Diagnostic value of the MZXBTCH scoring system for acute complex appendicitis by Tianyi Ma, Qian Zhang, Hongwei Zhao, Peng Zhang

“…Receiver operating characteristic (ROC) curves were plotted to compare the diagnostic efficacy of these scoring systems for complex appendicitis. Multivariate logistic regression analysis identified preoperative body temperature (odds ratio (OR) = 1.104; 95% confidence interval (CI) 1.067–1.143; P < 0.001), preoperative C-reactive protein (CRP) level (OR = 1.002; 95% CI 1.001–1.002; P < 0.001), lymphocyte percentage (OR = 0.994; 95% CI 0.990–0.996; P < 0.001), appendiceal fecal stones (OR = 1.127; 95% CI 1.068–1.190; P < 0.001), periappendiceal fat stranding (OR = 1.133; 95% CI 1.072–1.198; P < 0.001), and appendix diameter (OR = 1.013; 95% CI 1.004–1.022; P < 0.001) as independent risk factors for complex appendicitis. …”
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MODELS IN MECHANICAL RELIABILITY DATA by Adrian Stere PARIS

“…The state-of-the-art in the domain implies extended use of specialised software, from simple freeware to complex, expensive ones; the paper proposes an investigation of the capabilities of some useful software, mainly applying the regression analysis of experimental data and some numerical examples and extends the discussion to dependability and performability.…”
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Generative learning for forecasting the dynamics of high-dimensional complex systems by Han Gao, Sebastian Kaltenbach, Petros Koumoutsakos

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A New Hyperparameter Tuning Framework for Regression Tasks in Deep Neural Network: Combined-Sampling Algorithm to Search the Optimized Hyperparameters by Nguyen Huu Tiep, Hae-Yong Jeong, Kyung-Doo Kim, Nguyen Xuan Mung, Nhu-Ngoc Dao, Hoai-Nam Tran, Van-Khanh Hoang, Nguyen Ngoc Anh, Mai The Vu

“…This paper introduces a novel hyperparameter optimization framework for regression tasks called the Combined-Sampling Algorithm to Search the Optimized Hyperparameters (CASOH). …”
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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

“…In this paper, we study the transfer learning problem under high-dimensional linear models with t-distributed error (Trans-PtLR), which aims to improve the estimation and prediction of target data by borrowing information from useful source data and offering robustness to accommodate complex data with heavy tails and outliers. In the oracle case with known transferable source datasets, a transfer learning algorithm based on penalized maximum likelihood and expectation-maximization algorithm is established. …”
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Prediction of ball mill power in iron ore concentration plants: A comparison between radial basis functions and linear regression by Javad Taghavi, Mahdi Gharabaghi

“…Findings indicate that the RBF model outperforms linear regression in terms of accuracy and precision, exhibiting a lower standard deviation (0.0782 MW for RBF compared to 0.1207 MW for linear regression) and a smaller average absolute error (0.0585 MW for RBF compared to 0.0899 MW for linear regression).. …”
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Comparative Analysis of Random Forest and Logistic Regression Methods in Predicting Leukemia Blood Cancer Using Microscopic Blood Cell Images by Jepri Banjarnahor, Galuh Wira Relungwangi

“…These findings suggest that ensemble methods like RF are particularly well-suited for detecting one of the most deadly blood cancers, leukemia, due to their ability to handle complex feature interactions in medical imaging data. …”
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Pearson and Deviance Residual-Based Control Charts for the Inverse Gaussian Ridge Regression Process: Simulation and an Application to Air Quality Monitoring by Muhammad Amin, Samra Rani, Sadiah M. A. Aljeddani

“…When input variables exhibit multicollinearity, traditional MLE-based inverse Gaussian regression model (IGRM) control charts become unreliable. …”
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Multidimensional factors of depressive symptoms among Chinese undergraduate students: a cross-sectional study using binary logistic regression by Wenjia Chen, Jianfei Bai, Haitao Niu, Qingying Zhu, Xiuhan Zhao, Yanyu Dong

“…IntroductionThis cross-sectional study investigates the interplay of lifestyle, behavioral, and psychosocial factors in predicting depressive symptoms among Chinese college students (N=508) using binary logistic regression.MethodsParticipants were recruited from four geographically diverse provinces (Eastern: Shandong; Western: Shaanxi, Sichuan; Southern: Hainan) across 8 universities (5 comprehensive universities, 3 specialized institutions), with balanced urban (n=245, 48.22%) and rural (n=263, 51.78%) representation. …”
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A Hybrid Fault Detection Method of Independent Component Analysis and Auto-Associative Kernel Regression for Process Monitoring in Power Plant by Seunghwan Jung, Jonggeun Kim, Sungshin Kim

