Learning Regionalization Using Accurate Spatial Cost Gradients Within a Differentiable High‐Resolution Hydrological Model: Application to the French Mediterranean Region by Ngo Nghi Truyen Huynh, Pierre‐André Garambois, François Colleoni, Benjamin Renard, Hélène Roux, Julie Demargne, Maxime Jay‐Allemand, Pierre Javelle

“…A possible approach is to search for a transfer function that quantitatively relates physical descriptors to conceptual model parameters. This paper introduces a Hybrid Data Assimilation and Parameter Regionalization (HDA‐PR) approach incorporating learnable regionalization mappings, based on either multi‐linear regression or artificial neural networks (ANNs), into a differentiable hydrological model. …”
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A population-based observational study using statistical modeling to assess the association between depressive symptom severity and sleep disorders in postmenopausal women by Ying Cui, Huimin Du

“…Weighted multivariable logistic regression models were used to analyze the association between depressive symptom severity and sleep disorders, adjusting for potential confounders. …”
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Development of a model for predicting money laundering rate by E. S. Anisimov, J. M. Beketnova

“…Using a seasonally integrated autoregressive moving average (SARIMA) model, it compares different regression models for the research tasks (linear regression, logistic regression, autoregressive and SARIMA). …”
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Video Quality Prediction Models Based on Video Content Dynamics for H.264 Video over UMTS Networks by Asiya Khan, Lingfen Sun, Emmanuel Ifeachor, Jose-Oscar Fajardo, Fidel Liberal, Harilaos Koumaras

“…The loss models considered are 2-state Markov models. Both the models are trained with a combination of physical and application layer parameters and validated with unseen dataset. …”
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Teaching innovation in a pharmacy course: integration of “Questioning-Training of Comprehensive Knowledge Application” and a “Teacher-AI-Student Interaction Model” by Liwei Wang, Yantao Xu, Mianmian Zhang, Renren Bai, Tian Xie

“…The students’ questioning and knowledge application scores in both classes were analyzed using R statistical software, interaction model regression analysis, and prediction model Holt linear trend method analysis. …”
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Machine Learning-Based Model in Predicting the Plate-End Debonding of FRP-Strengthened RC Beams in Flexure by Tianyu Hu, Guibing Li

“…The results of reliability evaluation and parameter analysis reveal that ACI, CNR, fib-1, fib-2, and TR55-2 are a little conservative; AS and TR55-1 have the problem of overestimating the shear force; the accuracy and robustness of the SSA-BP model developed in this paper are good; the stirrup reinforcement has the greatest effect on PE debonding; and each parameter shows a complex nonlinear relationship with the shear force when PE debonding occurs.…”
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Improving rainfall forecasting using deep learning data fusing model approach for observed and climate change data by Farhan Amir Fardush Sham, Ahmed El-Shafie, Wan Zurina Binti Wan Jaafar, S. Adarsh, Mohsen Sherif, Ali Najah Ahmed

“…Traditional forecasting methods, such as linear regression, autoregressive models, and time-series analysis, are limited in their ability to capture the intricate and dynamic nature of rainfall patterns. …”
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QSPR modeling of some COVID-19 drugs using neighborhood eccentricity-based topological indices: A comparative analysis. by Yeliz Kara, Yeşim Saǧlam Özkan, Asad Ullah, Yasser Salah Hamed, Melaku Berhe Belay

“…Additionally, a comparison of the known values and the calculated values from the regression models discussed is obtained.…”
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Prediction of Remaining Useful Life of Operating Mechanism Driven by Deep Feature in Classification Model by Xilei Dong, Fuping Zhang, Yunyang Ye, Huan Zhang

“…To address the drawbacks of high computational complexity and slow convergence speed in existing data-driven regression models, in this research, based on deep feature linear fitting of classification models, a remaining useful life prediction method for operating mechanism is proposed. …”
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Hybrid Tree-Based Machine Learning Models for State-of-Charge and Core Temperature Estimation in EV Batteries by Aya Haraz, Khalid Abualsaud, Ahmed M. Massoud

