Influence of Modal Decomposition Algorithms on Nonlinear Time Series Machine Learning Prediction Models in Engineering: A Case Study of Subway Tunnel Settlement by Qingmeng Shen, Yuming Wu, Limin Wan, Qian Chen, Yue Li, Zichao Liao, Wenbo Wang, Feng Li, Tao Li, Jiajun Shu

“…Finally, the decomposed signals are fed into the machine learning model to construct a high-precision settlement prediction model based on rolling decomposition. …”
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Signal Processing-Free Intelligent Model for Power Quality Disturbances Identification by Mohammed F. Al-Mashdali, Asif Islam, Abdulbasit Hassan, Md Shafiullah, Mujahed Al-Dhaifallah, Khalid Al Fuwail

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Intelligent Feature Selection Ensemble Model for Price Prediction in Real Estate Markets by Daniel Cristóbal Andrade-Girón, William Joel Marin-Rodriguez, Marcelo Gumercindo Zuñiga-Rojas

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A Simple Emulator That Enables Interpretation of Parameter‐Output Relationships, Applied to Two Climate Model PPEs by Qingyuan Yang, Gregory S. Elsaesser, Marcus van Lier‐Walqui, Trude Eidhammer

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Prediction of microvascular obstruction from angio-based microvascular resistance and available clinical data in percutaneous coronary intervention: an explainable machine learning... by Zhe Zhang, Yang Dai, Peng Xue, Xue Bao, Xinbo Bai, Shiyang Qiao, Yuan Gao, Xuemei Guo, Yanan Xue, Qing Dai, Biao Xu, Lina Kang

“…This study aimed to validate the correlation between AMR and CMR-derived parameters and to construct an interpretable machine learning (ML) model, incorporating AMR and clinical data, to forecast MVO in ST-segment elevation myocardial infarction (STEMI) patients undergoing primary percutaneous coronary intervention (PPCI). …”
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Wheels turning: CHO cell modeling moves into a digital biomanufacturing era by Sandeep Ranpura, Vishwanathgouda Maralingannavar, Alexandra-Gabriela Gheorghe, Edward Ma, James Morrissey, Michael J. Betenbaugh, Deniz Demirhan

“…Finally, we summarize the application of machine learning and hybrid models to CHO bioprocesses, aiming to develop and manufacture drugs more efficiently and at a lower cost for patients.…”
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Development and external validation of a machine learning-based model to predict postoperative recurrence in patients with duodenal adenocarcinoma: a multicenter, retrospective coh... by Xu Liu, Qifeng Xiao, Zongting Gu, Xin Wu, Chunhui Yuan, Xiaolong Tang, Fanbin Meng, Dong Wang, Ren Lang, Kaiqing Guo, Xiaodong Tian, Yu Zhang, Enhong Zhao, Zhenzhou Wu, Jingyong Xu, Ying Xing, Feng Cao, Chengfeng Wang, Jianwei Zhang

“…Methods Our objective is to develop a machine learning-based model to predict the postoperative recurrence of DA. …”
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Real-Time Waste Detection and Classification Using YOLOv12-Based Deep Learning Model by Mosharof Hossain Dipo, Fahmid Al Farid, Md. Sifti Al Mahmud, Muntasir Momtaz, Shakila Rahman, Jia Uddin, Hezerul Abdul Karim

“…To enable the recycling process to be optimized and to minimize environmental impact, waste materials must be well detected and classified. …”
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Application of a Hybrid Model for Data Analysis in Hydroponic Systems by Kuanysh Bakirov, Jamalbek Tussupov, Akhmet Tussupov, Ibraheem Shayea, Aruzhan Shoman

“…This study presents a hybrid data analysis approach to optimize the growing conditions for beetroot and tarragon microgreens cultivated in hydroponic systems. …”
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Synthesizing Explainability Across Multiple ML Models for Structured Data by Emir Veledar, Lili Zhou, Omar Veledar, Hannah Gardener, Carolina M. Gutierrez, Jose G. Romano, Tatjana Rundek

