Automatic Generation Technology of Safety Measures for Digital Substation Based on Improved Support Vector Machine by Yabing YAN, Xu CHU, Haolong XIAO, Wenwu LIANG, Hui LI, Zhenxing XIA

“…Firstly, construct a secondary circuit model and equipment model based on adjacency matrix, and further integrate the secondary security measure rule library to form a sample dataset; Secondly, support vector machines were used to classify secondary security measures, and bacterial foraging algorithms were introduced to optimize penalty factors and kernel parameters, effectively improving the training effectiveness of the automatic generation model for security measures; Finally, the effectiveness of the proposed method was verified through numerical examples.…”
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Machine learning and thermodynamic modeling for optimizing hydrogen production via algae-biomass co-gasification by Thanadol Tuntiwongwat, Takashi Yukawa, Thongchai Rohitatisha Srinophakun, Kanit Manatura, Somboon Sukpancharoen, Seyedali Mirjalili

“…Existing studies rely on either experimental trials or simulation modeling in isolation, missing opportunities for comprehensive optimization through machine learning (ML)-enhanced thermodynamic analysis. …”
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Predictive machine learning algorithm for COPD exacerbations using a digital inhaler with integrated sensors by Michael Reich, Njira Lugogo, Laurie D Snyder, Megan L Neely, Guilherme Safioti, Randall Brown, Michael DePietro, Roy Pleasants, Thomas Li, Lena Granovsky

“…Features of the model with the highest weight were baseline inhalation parameters and changes in inhalation parameters before an exacerbation compared with baseline.Conclusion We demonstrated the development of a proof-of-concept machine learning model predictive of impending COPD exacerbations using data from the integrated digital reliever inhaler. …”
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Predicting bearing capacity of gently inclined bauxite pillar based on numerical simulation and machine learning by Deyu WANG, Defu ZHU, Biaobiao YU, Chen WANG

“…A coupled FLAC3D-3DEC simulation method was employed to conduct tests on the bearing characteristics of a gently inclined pillar, based on the rock mass and joint parameters that had been calibrated by the trial-and-error method, monitor and build a machine learning gently inclined pillar strength dataset and verify its reliability. …”
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Research on the Influence of Rotating Pair Clearance on the Dynamic Performance of the Main Mechanism of Gear Shaping Machine by Yayin He, Zhihong Liang

“…In order to study the influence of the rotating pair clearance on the dynamic performance of the main mechanism of gear shaping machine， the non-linear contact impact force model of the clearance with friction and the virtual prototype model of the main mechanism of gear shaping machine with the clearance rotating pair are established. …”
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Crude Oil Price Prediction Based on a Dynamic Correcting Support Vector Regression Machine by Li Shu-rong, Ge Yu-lei

“…A new accurate method on predicting crude oil price is presented, which is based on ε-support vector regression (ε-SVR) machine with dynamic correction factor correcting forecasting errors. …”
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Modelling and optimization of a machine production process: Buffer stock supply implementation and replenishment control by Elbaraka Saad, Mokhlis Salah-eddine, Barra Adil, Fouraiji Hicham

“…Initially, one will present a modelling of the manufacturing system conceptualized as an industrial machine, as well as the replenishment policy, that will be conceived as a control loop tuning the raw material supplies quantities into the production machine following a proposed architecture. …”
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Prediction of Splitting Tensile Strength from Cylinder Compressive Strength of Concrete by Support Vector Machine by Kezhen Yan, Hongbing Xu, Guanghui Shen, Pei Liu

“…Compressive strength and splitting tensile strength are both important parameters that are utilized for characterization concrete mechanical properties. …”
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Machine-Learning Insights from the Framingham Heart Study: Enhancing Cardiovascular Risk Prediction and Monitoring by Emi Yuda, Itaru Kaneko, Daisuke Hirahara

“…This study utilized the Framingham Heart Study dataset to develop and evaluate machine-learning models for predicting mortality risk based on key cardiovascular parameters. …”
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Multifractal analysis and support vector machine for the classification of coronaviruses and SARS-CoV-2 variants by J. P. Correia, L. R. da Silva, R. Silva

