A hybrid prediction and multi-objective optimization framework for limestone calcined clay cement concrete mixture design by Xi Chen, Weiyi Chen, Zongao Li, Pu Zhang

“…This study proposes a hybrid framework combining machine learning (ML) and multi-objective optimization (MOO) to design cost-effective and eco-friendly LC3 mixtures. …”
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Advanced KNN-based cost-efficient algorithm for precision localization and energy optimization in dynamic underwater sensor networks by Nadia Shamshad, Lei Wang, Kiran Saleem, Danish Sarwr, Salil Bharany, Ahmad Almogren, Jaeyoung Choi, Ateeq Ur Rehman, Ayman Altameem

“…This paper proposes a KNN-based cost-efficient machine-learning algorithm aimed at optimizing underwater context acquisition with sensor nodes. …”
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Version [2.0] - [VIC-Borg: Multiobjective automatic calibration toolkit for VIC model] by Jinfeng Ma, Hua Zheng, Ruonan Li, Kaifeng Rao, Yanzheng Yang, Weifeng Li

“…The VIC-Borg tool facilitates multi-objective automatic calibration for the variable infiltration capacity (VIC) model, but its efficiency was constrained by single-machine performance. …”
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Personalization of AI-Based Digital Twins to Optimize Adaptation in Industrial Design and Manufacturing—Review by Izabela Rojek, Dariusz Mikołajewski, Ewa Dostatni, Jan Cybulski, Mirosław Kozielski

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Study on Structure Design and Parameter Optimization of Diversion Rifled Feeder Based on CFD-DEM by Wancheng Dong, Xiongye Zhang, Zhen Jiang, Xue Hu, Yun Ge, Lixin Zhang

“…The optimized feeder design effectively improves the stability and air–fertilizer mixing uniformity of cotton pneumatic fertilizing machines, providing valuable theoretical and technical support for their design optimization and performance enhancement.…”
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Optimal mean arterial pressure for favorable neurological outcomes in patients after cardiac arrest by Sijin Lee, Kwang-Sig Lee, Kap Su Han, Juhyun Song, Sung Woo Lee, Su Jin Kim

“…Methods This retrospective observational study included 291 post-cardiac arrest patients treated at a tertiary care center. Five machine learning models to predict favorable neurological outcomes using hourly MAP measurements during the first 24 h after return of spontaneous circulation (ROSC) were compared and Random Forest model was selected due to its superior performance. …”
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COMPARATIVE ANALYSIS THE PERFORMANCE OF CLIENT-SIDE AND SERVER-SIDE MACHINE LEARNING TECHNOLOGIES by I. Mysiuk, Roman Shuvar

“…The performance analysis of client-side and server-side machine learning technologies is important for understanding the optimal way to model optimization. …”
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Identifying liver cirrhosis in patients with chronic hepatitis B: an interpretable machine learning algorithm based on LSM by Xueting Bai, Chunwen Pu, Wenchong Zhen, Yushuang Huang, Qian Zhang, Zihan Li, Yixin Zhang, Rongxuan Xu, Zhihan Yao, Wei Wu, Mei Sun, Xiaofeng Li

“…The SHAP analysis indicated that LSM contributed the most to the model. The model still showed strong discriminative power when using only LSM or traditional indicators alone.Conclusions Machine learning models, especially the RF model, can effectively identify LC in CHB patients. …”
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Analysis of Combined Strength Training with Small-Sided Games in Football Education Using Machine Learning Methods by Huseyin Guneralp, Hasan Ulas Yavuz, Boran Sekeroglu, Musa Oytun, Cevdet Tinazci

“…Eighteen physical measurements of the players were obtained using sensitive devices before and after they were completed. Four tree-based machine learning models, decision tree, random forest, gradient boosting, and extreme gradient boosting, were applied to solve the complex pattern of training strategies using the measurements. …”
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Inversion of Aerosol Chemical Composition in the Beijing–Tianjin–Hebei Region Using a Machine Learning Algorithm by Baojiang Li, Gang Cheng, Chunlin Shang, Ruirui Si, Zhenping Shao, Pu Zhang, Wenyu Zhang, Lingbin Kong

“…By comparing the inversion accuracies of single models—namely MLR (Multivariable Linear Regression) model, SVR (Support Vector Regression) model, RF (Random Forest) model, KNN (K-Nearest Neighbor) model, and LightGBM (Light Gradient Boosting Machine)—with that of the combined model (CM) after selecting the optimal model, we found that although the accuracy of the KNN model was the highest among the other single models, the accuracy of the CM model was higher. …”
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Using machine learning techniques to evaluate the impact of future climate change on wheat yields in Xinjiang, China by Xuehui Gao, Jian Liu, Haixia Lin, Tehseen Javed, Feihu Yin, Rui Chen, Yue Wen, Jinzhu Zhang, Kefan Yi, Zhenhua Wang

