Combination model for freshness prediction of pork using VIS/NIR hyperspectral imaging with chemometrics by Minwoo Choi, Hye-Jin Kim, Azfar Ismail, Hyun-Jun Kim, Heesang Hong, Ghiseok Kim, Cheorun Jo

“…Furthermore, the combination model (Model 4), utilizing HSI spectral data and predicted metabolites from Model 2 to predict freshness indicators, improved the prediction coefficients compared to Model 3; TBC R2p = 0.7583 and VBN R2p = 0.8441. …”
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International Natural Uranium Price Prediction Based on TF-CNN-BiLSTM Model by YANG Jingzhe, XUE Xiaogang

“…Thus, there is a pressing need for advanced predictive models capable of handling the multifaceted nature of uranium price fluctuations. …”

A compositional model for effort‐aware Just‐In‐Time defect prediction on android apps by Kunsong Zhao, Zhou Xu, Meng Yan, Lei Xue, Wei Li, Gemma Catolino

“…In this study, a novel compositional model, called KPIDL, is proposed to conduct the JIT defect prediction task for Android apps. …”
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Personalized prediction of lifetime benefits with statin therapy for asymptomatic individuals: a modeling study. by Bart S Ferket, Bob J H van Kempen, Jan Heeringa, Sandra Spronk, Kirsten E Fleischmann, Rogier L G Nijhuis, Albert Hofman, Ewout W Steyerberg, M G Myriam Hunink

“…<h4>Background</h4>Physicians need to inform asymptomatic individuals about personalized outcomes of statin therapy for primary prevention of cardiovascular disease (CVD). However, current prediction models focus on short-term outcomes and ignore the competing risk of death due to other causes. …”
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Urban land use function prediction method based on RF and cellular automaton model by Wenjun Song, Min Ling

“…Moreover, the study also integrates random forest algorithm and cellular automaton model, and finally proposes a new urban land use function prediction method based on random forest algorithm and cellular automaton model. …”
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Distributed user privacy preserving adjustable personalized QoS prediction model for cloud services by Jianlong XU, Jian LIN, Yusen LI, Zhi XIONG

“…Personalized quality of service （QoS） prediction is crucial for developing high-quality cloud service system.However, the traditional collaborative filtering method, based on centralized training, presents challenges in protecting user privacy.In order to effectively protect user privacy while obtaining highly accurate prediction effect, a distributed user privacy adjustable personalized QoS prediction model for cloud services （DUPPA） was proposed.The model adopted a “server-multi-user” architecture, in which the server coordinated multiple users, handled multiple users’ requests for uploading model gradients and downloading global model, and maintained global model parameters.To further protect user privacy, a user privacy adjustment strategy was proposed to balance privacy and prediction accuracy by adjusting the initialization proportion of local model parameters and gradient upload proportion.In the local model initialization stage, the user calculated the difference matrix between the local model and the global model, and selected the global model parameters corresponding to the larger elements in the difference matrix to initialize the local model parameters.In the gradient upload stage, the user can select some important gradients to upload to the server to meet the privacy protection requirements of different application scenarios.To evaluate the privacy degree of DUPPA, a data reconstruction attack method was proposed for the distributed matrix factorization model gradient sharing scheme.The experimental results show that when DUPPA sets the gradient upload proportion to 0.1 and the local model parameter initialization proportion to 0.5, the predicted MAE and RMSE are reduced by 1.27% and 0.91%, respectively, compared with the traditional centralized matrix factorization model.Besides, when DUPPA sets the gradient upload proportion to 0.1, the privacy degree is 5 times higher than when the gradient upload proportion is 1.And when DUPPA sets the local model parameter initialization proportion to 0.5, the privacy degree is 3.44 times higher than when the local model parameter initialization proportion is 1.…”
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Elastic net with Bayesian Density Estimation model for feature selection for photovoltaic energy prediction by Venkatachalam Mohanasundaram, Balamurugan Rangaswamy

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Establishment of a prediction model for major adverse cardiovascular and cerebrovascular events in prehypertensive patients by HE Zhi,an，LAI Jiangqiong，ZHENG Tao