“…Using a Support Vector Machine (SVM) as a classifier further enhanced the performance. …”
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Prediction of sleep disorders using Novel decision support neutrosophic based machine learning models by Nihar Ranjan Panda, Surapati Paramanik, Prasanta Kumar Raut, Ruchi Bhuyan

“…This study introduces a novel decision support system utilizing a neutrosophic machine learning prediction model to enhance the accuracy and reliability of sleep disorder diagnosis. …”
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Vibration Sensor Based Intelligent Fault Diagnosis System for Large Machine Unit in Petrochemical Industries by Qinghua Zhang, Aisong Qin, Lei Shu, Guoxi Sun, Longqiu Shao

“…Aiming at dynamic real-time vibration monitoring and vibration signal analysis for large machine unit in petrochemical industry, which cannot realize real-time, online, and fast fault diagnosis, an intelligent fault diagnosis system using artificial immune algorithm and dimensionless parameters is developed in this paper, innovated with a focus on reliability, remote monitoring, and practicality and applied to the third catalytic flue gas turbine in a petrochemical enterprise, with good effects.…”
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Temperature Sensor Fault Detection in Chiller Based on One-class Support Vector Machine Algorithm by Mao Qianjun, Liang Zhiyuan, Liu Donghua, Hu Yunpeng, Li Guannan, Fang Xi

“…The optimized model parameters were obtained by the 10-fold cross validation method. …”
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Interpretable material descriptors for critical pitting temperature in austenitic stainless steel via machine learning by Faguo Hou, Hong-Hui Wu, Dexin Zhu, Jinyong Zhang, Liudong Hou, Shuize Wang, Guilin Wu, Junheng Gao, Jing Ma, Xinping Mao

“…Utilizing interpretable machine learning techniques, a predictive model for CPT is developed and confirmed via cross-validation, demonstrating superior predictive accuracy. …”
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Adaptive Recognition and Control of Shield Tunneling Machine in Soil Layers Containing Plastic Drainage Boards by Qiuping Wang, Wanli Li, Zhikuan Xu, Yougang Sun

“…The underground plastic vertical drains (PVDs) are a significant problem for shield machines in tunneling construction. At present, the main method to deal with PVDs is to manually adjust the parameters of the shield machine. …”
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Combining machine-learned and empirical force fields with the parareal algorithm: application to the diffusion of atomistic defects by Gorynina, Olga, Legoll, Frédéric, Lelièvre, Tony, Perez, Danny

“…We also identify a large regime of numerical parameters for which statistical accuracy is reached without being a consequence of trajectorial accuracy.…”
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Structure design and field test of vibration swing type seedling lifting and soil cleaning machine by HUO Peng, LI Jianping, YANG Xin, XU Shucai, FAN Xiaowen

“…The results showed that the main root length of the seedlings was between 120 mm and 150 mm, and the injury rate of the seedlings was low, which met the requirements of agronomy. The size parameters of the machine are expected to provide a reference for the design and research of the seedling lifting and soil cleaning device.…”
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Nonlinear signal processing, spectral, and fractal based stridor auscultation: A machine learning approach by VIMAL RAJ, A RENJINI, M S SWAPNA, S SREEJYOTHI, S SANKARARAMAN

“…The study reveals that the fractal dimension and sample entropy values are higher for BR, which may be due to the musical ordered behaviour of ST. The machine learning techniques based on the features extracted from the PSD data and phase portrait parameters offer good predictability, besides the classification of BR and ST, and thereby revealing its potential in pulmonary auscultation. …”
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Improving the Routing Security in Wireless Sensor Networks using Neutrosophic Set and Machine Learning Models by Hanadi Ahmad Simmak, Ahmed A El-Douh, Tareef S Alkellezli, Rabih Sbera, Darin shafek, Ahmed Abdelhafeez

“…We use the XGBoost and Random Forest (RF) models with different parameters. Then the bipolar neutrosophic set is used to deal with uncertainty and vague information. …”
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Advanced hybrid machine learning based modeling for prediction of properties of ionic liquids at different temperatures by Saud Bawazeer

“…The main aim of this work is to study three significant machine learning models to estimate the surface tension of ionic liquids via advanced hybrid computational techniques. …”
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