-number":["2321939"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>What makes a wine exceptional enough to score a perfect 10 from experts? This study explores a data-driven approach to identify the ideal physicochemical composition for wines that could achieve this highest possible rating. Using a dataset of 11 measurable attributes, including alcohol, sulfates, residual sugar, density, and citric acid, for wines rated up to a maximum quality score of 8 by expert tasters, we sought to predict compositions that might enhance wine quality beyond current observations. Our methodology applies a second-degree polynomial ridge regression model, optimized through an exhaustive evaluation of feature combinations. Furthermore, we propose a specific chemical and physical composition of wine that our model predicts could achieve a quality score of 10 from experts. While further validation with winemakers and industry experts is necessary, this study aims to contribute a practical tool for guiding quality exploration and advancing predictive modeling applications in food and beverage sciences.<\/jats:p>","DOI":"10.3390\/bdcc9030055","type":"journal-article","created":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T11:22:12Z","timestamp":1740568932000},"page":"55","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Exploring Predictive Modeling for Food Quality Enhancement: A Case Study on Wine"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-9552-1731","authenticated-orcid":false,"given":"Cemil","family":"Yavas","sequence":"first","affiliation":[{"name":"Department of Information Technology, Georgia Southern University, Statesboro, GA 30460, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1068-9855","authenticated-orcid":false,"given":"Jongyeop","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Georgia Southern University, Statesboro, GA 30460, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3919-8056","authenticated-orcid":false,"given":"Lei","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Georgia Southern University, Statesboro, GA 30460, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2377-9113","authenticated-orcid":false,"given":"Christopher","family":"Kadlec","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Georgia Southern University, Statesboro, GA 30460, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7115-8577","authenticated-orcid":false,"given":"Yiming","family":"Ji","sequence":"additional","affiliation":[{"name":"College of Computing and Software Engineering, Kennesaw State University, Kennesaw, GA 30060, USA"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cemil Emre Yavas, J.K., and Chen, L. (June, January 30). Exploring Flavors Through AI: The Future of Culinary Taste Prediction. Proceedings of the 2024 IEEE\/ACIS 22nd International Conference on Software Engineering Research, Management and Applications (SERA), Honolulu, HI, USA.","DOI":"10.1109\/SERA61261.2024.10685608"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.foodres.2017.10.015","article-title":"Development of a Partial Least Squares-Artificial Neural Network (PLS-ANN) Hybrid Model for the Prediction of Consumer Liking Scores of Ready-to-Drink Green Tea Beverages","volume":"103","author":"Yu","year":"2018","journal-title":"Food Res. Int."},{"key":"ref_3","first-page":"239","article-title":"Development of Fermented Millet Sprout Milk Beverage Based on Physicochemical Property Studies and Consumer Acceptability Data","volume":"75","author":"Sudha","year":"2016","journal-title":"J. Sci. Ind. Res. (JSIR)"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"278","DOI":"10.4236\/ojs.2021.112015","article-title":"Prediction of Wine Quality Using Machine Learning Algorithms","volume":"11","author":"Dahal","year":"2021","journal-title":"Open J. Stat."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"105429","DOI":"10.1109\/ACCESS.2024.3436603","article-title":"Mastering Precision in Pivotal Variables Defining Wine Quality via Incremental Analysis of Baseline Accuracy","volume":"12","author":"Yavas","year":"2024","journal-title":"IEEE Access"},{"key":"ref_6","first-page":"60","article-title":"A Generalized Wine Quality Prediction Framework by Evolutionary Algorithms","volume":"6","author":"Wu","year":"2021","journal-title":"Int. J. Interact. Multimed. Artif. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.procs.2017.12.041","article-title":"Selection of important features and predicting wine quality using machine learning techniques","volume":"125","author":"Gupta","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_8","first-page":"100261","article-title":"A machine learning application in wine quality prediction","volume":"8","author":"Bhardwaj","year":"2022","journal-title":"Mach. Learn. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"115430","DOI":"10.1109\/ACCESS.2023.3325676","article-title":"Determining the Importance of Physicochemical Properties in the Perceived Quality of Wines","volume":"11","author":"Luque","year":"2023","journal-title":"IEEE Access"},{"key":"ref_10","unstructured":"Kelly, M., Longjohn, R., and Nottingham, K. (2024, June 01). The UCI Machine Learning Repository. Available online: https:\/\/archive.ics.uci.edu."},{"key":"ref_11","unstructured":"Paulo Cortez, A.C. (2025, February 23). Wine Quality. 2009. Available online: https:\/\/doi.org\/10.24432\/C56S3T."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1080\/00401706.1970.10488634","article-title":"Ridge Regression: Biased Estimation for Nonorthogonal Problems","volume":"12","author":"Hoerl","year":"1970","journal-title":"Technometrics"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning Representations by Back-Propagating Errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1080\/14786440109462720","article-title":"LIII. On Lines and Planes of Closest Fit to Systems of Points in Space","volume":"2","author":"Pearson","year":"1901","journal-title":"Lond. Edinb. Dublin Philos. Mag. J. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1080\/14999013.2015.1033112","article-title":"Predictive Validity of the Short-Term Assessment of Risk and Treatability (START) for Aggression and Self-Harm in a Secure Mental Health Service: Gender Differences","volume":"14","author":"Dickens","year":"2015","journal-title":"Int. J. Forensic Ment. Health"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1177\/1948550615609735","article-title":"Do Bad Guys Get Ahead or Fall Behind? Relationships of the Dark Triad of Personality with Objective and Subjective Career Success","volume":"7","author":"Spurk","year":"2016","journal-title":"Soc. Psychol. Personal. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1851","DOI":"10.1108\/IJEBR-08-2021-0624","article-title":"Does Practice Make Perfect? Assessing the Formation of Expertise amongst New Venture Founders","volume":"28","author":"Nogueira","year":"2022","journal-title":"Int. J. Entrep. Behav. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1111\/bjhp.12001","article-title":"Social Support and Trait Personality Are Independently Associated with Resting Cardiovascular Function in Women","volume":"18","author":"Creaven","year":"2013","journal-title":"Br. J. Health Psychol."},{"key":"ref_20","first-page":"183","article-title":"Measuring Creativity in Academic Writing: An Analysis of Essays in Advanced Placement Language and Composition","volume":"34","author":"Bower","year":"2023","journal-title":"J. Adv. Acad."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"754","DOI":"10.4236\/ojs.2015.57075","article-title":"Variance Inflation Factor: As a Condition for the Inclusion of Suppressor Variable(s) in Regression Analysis","volume":"05","author":"Akinwande","year":"2015","journal-title":"Open J. Stat."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.adolescence.2012.11.004","article-title":"Perceived Support from Adults, Interactions with Police, and Adolescents\u2019 Depressive Symptomology: An Examination of Sex, Race, and Social Class","volume":"36","year":"2013","journal-title":"J. Adolesc."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yin, X., Wei, X., Irfan, M., and Yasin, S. (2023). Revitalizing Organizational Efficiency: Unpacking the Relationship between CEO Turnover, Research and Development, and Pay-Performance Sensitivities in the Financial Sector of Pakistan. Sustainability, 15.","DOI":"10.3390\/su151310578"},{"key":"ref_24","first-page":"227","article-title":"Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies","volume":"6","author":"Vatcheva","year":"2016","journal-title":"Epidemiol. Open Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Warmenhoven, J., Bargary, N., Liebl, D., Harrison, A., Robinson, M.A., Gunning, E., and Hooker, G. (2021). PCA of Waveforms and Functional PCA: A Primer for Biomechanics. J. Biomech., 116.","DOI":"10.1016\/j.jbiomech.2020.110106"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5445","DOI":"10.1007\/s10462-022-10297-z","article-title":"Tutorial on PCA and Approximate PCA and Approximate Kernel PCA","volume":"56","author":"Marukatat","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_27","first-page":"41","article-title":"Performance Comparison of PCA, DWT-PCA And LWT-PCA for Face Image Retrieval","volume":"2","author":"Madhavan","year":"2012","journal-title":"Comput. Sci. Eng. Int. J."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2","DOI":"10.7603\/s40601-013-0002-4","article-title":"Face Recognition Using Holistic Features and Within Class Scatter-Based PCA","volume":"3","author":"Wijaya","year":"2013","journal-title":"GSTF Int. J. Comput. (JoC)"},{"key":"ref_29","first-page":"14","article-title":"Performance Evaluation of Face Recognition Using PCA and N-PCA","volume":"76","author":"KumarBansal","year":"2013","journal-title":"Int. J. Comput. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3469","DOI":"10.11113\/jt.v70.3469","article-title":"Fault Detection and Monitoring Using Multiscale Principal Component Analysis at a Sewage Treatment Plant","volume":"70","author":"Mirin","year":"2014","journal-title":"J. Teknol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"210","DOI":"10.14710\/jtsiskom.2020.13590","article-title":"Face Recognition System with PCA-GA Algorithm for Smart Home Door Security Using Rasberry Pi","volume":"8","author":"Subiyanto","year":"2020","journal-title":"J. Teknol. Dan Sist. Komput."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"135","DOI":"10.3923\/itj.2003.135.139","article-title":"Type I Error Rate and Power of Three Normality Tests","volume":"2","author":"Mendes","year":"2003","journal-title":"Inf. Technol. J."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1108\/JFM-12-2021-0153","article-title":"Barriers to Building Information Modelling and Facility Management Practices Integration in Nigeria","volume":"21","author":"Okwe","year":"2023","journal-title":"J. Facil. Manag."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"844","DOI":"10.3390\/jcp3040038","article-title":"Challenging Assumptions of Normality in AES S-Box Configurations under Side-Channel Analysis","volume":"3","author":"Carper","year":"2023","journal-title":"J. Cybersecur. Priv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1198\/sbr.2009.08089","article-title":"Tests for Normality Based on Entropy Divergences","volume":"2","author":"Guo","year":"2010","journal-title":"Stat. Biopharm. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3002","DOI":"10.1080\/00949655.2014.947986","article-title":"Shapiro\u2013Francia Test Compared to Other Normality Test Using Expected p-Value","volume":"85","author":"Mbah","year":"2015","journal-title":"J. Stat. Comput. Simul."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1111\/vsu.14034","article-title":"Verifying Model Assumptions and Testing Normality","volume":"53","author":"Evans","year":"2024","journal-title":"Vet. Surg."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"63","DOI":"10.60084\/ijds.v1i2.130","article-title":"Statistical Clustering Approach: Mapping Population Indicators Through Probabilistic Analysis in Aceh Province, Indonesia","volume":"1","author":"Sasmita","year":"2023","journal-title":"Infolitika J. Data Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10260-007-0046-8","article-title":"Testing Normality in the Presence of Outliers","volume":"17","author":"Coin","year":"2008","journal-title":"Stat. Methods Appl."