Self-adaptive prediction and prewarning model of mine gas concentration by Dingwen Dong

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Ensemble prediction modeling of flotation recovery based on machine learning by Guichun He, Mengfei Liu, Hongyu Zhao, Kaiqi Huang

“…First, the outliers are processed using the box chart method and filtering algorithm. Then, the decision tree (DT), support vector regression (SVR), random forest (RF), and the bagging, boosting, and stacking integration algorithms are employed to construct a flotation recovery prediction model. …”
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Fast Task Scheduling With Model Predictive Control Integrating a Priority-Based Heuristic by Francesco Liberati, Manuel Donsante, Chiara Maria Francesca Cirino, Andrea Tortorelli

“…This paper presents a scalable Model Predictive Control (MPC) algorithm for task scheduling and real time re-scheduling. …”
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Advancing Knowledge on Machine Learning Algorithms for Predicting Childhood Vaccination Defaulters in Ghana: A Comparative Performance Analysis by Eliezer Ofori Odei-Lartey, Stephaney Gyaase, Dominic Asamoah, Thomas Gyan, Kwaku Poku Asante, Michael Asante

“…This study evaluated the utility of machine learning algorithms for predicting childhood vaccination defaulters in Ghana, addressing the limitations of traditional statistical methods when handling complex, high-dimensional health data. …”
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Developing predictive models for tocilizumab response in rheumatoid arthritis: a gene expression and machine learning approaches by Weiya Lan, Wei Du, Wukai Ma, Xueming Yao, Zong Jiang, Jing Zhou, Changming Chen, Chunxia Wang, Fang Tang

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Maintenance Time Prediction for Predictive Maintenance of Ship Engines by Seunghun Lim, Jungmo Oh, Jinkyu Park

“…However, due to the nature of ship operation, data collection is difficult, and most studies focus on fault detection, hindering the application of predictive maintenance to ships. In this study, we developed a maintenance time prediction algorithm using the revision generator engine condition criterion (RGCCV) value and the cylinder exhaust gas temperature, as developed in a previous study for marine generator engines. …”
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Enhanced Model for Gestational Diabetes Mellitus Prediction Using a Fusion Technique of Multiple Algorithms with Explainability by Ahmad Hassan, Saima Gulzar Ahmad, Tassawar Iqbal, Ehsan Ullah Munir, Kashif Ayyub, Naeem Ramzan

“…These risks highlight the need for accurate GDM prediction. This research proposes a novel fusion model for early GDM prediction. …”
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Water quality prediction and carbon reduction mechanisms in wastewater treatment in Northwest cities using Random Forest Regression model by Jingjing Sun, Xin Guan, Xiaojun Sun, Xiaojing Cao, Yepei Tan, Jiarong Liao

“…The RFR algorithm integrates Bagging ensemble learning and random subspace theory to construct multiple decision trees and aggregate their predictions, thereby enhancing the model’s prediction accuracy and stability. …”
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Mechanism- and data-driven algorithms of electrical energy consumption accounting and prediction for medium and heavy plate rolling by Qiang Guo, Zimeng Zhou, Jie Li, Fengwei Jing

“…Furthermore, a random forest regression model of additional power consumption based on data was established, and then a prediction algorithm for the comprehensive power consumption of billet rolling was given. …”
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Applying Probability Theory for the Quality Assessment of a Wildfire Spread Prediction Framework Based on Genetic Algorithms by Andrés Cencerrado, Ana Cortés, Tomàs Margalef

“…For this purpose, we rely on a two-stage prediction process to enhance the quality of traditional predictions, taking advantage of parallel computing. …”
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Investigation of ML algorithms for prediction of CFD data of fluid flow inside a packed-bed reactor by Yujiang Qiu, Pinxiao Liu

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Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms. by Alemu Birara Zemariam, Biruk Beletew Abate, Addis Wondmagegn Alamaw, Eyob Shitie Lake, Gizachew Yilak, Mulat Ayele, Befkad Derese Tilahun, Habtamu Setegn Ngusie

“…Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of stunting among adolescent girls in Ethiopia.…”
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Comparative Analysis of Machine Learning Algorithms for Predicting Patient Admission in Emergency Departments Using EHR Data by Ahmad Abdul Chamid, Ratih Nindyasari, Muhammad Imam Ghozali

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Optimization and Multimachine Learning Algorithms to Predict Nanometal Surface Area Transfer Parameters for Gold and Silver Nanoparticles by Steven M. E. Demers, Christopher Sobecki, Larry Deschaine

“…Overall, the metal nanoparticles followed more closely the NSET prediction than for Förster resonance energy transfer, though scattering effects began to occur at 20 nm in the nanoparticle diameter. …”
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Predicting Irrigation Water Quality Indices Based on Data-Driven Algorithms: Case Study in Semiarid Environment by Dimple Dimple, Jitendra Rajput, Nadhir Al-Ansari, Ahmed Elbeltagi

“…The KR and SAR values were predicted accurately by the SVM model in comparison to the observed values. …”
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Radial Basis Function Neural Network with Particle Swarm Optimization Algorithms for Regional Logistics Demand Prediction by Zhineng Hu, Yixin Zhang, Liming Yao

“…In this paper, the PSO-RBFNN model, a radial basis function neural network (RBFNN) combined with particle swarm optimization (PSO) algorithm, is studied. The PSO-RBFNN model is trained by indicators data in a region to predict the regional logistics demand. …”
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A comparative analysis of machine learning algorithms with tree-structured parzen estimator for liver disease prediction by Rakibul Islam, Azrin Sultana, MD. Nuruzzaman Tuhin

“…No previous literature research has utilized ML algorithms with TPE to predict LD. For this research, the Indian Liver Patients’ Dataset with 583 instances and 11 attributes was used. …”
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Application of machine learning algorithms and SHAP explanations to predict fertility preference among reproductive women in Somalia by Jamilu Sani, Salad Halane, Abdiwali Mohamed Ahmed, Mohamed Mustaf Ahmed

“…Predictor variables included sociodemographic factors, such as age, education, parity, wealth, residence, and distance to health facilities. Seven ML algorithms were evaluated for predictive performance, with Random Forest emerging as the optimal model based on metrics such as accuracy, precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUROC). …”
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Precision forecasting for hybrid energy systems using five deep learning algorithms for meteorological parameter prediction by Ceren Ceylan, Zehra Yumurtacı

“…Key results demonstrate that GRU achieves superior performance in wind speed prediction (RMSE: 0.049 m/s, R2: 0.8634) and solar radiation forecasting (RMSE: 0.146 W/m2, R2: 0.6643), while CNN-LSTM excels in ambient temperature prediction (RMSE: 0.011 °C, R2: 0.9976). …”
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Study for Predicting Land Surface Temperature (LST) Using Landsat Data: A Comparison of Four Algorithms by Elhadi K. Mustafa, Yungang Co, Guoxiang Liu, Mosbeh R. Kaloop, Ashraf A. Beshr, Fawzi Zarzoura, Mohammed Sadek

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