Optimizing Artificial Neural Networks with Particle Swarm Optimization for Accurate Prediction of Insulator Flashover Voltage Under Dry and Rainy Conditions by Abdelhalim Mahdjoubi, lazreg taibaoui, Boubakeur Zegnini

“…The research emphasizes the critical role of raindrops in reducing flashover voltage. A hybrid model combining Artificial Neural Networks (ANN) with Particle Swarm Optimization (PSO) is developed to address these challenges. …”
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Processing of Polymers Stress Relaxation Curves Using Machine Learning Methods by Anton S. Chepurnenko, Tatiana N. Kondratieva, Ebrahim Al-Wali

“…The aim of this work is to develop machine learning models for determining the rheological properties of polymers from experimental stress relaxation curves. …”
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Various Designs of Spoke-Type Permanent Magnet Motor for Performance Optimization by Vasilija Sarac, Dragan Minovski, Sara Aneva, Petar Janiga, Miroslava Farkas Smitkova, Darko Bogatinov, Ana Atanasova

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Lattice protein folding with variational annealing by Shoummo A Khandoker, Estelle M Inack, Mohamed Hibat-Allah

“…This scheme is generalizable to three spatial dimensions and can be extended to lattice protein models with larger alphabets. Our findings emphasize the potential of advanced machine learning techniques in tackling complex protein folding problems and a broader class of constrained combinatorial optimization challenges.…”
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Hybrid deep learning optimization for smart agriculture: Dipper throated optimization and polar rose search applied to water quality prediction. by Amal H Alharbi, Faris H Rizk, Khaled Sh Gaber, Marwa M Eid, El-Sayed M El-Kenawy, Ehsan Khodadadi, Nima Khodadadi

“…We applied this hybrid strategy to a Radial Basis Function Network (RBFN), and validated its performance improvements through extensive experiments, including ANOVA and Wilcoxon tests for both feature selection and optimization phases. The optimized model achieved a classification accuracy of 99.46%, significantly outperforming classical machine learning and unoptimized deep learning models. …”
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Machine learning assisted estimation of total solids content of drilling fluids by B.T. Gunel, Y.D. Pak, A.Ö. Herekeli, S. Gül, B. Kulga, E. Artun

“…Further optimization of the random forests model resulted in a mean absolute percentage error (MAPE) of 3.9% and 9.6% and R2 of 0.99 and 0.93 for the training and testing sets, respectively. …”
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Optimized layout and excavation sequence of deep chambers by Shenghua YIN, Yongyuan KOU, Yun ZHOU, Zepeng YAN, Dexin ZENG

“…Regarding the excavation sequencing, this study optimized the excavation order based on an incremental stability characterization model for the chamber group. …”
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Production Lot Sizing and Process Targeting under Process Deterioration and Machine Breakdown Conditions by Muhammad Al-Salamah, Ali Abudari

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Optimal Design of High-Precision Focusing Mechanism Based on Flexible Hinge by Zhanwei Huo, Guangzhen Li, Luyang Tan, Tianwen Yang, Dapeng Tian, Ji Li

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TKEO-Enhanced Machine Learning for Classification of Bearing Faults in Predictive Maintenance by Xuanbai Yu, Olivier Caspary

“…Advanced classifiers, including support vector machines and random forests, demonstrated that TKEO effectively improved model accuracy in the capture of fault-related signal dynamics. …”
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FAIRness Along the Machine Learning Lifecycle Using Dataverse in Combination with MLflow by Lincoln Sherpa, Valentin Khaydarov, Ralph Müller-Pfefferkorn

“…Typical Machine Learning (ML) approaches are characterized by their iterative and exploratory nature: continuously refining and adapting not only code but also ML models to optimize the results and the performance on new data. …”
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Subsystem-Based Fault Detection in Robotics via <italic>L2</italic> Norm and Random Forest Models by Elham Abdullah Alamoudi

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Probabilistic Evaluation of Hydraulic Fracture Performance Using Ensemble Machine Learning by Xiaoping Xu, Xianlin Ma, Jie Zhan

“…We present a probabilistic evaluation approach that integrates ensemble machine learning with Monte Carlo simulation. In the method, we employ the ensemble learning to develop a predictive model between well productivity and its influential factors including both geological properties and HF treatment parameters. …”
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Prediction of Freezing Time During Hydrogen Fueling Using Machine Learning by Ji-Ah Choi, Ji-Seong Jang, Sang-Won Ji

“…Hyperparameter optimization was performed using random search to enhance model performance. …”
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MIMD Programs Execution Support on SIMD Machines: A Holistic Survey by Dheya Mustafa, Ruba Alkhasawneh, Fadi Obeidat, Ahmed S. Shatnawi

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Application of machine learning in identifying risk factors for low APGAR scores by Haifa Fahad Alhasson, Nagat Elhag, Shuaa Saleem Alharbi, Ishag Adam

“…Methods This study aimed to develop a machine-learning model that predicts low APGAR scores by incorporating maternal, fetal, and perinatal factors in Wad Medani, Sudan. …”
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Compensation of the Radial Error of Measuring Head based on Forming Grinding Machine by Wang Zhonghou, Cao Huan, Li Gang, Liu Xingrong

“…According to the principle of polar coordinates measuring and tooth profile deviation calculation,an optimization model with purpose of minimum tooth profile and variable parameter of radial error is established based on the object of standard involute spur gear.After resolving this radial error and compensating the machine with the error,a helical involute gear is measured and the measured result is compared with the testing result of the three coordinate of the testing results of the three coordinate measuring instrument,the rationality of the radial error solving of the measuring head is obtained.…”
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New Heuristics Method for Malicious URLs Detection Using Machine Learning by Maher Kassem Hasan

“…We implemented and optimized three models—Logistic Regression, Random Forest, and Support Vector Machines (SVM)—based on the literature available that indicates the effectiveness of these models. …”
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Large dataset on Fourier transform near infrared (FT-NIR) spectroscopy of green and roasted specialty coffee: Preprocessed infrared spectra and sensory scores for machine learning-... by Andrés F. Bahamón-Monje, Ever M. Morales-Angulo, Gentil A. Collazos-Escobar, Nelson Gutiérrez-Guzmán

“…This approach was designed to establish a robust dataset for calibrating machine learning-based predictive models of coffee cup quality. …”
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Design and Dimension Optimization of Rigid–Soft Hand Function Rehabilitation Robots by Rui Zhang, Meng Ning, Yuqian Wang, Jun Yang

“…Hierarchical constraint modeling and an improved artificial bee colony algorithm optimize linkage dimensions and control strategies, achieving enhanced human–robot kinematic matching. …”
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