},{"key":"ref_40","first-page":"14532","article-title":"Analysis of Attitudes of Students Attending Secondary Education Institutions towards Physical Education and Sports According to Various Variables","volume":"14","year":"2023","journal-title":"Rev. Gest\u00e3o Secr. (Manag. Adm. Prof. Rev.)"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"012008","DOI":"10.1088\/1755-1315\/1148\/1\/012008","article-title":"Sensor BMP280 Statistical Analysis for Barometric Pressure Acquisition","volume":"1148","author":"Kusuma","year":"2023","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"115","DOI":"10.4028\/www.scientific.net\/AMM.611.115","article-title":"Methodology and Application of the Kruskal-Wallis Test","volume":"611","author":"Ostertag","year":"2014","journal-title":"Appl. Mech. Mater."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Yang, X., Yin, X., Huang, W., Duan, Z., Wu, H., Li, Y., Li, M., Zhang, T., Zhou, C., and Xu, H. (2023). Distribution of Cardiorespiratory Fitness in Children and Adolescents at Different Latitudes. Am. J. Hum. Biol., 35.","DOI":"10.1002\/ajhb.23908"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"67","DOI":"10.18276\/sip.2019.55-06","article-title":"The ANOVA Method as a Popular Research Tool","volume":"55","author":"Nowakowski","year":"2019","journal-title":"Stud. Pr. WNEiZ"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1049\/el.2019.2478","article-title":"Blind Watermark Detection Based on K-S Test in Radio-frequency Signals","volume":"56","author":"Xie","year":"2020","journal-title":"Electron. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"914","DOI":"10.1021\/acsomega.9b03940","article-title":"Introducing Kolmogorov\u2013Smirnov Tests under Uncertainty: An Application to Radioactive Data","volume":"5","author":"Aslam","year":"2020","journal-title":"ACS Omega"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1755","DOI":"10.4028\/www.scientific.net\/AMR.864-867.1755","article-title":"Metals Distribution (Cu, Zn, Pb, Mn and Ni) in Campus Wastewater: K-S Test and Friedman ANOVA","volume":"864\u2013867","author":"Sundari","year":"2013","journal-title":"Adv. Mater. Res."},{"key":"ref_48","first-page":"1","article-title":"Detection of Non-Normality in Data Sets and Comparison between Different Normality Tests","volume":"5","author":"Biu","year":"2020","journal-title":"Asian J. Probab. Stat."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Arsham, H. (2013). Adaptive K-S Tests for White Noise in the Frequency Domain. Int. J. Pure Apllied Math., 82.","DOI":"10.12732\/ijpam.v82i4.2"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Wee, S., Choi, C., and Jeong, J. (2021). Blind Interleaver Parameters Estimation Using Kolmogorov\u2013Smirnov Test. Sensors, 21.","DOI":"10.3390\/s21103458"},{"key":"ref_51","first-page":"1","article-title":"Programming Development of Kolmogorov-Smirnov Goodness-of-Fit Testing of Data Normality as a Microsoft Excel\u00ae Library Function","volume":"2015","author":"Olusegun","year":"2015","journal-title":"J. Softw. Syst. Dev."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1631","DOI":"10.46481\/jnsps.2024.1631","article-title":"On the Fourth-Order Hybrid Beta Polynomial Kernels in Kernel Density Estimation","volume":"6","author":"Afere","year":"2024","journal-title":"J. Niger. Soc. Phys. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1175\/2007WAF2007023.1","article-title":"Modeling the Distribution of Precipitation Forecasts from the Canadian Ensemble Prediction System Using Kernel Density Estimation","volume":"23","author":"Peel","year":"2008","journal-title":"Weather Forecast."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1007\/s11749-009-0168-4","article-title":"Multivariate Plug-in Bandwidth Selection with Unconstrained Pilot Bandwidth Matrices","volume":"19","author":"Duong","year":"2010","journal-title":"TEST"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"105122","DOI":"10.1088\/1361-6501\/ac7a91","article-title":"Real-Time Remaining Useful Life Prediction Based on Adaptive Kernel Window Width Density","volume":"33","author":"Zhang","year":"2022","journal-title":"Meas. Sci. Technol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s11265-006-9772-7","article-title":"Gaussianization: An Efficient Multivariate Density Estimation Technique for Statistical Signal Processing","volume":"45","author":"Erdogmus","year":"2006","journal-title":"J. VLSI Signal Process. Syst. Signal Image Video Technol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1080\/10618600.2018.1549052","article-title":"Fast and Stable Multivariate Kernel Density Estimation by Fast Sum Updating","volume":"28","author":"Warin","year":"2019","journal-title":"J. Comput. Graph. Stat."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1007\/s11227-015-1577-7","article-title":"Kernel Density Estimation in Accelerators: Implementation and Performance Evaluation","volume":"72","author":"Mendiburu","year":"2016","journal-title":"J. Supercomput."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Duong, T. (2007). ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R. J. Stat. Softw., 21.","DOI":"10.18637\/jss.v021.i07"},{"key":"ref_60","first-page":"1003","article-title":"The Bias Reduction in Density Estimation Using a Geometric Extrapolated Kernel Estimator","volume":"47","author":"Shadrokh","year":"2016","journal-title":"Hacet. J. Math. Stat."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1111\/1467-842X.00283","article-title":"Variable Kernel Density Estimation","volume":"45","author":"Hazelton","year":"2003","journal-title":"Aust. N. Z. J. Stat."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1109\/TIT.2022.3205781","article-title":"Uncertainty Quantification for Nonconvex Tensor Completion: Confidence Intervals, Heteroscedasticity and Optimality","volume":"69","author":"Cai","year":"2023","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"14713","DOI":"10.48084\/etasr.7573","article-title":"Optimal Surface Grinding Regression Model Determination with the SRP Method","volume":"14","author":"Thinh","year":"2024","journal-title":"Eng. Technol. Appl. Sci. Res."