Comparative analysis of supervised learning models for effluent quality prediction in wastewater treatment plants. by Liu Bo-Qi, Zhou Ding-Jie, Zhao Yang, Shi Long-Yu

“…Nevertheless, the findings provide valuable insights into selecting effective machine learning models for effluent quality prediction, supporting data-driven decision-making in wastewater management and advancing intelligent process optimization in WWTP.…”
Get full text

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. …”
Get full text

Assessment of spatial autocorrelation and scalability in fine-scale wildfire random forest prediction models by Madeleine Pascolini-Campbell, Joshua B. Fisher, Kerry Cawse-Nicholson, Christine M. Lee, Natasha Stavros

“…Abstract Wildfire prediction models that can be applied across diverse regions at fine scales (< 100 m) are critical for wildfire management. …”
Get full text

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. …”
Get full text

Comparative Study of OLGA and LedaFlow Models for Mechanistic Predictions of Hydrate Transport Dynamics by Anqi Qu, Luiz F. R. Dalla, Luis E. Zerpa, Thierry Palermo, Khalid Mateen, Annie Fidel-Dufour, Carolyn A. Koh

Get full text

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.…”
Get full text

Elastic net with Bayesian Density Estimation model for feature selection for photovoltaic energy prediction by Venkatachalam Mohanasundaram, Balamurugan Rangaswamy

Subjects: Get full text

Predicting the time to get back to work using statistical models and machine learning approaches by George Bouliotis, M. Underwood, R. Froud

“…Objectives To compare model performance and predictive accuracy of classic regressions and machine learning approaches using data from the Inspiring Families programme. …”
Get full text

Development and application of advanced learning models for predicting the land subsidence due to coal mining by Shirin Jahanmiri, Majid Noorian-Bidgoli

“…Three hybrid models—BBO-GEP, GWO-GEP, and SSA-GEP—were developed and tested to enhance prediction accuracy and reduce model uncertainty. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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

Subjects: Get full text

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. …”
Get full text

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. …”
Get full text

On the Prediction and Forecasting of PMs and Air Pollution: An Application of Deep Hybrid AI-Based Models by Youness El Mghouchi, Mihaela Tinca Udristioiu

Subjects: Get full text

Based on the Prediction of Future Drought Evolution in Heilongjiang Province under the CMIP6 Model by LIU Tao, SI Zhenjiang, LIU Yan

Get full text

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. …”
Get full text

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. …”
Get full text

Advanced machine learning models for the prediction of ceramic tiles’ properties during the firing stage by V. Vasic, Milica, Awoyera, Paul O., Fadugba, Oladlu George, Barisic, Ivana, Nettinger Grubeša, Ivanka

“…Among the four ensemble ML models evaluated, CatBoost demonstrated the highest predictive performance. …”
Get full text

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. …”
Get full text