“…Explainable Machine Learning (XML) in high-stakes domains demands reproducible methods to aggregate feature importance across multiple models applied to the same structured dataset. …”
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EVALUATION OF THE RELATIONSHIP BETWEEN SPRAY LIQUID PRESSURE AND MACHINE SPEED USING THREE TYPES OF NOZZLES ON A BOOM SPRAYER by G. Tihanov, G. Hristova, P. Veleva

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Random Forest-Based Machine Learning Model Design for 21,700/5 Ah Lithium Cell Health Prediction Using Experimental Data by Sid-Ali Amamra

“…Two machine learning models: support vector regression (SVR) and random forest (RF) were designed and evaluated. …”
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Maritime Risk Assessment: A Cutting-Edge Hybrid Model Integrating Automated Machine Learning and Deep Learning with Hydrodynamic and Monte Carlo Simulations by Egemen Ander Balas, Can Elmar Balas

“…The machine learning models of Light Gradient Boosting (LightGBM), XGBoost, Random Forest, and Multilayer Perceptron (MLP) were employed. …”
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A recommender system with multi-objective hybrid Harris Hawk optimization for feature selection and disease diagnosis by Madhusree Kuanr, Puspanjali Mohapatra

“…The proposed recommender system uses the Tree-based Pipeline Optimization Tool (TPOT) automated machine learning model to recommend the most suitable machine learning prediction model with the best classifier in terms of classification accuracy for a disease with the selected features. …”
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Predicting pull-out strength and failure modes of metal anchors embedded in masonry structures using explainable machine learning models and empirical equations by Aryan Baibordy, Mohammad Yekrangnia

“…Optimization techniques were then used to fine-tune the hyperparameters, improving the performance of each ML model. …”
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Methodological Validation of Machine Learning Models for Non-Technical Loss Detection in Electric Power Systems: A Case Study in an Ecuadorian Electricity Distributor by Carlos Arias-Marín, Antonio Barragán-Escandón, Marco Toledo-Orozco, Xavier Serrano-Guerrero

“…Following the process, several popular classification models were trained. Hyperparameter optimization was performed by using grid search, and the models were validated by using cross-validation techniques, finding that the ensemble methods Categorical Boosting (CGB), Light Gradient Boosting Machine (LGB) and Extreme Gradient Boosting (EGB) are the most suitable for identifying losses, achieving high performance and reasonable computational cost. …”
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Advancement of Artificial Intelligence in Cost Estimation for Project Management Success: A Systematic Review of Machine Learning, Deep Learning, Regression, and Hybrid Models by Md. Mahfuzul Islam Shamim, Abu Bakar bin Abdul Hamid, Tadiwa Elisha Nyamasvisva, Najmus Saqib Bin Rafi

“…Key AI techniques, including support vector machines (SVMs) (7.90% of studies), decision trees, and gradient-boosting models, offer substantial improvements in cost prediction and resource optimization. …”
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Development of a Predictive Model for the Biological Activity of Food and Microbial Metabolites Toward Estrogen Receptor Alpha (ERα) Using Machine Learning by Maksim Kuznetsov, Olga Chernyavskaya, Mikhail Kutuzov, Daria Vilkova, Olga Novichenko, Alla Stolyarova, Dmitry Mashin, Igor Nikitin

“…In this study, we evaluated a suite of 27 machine learning models and, following systematic optimization and rigorous performance comparison, identified linear discriminant analysis (LDA) as the most effective predictive approach. …”
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Modeling of Six Openers for Seeding Machines, Cross-Shaped, T-Inverse, U-Shaped, V-Shaped, V-Shaped with Two Plates and Tulip-Shaped by Behnam Sepehr, isa Hazbawi

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Advanced machine learning applications in fibromyalgia to assess the relationship between 3D spinal alignment with clinical outcomes by Ibrahim M. Moustafa, Iman Akef Khowailed, Shima A. Mohammad Zadeh, Dilber Uzun Ozsahin, Mubarak Taiwo Mustapha, Paul A. Oakley, Deed E. Harrison

“…Abstract This study leveraged machine learning (ML) models to explore the relationship between three-dimensional (3D) spinal alignment parameters and clinical outcomes in patients suffering from fibromyalgia syndrome (FMS). …”
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