Machine Learning and Parameter Optimization for Banking Stability Prediction and Determinants Identification in ASEAN by Pham Thuy Tu, Dao Le Kieu Oanh, Do Doan Trang

“…Empirical results reveal that key drivers of financial stability include equity capital, financial leverage, return on equity, GDP growth, inflation, technological advancements, and systemic shocks like the COVID-19 pandemic. Notably, the Ridge-optimized XGBRegressor model achieves the highest predictive accuracy (~89%), demonstrating the efficacy of hybrid machine learning approaches in financial stability forecasting. …”
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Improved War Strategy Optimization with Extreme Learning Machine for Health Data Classification by İbrahim Berkan Aydilek, Arzu Uslu, Cengiz Kına

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Research on Fractional-Order Control of Anchor Drilling Machine Optimized by Intelligent Algorithms by Jingkai Li, Jun Zhang, Jiaquan Xie, Wei Shi, Jianzhong Zhao

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Optimization Design of Lazy-Wave Dynamic Cable Configuration Based on Machine Learning by Xudong Zhao, Qingfen Ma, Jingru Li, Zhongye Wu, Hui Lu, Yang Xiong

“…To address this challenge, this study proposes a closed-loop optimization framework that couples machine learning with intelligent optimization algorithms for a dynamic cable configuration design. …”
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Optimizing Concrete Mix Design for Cost and Carbon Reduction Using Machine Learning by Angga T. Yudhistira, Arief S. B. Nugroho, Iman Satyarno, Tantri N. Handayani, Malindu Sandanayake, Rimba Erlangga, Jonathan Lianto, Alfa Rosyid Ernanto

“…XGBoost Machine Learning Algorithm is used to make predictions, and PSO is used to obtain the optimal mixture. …”
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Multi-Objective Machine Learning Optimization of Cylindrical TPMS Lattices for Bone Implants by Mansoureh Rezapourian, Ali Cheloee Darabi, Mohammadreza Khoshbin, Irina Hussainova

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Machine Learning Exploration of Experimental Conditions for Optimized Electrochemical CO2 Reduction by Vuri Ayu Setyowati, Shiho Mukaida, Kaito Nagita, Takashi Harada, Shuji Nakanishi, Kazuyuki Iwase

“…An inverse analysis based on the ML model suggested the optimal experimental conditions for achieving the desired characteristics of the electrolysis system, with the proposed conditions experimentally validated. …”
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Optimization of parameters of resource-saving machine technologies and operating modes of corn harvesters by Ya. P. Lobachevsky, E. I. Trubilin, E. V. Truflyak

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Transforming mining energy optimization: a review of machine learning techniques and challenges by Sravani Parvathareddy, Abid Yahya, Lilian Amuhaya, Ravi Samikannu, Raymond Sogna Suglo

“…Effective energymanagement is critical to addressing these challenges, particularly in the context of decarbonizationtargets and the complexities of remote site operations. Machine Learning (ML) offers domain-specificopportunities for optimizing energy usage through predictive maintenance, demand forecasting, and realtime process control. …”
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Machine Learning-Based Normal White Blood Cell Multi-Classification Optimization by Taeyeon Gil, Sukjun Lee, Onseok Lee

“…Therefore, in this study, we proposed an ML-based optimization system for five normal WBC classifications. …”
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Interpretable Machine Learning for Population Spatialization and Optimal Grid Scale Selection in Shanghai by Yuan Cao, Hefeng Wang, Lanxuan Guo, Anbing Zhang, Xiaohu Wu

“…This study introduced a combined approach using eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanation (SHAP) to estimate population spatialization at various grid scales and interpret the key influencing factors, then we applied accuracy evaluation metrics and landscape ecology indices to identify the optimal grid scale. The results showed that the XGBoost model outperformed the WorldPop dataset in accuracy across all grid scales, with determination coefficients (R²) consistently exceeding 0.83. …”
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Low Capillary Elastic Flow Model Optimization Using the Lattice Boltzmann Method and Non-Dominated Sorting Genetic Algorithm by Yaqi Hou, Wei Zhang, Jiahua Hu, Feiyu Gao, Xuexue Zong

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Committee Machine Learning for Electrofacies-Guided Well Placement and Oil Recovery Optimization by Adewale Amosu, Dung Bui, Oluwapelumi Oke, Abdul-Muaizz Koray, Emmanuel Appiah Kubi, Najmudeen Sibaweihi, William Ampomah

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Optimizing Machine Learning-Based Ovarian Cancer Prediction Through Normalization Strategies by Roopashri Shetty, Siddhant Gupta, Vansh Mediratta, Shwetha Rai, M. Geetha

“…Additionally, unsupervised models like K-Means and DBSCAN clustering are implemented to study further subgroups of the Ovarian Cancer dataset optimizing results. …”
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Optimization and test of the heating parts of gas-fired tea fixation and shaping machine by Qilong Wu, Qilong Wu, Lei Yu, Chengmao Cao, Chengmao Cao, Kuan Qin, Kuan Qin, Yi Jing, Yuhao Dai, Yuhao Dai, Xinliang Guo, Xinliang Guo, Lili Fan, Lili Fan, Junhao Zhang, Junhao Zhang, Chaoyue Fan, Chaoyue Fan, Haijun Bi, Haijun Bi

“…IntroductionThe uneven heating of the U-shaped multi-groove pot in gas-fired tea fixation and shaping machines results in inconsistent quality of tea leaves.MethodsA three-dimensional model of the gas-fired tea fixation and shaping machine was established by using SOLIDWORKS software. …”
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Thermal Load Predictions in Low-Energy Buildings: A Hybrid AI-Based Approach Integrating Integral Feature Selection and Machine Learning Models by Youness El Mghouchi, Mihaela Tinca Udristioiu

“…Initially, 13 machine learning (ML) models were assessed to predict heating and cooling loads. …”
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Enhancing Education with Machine Learning: Predicting Student Readability Scores by Claire Bell

“…This study explores the use of machine learning techniques to predict students' reading scores, with a particular focus on Random Forest Classification (RFC) as a reliable baseline model. …”
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Comparisons of Machine Learning Models for Prediction of Susceptibility to Diabetes by Jiao Yutian

“…This study compares the predictive performance of machine learning models such as Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM). …”
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Research Progress on Process Optimization of Metal Materials in Wire Electrical Discharge Machining by Xinfeng Zhao, Binghui Dong, Shengwen Dong, Wuyi Ming

“…Subsequently, this paper reviews AI-based approaches, traditional machine-learning methods (e.g., neural networks, support vector machines, and random forests), and deep-learning models (e.g., convolutional neural networks and deep neural networks) in aspects such as state recognition, process prediction, multi-objective optimization, and intelligent control. …”
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Design of turning hydraulic engines for manipulators of mobile machines on the basis of multicriterial optimization by Lagerev I.A., Shatunova E.A.

“…In this paper the mathematical models of the main types of turning hydraulic engines, which at the present time widely used in the construction of handling systems of domestic and foreign mobile transport-technological machines wide functionality. …”
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