Multiphase Transport Network Optimization: Mathematical Framework Integrating Resilience Quantification and Dynamic Algorithm Coupling by Linghao Ren, Xinyue Li, Renjie Song, Yuning Wang, Meiyun Gui, Bo Tang

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TIM-FEM-ML synthetic technology for longitudinal optimization of tunnel excavated in the interlayered rock mass by Hui Li, Weizhong Chen, Xiaoyun Shu, Xianjun Tan, Qun Sui

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AI-driven model for optimized pulse programming of memristive devices by Benjamin Spetzler, Markus Fritscher, Seongae Park, Nayoun Kim, Christian Wenger, Martin Ziegler

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A Dendritic Neural Network-Based Model for Residential Electricity Consumption Prediction by Ting Jin, Rui Xu, Kunqi Su, Jinrui Gao

Subjects: “…dendritic neural network-based model…”
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A Probabilistic Approach to Surrogate‐Assisted Multi‐Objective Optimization of Complex Groundwater Problems by Reygie Q. Macasieb, Jeremy T. White, Damiano Pasetto, Adam J. Siade

Subjects: “…multi‐objective optimization…”
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Prediction and Optimization for Multi-Product Marketing Resource Allocation in Cross-Border E-Commerce by Yi Xie, Heng-Qing Ye, Wenbin Zhu

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An improved deep learning model for soybean future price prediction with hybrid data preprocessing strategy by Dingya CHEN, Hui LIU, Yanfei LI, Zhu DUAN

“…Finally, the high frequency component is decomposed secondarily using variational mode decomposition optimized by beluga whale optimization algorithm. In the deep learning prediction stage, a deep extreme learning machine optimized by the sparrow search algorithm was used to obtain the prediction results of all subseries and reconstructs them to obtain the final soybean future price prediction results. …”
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Vortex-Induced Vibration Performance Prediction of Double-Deck Steel Truss Bridge Based on Improved Machine Learning Algorithm by Yang Yang, Huiwen Hou, Gang Yao, Bo Wu

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AI-enhanced automation of building energy optimization using a hybrid stacked model and genetic algorithms: Experiments with seven machine learning techniques and a deep neural network by Mohammad H. Mehraban, Samad ME Sepasgozar, Alireza Ghomimoghadam, Behrouz Zafari

“…This approach balances multiple objectives, such as energy consumption and thermal comfort, to streamline the identification of optimal building configurations. Seven machine learning (ML) models, including Linear Regression (LR), Decision Trees (DT), Random Forest Regressor (RFR), Gradient Boosting Machines (GBM), Support Vector Regressor (SVR), K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGB), and a deep Feedforward Neural Network (FNN) are developed and assessed in predicting three key performance metrics: Energy Use Intensity (EUI), Predicted Percentage Dissatisfied (PPD), and Heating Load.A hybrid stacked model, combining FNN with XGB, using GBM meta learner, emerged as the top performer, achieving an impressive Coefficient of Determination (R²) of 0.99 and Mean Absolute Percentage Error (MAPE) of 0.02 across all targets. …”
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Machine Learning-Based Recommender System for Pulsed Laser Ablation in Liquid: Recommendation of Optimal Processing Parameters for Targeted Nanoparticle Size and Concentration Using Cosine Similarity and KNN Models by Anesu Nyabadza, Dermot Brabazon

“…The XGBoost model was optimal for predicting the NP concentration attaining a competitive MPE of 2%. …”
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Gearbox Fault Diagnosis Based on the LMD Cloud Model and PSO-KELM by Zhao Xiaohui, Tan Qi, Hu Sheng, Yang Wenbin, Huan Kaixuan, Zhang Zhijie

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Optimal Time Series Forecasting Through the GARMA Model by Adel Hassan A. Gadhi, Shelton Peiris, David E. Allen, Richard Hunt

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Hybrid ML-based predictive modeling and GUI development of calcium aluminate cement hydration and strength optimization for advanced and durable construction applications by Muwaffaq Alqurashi

“…These models, combined with clustering insights, enable precise mix design optimization, demonstrating the transformative potential of hybrid ML in cement science for applications in specialized environments.…”
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A New Reliability Rock Mass Classification Method Based on Least Squares Support Vector Machine Optimized by Bacterial Foraging Optimization Algorithm by S. Zheng, A. N. Jiang, X. R. Yang, G. C. Luo

“…This paper presents a new reliability rock mass classification method based on a least squares support vector machine (LSSVM) optimized by a bacterial foraging optimization algorithm (BFOA). …”
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Development of Mathematical and Computational Models for Predicting Agricultural Soil–Water Management Properties (ASWMPs) to Optimize Intelligent Irrigation Systems and Enhance Cr... by Brigitta Tóth, Oswaldo Guerrero-Bustamante, Michel Murillo, Jose Duque, Rodrigo Polo-Mendoza

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Physically-constrained evapotranspiration models with machine learning parameterization outperform pure machine learning: Critical role of domain knowledge. by Yeonuk Kim, Monica Garcia, T Andrew Black, Mark S Johnson

“…These results imply that conventional parameterizations may require reevaluated to effectively integrate physical models with machine learning, as conventional choices may not be optimal for this new, hybrid, paradigm. …”
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Enhancing shear strength predictions of UHPC beams through hybrid machine learning approaches by Sanjog Chhetri Sapkota, Ajad Shrestha, Moinul Haq, Satish Paudel, Waiching Tang, Hesam Kamyab, Daniele Rocchio

“…The accuracy needed for precise predictions is frequently lacking in current empirical equations and traditional machine learning (ML) techniques. This study proposes hybrid ML models that integrate three nature inspired metaheuristic algorithms—Giant Armadillo Optimization (GOA), Spotted Hyena Optimization (SHO) and Leopard seal optimization (LSA)- Extreme Gradient Boosting (XGB) to predict the shear strength of UHPC beams. …”
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Hybridization of Swarm for Features Selection to Modeling Heart Attack Data by Omar Shakir, Ibrahim Saleh

“…In addition, the data sets are excessively unbalanced, which leads to the bias of machine learning models when modeling heart attacks. …”
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Optimized Breast Cancer Classification Using PCA-LASSO Feature Selection and Ensemble Learning Strategies With Optuna Optimization by Prabhat Kumar Sahu, Taiyaba Fatma

“…This study presents a novel and optimized breast cancer classification system using machine learning models enhanced through advanced hyperparameter tuning techniques and statistical validation methods. …”
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An Approach to Truck Driving Risk Identification: A Machine Learning Method Based on Optuna Optimization by Zhaofei Wang, Hao Li, Qiuping Wang

“…The results are shown to indicate that the machine learning model based on Optuna optimization can effectively identify truck driving risks. …”
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