Energy Management of Industrial Energy Systems via Rolling Horizon and Hybrid Optimization: A Real-Plant Application in Germany by Loukas Kyriakidis, Rushit Kansara, Maria Isabel Roldán Serrano

“…To solve the resulting nonlinear and constrained optimization problem at each RHA iteration, we propose a novel hybrid algorithm that combines Bayesian optimization (BO) with the Interior Point OPTimizer (IPOPT). …”
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Probabilistic back analysis method for determining surrounding rock parameters of deep hard rock tunnel by WU Zhong-guang, WU Shun-chuan

“…Second, a multi-output support vector machine (MSVM) was optimized by particle swarm optimization (PSO) algorithm, and an intelligent response surface model was established to reflect the nonlinear mapping relationship between back-analyzed parameters and field monitoring data. …”
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Deep VMD-attention network for arrhythmia signal classification based on Hodgkin-Huxley model and multi-objective crayfish optimization algorithm. by Hang Zhao, Xiongfei Yin

“…The model based on MOCOA-VMD achieves the highest accuracy of 94.46%, outperforming models constructed using EEMD, VMD, CNN and LSTM modules. Bayesian optimization was employed to fine-tune the hyperparameters and further enhance the performance of the deep model, with the best accuracy of the deep attention model after TPE optimization reaching 96.11%. …”
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A Fast Two-Dimensional Direction-of-Arrival Estimator Using Array Manifold Matrix Learning by Jieyi Lu, Long Yang, Yixin Yang, Lu Wang

“…This paper proposes a fast 2D DOA estimator using array manifold matrix learning, where source-associated grid points are progressively selected from the set of predefined angular grids based on marginal likelihood maximization in the sparse Bayesian learning framework. …”
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Deconvolution of continuous paleomagnetic data from pass‐through magnetometer: A new algorithm to restore geomagnetic and environmental information based on realistic optimization... by Hirokuni Oda, Chuang Xuan

“…We acquired reliable sensor response of an SRM at the Oregon State University based on repeated measurements of a precisely fabricated magnetic point source. In addition, we present an improved deconvolution algorithm based on Akaike's Bayesian Information Criterion (ABIC) minimization, incorporating new parameters to account for errors in sample measurement position and length. …”
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Diverging trends in the global burden of ischemic heart disease attributable to non-optimal temperatures: a historical analysis (1990–2021) and 2050 projections by Zehao Jin, Zehao Jin, Yuting Pang, Ziyang Huang, Jialin Liu, Xiaoyi Zhan, Kangwei Wang

“…Analysis of the Bayesian modeling projections for 2050 revealed divergent trajectories: high non-optimal temperature-related age-standardized death rates and DALYs rates are likely to increase by 2.85 per 100,000 and 66.83 per 100,000, respectively. …”
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Data driven models for predicting pH of CO2 in aqueous solutions: Implications for CO2 sequestration by Mohammad Rasool Dehghani, Moein Kafi, Hamed Nikravesh, Maryam Aghel, Erfan Mohammadian, Yousef Kazemzadeh, Reza Azin

“…To fill this research gap, this study developed 15 models comprising five machine learning methods: regression trees, support vector regression, Gaussian process regression, bagged trees, and boosted trees, and three optimization algorithms: random search, grid search, and Bayesian optimization. …”
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Pose measurement method for coal mine drilling robot based on deep learning by Jiangnan LUO, Jianping LI, Hongxiang JIANG, Deyi ZHANG

“…A focal loss function was employed to enhance the model's focus on the drill head and gripper, and Bayesian parameter optimization was used for hyperparameter tuning. …”
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Developing a cost-effective tool for choke flow rate prediction in sub-critical oil wells using wellhead data by Zhiwei Xun, Farag M. A. Altalbawy, Prakash Kanjariya, R. Manjunatha, Debasish Shit, M. Nirmala, Ajay Sharma, Sarbeswara Hota, Shirin Shomurotova, Fadhil Faez Sead, Hojjat Abbasi, Mohammad Mahtab Alam

“…Gradient boosting machine (GBM) models were optimized using advanced algorithms like self-adaptive differential evolution (SADE), evolution strategy (ES), Bayesian probability improvement (BPI), and Batch Bayesian optimization (BBO). …”
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Mathematical Formalization and Algorithmization of the Main Modules of Organizational and Technical Systems by A. A. Solodov

