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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Combined stochastic modelling of pathogenic and spoilage microorganisms by Nikola Maciejewska, Constantine‐Richard Stefanou, Leonardos Stathas, Konstantinos Koutsoumanis

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Prospective Models of Financial Forecasting of budget Revenues by A. K. Karaev, O. V. Borisova

Subjects: “…predictive model…”
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Frailty risk prediction models in maintenance hemodialysis patients: a systematic review and meta-analysis of model performance and methodological quality by Zihan Chen, Lidan Zhu, Caixia Ma, Juanjuan Li, Le Liu, Wenyi Xiao, Yurong Ge

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Predicting the Phenology of Herbivorous Insects by Zimo Yang, Elise Woodruff, David Held, Nate B. Hardy

“…Here, with multilevel Bayesian models, we performed a comparative analysis of 601 published herbivorous insect phenology models. …”
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Robust Predictive Functional Control for Quadrotor Flight Systems by Kai Masuda, Kenji Uchiyama

Subjects: “…model predictive control…”
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Heating, Ventilation, and Air Conditioning (HVAC) Temperature and Humidity Control Optimization Based on Large Language Models (LLMs) by Xuanrong Zhu, Hui Li

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First-Order Decay Models for the Estimation of Methane Emissions in a Landfill in the Metropolitan Area of Oaxaca City, Mexico by Pérez Belmonte Nancy Merab, Sandoval Torres Sadoth, Belmonte Jiménez Salvador Isidro

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Article 700 Identification in Judicial Judgments: Comparing Transformers and Machine Learning Models by Sid Ali Mahmoudi, Charles Condevaux, Guillaume Zambrano, Stéphane Mussard

“…Predictive justice, which involves forecasting trial outcomes, presents significant challenges due to the complex structure of legal judgments. …”
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A preliminary nomogram model for predicting relapse of patients with primary membranous nephropathy by Min Li, Huifang Wang, Xiaoying Lai, Dandan Guo, Chunhui Jiang, Zixuan Fu, Xuemei Liu

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Risk factors for early diagnosis and prediction model development of of neonatal ABO-HDFN by ZHANG Wenhua, LIU Dan, ZHANG Wenting, LING Jing

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Prediction of postpartum depression in women: development and validation of multiple machine learning models by Weijing Qi, Yongjian Wang, Yipeng Wang, Sha Huang, Cong Li, Haoyu Jin, Jinfan Zuo, Xuefei Cui, Ziqi Wei, Qing Guo, Jie Hu

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Construction and verification of a risk factor prediction model for neonatal severe pneumonia by Weihua Gong, Kaijie Gao, Jiajia Ni, Ying Shi, Zhiming Shan, Hongqi Sun, Shanshan Wang, Jiangtao Xu, Junmei Yang

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A Predictive Model for Secondary Posttonsillectomy Hemorrhage in Pediatric Patients: An 8‐Year Retrospective Study by Yuting Ge, Wenchuan Chang, Lixiao Xie, Yan Gao, Yue Xu, Huie Zhu

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A predictive model based on the GLIM diagnosis for malnutrition in older adult heart failure patients by Xinyu Tang, Changying Zhang, Feng Xiao, Fang Yang, Xiaoya Zhu, Yunlai Gao

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P2 Hybrid AMT Shift Clutch Engagement Control Strategy based on Model Prediction by Changqing Du, Huan Zhang, Xiliang Cao, Huawu Wang

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Construction of a risk prediction model for occupational noise-induced hearing loss using routine blood and biochemical indicators in Shenzhen, China: a predictive modelling study by Wenting Feng, Wen Zhang, Yan Guo, Naixing Zhang, Liang Zhou, Dafeng Lin, Linlin Chen, Caiping Li, Liuwei Shi, Xiangli Yang, Peimao Li, Dianpeng Wang

“…Routine blood and biochemical indicators were extracted from the case data, and a range of machine learning algorithms including extreme gradient boosting (XGBoost) were employed to construct predictive models. The model underwent refinement to identify the most representative variables, and decision curve analysis was conducted to evaluate the net benefit of the model across various threshold levels.Primary outcome measures Model creation data set and validation data sets: ONIHL.Results The prediction model, developed using XGBoost, demonstrated exceptional performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.942, a sensitivity of 0.875 and a specificity of 0.936 on the validation data set. …”
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Predicting the impact of climate change on the re-emergence of malaria cases in China using LSTMSeq2Seq deep learning model: a modelling and prediction analysis study by Eric Kamana, Jijun Zhao, Di Bai

“…The LSTMSeq2Seq model achieved an average prediction accuracy of 87.3%.Conclusions The LSTMSeq2Seq model significantly improved the prediction of malaria re-emergence based on the influence of climatic factors. …”
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RESEARCH ON MODELING METHOD OF EQUIVALENT TURNING FORCE MODEL (MT) by WU MingZhou, ZHANG GuangPeng, GOU JianFeng, ZHANG Peng

“…A turning force prediction model based on the equivalent model is established. …”
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Prediction of Chlorophyll a in Kurtboğazı and Çamlıdere Reservoirs by Handan Çamdeviren, Sıddık Keskin, Nilsun Demır, &ccedil -

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