“…Among the combinations tested, the Extra Trees Regressor-Random Forest (ETR-RF) model delivered the highest estimation accuracy, while the Decision Tree-LightGBM (DT-LGBM) model exhibited the fastest training time. …”
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Hourly surface nitrogen dioxide retrieval from GEMS tropospheric vertical column densities: benefit of using time-contiguous input features for machine learning models by J. Gödeke, A. Richter, K. Lange, P. Maaß, H. Hong, H. Lee, J. Park

“…In other words, we inspect the models' ability to generalize to unseen locations. …”
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Ensemble machine learning prediction model for clinical refraction using partial interferometry measurements in childhood. by Sa Ra Kim, Dong Hyun Kang, Gon Soo Choe, Dae Hee Kim

“…Root mean squared error (RMSE) was used to compare the accuracy among the models. The accuracy of the ensemble models was compared with that of a previously developed multiple linear regression model.…”
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A New Mathematical Model for Flank Wear Prediction Using Functional Data Analysis Methodology by Sonja Jozić, Branimir Lela, Dražen Bajić

“…In order to be able to predict flank wear a new linear regression mathematical model has been developed by utilizing functional data analysis methodology. …”
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The Integration Model of Kano Model and Importance-Performance and Gap Analysis—Application of Mutual Information by Shu-Ping Lin, Ming-Chun Tsai

“…This study proposes the Kano-IPGA (KIPGA) model, incorporating mutual information (MI) to bridge the gap between these two models. …”
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Development of a simple mechanical model to evaluate running economy for designing running augmentation suits by Hiromu Mori, Takayuki Tanaka, Akihiko Murai, Takashi Kusaka, Yumeko Imamura

“…We extended a spring-loaded inverted pendulum model, considering the biomechanical determinants of running economy (RE), to develop a simplified mechanical model that can accurately represent the $$\epsilon _{EI}$$ ϵ EI calculated by a detailed running model. …”
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Predictive modeling of longitudinal cracking in CRCP using PSO-tuned gradient boosting machines by Ali Alnaqbi, Ghazi G. Al-Khateeb, Waleed Zeiada

“…Compared to traditional and untuned models, the PSO-GBM offers improved generalization and a stronger ability to capture nonlinear interactions among variables. …”
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Scoring model for exploring factors influencing mortality in dengue patients at a tertiary care hospital: a retrospective study by Abhishek S. Rao, B. H. Karthik Pai, K. Adithi, Lakshmi Belur Keshav, Karan Malhotra, Sneha Nayak, H. K. Sachidananda

“…A practical score-based model (DENScore) was developed by calculating the risk score for each attribute. …”
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Forest cover and canopy health mapping in Australian subalpine landscape: supervised machine learning models for Sentinel-2 and Landsat images by Weerach Charerntantanakul, Marta Yebra, Hilary Rose Dawson, Adrienne Beth Nicotra, Saul Alan Cunningham, Matthew Theodore Brookhouse

“…The models built on Landsat surface reflectance performed similarly. …”
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Accuracy of photorespiration and mitochondrial respiration in the light fitted by CO2 response model for photosynthesis by Zhengwen Niu, Zi-Wu-Yin Ye, Qi Huang, Chunju Peng, Huajing Kang

“…Furthermore, the effects of ambient CO2 concentration (0~1200 μmol·mol-1) on the measured Rp and Rd were quantified through polynomial regression.ResultsThe A/Ca model achieved superior fitting performance over the A/Ci model. …”
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Modelling the Effects of Marketing Strategies on Tourist Satisfaction in the Tourism Industry (Case Study: Tourist Attractions in Tehran) by Roozbeh Janatkhah, Leila Andervazh, Tahereh Hasomei

“…Data analysis in this research was performed using correlation and stepwise regression tests in SPSS software. Additionally, due to the non-normality of the data, Structural Equation Modelling (SEM) was employed using the Partial Least Squares (PLS) method with SMART PLS4 software.Finding: The findings showed that tourism marketing strategies with a T value (36.279) and path coefficient value (0.771) have a positive effect on tourists' satisfaction. …”
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