“…<b>Objective</b> To explore the predictive efficacy of logistic regression model based on carotid artery elastic ultrasound parameters for the occurrence of major adverse cardiovascular and cerebrovascular events（MACCE ）in patients with prehypertension. …”
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Simple prediction model for unfavorable outcome in ischemic stroke after intravenous thrombolytic therapy by Daniel Amitrano, Ivan Rocha Ferreira da Silva, Bernardo B. Liberato, Valéria Batistella, Janaina Oliveira, Osvaldo J. M. Nascimento

“…Multivariate logistic regression was used to develop a predictive model. Results We analyzed a total of 82 patients. …”
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Interpretable machine learning model for predicting post-hepatectomy liver failure in hepatocellular carcinoma by Tianzhi Tang, Tianyu Guo, Bo Zhu, Qihui Tian, Yang Wu, Yefu Liu

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Machine Learning Model for Predicting Global Ionospheric TEC Maps Based on Constraint Conditions by Qingfeng Li, Hanxian Fang, Chao Xiao, Die Duan, Hongtao Huang, Ganming Ren

“…In this context, we propose a machine learning prediction model [predictive GAN variational autoencoder-label (PGVAE-label)] using a labeled graph of image segmentation as a constraint to predict the global ionospheric TEC. …”
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EUR Prediction for Shale Gas Wells Based on the ROA-CatBoost-AM Model by Weikang He, Xizhe Li, Yujin Wan, Honming Zhan, Nan Wan, Sijie He, Yaoqiang Lin, Longyi Wang, Wenxuan Yu, Liqing Chen

“…The results indicated that the ROA-CatBoost-AM model exhibited superior performance in both fitting accuracy and prediction effectiveness. …”
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Based on the Prediction of Future Drought Evolution in Heilongjiang Province under the CMIP6 Model by LIU Tao, SI Zhenjiang, LIU Yan

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An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems by V. Ranganayaki, S. N. Deepa

“…Various criteria are proposed to select the number of hidden neurons in artificial neural network (ANN) models and based on the criterion evolved an intelligent ensemble neural network model is proposed to predict wind speed in renewable energy applications. …”
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Prediction of Blast Vibration Velocity of Buried Steel Pipe Based on PSO-LSSVM Model by Hongyu Zhang, Shengwu Tu, Senlin Nie, Weihua Ming

“…A least squares support vector machine (LS-SVM) model was established to predict the peak vibration velocity of the pipeline and determine the best parameter combination in the LS-SVM model through a local particle swarm optimization (PSO), and the results of the PSO-LSSVM model were predicted. …”
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Development of a machine learning-derived model to predict unplanned ICU admissions after major non-cardiac surgery by Catherine Chiu, Matthias R. Braehler, Anne L. Donovan, Atul J. Butte, Romain Pirracchio, Andrew M. Bishara

“…We describe the development of a machine-learning derived model to predict UIAs using only widely used preoperative variables. …”
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Efficacy and validation of a clinical predictive model for chronic atrophic gastritis in patients: a multi-center retrospective analysis by Xiang Fang, Wenjing Ding, Xiaolong Xu, Hui Chen, Bei Pei, Yi Zhang, Biao Song, Xuejun Li, Li Yao

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Development of a morphological-based predictive model for mechanical properties of binary immiscible polymer blends considering interfacial effects by Nima Arjomand, Mahboube Mohamadi, Javad Alizadeh Kaklar

Subjects: “…Predictive modeling…”
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Genetic programming-based algorithms application in modeling the compressive strength of steel fiber-reinforced concrete exposed to elevated temperatures by Mohsin Ali, Li Chen, Qadir Bux Alias Imran Latif Qureshi, Deema Mohammed Alsekait, Adil Khan, Kiran Arif, Muhammad Luqman, Diaa Salama Abd Elminaam, Amir Hamza, Majid Khan

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An Improved Real-Time LOS-Based Model Predictive Control for the Semi-Submersible Offshore Platform Under Ocean Disturbances by Shunli Wang, Xiufen Ye, Ronghao Zhang, Meng Luo

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