Enhancing Generalizability of a Machine Learning Model for Infrared Thermographic Defect Detection by Using 3D Numerical Modeling by Vladimir Vavilov, Arsenii Chulkov, Alexey Moskovchenko

“… The paper describes the implementation of 3D numerical simulation in machine learning models used in infrared thermographic nondestructive testing.  …”
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Applying genetic algorithm to extreme learning machine in prediction of tumbler index with principal component analysis for iron ore sintering by Senhui Wang

“…The tumbler index (TI) is one of the most important indices to characterize the quality of sinter, which depends on the raw materials proportion, operating system parameters and the chemical compositions. To accurately predict TI, an integrate model is proposed in this study. …”
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Prediction of tablet disintegration time based on formulations properties via artificial intelligence by comparing machine learning models and validation by Mohammed Ghazwani, Umme Hani

“…Drug and formulation properties were considered as the inputs to estimate the output which is tablet disintegration time. Advanced machine learning methods, including Bayesian Ridge Regression (BRR), Relevance Vector Machine (RVM), and Sparse Bayesian Learning (SBL) were utilized after comprehensive preprocessing involving outlier detection, normalization, and feature selection. …”
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Developing machine learning frameworks to predict mechanical properties of ultra-high performance concrete mixed with various industrial byproducts by Kennedy C. Onyelowe, Shadi Hanandeh, Nestor Ulloa, Ruth Barba-Vera, Arif Ali Baig Moghal, Ahmed M. Ebid, Krishna Prakash Arunachalam, Ateekh Ur Rehman

“…Sensitivity analyses using SHAP and Hoffman & Gardener’s methods identified the most influential parameters affecting each UHPC property, providing insights into the key factors driving concrete performance. …”
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A smarter approach to liquefaction risk: harnessing dynamic cone penetration test data and machine learning for safer infrastructure by Shubhendu Vikram Singh, Sufyan Ghani

“…ML models, including Support Vector Machine (SVM) optimized with Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Firefly Algorithm (FA), were employed to predict the e/qd ratio using key geotechnical parameters, such as fine content, peak ground acceleration, reduction factor, and penetration rate. …”
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Optimizing travel time reliability with XAI: A Virginia interstate network case using machine learning and meta-heuristics by Navid Khorshidi, Shahriar Afandizadeh Zargari, Soheil Rezashoar, Hamid Mirzahossein

“…This paper applies machine learning models to predict travel time reliability in transportation networks, using XGBoost, LightGBM, and CatBoost optimized with seven metaheuristic algorithms. …”
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Optimal Operation of a Tablet Pressing Machine Using Deep-Neural-Network-Embedded Mixed-Integer Linear Programming by Jialong Li, Lan Wu, Yuang Qin, Haojun Zhi

“…This paper presents a deep neural network (DNN)-embedded mixed-integer linear programming (MILP) model for fault prediction and production optimization in tablet pressing machines. The DNN predicts the probability of failures during the tablet pressing process by analyzing key operational parameters such as pressure, temperature, humidity, speed, vibration, and number of maintenance cycles. …”
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Analysis of Weak Links in the Mechanized Mining of Underground Metal Mines: Insights from Machine Learning and SHAP Explainability Models by Chengye Yang, Keping Zhou, Jielin Li

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Nonlinear Model Predictive Control for Pumped Storage Plants Based on Online Sequential Extreme Learning Machine with Forgetting Factor by Chen Feng, Chaoshun Li, Li Chang, Zijun Mai, Chunwang Wu

“…Specifically, the initial learning parameters are optimized by prior-knowledge learning and a new self-adaptive adjustment strategy is also put forward. …”
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Development of a Self-Updating System for the Prediction of Steel Mechanical Properties in a Steel Company by Machine Learning Procedures by Valerio Zippo, Elisa Robotti, Daniele Maestri, Pietro Fossati, David Valenza, Stefano Maggi, Gennaro Papallo, Masho Hilawie Belay, Simone Cerruti, Giorgio Porcu, Emilio Marengo