“…Additionally, the impacts of climate change scenarios on wheat yield were predicted using two emission scenarios (SSP45 and SSP85) from global climate models (GCMs) and machine learning (ML) algorithms. …”
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Assessing HMM and SVM for Condition-Based Monitoring and Fault Detection in HEV Electrical Machines by Riham Ginzarly, Nazih Moubayed, Ghaleb Hoblos, Hassan Kanj, Mouhammad Alakkoumi, Alaa Mawas

“…Hence, the aim of this paper is to present two data-based fault detection approaches, which are the support vector machine (SVM) and the Hidden Markov Model (HMM). Their capability to detect primitive faults like tiny cracks in the machine’s magnet will be shown. …”
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Software Package for Optimization of Burner Devices on Dispersed Working Fluids by Ruslan V. Fedorov, Igor I. Shepelev, Mariia A. Malyoshina, Dmitry A. Generalov, Vyacheslav V. Sherkunov, Valeriy V. Sapunov

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Machine Learning-Augmented Triage for Sepsis: Real-Time ICU Mortality Prediction Using SHAP-Explained Meta-Ensemble Models by Hülya Yilmaz Başer, Turan Evran, Mehmet Akif Cifci

“…To address class imbalance and missing data, we employed the Synthetic Minority Oversampling Technique and systematic imputation methods, respectively. Our hybrid modeling approach integrates ensemble-based ML algorithms with deep learning architectures, optimized through the Red Piranha Optimization algorithm for feature selection and hyperparameter tuning. …”
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Predicting Superaverage Length of Stay in COPD Patients with Hypercapnic Respiratory Failure Using Machine Learning by Zuo B, Jin L, Sun Z, Hu H, Yin Y, Yang S, Liu Z

“…Cerebrovascular disease, hematocrit, activated partial thromboplastin time, partial pressure of carbon dioxide, reduced hemoglobin and oxyhemoglobin were independent risk factors for superaverage length of stay in COPD patients with hypercapnic respiratory failure. The Catboost model is the optimal model on both the modeling dataset and the external validation set. …”
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Comparison and integration of physical and interpretable AI-driven models for rainfall-runoff simulation by Sara Asadi, Patricia Jimeno-Sáez, Adrián López-Ballesteros, Javier Senent-Aparicio

“…The use of Shapley Additive Explanations (SHAP) methodology allowed the results of the ensemble with machine learning to be more interpretable by explaining how each model contributes to the prediction. …”
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Predictive Maintenance of Old Grinding Machines Using Machine Learning Techniques by Primawati Primawati, Fitrah Qalbina, Mulyanti Mulyanti, Ferra Yanuar, Dodi Devianto, Remon Lapisa, Fazrol Rozi

“…The random forest model achieved the highest accuracy of 94.59%, demonstrating its effectiveness in predicting machine failures. …”
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Short-Term Power Load Forecasting Using Adaptive Mode Decomposition and Improved Least Squares Support Vector Machine by Wenjie Guo, Jie Liu, Jun Ma, Zheng Lan

“…Different frequency features are effectively extracted by using the proposed combination kernel structure, which can achieve the balance of learning capacity and generalization capacity for each unique load component. Further, an optimized genetic algorithm is deployed to optimize model parameters in ILSSVM by integrating the adaptive genetic algorithm and simulated annealing to improve load forecasting accuracy. …”
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Comparison of Machine Learning Algorithms for Daily Runoff Forecasting with Global Rainfall Products in Algeria by Rayane Bounab, Hamouda Boutaghane, Tayeb Boulmaiz, Yves Tramblay

“…In Algeria, to identify a relevant modeling approach using this new source of rainfall information, the present research aims to (i) compare a conceptual model (GR4J) and seven machine learning algorithms (FFNN, ELM, LSTM, LSTM2, GRU, SVM, and GPR) and (ii) compare different types of precipitation inputs, including four satellite products (CHIRPS, SM2RAIN, GPM, and PERSIANN), one reanalysis product (ERA5), and observed precipitation, to assess which combination of models and precipitation data provides the optimal performance for river discharge simulation. …”
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Towards ML Models’ Recommendations by Lara Kallab, Elio Mansour, Richard Chbeir

“…A prominent way to achieve this is machine learning (ML), which optimizes system performance by employing learning algorithms to create models based on data and its inherent patterns. …”
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