The Finite-Time Turnpike Property in Machine Learning by Martin Gugat

“…The finite-time turnpike property describes the situation in an optimal control problem where an optimal trajectory reaches the desired state before the end of the time interval and remains there. We consider a machine learning problem with a neural ordinary differential equation that can be seen as a homogenization of a deep ResNet. …”
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Churn prediction for SaaS company with machine learning by Hugo Eduardo Sanches, Ayslan Trevizan Possebom, Linnyer Beatrys Ruiz Aylon

“…Design/methodology/approach – Through a preprocessing and normalization of data, seven machine learning algorithms were applied. The models were trained, and also cross-validation and parameter tuning techniques were applied to improve results. …”
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Model of Parametric Reliability of Woodworking Machine Tools by Lidiia Dziuba, Mariia Pylypchuk, Oksana Chmyr, Roman Pavliuk

“…The probabilities of failure-free operation were calculated by considering the alpha probability density function for operating time intervals to failure of woodworking machine tools. Finally, it was found that the probability of failure-free operation of machine tools significantly depends on the time-depending parameter, which characterizes the relative durability of the machine tool.…”
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A hybrid unsupervised machine learning model with spectral clustering and semi-supervised support vector machine for credit risk assessment. by Tao Yu, Wei Huang, Xin Tang, Duosi Zheng

“…Despite the efficacy of unsupervised learning methods in handling unlabeled datasets, their performance remains limited owing to challenges such as imbalanced data, local optima, and parameter adjustment complexities. Thus, this paper introduces a novel hybrid unsupervised classification method, named the two-stage hybrid system with spectral clustering and semi-supervised support vector machine (TSC-SVM), which effectively addresses the unsupervised imbalance problem in credit risk assessment by targeting global optimal solutions. …”
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Effect of Key Parameters on Ploughing Force Performance of Planing-Type Anti-Climbers by Zhuyao Li, Jiyou Fei, Dongxue Song, Hong He, Chang Liu, Chong Zhang

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Numerical Analysis of the Structural Parameters on the Performance of Oil-Injected Rotary Vane Compressors by Fanghua Ye, Huiyang Zhu, Yexin Peng, Giuseppe Bianchi, Sham Rane, Yuande Dai

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Comparative analysis of machine learning models for wind speed forecasting: Support vector machines, fine tree, and linear regression approaches by Yousef Altork

“…Wind speed is an important parameter of wind energy conversion, and its forecast is significant for optimal power generation and maintaining the stability of the electricity supply. …”
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Machine-Learning-Based Optimal Feed Rate Determination in Machining: Integrating GA-Calibrated Cutting Force Modeling and Vibration Analysis by Yu-Peng Yeh, Han-Hao Tsai, Jen-Yuan Chang

“…These findings validate the model’s ability to enhance machining performance and demonstrate the practical value of integrating simulated dynamics and machine learning for data-driven parameter optimization in robotic systems.…”
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Advances in Machine Learning for Mechanically Ventilated Patients by Xu Y, Xue J, Deng Y, Tu L, Ding Y, Zhang Y, Yuan X, Xu K, Guo L, Gao N

“…The review also examined challenges of integrating machine learning into clinical practice, such as data integration, model interpretability, and real - time performance requirements.Results: Machine learning models have demonstrated significant potential in predicting successful extubation, optimizing oxygenation strategies through non-invasive blood gas prediction, and dynamically adjusting ventilator parameters using reinforcement learning. …”
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Parameter Matching and Control Strategy Development for AMT Parallel Plug-in System by SONG Chao, WANG Kunjun, XIE Yongbo, LI Yonghua

“…Through the simulation of working range of each power component under typical road conditions, the theoretical calculation parameters are completed. Fuel economy and ride comfort of the system are improved by designing a system state machine and clutch control strategy that matches component efficiency characteristics.…”
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Reducing the Parameter Dependency of Phase-Picking Neural Networks with Dice Loss by Yongsoo Park, Gregory C. Beroza

“…When strategically used, models trained on the Dice loss can reduce the parameter dependency of machine learning-based seismic monitoring.…”
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Surface and subsurface characteristics of wire-electrical discharge machined Al-alloy and composite: a fundamental study on the role of machining variables by Gaurav Anand, Santanu Sardar, Satesh Sah, Ashim Guha, Ibrahim Albaijan, Debdulal Das

“…The wire-EDM process involves a large number of machining variables apart from parameters associated with the workpiece, dielectric, and electrode; therefore, research on wire-EDM is often performed on the basis of the design of experiments, which is somewhat limited the fundamental understanding of the role of various influencing variables. …”
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An Ensemble Method for Soil Parameter Prediction Based on Multisource Data Fusion by Mingyuan Wang, Shaoxiang Zeng, Zuguo Zhang, Songting Chen, Jun Wang

“…Specifically, integrating MASW data increases the R2 at location CPT3 from 0.477 to 0.758, demonstrating that the proposed method can improve the predictions of soil parameters in areas with sparse data.…”
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Improving SLICES crystal representation through CHGNet integration and parameter tuning by Bizhu Zhang, Kedeng Wu, Chang Zhang, Hang Xiao, Liangliang Zhu

“…Furthermore, through systematic optimization of key parameters, including bond scaling, Δx, and lower bounds of lattice scaling, we achieved enhanced reconstruction performance. …”
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Investigating Bayesian Parameter Identification Using Non-Standard Laboratory Specimens by Matej Šodan, Vladimir Divić, Noémi Friedman, Mijo Nikolić

“…This work investigates the applicability of Bayesian inverse analysis for identifying parameters from non-standard aluminum specimens with notches that induce stress concentrations. …”
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Stability Analysis and Construction Parameter Optimization of Tunnels in the Fractured Zone of Faults by Banma Huang, Haibo Chen, Chenglong Duan, Wenhu Li

“…Then, this paper discusses the minimum smooth blasting parameters under these conditions. Finally, the actual blasting effect of tunnel construction is tested and the optimization algorithm model of tunnel fault drilling and blasting parameters is established. …”
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Efficient Seismic Denoising Transformer with Gradient Prediction and Parameter-Free Attention by GAO Lei, QIAO Haowei, LIANG Dongsheng, MIN Fan, YANG Mei

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Parameter Optimization Method for Centrifugal Feed Disc Discharging Based on Numerical Simulation and Response Surface by Kai Lu, Cheng Yin, Jing Qian, Zhiyan Sun, Liqiang Wang

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Multi-Objective Parameter Optimization of Electro-Hydraulic Energy-Regenerative Suspension Systems for Urban Buses by Zhilin Jin, Xinyu Li, Shilong Cao

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Early-Fault Feature Extraction for Rolling Bearings Based on Parameter-Optimized Variation Mode Decomposition by Junjie Ni, Gangjin Huang, Jing Yang, Nan Wang, Junheng Fu

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