Physics-Informed Deep Learning for Three Dimensional Black Holes by Emad Yaraie, Hossein Ghaffarnejad, Mohammad Farsam

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Analysis of service oriented network slicing at infrastructure layer in LoRa for IoT applications by Shilpi Verma, Sindhu Hak Gupta, Vipin Balyan

“…Additionally, different machine learning models have been employed to evaluate the effectiveness of the proposed slice classification strategy, confirming the reliability of communication-parameter-based slice allocation.…”
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Online identification of stability region for large-scale wind farms, Part II: Construction of stability region boundary by Jia Luo, Peng Wang, Haoran Zhao, Yonghan Liu, Vladimir Terzija

“…Secondly, based on stability margin data at diverse operating points, a k-nearest neighbor support vector machine is proposed to quantify the multi-parameter stability region. …”
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Simulation Investigation of an Active Vibration Protection System of an Operator of a Hand-Held Percussive Tool by Zygmunt Basista

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Algorithm for designing domestic wastewater treatment process flow based on BP-SVM by ZHANG Jianmin, CHEN Minjie

“…Multiple BP-SVMs were set up to solve the multi-classification problem. Parameter optimization of BP-SVM parameters was performed using genetic algorithm (GA). …”
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A Control Strategy of Grid-connected Photovoltaic-storage Inverter Based on Improved Virtual Synchronous Generator by HUANG Xuezheng, QIAN Junxia, XU Hai, ZHAO Shengjie, MEI Jianchun

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Water Level Classification for Detect Flood Disaster Status Using KNN and SVM by Jiwa Akbar, Muchtar Ali Setyo Yudono

“…The SVM algorithm used parameter C ranging from 1 to 10 in the scenarios, and the RBF kernel achieved 100% accuracy. …”
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Ulcer detection in Wireless Capsule Endoscopy images using deep CNN by Vani V, K.V. Mahendra Prashanth

“…The test results were subjected to hyper-parameter optimization for various tweaking parameters such as epochs, pooling schemes, learning rate, number of layers, optimizer, activation functions and drop out scheme. …”
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Research on the Prediction of Pipelines Corrosion Rate Based on GA-LSSVM by CHEN Yong-hong, SU Yong-sheng, HU Ping

“…Corrosion rate is an important characteristic parameter to reflect the corrosion dynamics process of pipeline.In order to accurately evaluate the long-term operation reliability and remaining life of pipeline, the prediction of corrosion rate is particularly important.Least squares support vector machine(LSSVM)is a method based on machine learning, which is often used in classification and prediction research.Since penalty parameters γ and kernel parameters σ2 are two important parameters of LSSVM, the value of these two parameters can only be obtained by experience in calculation, causing a great impact on the calculation results.In this paper, the genetic algorithm(GA)was used to optimize the parameters, the GA-LSSVM prediction model was built and the model was applied to the prediction of pipeline corrosion rate.Compared with the results of other prediction models, the results showed that the accuracy of GA-LSSVM model and prediction results were relatively higher, which could realize the prediction of pipeline corrosion rate.…”
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Advancing Geotechnical Evaluation of Wellbores: A Robust and Precise Model for Predicting Uniaxial Compressive Strength (UCS) of Rocks in Oil and Gas Wells by Mohammadali Ahmadi

“…This study examines the efficacy of various machine learning models for predicting the uniaxial compressive strength (UCS) of rocks in oil and gas wells, which are essential for ensuring wellbore stability and optimizing drilling operations. …”
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Electrical Fault Diagnosis Method for Tunneling Equipment Based on Fault Simulation and Optimized Particle Swarm Algorithm by Huan Zou, Xueping Zhang, Xin Wang, Yu Li

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SVMWPE-HLLE Based Fault Diagnosis Approach for Rolling Bearing by Chunhui Li, Youfu Tang, Guolin Xu

“…By analyzing WGN signal and 1/f noise signal, the parameter selections for SVMWPE were studied, and the stability and superiority were investigated by comparing SVMWPE with MPE, MWPE, and SVMPE. …”
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Recent advances in the inverse design of silicon photonic devices and related platforms using deep generative models by Sun Jae Baek, Minhyeok Lee

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A Unified Surrogate Framework for Data-Driven Reliability Analysis of Mechanical Systems from Low to Multi-DOF by Lun Shao, Alexandre Saidi, Abdel-Malek Zine, Mohamed Ichchou

“…The methodology integrates four main components: (i) probabilistic uncertainty modeling for mass, damping, and stiffness, (ii) Latin Hypercube Sampling (LHS) to efficiently explore parameter variations, (iii) Monte Carlo simulation (MCS) for estimating failure probabilities under stochastic excitations, and (iv) machine learning models, including Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Neural Networks (NNs), to predict structural responses and failure probabilities. …”
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Prediction of COVID-19 Effect on Patients during Six Month After Recovery, by Using AI Algorithm by hanan aldabagh, Ghaydaa Al-Talib

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Dental bur detection system based on asymmetric double convolution and adaptive feature fusion by HongLing Hou, Ao Yang, Xiangyao Li, Kangkai Zhu, Yandi Zhao, Zhiqiang Wu

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Hydrological Impact of Remotely Sensed Interannual Vegetation Variability in the Upper Colorado River Basin by Qianqiu Longyang, Ruijie Zeng

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Data and Knowledge Dual-Driven Creep Life Prediction for Austenitic Heat-Resistance Steel by Xiaochang Xie, Mutong Liu, Ping Yang, Zenan Yang, Chengbo Pan, Chenchong Wang, Xiaolu Wei

“…In this study, we collected 216 creep data of austenitic heat-resistant steel, selected a variety of different machine learning algorithms to establish creep life prediction models, calculated and introduced a large amount of physical metallurgy knowledge highly related to creep based on Thermo-Calc, and converted the creep life into the form of the Larson–Miller parameter to optimize the data distribution, which effectively improved the prediction accuracy and interpretability of the model. …”
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Intelligent System Using Data to Support Decision-Making by Viera Anderková, František Babič, Zuzana Paraličová, Daniela Javorská

“…Interest in explainable machine learning has grown, particularly in healthcare, where transparency and trust are essential. …”
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Nonlinear compressive reduced basis approximation for PDE’s by Cohen, Albert, Farhat, Charbel, Maday, Yvon, Somacal, Agustin

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