“…The formalization and algorithmization of the organizational and technical systems behavior is undertaken mainly in terms of the Bayesian criterion of optimal statistical estimates. …”
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Daily-Scale Fire Risk Assessment for Eastern Mongolian Grasslands by Integrating Multi-Source Remote Sensing and Machine Learning by Risu Na, Byambakhuu Gantumur, Wala Du, Sainbuyan Bayarsaikhan, Yu Shan, Qier Mu, Yuhai Bao, Nyamaa Tegshjargal, Battsengel Vandansambuu

“…Model performance was enhanced using Bayesian hyperparameter optimization via Optuna. Results indicate that the Bayesian-optimized XGBoost model achieved the best generalization performance, with an overall accuracy of 92.3%. …”
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Enhanced water saturation estimation in hydrocarbon reservoirs using machine learning by Ali Akbari, Ali Ranjbar, Yousef Kazemzadeh, Dmitriy A. Martyushev

“…Nine well log parameters—Depth (DEPT), High-Temperature Neutron Porosity, True Resistivity, Computed Gamma Ray, Spectral Gamma Ray, Hole Caliper, Compressional Sonic Travel Time, Bulk Density, and Temperature—were used as input features to train and test five ML algorithms: Linear Regression, Support Vector Machine (SVM), Random Forest, Least Squares Boosting, and Bayesian methods. To improve model performance, a Gaussian outlier removal technique was applied to eliminate anomalous data points. …”
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An overview of AI in Biofunctional Materials by Dazhou Li

“…Case studies include rapid optimization of nanoparticle synthesis via Bayesian frameworks and the discovery of biodegradable stent materials through random forest screening. …”
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Machine Learning-Enabled Fast Prediction of GGNMOS Performance and Inverse Design for Electrostatic Discharge Applications by Zihan Wang, Ruichen Chen, Shengyao Lu, Ian Then, Di Niu, Xihua Wang

“…Additionally, Bayesian optimization was employed for inverse design, allowing rapid identification of optimal structural parameters for desired performance metrics. …”
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A Geometric Semantic Enhanced TomoSAR Reconstruction Algorithm in an Urban Area: Analysis and Application by Chunyi Wang, Qiancheng Yan, Xiaolan Qiu, Yitong Luo, Lingxiao Peng, Zhe Zhang

“…Geo-SETRA integrates geometric structures, extracted from TomoSAR point clouds, as prior distributions for elevation estimation using Bayesian methods. …”
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Prediction of tablet disintegration time based on formulations properties via artificial intelligence by comparing machine learning models and validation by Mohammed Ghazwani, Umme Hani

“…Grey Wolf Optimization (GWO) was utilized for model optimization to obtain optimal combinations of hyper-parameters. …”
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Improved Quantile Convolutional and Recurrent Neural Networks for Electric Vehicle Battery Temperature Prediction by Andreas M. Billert, Runyao Yu, Stefan Erschen, Michael Frey, Frank Gauterin

“…The Q*NN hyperparameters are optimized using an efficient Bayesian optimization, before the Q*NN models are compared with regression and quantile regression models for four horizons. …”
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Stereo Online Self-Calibration Through the Combination of Hybrid Cost Functions with Shared Characteristics Considering Cost Uncertainty by Wonju Lee

“…Both constraints work in the same direction to reduce the difference in the y-axis coordinates of corresponding points. As a result, the optimization process proceeds smoothly, and it helps reduce the likelihood of overfitting. …”
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Efficiency Comparison of New Adjusted Nonparametric and Parametric Statistics Interval Estimation Methods in the Simple Linear Regression Model by Saichon Sinsomboonthong, Juthaphorn Sinsomboonthong

“…The independent variable and the error came from normal, scale-contaminated normal, and gamma distributions. Six point estimations were performed, for example, least squares, Bayesian, Jack knife, Theil, optimum-type Theil, and new adjusted Theil–Sen and Siegel methods in the simple linear regression model with 1,000 iterations. …”
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Beyond labels: determining the true type of blood gas samples in ICU patients through supervised machine learning by Johan Helleberg, Anna Sundelin, Johan Mårtensson, Olav Rooyackers, Ragnar Thobaben

“…Training was performed using cross-validation in the training set, with forward stepwise feature selection and Bayesian hyperparameter optimization, and accuracy was assessed using area under the precision recall curve (AUCPR) in the test set. …”
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