Machine Learning-Driven Prediction of CO₂ Solubility in Brine: A Hybrid Grey Wolf Optimizer (GWO)-Assisted Gaussian Process Regression (GPR) Approach by Seyed Hossein Hashemi, Farshid Torabi, Paitoon Tontiwachwuthikul

“…These results highlight how machine learning can improve CO₂ injection processes, both for underground carbon storage and enhanced oil production.…”
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Effects of Cu content and Sintering temperature on microstructure and mechanical properties of SiCp/Al-Cu-Mg composites through experimental study, CALPHAD-type simulation and mach... by Wei Yang, Yiwei Wang, Xiaozhong Huang, Shuhong Liu, Peisheng Wang, Yong Du

“…The RFReg model was selected as the best model and used to design the composition and process parameters of the composite alloy. A new composite was designed by the machine learning models. …”
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A novel cascade heat integration configuration for electricity/freshwater/hydrogen outputs using SOFC-GT, multi-effect desalination, and PEM electrolysis using machine learning opt... by Zhaoyang Zuo, Junhua Wang, Sarminah Samad, Nashwan Adnan Othman, Ahmad Almadhor, Raymond Ghandour, Ibrahim A. Alsayer, Dilsora Abduvalieva, Salem Alkhalaf, Samah G. Babiker

“…Hence, a real-time machine learning framework is implemented to optimize key operational parameters dynamically, ensuring efficient fuel utilization, energy distribution, and load balancing across the system. …”
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Machine learning-driven prognostic prediction model for composite small cell lung cancer: identifying risk factors with network tools and validation using SEER data and external co... by Fei Li, Mengfan Zhao, Linlin Cao, Shuai Qie

“…Subsequently, through 10-fold cross-validation and grid search for optimal parameters, we selected the XGBoost model as the best-performing one among four candidates. …”
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Bulanık Bozulma ve Öğrenme Etkileri Altında Tek Makine Erken/Geç Tamamlanma Probleminin Bulanık Şans Kısıtlı Programlama Tekniği ile İncelenmesi by Oğuzhan Ahmet Arık, Mehmet Duran Toksarı

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Development and Validation of a Prediction Model Using Sella Magnetic Resonance Imaging–Based Radiomics and Clinical Parameters for the Diagnosis of Growth Hormone Deficiency and I... by Kyungchul Song, Taehoon Ko, Hyun Wook Chae, Jun Suk Oh, Ho-Seong Kim, Hyun Joo Shin, Jeong-Ho Kim, Ji-Hoon Na, Chae Jung Park, Beomseok Sohn

“…ObjectiveThis study aimed to develop a machine learning–based model using sella MRI–based radiomics and clinical parameters to diagnose GHD and ISS. …”
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Well Production Forecasting in Volve Field Using Kolmogorov–Arnold Networks by Xingyu Lu, Jing Cao, Jian Zou

“…However, traditional methods often struggle to capture the complex dynamics of reservoirs, and existing machine learning models rely on large parameter sets, resulting in high computational costs and limited scalability. …”
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ICARus: a pipeline to extract robust gene expression signatures from transcriptome datasets by Zhaorong Li, Juan I. Fuxman Bass, Juan I. Fuxman Bass, Juan I. Fuxman Bass, Juan I. Fuxman Bass

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Experimental Investigation of Magnetic Abrasive Finishing for Post-Processing Additive Manufactured Inconel 939 Parts by Michał Marczak, Dorota A. Moszczyńska, Aleksander P. Wawrzyszcz

“…This research focuses on optimizing the process parameters—eccentricity, rotational speed, and machining time—to enhance surface integrity following preliminary vibratory machining. …”
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A Fusion Modeling Approach for Six-Phase Hybrid Excitation Synchronous Motor, Leveraging Finite-Element Analysis, and Experimental Data-Driven by Xiao Zeng, Yunhao Ma, Jiali Wan, Yiyu Liu

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Determination of stability of the cutting process under dynamic loading conditions by A.S. Manokhin, S.An. Klimenko, S.A. Klimenko, M.Yu. Kopieikina, Yu.O. Melniychuk, A.O. Chumak, A.G. Naidenko