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"149","DOI":"10.21303\/2461-4262.2024.003294","article-title":"Towards enhanced surface roughness modeling in machining: An analysis of data transformation techniques","volume":"2","author":"Thinh","year":"2024","journal-title":"EUREKA Phys. Eng."},{"key":"ref_65","first-page":"349","article-title":"Compositional data analysis by the square-root transformation: Application to NBA USG% data","volume":"31","author":"Lee","year":"2024","journal-title":"Commun. Stat. Appl. Methods"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Gerke, O., and M\u00f6ller, S. (2023). Modeling Bland\u2013Altman Limits of Agreement with Fractional Polynomials\u2014An Example with the Agatston Score for Coronary Calcification. Axioms, 12.","DOI":"10.3390\/axioms12090884"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1111\/1365-2745.14315","article-title":"Gymnosperms demonstrate patterns of fine-root trait coordination consistent with the global root economics space","volume":"112","author":"Langguth","year":"2024","journal-title":"J. Ecol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1177\/09670335241240309","article-title":"Near infrared spectroscopy for determination of moisture content in lyophilized formulation","volume":"32","author":"Khanolkar","year":"2024","journal-title":"J. Near Infrared Spectrosc."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Saffari, S.E., Soo, S.A., Mohammadi, R., Ng, K.P., Greene, W., and Kandiah, N. (2024). Modelling the Distribution of Cognitive Outcomes for Early-Stage Neurocognitive Disorders: A Model Comparison Approach. Biomedicines, 12.","DOI":"10.3390\/biomedicines12020393"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1692","DOI":"10.1109\/JSTARS.2022.3145874","article-title":"Saline-Sodic Soil EC Retrieval Based on Box-Cox Transformation and Machine Learning","volume":"15","author":"Li","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Loaiza, J.G., Rangel-Peraza, J.G., Monjard\u00edn-Armenta, S.A., Bustos-Terrones, Y.A., Bandala, E.R., Sanhouse-Garc\u00eda, A.J., and Renter\u00eda-Guevara, S.A. (2023). Surface Water Quality Assessment through Remote Sensing Based on the Box\u2013Cox Transformation and Linear Regression. Water, 15.","DOI":"10.3390\/w15142606"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"24813","DOI":"10.1109\/ACCESS.2024.3365496","article-title":"Forecasting the Friction Factor of a Mine Airway Using an Improvement Stacking Ensemble Learning Approach","volume":"12","author":"Qi","year":"2024","journal-title":"IEEE Access"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.1111\/1752-1688.13138","article-title":"Evaluation of streamflow as a covariate in models for predicting daily pesticide concentrations","volume":"59","author":"Mosquin","year":"2023","journal-title":"JAWRA J. Am. Water Resour. Assoc."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/s10291-024-01741-4","article-title":"Anomalous Ambiguity Detection Between Reference Stations Based on Box-Cox Transformation of Tropospheric Residual Estimation","volume":"28","author":"Zhang","year":"2024","journal-title":"GPS Solut."},{"key":"ref_75","first-page":"175","article-title":"Implementation of remote sensing algorithms to estimate TOC, Chl-a and TDS in a tropical water body; Sanalona reservoir, Sinaloa, Mexico","volume":"196","year":"2023","journal-title":"Environ. Monit. Assess."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"e2023WR034942","DOI":"10.1029\/2023WR034942","article-title":"Reliability of Ensemble Climatological Forecasts","volume":"59","author":"Huang","year":"2023","journal-title":"Water Resour. Res."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"293","DOI":"10.54254\/2755-2721\/50\/20241679","article-title":"Research on the prediction method of first-day box office considering the holiday factor","volume":"50","author":"Kuang","year":"2024","journal-title":"Appl. Comput. Eng."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s10549-024-07484-7","article-title":"Tumor infiltrating lymphocytes and change in tumor load on MRI to assess response and prognosis after neoadjuvant chemotherapy in breast cancer","volume":"209","author":"Janssen","year":"2024","journal-title":"Breast Cancer Res. Treat."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Oliveira, T.S.D., Silva, I.N., Aniceto, E.S., and J\u00fanior, J.R.M. (2023). Application of the multivariate and univariate analyses to estimate the feed efficiency in beef cattle. Biosci. J., 39.","DOI":"10.14393\/BJ-v39n0a2023-67310"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"e35","DOI":"10.3346\/jkms.2024.39.e35","article-title":"Data Distribution: Normal or Abnormal?","volume":"39","author":"Habibzadeh","year":"2024","journal-title":"J. Korean Med. Sci."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Bosse, N.I., Abbott, S., Cori, A., Van Leeuwen, E., Bracher, J., and Funk, S. (2023). Scoring epidemiological forecasts on transformed scales. PLoS Comput. Biol., 19.","DOI":"10.1101\/2023.01.23.23284722"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1002\/jrsm.1655","article-title":"Accounting for time dependency in meta-analyses of concordance probability estimates","volume":"14","author":"Schmid","year":"2023","journal-title":"Res. Synth. Methods"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Zhang, R.P., Zhou, J.H., Guo, J., Miao, Y.H., and Zhang, L.L. (2023). Inversion models of aboveground grassland biomass in Xinjiang based on multisource data. Front. Plant Sci., 14.","DOI":"10.3389\/fpls.2023.1152432"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Yu, G., Siddiqui, M.K., Hussain, M., Hussain, N., Saddique, Z., and Petros, F.B. (2024). On topological indices and entropy measures of beryllonitrene network via logarithmic regression model. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-57601-1"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Rasheed, M.W., Mahboob, A., and Hanif, I. (2024). Investigating the properties of octane isomers by novel neighborhood product degree-based topological indices. Front. Phys., 12.","DOI":"10.3389\/fphy.2024.1369939"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Bueno-L\u00f3pez, S.W., Caraballo-Rojas, L.R., and Torres-Herrera, J.G. (2024). Evaluation of Different Modeling Approaches to Estimating Total Bole Volume for Pinus occidentalis, Swartz in Different Ecological Zones. Preprints.","DOI":"10.20944\/preprints202405.0179.v1"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"e14002","DOI":"10.1111\/tid.14002","article-title":"Infection evaluation in the early period after liver transplantation: A single-center exploration","volume":"25","author":"Tu","year":"2023","journal-title":"Transpl. Infect. Dis."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Pizarro, C., Bosse, F.L., Begrich, C., Reznakova, B., Beiert, T., Schrickel, J.W., Nickenig, G., Skowasch, D., and Momcilovic, D. (2023). Cardiac autonomic dysfunction in adult congenital heart disease. BMC Cardiovasc. Disord., 23.","DOI":"10.1186\/s12872-023-03558-4"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Subi, X., Eziz, M., and Zhong, Q. (2023). Hyperspectral Estimation Model of Organic Matter Content in Farmland Soil in the Arid Zone. Sustainability, 15.","DOI":"10.3390\/su151813719"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"012010","DOI":"10.1088\/1755-1315\/1226\/1\/012010","article-title":"Estimating the Chlorophyll-a in the Nha Trang Bay using Landsat-8 OLI data","volume":"1226","author":"Hieu","year":"2023","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"2067","DOI":"10.1175\/WAF-D-19-0121.1","article-title":"Factors Influencing the Performance of Regression-Based Statistical Postprocessing Models for Short-Term Precipitation Forecasts","volume":"34","author":"Li","year":"2019","journal-title":"Weather Forecast."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"8","DOI":"10.3758\/s13428-010-0044-x","article-title":"Problematic Standard Errors and Confidence Intervals for Skewness and Kurtosis","volume":"43","author":"Wright","year":"2011","journal-title":"Behav. Res. Methods"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"635","DOI":"10.3319\/TAO.2001.12.4.635(A)","article-title":"Statistics of 6-Hour Forecast Errors Derived from Global Data Assimilation System at the Central Weather Bureau in Taiwan","volume":"12","author":"Hong","year":"2001","journal-title":"Terr. Atmos. Ocean. Sci."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1080\/00031305.2014.917055","article-title":"Kurtosis as Peakedness, 1905\u20132014","volume":"68","author":"Westfall","year":"2014","journal-title":"R.I.P. Am. Stat."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1027\/1614-2241\/a000057","article-title":"Skewness and Kurtosis in Real Data Samples","volume":"9","author":"Blanca","year":"2013","journal-title":"Methodology"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"71","DOI":"10.18267\/j.efaj.227","article-title":"Forecasting Cross-Section of Stock Returns with Realised Moments","volume":"14","year":"2019","journal-title":"Eur. Financ. Account. J."},{"key":"ref_97","first-page":"236","article-title":"Performance of Risk Measures in Portfolio Construction on Central and South-East European Emerging Markets","volume":"01","author":"Vidovic","year":"2011","journal-title":"Am. J. Oper. Res."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"4438","DOI":"10.1021\/acs.jchemed.3c00402","article-title":"Teaching Descriptive Statistics and Hypothesis Tests Measuring Water Density","volume":"100","author":"Borges","year":"2023","journal-title":"J. Chem. Educ."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1080\/00949650213536","article-title":"The Combination Test for Multivariate Normality","volume":"72","author":"Hwu","year":"2002","journal-title":"J. Stat. Comput. Simul."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"24002","DOI":"10.1051\/e3sconf\/202338224002","article-title":"Probability Distribution of Soil Suction of Engineered Turf Cover and Compacted Clay Cover","volume":"382","author":"Alam","year":"2023","journal-title":"E3S Web Conf."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Galwey, N.W. (2023). A Q-Q Plot Aids Interpretation of the False Discovery Rate. Biom. J., 65.","DOI":"10.1002\/bimj.202100309"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1080\/00031305.2015.1077728","article-title":"Variations of Q\u2013Q Plots: The Power of Our Eyes!","volume":"70","author":"Loy","year":"2016","journal-title":"Am. Stat."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"731","DOI":"10.21105\/joss.00731","article-title":"Qqman: An R Package for Visualizing GWAS Results Using Q-Q and Manhattan Plots","volume":"3","author":"Turner","year":"2018","journal-title":"J. Open Source Softw."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v106.i10","article-title":"Application of Equal Local Levels to Improve Q-Q Plot Testing Bands with R Package Qqconf","volume":"106","author":"Weine","year":"2023","journal-title":"J. Stat. Softw."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1177\/1536867X0700700213","article-title":"Stata Tip 47: Quantile\u2013Quantile Plots without Programming","volume":"7","author":"Cox","year":"2007","journal-title":"Stata J. Promot. Commun. Stat. Stata"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"137","DOI":"10.3846\/1392-6292.2010.15.137-151","article-title":"Two-Sample Problems in Statistical Data Modelling","volume":"15","author":"Valeinis","year":"2010","journal-title":"Math. Model. Anal."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1038\/ejhg.2013.166","article-title":"Meta-Analysis of SNPs Involved in Variance Heterogeneity Using Levene\u2019s Test for Equal Variances","volume":"22","author":"Deng","year":"2014","journal-title":"Eur. J. Hum. Genet."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1214\/09-STS301","article-title":"The Impact of Levene\u2019s Test of Equality of Variances on Statistical Theory and Practice","volume":"24","author":"Gastwirth","year":"2009","journal-title":"Stat. Sci."