A Multi-Category Defect Detection Model for Rail Fastener Based on Optimized YOLOv8n by Mei Chen, Maolin Zhang, Jun Peng, Jiabin Huang, Haitao Li

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Complex Environmental Geomagnetic Matching-Assisted Navigation Algorithm Based on Improved Extreme Learning Machine by Jian Huang, Zhe Hu, Wenjun Yi

“…To overcome this challenge, this paper proposes an NGO-ELM geomagnetic matching-assisted navigation algorithm, in which the Northern Goshawk Optimization (NGO) algorithm is used to optimize the initial weights and biases of the Extreme Learning Machine (ELM). …”
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Simulation-based deep reinforcement learning for multi-objective identical parallel machine scheduling problem by Sohyun Nam, Young-in Cho, Jong Hun Woo

“…This study proposes a novel Markov decision process model for the multi-objective scheduling problems for the welding process, incorporating setup requirements and due date-related constraints into the state representation, action modelling, and reward design. …”
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Ensemble Learning-Based Wine Quality Prediction Using Optimized Feature Selection and XGBoost by Sonam Tyagi, Ishwari Singh Rajput, Bhawnesh Kumar, Harendra Singh Negi

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Evaluating the strength properties of high-performance concrete in the form of ensemble and hybrid models using deep learning techniques by Zhe Wang, Tao Sun, Yan Sun, Na Liu

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Machine learning algorithms for manufacturing quality assurance: A systematic review of performance metrics and applications by Ashfakul Karim Kausik, Adib Bin Rashid, Ramisha Fariha Baki, Md Mifthahul Jannat Maktum

“…Adopting Machine Learning (ML) in manufacturing quality assurance (QA) has accelerated with Industry 4.0, enabling automated defect detection, predictive maintenance, and real-time process optimization. …”
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An efficient machine learning-enhanced DTCO framework for low-power and high-performance circuit design by Mingyang Liu, Zhengguang Tang, Hailong You, Cong Li, Guangxin Guo, Zeyuan Wang, Linying Zhang, Xingming Liu, Yu Wang, Yong Dai, Geng Bai, Xiaoling Lin

“…To assist designers in establishing a bridge between device parameters and circuit metrics efficiently, and provide guidance for parameter optimization in the early stages of circuit design. In this paper, we propose an efficient machine learning (ML)-enhanced DTCO framework. …”
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An Analytical Cost Function Design and Implementation for Predictive Control of Induction Machine Drives by Wei Wei, Liming Yan, Shun Tian, Xisheng Xu, Keke Sun

“…In finite control set-model predictive torque control (FCS-MPTC) of induction machine (IM), the optimal design of weighting factors for the cost function has always been a research difficulty in community of scholars. …”
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Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method by Tao Huang, Yuanyuan Li, Simin Wang, Shijie Qiao, Xiujuan Zheng, Wenhui Xiong, Menghan Yang, Xirui Huang, Bizhen Gao

“…However, there is a lack of machine-learning (ML)-based predictive models to assess individual genetic susceptibility to MetS. …”
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Early thrombus detection in ECMO with optimized impedance measurements: A simulative study by Slapal Filip, Silva Diogo F., Leonhardt Steffen, Walter Marian

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Predicting postoperative neurological outcomes of degenerative cervical myelopathy based on machine learning by Shuai Zhou, Shuai Zhou, Shuai Zhou, Shuai Zhou, Zexiang Liu, Zexiang Liu, Zexiang Liu, Haoge Huang, Haoge Huang, Haoge Huang, Hanxu Xi, Xiao Fan, Xiao Fan, Xiao Fan, Yanbin Zhao, Yanbin Zhao, Yanbin Zhao, Xin Chen, Xin Chen, Xin Chen, Yinze Diao, Yinze Diao, Yinze Diao, Yu Sun, Yu Sun, Yu Sun, Hong Ji, Feifei Zhou, Feifei Zhou, Feifei Zhou

“…After training and optimizing multiple ML algorithms, we generated a model with the highest area under the receiver operating characteristic curve (AUROC) to predict short-term outcomes following DCM surgery. …”
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Global miniaturization of broadband antennas by prescreening and machine learning by Slawomir Koziel, Anna Pietrenko-Dabrowska, Ubaid Ullah

