Modeling Population Growth in R with the biogrowth Package by Alberto Garre, Jeroen Koomen, Heidy M. W. den Besten, Marcel H. Zwietering

Subjects: “…kinetic modelling…”
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Prediction of Carbon Dioxide Concentrations in Strawberry Greenhouse by Using Time Series Models by Seung Hyun Shin, Nibas Chandra Deb, Elanchezhian Arulmozhi, Niraj Tamrakar, Oluwasegun Moses Ogundele, Junghoo Kook, Dae Hyun Kim, Hyeon Tae Kim

“…Therefore, the objectives of this study were to predict CO₂ concentrations in the greenhouse by applying time series models using five datasets. …”
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Analysis of the dynamics of changes in factor load models for predicting failures of a spacecraft by Kraev Konstantin, Zaitsev Pavel, Kazakovtsev Vladimir

“…The structure of the methodology for preparing data and creating factor load models is described. A methodology for analyzing the dynamics of changes in factor load models is proposed. …”
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Analysis of a double Poisson model for predicting football results in Euro 2020. by Matthew J Penn, Christl A Donnelly

“…This paper examines the pre-tournament predictions made using this model for the Euro 2020 football tournament. …”
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Predicting Market Value of Iranian Football Players using Linear Modeling Techniques by Rasool Nazari, Saeid Azari

“…Purpose: The purpose of this study was to predict the market value of professional football players in the Iranian Premier League in the 2020-2021 season of Iranian football.Methods: method of the present study was descriptive-survey. …”
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Predicting Course Grades Through Comprehensive Modeling of Students’ Learning Behavioral Patterns by Danial Hooshyar, Yeongwook Yang

“…This study aims to model students’ online learning behavior and accordingly predict their course achievement. …”
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COMPUTER MODELLING METHODS FOR PREDICTING PERFORMANCE OF CATHODE MATERIALS FOR LITHIUM-ION BATTERIES by M.Yu.. Arsent'ev, P.A. Tikhonov, M.V. Kalinina, T.L. Egorova, A.V. Shmigel

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Performance of machine learning models for predicting high-severity symptoms in multiple sclerosis by Subhrajit Roy, Diana Mincu, Lev Proleev, Chintan Ghate, Jennifer S. Graves, David F. Steiner, Fletcher Lee Hartsell, Katherine Heller

“…Finally, we retrospectively developed three classical ML methods and two deep learning models to accurately and continually predict the incidence of five high-severity symptoms (fatigue, sensory disturbance, walking instability, depression or anxiety and cramps/spasms) three months in advance.…”
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A Robust Hybrid CNN–LSTM Model for Predicting Student Academic Performance by Kuburat Oyeranti Adefemi, Murimo Bethel Mutanga

“…However, many existing models struggle with generalizability and fail to effectively manage data challenges such as class imbalance and missing data, leading to suboptimal predictive performance. …”
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Predicting the Evolution of Shallow Cumulus Clouds With a Lotka‐Volterra Like Model by Jingyi Chen, Samson Hagos, Jerome Fast, Zhe Feng

“…Abstract In numerical weather prediction and climate models, boundary‐layer clouds are controlled by a wide range of subgrid‐scale processes. …”
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Numerically predicting deflections of mat-sand interaction model under gravity loadings by Md. Foisal Haque, Nure Ashraf, Jahidul Islam Chalkder, Abu Salem Musa

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Time series analysis for modeling and predicting confirmed cases of influenza a in Algeria by Djillali Seba, N. Benaklef, K. Belaide

“…This study employed a comprehensive approach to develop a model for predicting confirmed cases of Influenza A in Algeria. …”
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A deep learning model for predicting systemic lupus erythematosus-associated epitopes by Jiale He, Zixia Liu, Xiaopo Tang

“…These findings underscore the model’s robustness and capacity to generalize across complex epitope prediction tasks. …”
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Voice as a sensitive biomarker for predicting exercise intensity: a modelling study by Shuyi Zhou, Ruisi Ma, Wangjing Hu, Dandan Zhang, Rui Hu, Shengwei Zou, Dingyi Cai, Zikang Jiang, Hexiao Ding, Ting Liu

“…These features were analyzed using statistical models, including support vector machine (SVM), to classify exercise intensity.ResultsSignificant variations in speech characteristics, such as speech duration, fundamental frequency (F0), and pause times, were observed across different exercise intensities, with the models achieving high accuracy in distinguishing between exercise states.ConclusionThese findings suggest that speech analysis can provide a non-invasive, real-time method for monitoring exercise intensity. …”
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Predicting Remaining Useful Life Based on Hilbert–Huang Entropy with Degradation Model by Yuhuang Zheng

“…To assess the degradation of a machine, this paper presents a bearing remaining useful life (RUL) prediction method. The method relies on a novel health indicator and a linear degradation model to predict bearing RUL. …”
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The application of the motivation-opportunity-ability model in predicting green entrepreneurial behavior of farmers by Zobeyde Kouchaky, Fatemeh Razzaghi Borkhani, Taher Azizi-Khalkheili, Fatemeh Ghorbani Piralidehi

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Predicting customer subscription in bank telemarketing campaigns using ensemble learning models by Michael Peter, Hawa Mofi, Said Likoko, Julius Sabas, Ramadhani Mbura, Neema Mduma

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A hybrid model for predicting response to risperidone after first-episode psychosis by Giovany Oliveira Costa, Vanessa K. Ota, Matheus Rodrigues Luiz, Joice Santos Rosa, Gabriela Xavier, Jessica Honorato-Mauer, Marcos L. Santoro, Carolina Muniz Carvalho, Daniel A. Cavalcante, Amanda V.G. Bugiga, Rodrigo A. Bressan, Gerome Breen, Ary Gadelha, Cristiano Noto, Diego R. Mazzotti, Sintia I. Belangero

“…This study aimed to determine the performance of clinical, genetic, and hybrid models to predict the response of patients in first-episode psychosis (FEP) to the antipsychotic risperidone. …”
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Predicting purulent meningitis in very preterm infants: a novel clinical model by Xiaowei Sun, Rui Jing, Yang Li

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Proposing Optimized Random Forest Models for Predicting Compressive Strength of Geopolymer Composites by Feng Bin, Shahab Hosseini, Jie Chen, Pijush Samui, Hadi Fattahi, Danial Jahed Armaghani

“…Both hybrid models significantly outperformed traditional methods, demonstrating their higher accuracy and reliability in predicting the compressive strength of GePC. …”
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