Optimizing catalyst layer composition of PEM fuel cell via machine learning: Insights from in-house experimental data by Yuze Hou, Patrick Schneider, Linda Ney, Nada Zamel

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Fine-Tuned Machine Learning Classifiers for Diagnosing Parkinson’s Disease Using Vocal Characteristics: A Comparative Analysis by Mehmet Meral, Ferdi Ozbilgin, Fatih Durmus

“…The Chi-Square test was used for feature selection to determine the most important attributes that enhanced the accuracy of the classification. Six different machine learning classifiers, namely SVM, k-NN, DT, NN, Ensemble and Stacking models, were developed and optimized via Bayesian Optimization (BO), Grid Search (GS) and Random Search (RS). …”
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Digital Industrial Design Method in Architectural Design by Machine Learning Optimization: Towards Sustainable Construction Practices of Geopolymer Concrete by Xiaoyan Wang, Yantao Zhong, Fei Zhu, Jiandong Huang

“…This study explores the predictive capabilities of soft computing models in estimating the compressive strength of GeC, utilizing multi-layer perceptron (MLP) neural networks and hybrid systems incorporating the Gannet Optimization Algorithm (GOA) and Grey Wolf Optimizer (GWO). …”
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Machine learning based optimization of titanium electropolishing using artificial neural networks and Taguchi design in eco-friendly electrolytes by Hyun-Kyu Hwang, Seong-Jong Kim

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Data-driven machine learning with lattice distortion and thermodynamic parameters guided strength optimization of refractory high-entropy alloys by Shujian Ding, Yifan Zhang, Siyang Lei, Xiang Weng, Wenhui Li, Wei Ren, Jian Chen, Weili Wang

“…To address this difficulty, a data-driven machine learning (ML) model was established to predict the compressive yield strength (σ0.2) of RHEAs, which provides a design strategy with two pivotal descriptors concerning lattice distortion and thermodynamic parameters. …”
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Automatic Optimization of a Parallel-Plate Avalanche Counter with Optical Readout by María Pereira Martínez, Xabier Cid Vidal, Pietro Vischia

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An optimization protocol for MRI examination resource allocation based on demand forecasting and linear programming by Zhongbin Zhou, Hanyu Zhou, Yuanyuan Qiao, Zihan Gao, Ying Yang

“…Based on the prediction outcomes, an Integer Linear Programming model was employed to calculate the optimal number of MRI examinations per machine per day, targeting cost reduction. …”
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Interpretable machine learning for predicting optimal surgical timing in polytrauma patients with TBI and fractures to reduce postoperative infection risk by Xing Han, Jia-Hui Zhang, Xin Zhao, Xi-Guang Sang

“…Abstract This retrospective study leverages machine learning to determine the optimal timing for fracture reconstruction surgery in polytrauma patients, focusing on those with concomitant traumatic brain injury. …”
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On Explainability of Reinforcement Learning-Based Machine Learning Agents Trained with Proximal Policy Optimization That Utilizes Visual Sensor Data by Tomasz Hachaj, Marcin Piekarczyk

“…In this paper, we address the issues of the explainability of reinforcement learning-based machine learning agents trained with Proximal Policy Optimization (PPO) that utilizes visual sensor data. …”
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Landslide Displacement Prediction Using Kernel Extreme Learning Machine with Harris Hawk Optimization Based on Variational Mode Decomposition by Chenhui Wang, Gaocong Lin, Cuiqiong Zhou, Wei Guo, Qingjia Meng

“…Hence, this study proposes a novel landslide displacement prediction model based on variational mode decomposition (VMD) and a Harris Hawk optimized kernel extreme learning machine (HHO-KELM). …”
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Machine Learning Framework for Early Detection of Chronic Kidney Disease Stages Using Optimized Estimated Glomerular Filtration Rate by Samit Kumar Ghosh, Namareq Widatalla, Ahsan H. Khandoker

“…This study proposes a machine learning (ML) system that integrates regression-based eGFR estimation, metaheuristic optimization using the Grey Wolf Optimizer (GWO), and multi-class classification with various ML models to enhance CKD staging and classification. …”
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Enhanced water saturation estimation in hydrocarbon reservoirs using machine learning by Ali Akbari, Ali Ranjbar, Yousef Kazemzadeh, Dmitriy A. Martyushev

“…In this study, a comprehensive dataset consisting of 30,660 independent data points was utilized to develop machine learning (ML) models for Sw prediction. Nine well log parameters—Depth (DEPT), High-Temperature Neutron Porosity, True Resistivity, Computed Gamma Ray, Spectral Gamma Ray, Hole Caliper, Compressional Sonic Travel Time, Bulk Density, and Temperature—were used as input features to train and test five ML algorithms: Linear Regression, Support Vector Machine (SVM), Random Forest, Least Squares Boosting, and Bayesian methods. …”
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Online Traffic Crash Risk Inference Method Using Detection Transformer and Support Vector Machine Optimized by Biomimetic Algorithm by Bihui Zhang, Zhuqi Li, Bingjie Li, Jingbo Zhan, Songtao Deng, Yi Fang

“…The TAR-2 dataset is inputted into a Support Vector Machine (SVM) optimized using a hybrid algorithm and used to infer the risk of urban traffic crashes. …”
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Using optimized dimensionality reduction and machine learning to explain driving processes of phytoplankton community assembly in large mountain rivers by Jingxu Ye, Daikui Li, Qi Liu, Jianying Song, Jiawei Song, Zhigang Zu, Yujun Yi

“…In this study, we employed a methodology combining optimized dimensionality reduction with advanced machine learning to construct a path analysis model for explaining the driving processes underlying phytoplankton community assembly in large mountain rivers. …”
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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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Optimization of Structures and Composite Materials: A Brief Review by André Ferreira Costa Vieira, Marcos Rogério Tavares Filho, João Paulo Eguea, Marcelo Leite Ribeiro

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Multi objective optimization of FDM 3D printing parameters set via design of experiments and machine learning algorithms by Antonio Panico, Alberto Corvi, Luca Collini, Corrado Sciancalepore

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Machine Learning-Enabled Prediction and Optimization of Sulfur Recovery Units: A Step towards Industry 4.0 Integration by Imran Khan, Husnain Saghir, Muhammad Ahsan

“…In the next step, the Gaussian process regression model served as a surrogate for fitness function evaluations within a particle swarm optimization framework. …”
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GIS, remote sensing, and machine learning for identifying optimal locations for seawater intake and brine disposal in coastal desalination systems by Radwa Marzouk, Tareq M. Alkhaldi, Sakthivelu U, Saranya A, Arutperumjothi G, Srinivasan S

“…This study introduces an integrated framework using Geographic Information Systems (GIS), Remote Sensing (RS), and Machine Learning (ML) to identify optimal desalination sites. …”
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Intrusion Detection for Wireless Sensor Network Using Particle Swarm Optimization Based Explainable Ensemble Machine Learning Approach by Shaikh Afnan Birahim, Avijit Paul, Fahmida Rahman, Yamina Islam, Tonmoy Roy, Mohammad Asif Hasan, Fariha Haque, Muhammad E. H. Chowdhury

“…This paper proposes a novel Intrusion Detection System (IDS) leveraging Particle Swarm Optimization (PSO) and an ensemble machine learning approach combining Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbors (KNN) models to enhance the accuracy and reliability of intrusion detection in WSNs. …”
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