Prediction of lithium-ion battery SOC based on IGA-GRU and the fusion of multi-head attention mechanism by Pei Tang, Minnan Jiang, Weikai Xu, Zhengyu Ding, Mao Lv

“…Compared with the traditional parameter optimization approach, this paper uses the immune genetic algorithm to find the optimal hyperparameters of the model, which on the one hand has a wider choice of parameters, and on the other hand has been improved for the genetic algorithm is easy to fall into the local optimal solution, so as to improve the SOC estimation accuracy of the GRU model. …”
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Research on Creep Constitutive Model for High Temperature Reactor Based on Neural Network by ZHANG Fan, DAI Shoutong

“…For the secondary creep, the neural network was employed to perform reverse parameter calibration of the Norton Bailey’s power law. …”
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Trainable embedding quantum physics informed neural networks for solving nonlinear PDEs by Stefan Berger, Norbert Hosters, Matthias Möller

“…In direct comparison with classical PINNs, this approach showed an ability to achieve superior results while using the same number of parameters, highlighting their potential for more efficient optimization in high-dimensional parameter spaces, which could be transformative for future applications.…”
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Incorporating sparse labels into hidden Markov models using weighted likelihoods improves accuracy and interpretability in biologging studies. by Evan Sidrow, Nancy Heckman, Tess M McRae, Beth L Volpov, Andrew W Trites, Sarah M E Fortune, Marie Auger-Méthé

“…Including such rare labels often has a negligible influence on parameter estimates, which in turn does not meaningfully improve the accuracy of the decoded latent process. …”
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An Enhanced Deep Learning Model for Effective Crop Pest and Disease Detection by Yongqi Yuan, Jinhua Sun, Qian Zhang

“…Traditional machine learning methods struggle with plant pest and disease image recognition, particularly when dealing with small sample sizes, indistinct features, and numerous categories. …”
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Discriminative graph regularized representation learning for recognition. by Jinshan Qi, Rui Xu

“…Feature extraction has been extensively studied in the machine learning field as it plays a critical role in the success of various practical applications. …”
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The usage of a transformer based and artificial intelligence driven multidimensional feedback system in english writing instruction by Xiaofeng Zheng, Jian Zhang

“…Traditional systems, which depend on rule-based engines and shallow machine learning models, struggle to meet this demand. …”
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CBLN-YOLO: An Improved YOLO11n-Seg Network for Cotton Topping in Fields by Yufei Xie, Liping Chen

“…The positioning of the top bud by the topping machine in the cotton topping operation depends on the recognition algorithm. …”
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Space Precession Target Classification Based on Radar High-Resolution Range Profiles by Yizhe Wang, Cunqian Feng, Yongshun Zhang, Sisan He

“…Effective classification of space targets is of great significance for further micromotion parameter extraction and identification. Feature extraction is a key step during the classification process, largely influencing the final classification performance. …”
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Prediction of formation pressure in underground gas storage based on data-driven method by SUI Gulei, FU Yujiang, ZHU Hongxiang, LI Zunzhao, WANG Xiaolin

“…Formation pressure is a significant parameter for establishing the working system of injector-producer well and monitoring the operation of underground gas storage (UGS). …”
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Kolmogorov–Arnold Networks for Reduced-Order Modeling in Unsteady Aerodynamics and Aeroelasticity by Yuchen Zhang, Han Tang, Lianyi Wei, Guannan Zheng, Guowei Yang

“…Kolmogorov–Arnold Networks (KANs) are a recent development in machine learning, offering strong functional representation capabilities, enhanced interpretability, and reduced parameter complexity. …”
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A dynamic global backbone updating for communication-efficient personalised federated learning by Zhao Yang, Qingshuang Sun

“…Federated learning (FL) is an emerging distributed machine learning technique. However, when dealing with heterogeneous data, a shared global model cannot generalise all devices' local data. …”
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METHODOLOGY OF DETERMINING FORECASTING CONTROLLERS OF DISTRIBUTED GENERATION PLANTS by Yu. N. Bulatov, A. V. Kryukov, N. V. Huan

“…The paper presents a methodology of determining the parameters predictive links AEC and ASC and the results of research on the computer model railway power supply systems (RPSS) including in its membership following the DG plants: turbogenerator mini thermoelectric plant, hydro small power plant, wind power plant on the basis of the machine DC and solar power. …”
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TOPSIS vs Quality Loss Function Multi-Criteria Optimization of   Mechanical Performance in Laser Spot Welding Process by Ruba Khalil, Bassim Bachy

“…Tensile tests were conducted using a universal testing machine with a 100 kN load capacity. Parameters studied included laser power, welding velocity, laser focus diameter, and spot geometric size. …”
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Polarization optical detection and localization of subcutaneous lesions by Xinxian Zhang, Jiahao Fan, Jiawei Song, Nan Zeng, Honghui He, Valery V. Tuchin, Hui Ma

“…Further, the study extracts depth-sensitive polarization feature parameters (DSPFPs) for specific lesion types. Through experiments of tissue phantoms with various depth settings, the established machine learning regression models based on DSPFPs demonstrate their depth retrieval capabilities.…”
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A Surrogate Model for Studying Solar Energetic Particle Transport and the Seed Population by Atilim Guneş Baydin, Bala Poduval, Nathan A. Schwadron

“…In this scenario, adopting a machine learning approach to SEP modeling and prediction is desirable. …”
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Indicators to distinguish symptom accentuators from symptom producers in individuals with a diagnosed adjustment disorder: A pilot study on inconsistency subtypes using SIMS and MM... by Cristina Mazza, Graziella Orrù, Franco Burla, Merylin Monaro, Stefano Ferracuti, Marco Colasanti, Paolo Roma

“…The results indicated that the SIMS Total Score, Neurologic Impairment and Low Intelligence scales and the MMPI-2-RF Infrequent Responses (F-r) and Response Bias (RBS) scales successfully discriminated among symptom accentuators, symptom producers, and consistent participants. Machine learning analysis was used to identify the most efficient parameter for classifying these three groups, recognizing the SIMS Total Score as the best indicator.…”
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Application of optical flow method in metro train speed measurement by LYU Hongqiang, HUANG Tao, NIE Xingjia, CHEN Keyu, CAI Zhengkai, SHAN Qi

“…Subway speed is an important parameter of train control system. The traditional subway speed measurement methods have some defects. …”
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Bearing Fault Prediction Based on Mixed Domain Features and GWO-SVM by Xuan Zhou, Ruiyang Xia, Zhaodong Zhang, Sasa Duan, Mao Cheng, Chengjiang Zhou, Min Mao

“…We propose a bearing fault identification algorithm based on grey wolf optimizer (GWO) to address the common problems of high signal noise, inability of a single indicator to accurately reflect the true state of bearings, and optimization of support vector machine (SVM) prediction model parameters in bearing fault identification. …”
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Penentuan Kondisi Optimum Ukuran Partikel dan Bilangan Reynold Pada Sintesis Bioplastik Berbasis Sorgum by Yuli Darni

“…Moreover, the obtained film was also investigated for water uptake parameter. The result showed that the mechanical properties were improve by increasing mixing rate at smallest particle sizes of starch. …”
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