Estimation of Stator Resistance and Rotor Flux Linkage in SPMSM Using CLPSO with Opposition-Based-Learning Strategy by Jian He, Zhao-Hua Liu

“…Electromagnetic parameters are important for controller design and condition monitoring of permanent magnet synchronous machine (PMSM) system. …”
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Rapid learning with phase-change memory-based in-memory computing through learning-to-learn by Thomas Ortner, Horst Petschenig, Athanasios Vasilopoulos, Roland Renner, Špela Brglez, Thomas Limbacher, Enrique Piñero, Alejandro Linares-Barranco, Angeliki Pantazi, Robert Legenstein

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Distributed denial of service (DDoS) classification based on random forest model with backward elimination algorithm and grid search algorithm by Mohamed S. Sawah, Hela Elmannai, Alaa A. El-Bary, Kh. Lotfy, Osama E. Sheta

“…Abstract Distributed Denial of Service (DDoS) attacks pose significant threats to network security, disrupting critical services by overwhelming targeted systems with malicious traffic. In this study, a machine learning-based approach is proposed to classify DDoS attacks using multiple classification models, including Random Forest (RF), Naïve Bayes (NB), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM). …”
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Discussing the Construction of a Budget Management System Combining Multimedia Technology and Financial Risk Management by Xiaoyi Jiang

“…This paper mainly studies and innovates the data mining process and the support vector machine model and designs the data preprocessing method in the data mining process to perform feature selection and optimize the parameters of the support vector machine model. …”
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Thermal Modeling and Analysis of a HPMSM Coupling With Magnetic Bearings by Liu Bin, Yu Zhongjun, Fu Jia

“…On this basis, the lumped parameter thermal network (LPTN) model of the HPMSM set under the coupling state of the machine and magnetic bearings in totally enclosed environment is established. …”
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Prediction and optimization of acoustic absorption performance of quasi-Helmholtz acoustic metamaterials based on LightGBM algorithm by Jianxin Xu, Weikang Mao, Xiaoqian Yu, Bingfei Liu

“…The results of this study show that the acoustic performance of quasi-Helmholtz acoustic metamaterials can be predicted and optimized using machine learning methods. The study in this paper combines the method of machine learning with acoustic problems to provide a fast method for predicting the absorption performance of acoustic metamaterials.…”
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Implementasi Metode Longest Common Subsequences untuk Perbaikan Kata pada Kasus Analisis Sentimen Opini Pembelajaran Daring di Media Sosial Twitter by Alfisyar Jefry Pranata, Yuita Arum Sari, Edy Santoso

“…Dengan menggunakan metode Longest Common Subsequences untuk perbaikan kata dan metode Support Vector Machine untuk klasifikasi dengan nilai parameter terbaik yaitu learning rate (γ) = 0,0001, lambda (λ) = 0,1, complexity (C) = 0,001, epsilon (ϵ) = 0,0001 dan iterasi maksimum = 50 dapat menghasilkan nilai rata-rata hasil evaluasi yaitu precision = 0,5653, recall = 0,948, f-measure = 0,7047 dan accuracy = 0,598. …”
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An Interpretable Model for Salinity Inversion Assessment of the South Bank of the Yellow River Based on Optuna Hyperparameter Optimization and XGBoost by Xia Liu, Yu Hu, Xiang Li, Ruiqi Du, Youzhen Xiang, Fucang Zhang

“…However, machine learning model training requires many samples and hyper-parameter optimization and lacks solvability. …”
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A New Varying-Factor Finite-Time Recurrent Neural Network to Solve the Time-Varying Sylvester Equation Online by Haoming Tan, Junyun Wu, Hongjie Guan, Zhijun Zhang, Ling Tao, Qingmin Zhao, Chunquan Li

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Application of Fully Connected Neural Network-Based PyTorch in Concrete Compressive Strength Prediction by Xuwei Dong, Yang Liu, Jinpeng Dai

“…Compressive strength of concrete is an important parameter in the design of concrete structures and the prediction of their durability. …”
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Comparison of Fatigue Property Estimation Methods with Physical Test Data by Sebastian Raczek, Adam Niesłony, Krzysztof Kluger, Tomasz Łukasik

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Free-form型机床切齿优化（二）——混合进化遗传算法 by 张艳红, 郭九生, 王小椿

“…In view of the situation that the model of optimal synthesis for Free-form style machine tool setting parameters is a multidimensional, nonlinear and high order one, a hybrid evolutionary genetic algorithms is proposed in this paper. …”
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Free-form型机床的切齿优化（一）——优化模型的建立 by 张艳红, 郭九生, 王小椿

“…In this paper, Free-form style machine tool setting parameters is analyzed, and a mathematical model of optimal synthesis is established. …”
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Identifikasi Varietas Kayu Menggunakan Radially Average Power Spectrum Value Dan Orde Satu by Arya Mandiri, Soffiana Agustin

“…Hasil dari parameter radially average power spectrum value (RAPSV) dan orde satu, digunakan sebagai data untuk klasifikasi varietas kayu menggunakan metode support vektor machine (SVM). …”
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An Adversarial Attack via Penalty Method by Jiyuan Sun, Haibo Yu, Jianjun Zhao

“…Deep learning systems have achieved significant success across various machine learning tasks. However, they are highly vulnerable to attacks. …”
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Cascaded Sliding-Mode Observer for High-Order Systems with Lower-Triangular Structure by Tianhao Wen, Yuqing Lin, Yang Liu, Q. H. Wu, Yinsheng Su

“…During this sliding phase, the estimation errors rapidly converge to negligibly small values, determined by a parameter of the observer. Compared with standard high-gain observers and classical high-gain parameter embedded sliding-mode observers, the proposed observer achieves similar estimation error convergence speed with smaller gain coefficients. …”
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A Differential Evolutionary-Based XGBoost for Solving Classification of Physical Fitness Test Data of College Students by Baoyue Liang, Weifu Qin, Zuowen Liao

“…To accurately and timely identify the physical health status of college students, a hybrid model of DE-XGBoost is proposed in this study: a discrete coding strategy is designed to solve the XGBoost hyperparameter optimization problem, and differential evolution (DE) is used to achieve global parameter optimization. Based on 20,452 physical test records of a university in 2022, the empirical comparison shows that the accuracy rate, recall rate, and F1 value of the model are improved by 3.5–7.9% compared with support vector machine (SVM), gradient boosting machine (GBM), and multi-layer perceptron (MLP), showing significant performance advantages. …”
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Research on Credit Default Prediction Model Based on TabNet-Stacking by Shijie Wang, Xueyong Zhang

“…XGBoost (eXtreme Gradient Boosting), LightGBM (Light Gradient Boosting Machine), CatBoost (Category Boosting), KNN (K-NearestNeighbor), and SVM (Support Vector Machine) are selected as the first-layer base learners, and XGBoost is used as the second-layer meta-learner. …”
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AdditiveLLM: Large language models predict defects in metals additive manufacturing by Peter Pak, Amir Barati Farimani

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Adaptive mechanism-based grey wolf optimizer for feature selection in high-dimensional classification. by Genliang Li, Yaxin Cui, Jingyu Su

“…Feature Selection (FS) is a crucial component of machine learning and data mining. Its goal is to eliminate redundant and irrelevant features from a datasets, thereby enhancing the classifier's performance. …”
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