A hybrid approach for forecasting peak expiratory flow rate in asthma patients using combined linear regression and random forest model by Shayma Alkobaisi, Wan D. Bae, Muhammad Farhan Safdar, Najah Abed Abu Ali, Sungroul Kim, Choon-Sik Park, Robert Marek Nowak

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Sex determination and stature estimation using logistic and linear regression models: A population-specific study of tibia and Fibula in Northeastern Thais by Suthat Duangchit, Chanasorn Poodendaen, Phetcharat Phetnui, Pornpimol Tasu, Worrawit Boonthai, Nareelak Tangsrisakda, Sitthichai Iamsaard

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Predictive modelling of graphene-enhanced greases using classical feedback control and quantum kernel regression by Ethan Stefan-Henningsen, Amirkianoosh Kiani

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Implementing partial least squares and machine learning regressive models for prediction of drug release in targeted drug delivery application by Anupam Yadav, B. Jayaprakash, Laith Hussein Jasim, Mayank Kundlas, Maan Younis Anad, Ankur Srivastava, M. Janaki Ramudu, B. Bharathi, Prabhat Kumar Sahu

“…Uniquely, this work integrates glowworm swarm optimization (GSO) for model hyperparameter tuning, enhancing model accuracy and efficiency. …”
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Investigation of the Effect of Risk Perception, Socio-demographic and Obstetric Factors on the Level of Decision-making of Pregnant Women via the Internet: Multiple Linear Regression Analysis Model by Songül Aktaş, Tuğba Yazıcı Topçu, Özge Palancı Ay, Ruveyde Aydın

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Multivariate Regression Analysis for Identifying Key Drivers of Harmful Algal Bloom in Lake Erie by Omer Mermer, Ibrahim Demir

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Modeling the regulatory network of histone acetylation in Saccharomyces cerevisiae by Hung Pham, Roberto Ferrari, Shawn J Cokus, Siavash K Kurdistani, Matteo Pellegrini

“…We validate the model, both computationally and experimentally, to demonstrate that it yields accurate predictions of these regulatory mechanisms.…”
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REGRESSION MODELS OF INDIVIDUAL LINEAR DIMENSIONS NECESSARY FOR CONSTRUCTING THE CORRECT FORM OF DENTAL ARCH IN YOUNG WOMEN WITH A WIDE FACE, DEPENDING ON THE FEATURES OF ODONTOMETRIC AND CEPHALOMETRIC INDICATORS by М. М. Shinkaruk-Dykovytska, T. P. Pozur, S. V. Koliadenko, V. O. Orlovskiy, G. M. Galunko

“…Materials and methods: for 20 girls with normal occlusion close to the orthognathic bite and with a wide face type, in the license package "Statistica 6.0" developed regression models of linear sizes necessary for constructing the correct shape of dental arches, depending on odontometric and cephalometric indicators. …”
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SOME PECULIARITIES ARISING IN THE STUDY OF NONLINEAR REGRESSION USING EXCEL AND OTHER PROGRAMS by Irina Vladlenovna Orlova, Viktor Borisovich Turundaevskiy

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Exploring Predictive Modeling for Food Quality Enhancement: A Case Study on Wine by Cemil Emre Yavas, Jongyeop Kim, Lei Chen, Christopher Kadlec, Yiming Ji

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Konsistensi Model Regresi Empat Variabel Pada Populasi dan Sampel untuk Prediksi Temperatur by Nurjannah Syakrani, Naufal Athaya S. R

“…Nine out of ten sample regression models agree with the population model, with positive coefficients for visibility and wind direction and negative values for humidity and wind speed. …”
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Developing a simple artificial intelligence fuzzy-based model for estimating saturated hydraulic conductivity of soil by Mohammad Naderianfar

“…After examining different models and combinations of available data, three models were proposed for estimating saturated hydraulic conductivity. …”
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Prediction of anisotropic property of activated metal inert gas welding by employing different supervised machine learning models by Ruturaj U. Kakade, Nitin Khedkar, Amol Dalavi

“…Material characterization was performed on samples with the highest and lowest TS to evaluate the correlation between microstructure and strength. Machine learning models Linear Regression, Random Forest Regression, and Support Vector Regression (SVR) were applied to predict TS based on welding parameters.• The SVR model achieved the best predictive performance, with an R² of 0.8750 and a model accuracy of 96.73 %.• The results confirm the potential of SVR for accurately forecasting TS in A-MIG welded EN10028, facilitating process optimization in pressure applications…”
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A Goodness-of-Fit Test for Log-Linearity in Cox Proportional Hazards Model Under Monotonic Covariate Effects by Huan Chen, Chuan-Fa Tang

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Modelling temporal change in inventory attributes from a LiDAR-derived inventory for the United Counties of Prescott and Russell, Ontario: A comparison of random forest and linear regression methods by Benjamin Gwilliam

“…Rather, the quadratic equations from the linear regression models were used to predict 2021 landscape values. …”
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A robust transfer learning approach for high-dimensional linear regression to support integration of multi-source gene expression data. by Lulu Pan, Qian Gao, Kecheng Wei, Yongfu Yu, Guoyou Qin, Tong Wang

“…Extensive simulation experiments as well as an application demonstrate that Trans-PtLR demonstrates robustness and better performance of estimation and prediction when heavy-tail and outliers exist compared to transfer learning for linear regression model with normal error distribution. …”
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A COSMIC function points based test effort estimation model for mobile applications by Anureet Kaur, Kulwant Kaur

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Econometric predictive model for assessing the functional state of students during the examination period: a cross-sectional exploratory pilot study by I. S. Knyazhev, L. V. Karaulova, O. V. Reztsov, A. P. Spitsin

“…The present study investigates factors associated with the adaptation potential of the cardiovascular system, develops multiple regression models for the dependence of the adaptive potential on these factors, and compares the significance of the linear model with non-linear ones. …”
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Regression model of fear of childbirth in pregnant women by Farzaneh Rashidi, Nazanin Hesari, Sahar Shariatnia, Abdollah Razi, SeyyedMohammad MohammadiAubi, Fatemeh Gorji, Faezeh Ghanbari

“…Therefore, a study was conducted to analyze the regression model of fear of childbirth among pregnant women. …”
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Lake Water Level Estimation with Artificial Intelligence Methods by Aysu AYAR, Fatih ÜNEŞ, Bestami TAŞAR, Hakan VARÇIN

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