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"199","DOI":"10.21608\/dsu.2022.117363.1098","article-title":"\u201cEvaluation of the Effect of Two Different Concentration of Arginine on Fluoride Uptake by Demineralized Enamel Surfaces\u201d in Vitro Study","volume":"3","author":"Abdslam","year":"2022","journal-title":"Dent. Sci. Updat."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Aldababseh, A., and Temimi, M. (2017). Analysis of the Long-Term Variability of Poor Visibility Events in the UAE and the Link with Climate Dynamics. Atmosphere, 8.","DOI":"10.3390\/atmos8120242"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"iyac158","DOI":"10.1093\/genetics\/iyac158","article-title":"Genetic Determinants of Polygenic Prediction Accuracy within a Population","volume":"222","author":"Lu","year":"2022","journal-title":"Genetics"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"2013","DOI":"10.35445\/alishlah.v15i2.2034","article-title":"Identifying Students\u2019 Religiosity and Character Strengths in a Multiculturalism Life Consequence","volume":"15","author":"Tukiyo","year":"2023","journal-title":"AL-ISHLAH J. Pendidik."},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Ul Islam, T. (2018). Preliminary Tests of Homogeneity-Type I Error Rates under Non-Normality. Biostat. Biom. Open Access J., 6.","DOI":"10.19080\/BBOAJ.2018.06.555699"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/bmsp.12103","article-title":"ANOVA and the Variance Homogeneity Assumption: Exploring a Better Gatekeeper","volume":"71","author":"Kim","year":"2018","journal-title":"Br. J. Math. Stat. Psychol."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1109\/LSP.2022.3140682","article-title":"Linear Regression Classification in the Quaternion and Reduced Biquaternion Domains","volume":"29","author":"Kamal","year":"2022","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1097\/01.NURSE.0000946844.96157.68","article-title":"Linear regression: A beginner\u2019s guide for nursing research","volume":"53","author":"Dombrowsky","year":"2023","journal-title":"Nursing"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"277","DOI":"10.34288\/jri.v3i3.238","article-title":"Predicting the Bitcoin Price Using Linear Regression Optimized with Exponential Smoothing","volume":"3","author":"Suryani","year":"2021","journal-title":"J. Ris. Inform."},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Bo\u017ei\u0107, D., Runje, B., and Razumi\u0107, A. (2024). Risk Assessment for Linear Regression Models in Metrology. Appl. Sci., 14.","DOI":"10.20944\/preprints202402.0948.v1"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1177\/00491241211036165","article-title":"Moving Beyond Linear Regression: Implementing and Interpreting Quantile Regression Models With Fixed Effects","volume":"53","author":"Maroto","year":"2024","journal-title":"Sociol. Methods Res."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.2147\/DMSO.S404222","article-title":"The Application of the Insulin to C-Peptide Molar Ratio (ICPR) in Primary Screening for Insulin Antibodies in Type 2 Diabetes Mellitus Patients: A Further Quantitative Study on the Relationship Between ICPR and Insulin Antibodies","volume":"16","author":"Hua","year":"2023","journal-title":"Diabetes Metab. Syndr. Obes."},{"key":"ref_121","first-page":"126","article-title":"The Distribution of the Coefficient of determination in Linear Regression Model: A Review","volume":"23","author":"Rady","year":"2021","journal-title":"J. Univ. Shanghai Sci. Technol."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"152","DOI":"10.54097\/hset.v61i.10287","article-title":"Prediction Of Medical Insurance Cost Through Linear Regression Model","volume":"61","author":"Cao","year":"2023","journal-title":"Highlights Sci. Eng. Technol."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"1442","DOI":"10.1080\/07350015.2021.1931241","article-title":"Hedging with Linear Regressions and Neural Networks","volume":"40","author":"Ruf","year":"2022","journal-title":"J. Bus. Econ. Stat."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"133","DOI":"10.22271\/maths.2023.v8.i6b.1463","article-title":"Application of linear regression with their advantages, disadvantages, assumption and limitations","volume":"8","author":"Anandhi","year":"2023","journal-title":"Int. J. Stat. Appl. Math."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"012032","DOI":"10.1088\/1755-1315\/1089\/1\/012032","article-title":"Integration of linear and non-linear regression for estimating land value","volume":"1089","author":"Sugito","year":"2022","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_126","first-page":"7","article-title":"Stock Forecasting Based on Linear Regression Model and Nonlinear Machine Learning Regression Model","volume":"57","author":"Zhou","year":"2024","journal-title":"Adv. Econ. Manag. Political Sci."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"115192","DOI":"10.1016\/j.cam.2023.115192","article-title":"Bayesian scale mixtures of normals linear regression and Bayesian quantile regression with big data and variable selection","volume":"428","author":"Chu","year":"2023","journal-title":"J. Comput. Appl. Math."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"012018","DOI":"10.1088\/1742-6596\/2261\/1\/012018","article-title":"On the Study of Thai Music Emotion Recognition Based on Western Music Model","volume":"2261","author":"Satayarak","year":"2022","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_129","first-page":"368","article-title":"Aboveground Carbon Stock Estimation Model Using Sentinel-2A Imagery in Mbeliling Lanscape in Nusa Tenggara Timur, Indonesia","volume":"2022","author":"Oktian","year":"2022","journal-title":"KnE Life Sci."