Extracting Optimal Number of Features for Machine Learning Models in Multilayer IoT Attacks by Badeea Al Sukhni, Soumya K. Manna, Jugal M. Dave, Leishi Zhang

“…The proposed framework managed to extract an optimal set of 13 significant features out of 64 in the Edge-IIoT dataset, which is crucial for the efficient detection and classification of multilayer attacks, and also outperforms the performance of the KNN model compared to other classifiers in binary classification. …”
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First-passage approach to optimizing perturbations for improved training of machine learning models by Sagi Meir, Tommer D Keidar, Shlomi Reuveni, Barak Hirshberg

“…Our work allows optimization of perturbations for improving the training of machine learning models using a first-passage approach.…”
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Machining Process Optimization Using a Model Based on Criterial Functional Dependence by Peter Pavol Monka, Katarina Monkova, Ondrej Bilek, Martin Reznicek

“…This research deals with the optimization of the machining process using a model based on criterial functional dependence hypothesis. …”
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Machine Learning‐Driven Surrogate Modeling for Optimization of Triboelectric Nanogenerator Design Parameters by Mohammad Abrar Uddin, Myeongju Lim, Rubiga Kim, Barrett London Burgess, Ken Roberts, Junghyun Kim, Taeil Kim

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Innovative data techniques for centrifugal pump optimization with machine learning and AI model. by Gaurav Sandeep Dave, Amar Pradeep Pandhare, Atul Prabhakar Kulkarni, Dhananjay Vasant Khankal

“…The quality of recorded data plays a crucial role and directly influences the data transformation phase in machine learning (ML) and deep learning (DL) models. …”
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A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes by Cosmina-Mihaela Rosca, Adrian Stancu

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Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits. by Mohsen Yoosefzadeh-Najafabadi, Dan Tulpan, Milad Eskandari

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Daily runoff forecasting using novel optimized machine learning methods by Peiman Parisouj, Changhyun Jun, Sayed M. Bateni, Essam Heggy, Shahab S. Band

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Comparing the Estimation Power of Machine Learning Models and Statistical Models in Predicting Profit Component Changes and Selecting the Optimal Model by Maryam Izadi, Ali Ashtab, Akbar Zavari Rezaei

“…These models exhibited the best performance and were selected as the optimal models.…”
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Enhancing Machine Learning Models Through PCA, SMOTE-ENN, and Stochastic Weighted Averaging by Youngjin Han, Inwhee Joe

“…An ensemble model combining seven machine learning algorithms—Logistic Regression, Support Vector Machine, KNN, Random Forest, XGBoost, LightGBM, and CatBoost—was applied to predict survival outcomes. …”
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Seven-parameter PEMFC model optimization using an battlefield optimization algorithm by Manish Kumar Singla, S.A. Muhammed Ali, Jyoti Gupta, Pradeep Jangir, Arpita, Ramesh Kumar, Reena Jangid, Mohammad Khishe

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Research on the optimization model of anti-breast cancer candidate drugs based on machine learning by Zhou Dong, Hong Chen, Yuchen Yang, Hairong Hao

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Research on optimal selection of runoff prediction models based on coupled machine learning methods by Xing Wei, Mengen Chen, Yulin Zhou, Jianhua Zou, Libo Ran, Ruibo Shi

“…Employing a “decomposition-reconstruction” strategy combined with robust optimization algorithms enhances the performance of machine learning prediction models, thereby significantly improving the runoff prediction capabilities in watershed hydrological models.…”
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Optimizing Methanol Injection Quantity for Gas Hydrate Inhibition Using Machine Learning Models by Mohammed Hilal Mukhsaf, Weiqin Li, Ghassan Husham Jani

“…Using a dataset of 74,000 samples, with 80% for training and 20% for testing, we enhanced model robustness with 50 Monte Carlo iterations and tenfold cross-validation. …”
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Optimizing Concrete Mix Designs With Synthetic Data Generation and Machine Learning Prediction Models by Mohanad A. Deif, Hani Attar, Waleed Alomoush, Mohamed A. Hafez

“…The highest performance among these was observed for the KNN at flexural strength with a R2 score of 0.8737, XGBoost for compressive strength with a R2 score of 0.8963, and RF for tensile strength with a R2 score of 0.9420. Bayesian optimization was employed to tune hyperparameters to enhance the accuracy of the model. …”
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Optimization and Machine Learning in Modeling Approaches to Hybrid Energy Balance to Improve Ports’ Efficiency by Helena M. Ramos, João S. T. Coelho, Eyup Bekci, Toni X. Adrover, Oscar E. Coronado-Hernández, Modesto Perez-Sanchez, Kemal Koca, Aonghus McNabola, R. Espina-Valdés

“…This research provides a comprehensive review of hybrid energy solutions and optimization models for ports and marine environments. …”
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Determination of the Machine and Tractor Fleet Optimal Composition for a Model Cotton­Textile Cluster by M. T. Toshboltaev, M. R. Dzhiyanov

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Enhanced Performance of Wastewater Membrane Bioreactor Using Machine Learning Model’s Prediction and Optimization by Jia-Chi Jiang, Nien-Hsun Li, Mo Chen, Kuan-Chung Chen, Chih-Hua Chang

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Using multiple machine learning algorithms to optimize the water quality index model and their applicability by Fei Ding, Shilong Hao, Wenjie Zhang, Mingcen Jiang, Liangyao Chen, Haobin Yuan, Nan Wang, Wenpan Li, Xin Xie

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Machine Learning Model Optimization for Antarctic Blowing Snow Height and Optical Depth Diagnosis by Surendra Bhatta, Yuekui Yang

“…Previous research developed an optimized machine learning (ML) model to diagnose blowing snow occurrence using meteorological fields from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). …”
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