“…This study introduces an innovative machine learning procedure for cost-effective global optimization-based miniaturization of antennas. …”
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Daily Runoff Prediction Model Based on Multivariate Variational Mode Decomposition and Correlation Reconstruction by DING Jie, TU Peng-fei, FENG Yu, ZENG Huai-en

“…Finally, the integrated prediction combining fluctuation and random terms under condition 5 yielded R2 of 0.87 and 0.93 for the overall prediction at Ankang and Baihe stations, respectively, demonstrating excellent model performance. [Conclusions](1) The MVMD decomposition method can control the number of decomposition layers, ensuring complete signal feature extraction without overfitting while improving processing speed.(2) Pearson correlation coefficient method enhances prediction accuracy through decomposed data classification.(3) The MEA-BP can improve signal-to-noise ratio, adapt to complex environments, enhance learning efficiency and generalization ability, and reduce computational complexity.(4) The GWO-ELM algorithm integrates grey wolf optimizer with extreme learning machine, providing a fast and adaptive solution for time-series prediction with reduced overfitting and improved efficiency.(5) The overall combined model can efficiently and stably process large amount of data while ensuring high accuracy.…”
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Development of digital twin of CNC unit based on machine learning methods by Yu. G. Kabaldin, D. A. Shatagin, M. S. Anosov, A. M. Kuzmishina

“…A neural network model of dynamic stability of the cutting process is proposed, which enables to optimize the machining process at the stage of work preparation. …”
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Mapping and interpretability of aftershock hazards using hybrid machine learning algorithms by Bo Liu, Haijia Wen, Mingrui Di, Junhao Huang, Mingyong Liao, Jingyuan Yu, Yutao Xiang

“…This study addresses gaps in aftershock prediction research by proposing an interpretable hybrid machine learning model that leverages multi-source data. …”
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Optimal design of hollow conductor for high‐speed synchronous motor exploiting adaptive‐sampling radial basis function algorithm by Ruiye Li, Peng Cheng, Hai Lan

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High-temperature protection, structure optimization, and damage detection for missile-borne electronic devices by Bixiao Li, Ruichan Lv

“…In this study, the high-temperature performance of hypersonic missile radomes was investigated through a combination of numerical simulations, experiments, and machine learning-based damage detection. Two- and three-dimensional steady-state and transient heat conduction models were developed in MATLAB and COMSOL to examine the effects of different materials (ceramic, air and copper), filler configurations, and geometric shapes (cylindrical vs. conical) on radome insulation. …”
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Advancement in public health through machine learning: a narrative review of opportunities and ethical considerations by Sumit Singh Dhanda, Deepak Panwar, Chia-Chen Lin, Tarun Kumar Sharma, Deependra Rastogi, Shantanu Bindewari, Anand Singh, Yung-Hui Li, Neha Agarwal, Saurabh Agarwal

“…Abstract This narrative review presents a comprehensive and state-of-the-art synthesis of how machine learning (ML) is transforming public health through enhanced prediction, personalized treatment, real-time surveillance, and intelligent resource optimization. …”
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The impact of cultural factors on digital marketing strategies with Machine learning and honey bee Algorithm (HBA) by Muhammad Khan, Masood Ahmad, Rakhmonov Dilshodjon Alidjonovich, Kalonov Mukhiddin Bakhritdinovich, Kurbanbekova Mohichehra Turobjonovna, Imomov Jamshidxon Odilovich

“…Experimental results demonstrate that machine learning models effectively capture cultural preferences, and HBA significantly enhances marketing effectiveness, leading to a 20% increase in engagement and a 15% improvement in click-through rates (CTR). …”
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Rapid and Low-Cost Detection of Thyroid Dysfunction Using Raman Spectroscopy and an Improved Support Vector Machine by Xiangxiang Zheng, Guodong Lv, Guoli Du, Zhengang Zhai, Jiaqing Mo, Xiaoyi Lv

“…Principal component analysis (PCA) was used for feature extraction and reduced the dimension of high-dimension spectral data; then, SVM was employed to establish an effective discriminant model. To improve the efficiency and accuracy of the SVM discriminant model, we proposed artificial fish coupled with uniform design (AFUD) algorithm to optimize the SVM parameters. …”
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