},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Sunori, S.K., Kant, S., Agarwal, P., and Juneja, P. (2023, January 6\u20138). Development of Rainfall Prediction Models using Linear and Non-linear Regression Techniques. Proceedings of the 2023 4th IEEE Global Conference for Advancement in Technology (GCAT), Bangalore, India.","DOI":"10.1109\/GCAT59970.2023.10353508"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"800","DOI":"10.35833\/MPCE.2020.000738","article-title":"Data-driven Power Flow Method Based on Exact Linear Regression Equations","volume":"10","author":"Chen","year":"2022","journal-title":"J. Mod. Power Syst. Clean Energy"},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Kaur, J., Goyal, A., Handa, P., and Goel, N. (2022, January 11\u201313). Solar power forecasting using ordinary least square based regression algorithms. Proceedings of the 2022 IEEE Delhi Section Conference (DELCON), New Delhi, India.","DOI":"10.1109\/DELCON54057.2022.9753619"},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Faye, C.M., Fonn, S., and Levin, J. (2019). Factors associated with recovery from stunting among under-five children in two Nairobi informal settlements. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0215488"},{"key":"ref_134","first-page":"699","article-title":"Sugarcane Yield Prediction Using Vegetation Indices in Northern Karnataka, India","volume":"10","author":"Jha","year":"2022","journal-title":"Univers. J. Agric. Res."},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Alam, F., Usman, M., Alkhammash, H.I., and Wajid, M. (2021). Improved Direction-of-Arrival Estimation of an Acoustic Source Using Support Vector Regression and Signal Correlation. Sensors, 21.","DOI":"10.3390\/s21082692"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1007\/s44227-022-00007-0","article-title":"Novel Approaches for Resource Management Across Edge Servers","volume":"11","author":"Surya","year":"2023","journal-title":"Int. J. Networked Distrib. Comput."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"561","DOI":"10.13189\/eer.2022.100504","article-title":"Trend Analysis on Water Quality Index Using the Least Squares Regression Models","volume":"10","author":"Zawawi","year":"2022","journal-title":"Environ. Ecol. Res."},{"key":"ref_138","first-page":"26","article-title":"Estimation and correlation of chlorophyll and nitrogen contents in Psidium guajava L. with destructive and non-destructive methods","volume":"14","year":"2020","journal-title":"Rev. Colomb. Cienc. Hort\u00edcolas"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1080\/10586458.2019.1706670","article-title":"Searching for Hyperbolic Polynomials with Span Less than 4","volume":"31","author":"Capparelli","year":"2022","journal-title":"Exp. Math."},{"key":"ref_140","first-page":"92","article-title":"Close-to-convexity of polynomial solutions of a differential equation of the second order with polynomial coefficients of the second degree","volume":"90","author":"Sheremeta","year":"2020","journal-title":"Visnyk Lviv. Universytetu Seriya Mekhaniko-Mat."},{"key":"ref_141","first-page":"6500411","article-title":"Acoustic Mode Measuring Approach Developed on Generalized Minimax-Concave Regularization and Tikhonov Regularization","volume":"71","author":"Li","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_142","first-page":"311","article-title":"Comparative Analysis of Ridge, LASSO, and Elastic Net Regularization Approaches in Handling Multicollinearity for Infant Mortality Data in South Sulawesi","volume":"20","author":"Nur","year":"2023","journal-title":"J. Mat. Stat. Dan Komputasi"},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Emura, T., Matsumoto, K., Uozumi, R., and Michimae, H. (2024). g. ridge: An R Package for Generalized Ridge Regression for Sparse and High-Dimensional Linear Models. Symmetry, 16.","DOI":"10.20944\/preprints202401.1119.v1"},{"key":"ref_144","first-page":"85","article-title":"Estimation of chlorophyll content in pepper leaves using spectral transmittance red-edge parameters","volume":"15","author":"Huang","year":"2022","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1080\/00401706.2020.1742207","article-title":"Ridge Regression: A Historical Context","volume":"62","author":"Hoerl","year":"2020","journal-title":"Technometrics"},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"033101","DOI":"10.1088\/1674-1056\/acac08","article-title":"Ridge regression energy levels calculation of neutral ytterbium (Z = 70)","volume":"32","author":"Yu","year":"2023","journal-title":"Chin. Phys. B"},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"139","DOI":"10.59670\/ml.v20i6.3468","article-title":"Optimizing Linear Regression Models with Lasso and Ridge Regression: A Study on UAE Financial Behavior during COVID-19","volume":"20","author":"Safi","year":"2023","journal-title":"Migr. Lett."},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"4","DOI":"10.47836\/pjst.28.4.04","article-title":"Ridge Regression as Efficient Model Selection and Forecasting of Fish Drying Using V-Groove Hybrid Solar Drier","volume":"28","author":"Lim","year":"2020","journal-title":"Pertanika J. Sci. Technol."},{"key":"ref_149","doi-asserted-by":"crossref","unstructured":"Arashi, M., Roozbeh, M., Hamzah, N.A., and Gasparini, M. (2021). Ridge regression and its applications in genetic studies. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0245376"},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"113","DOI":"10.2478\/stattrans-2022-0033","article-title":"An improved ridge type estimator for logistic regression","volume":"23","author":"Varathan","year":"2022","journal-title":"Stat. Transit. New Ser."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"121","DOI":"10.5958\/0974-0279.2021.00010.0","article-title":"An analysis of Indian agricultural workers: A ridge regression approach","volume":"34","author":"Kumar","year":"2021","journal-title":"Agric. Econ. Res. Rev."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1080\/00401706.2020.1805021","article-title":"Comment: From Ridge Regression to Methods of Regularization","volume":"62","author":"Yuan","year":"2020","journal-title":"Technometrics"},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"242","DOI":"10.2991\/jsta.d.210322.001","article-title":"Optimum Ridge Regression Parameter Using R-Squared of Prediction as a Criterion for Regression Analysis","volume":"20","author":"Irandoukht","year":"2021","journal-title":"J. Stat. Theory Appl."