“…The prediction of the dependence is carried out using machine learning based on the XGB method (extreme gradient acceleration). …”
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Machine Learning‐Driven Extraction of Hybrid Compact Models Integrating Neural Networks and Berkeley Short‐Channel Insulated‐Gate Field‐Effect Transistor Model‐Common Multigate for... by Seungjoon Eom, Seunghwan Lee, Hyeok Yun, Kyeongrae Cho, Soomin Kim, Rockhyun Baek

“…Conventional techniques for extracting physics‐based model parameters are inherently slow processes and often yield less accurate model parameters because of the inflexibility of physical equations. …”
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Research and application of adaptive algorithm for 5G voice quality evaluation by Yuxiang ZHAO, Yaxin JI, Li YU, Tianyi ZHOU, Hang ZHOU

“…MOS (mean opinion score) is usually used to evaluate voice quality in the industry.It can objectively and fairly reflect the user’s voice service perception.It is difficult and costly to obtain data by road test, so a trained supervised learning model is usually used to predict the MOS score.However, the operator voice data has the characteristics of low percentage of MOS low score data and time sequence change, which affects the accuracy and generalization of the model prediction.Based on the study of existing data acquisition systems and machine learning algorithms of operators, an adaptive algorithm for MOS evaluation of 5G speech quality was proposed.Firstly, POLQA algorithm test equipment based on full parameter evaluation obtained training data to ensure the accuracy of training samples.Secondly, by means of data enhancement, the difficulty of acquiring poor quality samples was solved.Finally, based on the adaptive algorithm selection, the optimal MOS prediction model could be selected periodically and dynamically according to the timing changes of data features, so as to achieve large-scale and intelligent evaluation of 5G voice quality.…”
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Machine learning-based disease risk stratification and prediction of metabolic dysfunction-associated fatty liver disease using vibration-controlled transient elastography: Result... by Liqiong Huang, Yu Luo, Li Zhang, Mengqi Wu, Lirong Hu

“…The area under the receiver operating characteristic curve (AUC) of the random forest model in the validation set was 0.80, and the individual AUC was 0.83, 0.66 and 0.79 for the low-, moderate-, and high-risk groups, respectively. Conclusion Our machine learning model has excellent performance in stratification of risk for MAFLD with readily available clinical and demographic parameters. …”
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Development and interpretation of machine learning-based prognostic models for predicting high-risk prognostic pathological components in pulmonary nodules: integrating clinical fe... by Dingxin Wang, Jianhao Qiu, Rongyang Li, Hui Tian

“…In this study, we aimed to build a multi-parameter machine learning predictive model to improve the discrimination accuracy of HRPPC. …”
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Comparative Analysis of Machine Learning Techniques for Predicting Bulk Specific Gravity in Modified Asphalt Mixtures Incorporating Polyethylene Terephthalate (PET), High-Density P... by Bhupender Kumar, Navsal Kumar, Rabee Rustum, Vijay Shankar

“…Additionally, sensitivity analysis identified bitumen content (BC) and volume of bitumen (V_b) as the most influential parameters affecting G_mb, emphasizing the need for precise parameter optimization in asphalt mix design. …”
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Development of hybrid computational model for simulation of heat transfer and temperature prediction in chemical reactors by Kamal Y. Thajudeen, Mohammed Muqtader Ahmed, Saad Ali Alshehri

“…CFD (Computational Fluid Dynamics) was employed for the simulations and linked to machine learning for advanced modeling. The models under investigation include Bayesian Ridge Regression, Support Vector Machine, Deep Neural Network, and Attention-based Deep Neural Network. …”
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Application of Feedforward Artificial Neural Networks to Predict the Hydraulic State of a Water Distribution Network by Leandro Evangelista, Débora Móller, Bruno Brentan, Gustavo Meirelles

“…The results showed that creating an individual MLP for each parameter can be a good strategy to improve MLP accuracy.…”
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Assessing the strength of lime-treated clayey soil reinforced with PET: A ML-based data-derived approach by Javadreza Vahedi, Mehdi Koohmishi

“…The ML model trained and tested based on the data acquired reveals that the content of PET within the structure of the clayey soil is the most important parameter influencing the strength of clayey soil represented by PLSI. …”
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Bathymetry Inversion Using a Deep‐Learning‐Based Surrogate for Shallow Water Equations Solvers by Xiaofeng Liu, Yalan Song, Chaopeng Shen

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