“…In complex industrial processes, distributed control systems (DCSs) are currently operated to prevent unplanned shutdowns and major accidents. …”
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Estimating Coastal Dyke Leakage Flow Using Support Vector Machine (SVM) and Multivariate Adaptive Regression Spline (MARS) Model by Adel Asakereh, Farhad Choobi, mohammad bagherzadeh, reza mirzaee

“…Soft computing methods can be used to easily model, analyze and control complex systems. This study uses Support Vector Machine (SVM) method to predict leakage discharge of coastal dykes. …”
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Online Tool Wear Monitoring via Long Short-Term Memory (LSTM) Improved Particle Filtering and Gaussian Process Regression by Hui Xu, Hui Xie, Guangxian Li

“…However, traditional Gaussian Process Regression (GPR) models are constrained by linear assumptions, while conventional filtering algorithms struggle in noisy environments with low signal-to-noise ratios. …”
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Performance Prediction and Optimization of High-Plasticity Clay Lime–Cement Stabilization Based on Principal Component Analysis and Principal Component Regression by Ibrahim Haruna Umar, Zaharaddeen Ali Tarauni, Abdullahi Balarabe Bello, Hang Lin, Jubril Izge Hassan, Rihong Cao

“…Multivariate analysis integrated principal component analysis (PCA) with regression modeling (PCR) for sensitivity and causality assessment. …”
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Mean limiting pressure factors determination in contiguous pile walls using RAFELA and nonlinear regression models in spatially random soil by Divesh Ranjan Kumar, Sittha Kaorapapong, Warit Wipulanusat, Suraparb Keawsawasvong

“…This study employs random adaptive finite element limit analysis (RAFELA) combined with advanced machine learning (ML) techniques to predict the mean limiting pressure factor in contiguous pile walls (CPWs). Two nonlinear regression models, multivariate adaptive regression splines (MARS) and the group method of data handling (GMDH), are developed to forecast the mean limiting pressure factor. …”
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Chronic stress in practice assistants: An analytic approach comparing four machine learning classifiers with a standard logistic regression model. by Arezoo Bozorgmehr, Anika Thielmann, Birgitta Weltermann

“…As machine learning strategies offer the opportunity to improve understanding of chronic stress by exploiting complex interactions between variables, we used data from our previous study to derive the best analytic model for chronic stress: four common machine learning (ML) approaches are compared to a classical statistical procedure.…”
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A Hybrid Regression–Kriging–Machine Learning Framework for Imputing Missing TROPOMI NO₂ Data over Taiwan by Alyssa Valerio, Yi-Chun Chen, Chian-Yi Liu, Yi-Ying Chen, Chuan-Yao Lin

“…This structure enables the framework to capture both spatial autocorrelation and complex relationships between NO₂ concentrations and environmental drivers. …”
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PERAMALAN DERET WAKTU MENGGUNAKAN MODEL FUNGSI BASIS RADIAL (RBF) DAN AUTO REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) by DT Wiyanti, R Pulungan

“…Dalam artikel ini dibahas penggabungan dua buah metode yaitu Auto Regressive Integrated Moving Average (ARIMA) dan Radial Basis Function (RBF). …”
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Temporal–Spatial Partial Differential Equation Modeling of Land Cover Dynamics via Satellite Image Time Series and Sparse Regression by Ming Kang, Zheng Zhang, Zhitao Zhao, Keli Shi, Junfang Zhao, Ping Tang

“…Land cover dynamics play a critical role in understanding environmental changes, but accurately modeling these dynamics remains a challenge due to the complex interactions between temporal and spatial factors. …”
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Hybrid AI-Based Framework for Renewable Energy Forecasting: One-Stage Decomposition and Sample Entropy Reconstruction with Least-Squares Regression by Nahed Zemouri, Hatem Mezaache, Zakaria Zemali, Fabio La Foresta, Mario Versaci, Giovanni Angiulli

“…To optimize forecasting accuracy, outputs from all models are combined using a least-squares regression technique that assigns optimal weights to each prediction. …”
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Linear regressive weighted Gaussian kernel liquid neural network for brain tumor disease prediction using time series data by Firoz Khan, Sardar Irfanullah Amanullah, Shitharth Selvarajan

“…However, conventional machine learning and deep learning detection models face challenges in achieving high accuracy in brain tumor disease prediction while minimizing time complexity. To address this, a novel Linear Regressive Weighted Gaussian Kernel Liquid Neural Network (LRWGKLNN) model is developed. …”
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