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"e7005","DOI":"10.1002\/cpe.7005","article-title":"On the performance of link functions in the beta ridge regression model: Simulation and application","volume":"34","author":"Mustafa","year":"2022","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2021.3072389","article-title":"Balanced Tikhonov and Total Variation Deconvolution Approach for Radar Forward-Looking Super-Resolution Imaging","volume":"19","author":"Huo","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"2703","DOI":"10.1007\/s10489-020-01942-7","article-title":"Forecasting COVID-19 outbreak progression using hybrid polynomial-Bayesian ridge regression model","volume":"51","author":"Saqib","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"107968","DOI":"10.1016\/j.oceaneng.2020.107968","article-title":"Machine learning and data-driven fault detection for ship systems operations","volume":"216","author":"Cheliotis","year":"2020","journal-title":"Ocean Eng."},{"key":"ref_158","doi-asserted-by":"crossref","unstructured":"\u0160inkovec, H., Heinze, G., Blagus, R., and Geroldinger, A. (2021). To tune or not to tune, a case study of ridge logistic regression in small or sparse datasets. BMC Med. Res. Methodol., 21.","DOI":"10.1186\/s12874-021-01374-y"},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"E2445","DOI":"10.1002\/joc.6857","article-title":"Analysis of monthly and annual rainfall variability using linear models in the state of Mato Grosso do Sul, Midwest of Brazil","volume":"41","author":"Abreu","year":"2021","journal-title":"Int. J. Climatol."},{"key":"ref_160","doi-asserted-by":"crossref","unstructured":"Kasimati, A., Espejo-Garcia, B., Vali, E., Malounas, I., and Fountas, S. (2021). Investigating a Selection of Methods for the Prediction of Total Soluble Solids Among Wine Grape Quality Characteristics Using Normalized Difference Vegetation Index Data From Proximal and Remote Sensing. Front. Plant Sci., 12.","DOI":"10.3389\/fpls.2021.683078"},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"218","DOI":"10.2174\/157341112800392634","article-title":"A Numerical Evaluation of the Classification of Portuguese Red Wine","volume":"8","author":"Beh","year":"2012","journal-title":"Curr. Anal. Chem."},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"639652","DOI":"10.1155\/2013\/639652","article-title":"Quality Evaluation Based on Multivariate Statistical Methods","volume":"2013","author":"Yin","year":"2013","journal-title":"Math. Probl. Eng."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"217","DOI":"10.17221\/370\/2014-CJFS","article-title":"Application of Multivariate Regression Methods to Predict Sensory Quality of Red Wines","volume":"33","author":"Alvarez","year":"2015","journal-title":"Czech J. Food Sci."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"770","DOI":"10.2991\/978-94-6463-042-8_110","article-title":"Prediction of Wine Quality Using Ensemble Learning Approach of Machine Learning","volume":"Volume 101","author":"Shvets","year":"2023","journal-title":"Proceedings of the 2022 International Conference on Mathematical Statistics and Economic Analysis (MSEA 2022)"},{"key":"ref_165","doi-asserted-by":"crossref","unstructured":"Tarlak, F. (2023). The Use of Predictive Microbiology for the Prediction of the Shelf Life of Food Products. Foods, 12.","DOI":"10.3390\/foods12244461"},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"215","DOI":"10.4265\/bio.21.215","article-title":"Prediction of Competitive Microbial Growth","volume":"21","author":"Fujikawa","year":"2016","journal-title":"Biocontrol Sci."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"3819","DOI":"10.51594\/ijmer.v6i11.1721","article-title":"The Role of Data Analytics in Reducing Healthcare Disparities: A Review of Predictive Models for Health Equity","volume":"6","author":"Edoh","year":"2024","journal-title":"Int. J. Manag. Entrep. Res."},{"key":"ref_168","first-page":"e49252","article-title":"Harnessing the Power of AI: A Comprehensive Review of Its Impact and Challenges in Nursing Science and Healthcare","volume":"15","author":"Yelne","year":"2023","journal-title":"Cureus"},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1007\/978-3-030-60036-5_9","article-title":"Big Data Analytics in Healthcare: A Review of Opportunities and Challenges","volume":"Volume 332","author":"Miraz","year":"2020","journal-title":"Emerging Technologies in Computing"},{"key":"ref_170","doi-asserted-by":"crossref","unstructured":"CI&DETS\/ESAV, Polytechnic Institute of Viseu, Department of Food Industry, Viseu, Portugal, and Guin\u00e9, R.P.F. (2019). The Use of Artificial Neural Networks (ANN) in Food Process Engineering. ETP Int. J. Food Eng., 5, 15\u201321.","DOI":"10.18178\/ijfe.5.1.15-21"},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"19","DOI":"10.14419\/ijsw.v1i2.1151","article-title":"Artificial Neural Networks (ANNs) in Food Science\u2014A Review","volume":"1","author":"Goyal","year":"2013","journal-title":"Int. J. Sci. World"},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"2372","DOI":"10.1002\/cjce.23507","article-title":"Experimental Methods in Chemical Engineering: Artificial Neural Networks\u2013ANNs","volume":"97","author":"Panerati","year":"2019","journal-title":"Can. J. Chem. Eng."},{"key":"ref_173","unstructured":"(2020). AI-Driven Automated Feature Engineering to Enhance Performance of Predictive Models in Data Science. Int. J. Control Autom., 13, 1558\u20131571."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/3\/55\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:43:23Z","timestamp":1760028203000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/3\/55"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,26]]},"references-count":173,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["bdcc9030055"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9030055","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,26]]}}}