“…A workflow for process data analysis has been developed, based on the use of machine learning algorithms to build an interface for data treatment to be directly used online. …”
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Inverse design of high-strength medium-Mn steel using a machine learning-aided genetic algorithm approach by Jin-Young Lee, Seung-Hyun Kim, Hyun-Bin Jeong, KeunWon Lee, KiSub Cho, Young-Kook Lee

“…We also optimized the hyper-parameters of a genetic algorithm (GA) using the Shannon diversity index to enhance search efficiency while retaining diversity. …”
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Designing Laves-phase RFe2-type alloy with excellent magnetostrictive performance by physics-informed interpretable machine learning by Pengqiang Hu, Chao Zhou, Ruisheng Zhang, Sidan Ding, Yuanjun Guo, Bo Wang, Dezhen Xue, Yizhe Ma, Zhiyong Dai, Yin Zhang, Fanghua Tian, Sen Yang

“…Herein, we employ a physics-informed interpretable machine learning-based strategy to facilitate the design of targeted alloys. …”
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Enhancing accuracy through ensemble based machine learning for intrusion detection and privacy preservation over the network of smart cities by Mudita Uppal, Yonis Gulzar, Deepali Gupta, Jayant Uppal, Mukesh Kumar, Shilpa Saini

“…In this study, various supervised machine learning algorithms for anomaly-based detection methods are compared. …”
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Human-machine co-adaptation to automated insulin delivery: a randomised clinical trial using digital twin technology by Boris P. Kovatchev, Patricio Colmegna, Jacopo Pavan, Jenny L. Diaz Castañeda, Maria F. Villa-Tamayo, Chaitanya L. K. Koravi, Giulio Santini, Carlene Alix, Meaghan Stumpf, Sue A. Brown

“…This randomised clinical trial tested human-machine co-adaptation to AID using new ‘digital twin’ replay simulation technology. …”
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Computational fluid dynamics analysis and machine learning study of heat transfer in solar air heaters with distinct ribs configuration by Eid S. Alatawi

“…This research pioneers SAH optimization by uniquely integrating Computational Fluid Dynamics (CFD) with machine learning (ML) to analyze and predict the performance of 15 SAH designs featuring distinct curved rib configurations. …”
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Improving Transformer Health Index Prediction Performance Using Machine Learning Algorithms with a Synthetic Minority Oversampling Technique by Muhammad Akmal A. Putra, Suwarno, Rahman Azis Prasojo

“…Machine learning (ML) has emerged as a powerful tool in transformer condition assessment, enabling more accurate diagnostics by leveraging historical test data. …”
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Systematic selection of best performing mathematical models for in vitro gas production using machine learning across diverse feeds by Hamed Ahmadi, Natascha Titze, Katharina Wild, Markus Rodehutscord

“…We hypothesized that distinct feed types exhibit unique GP characteristics, effectively captured by specific models, and that statistical and machine learning methodologies can streamline model selection. …”
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Classification of Anxiety Levels of IGD Patients at RSU Royal Prima Medan Using Support Vector Machine (SVM) Algorithm by Kharisma Gunanta Ginting, Nugroho Prasetyo, Al Vino Gunawan, Magdalena Sihombing, Adli Abdillah Nababan

“…This study aims to develop a patient anxiety level classification model in the ED using the Support Vector Machine (SVM) algorithm with the application of the Synthetic Minority Oversampling Technique (SMOTE) to address the class imbalance issue. …”
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Advancing Aviation Safety Through Predictive Maintenance: A Machine Learning Approach for Carbon Brake Wear Severity Classification by Patsy Jammal, Olivia Pinon Fischer, Dimitri N. Mavris, Gregory Wagner

“…Aircraft-specific metrics from flight data are augmented with weather and airport parameters from FlightAware^® to better capture the operational environment. …”
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Predictive modeling of ultimate tensile strength in dissimilar friction stir welded aluminum alloys via machine learning approach by Meghavath Mothilal, Atul Kumar

“…The purpose of this study is to evaluate the effectiveness of various machine learning algorithms in predicting the ultimate tensile strength (UTS) of friction stir welded joints. …”
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