Q-tables formation method for automated monitoring of electromechanical converters parameters with application of linear integral criterion by N. A. Malev, O. V. Pogoditsky, A. S. Malacion

“…In the process of functioning working sets with electromechanical converters included in their composition, it is necessary to take into account the influence of endogenous and exogenous disturbances that cause deviations of the parameters of electric machines from the nominal values given by the manufacturer in the appropriate documentation. …”
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Comparison of Regression and Artificial Neural Network Models for Surface Roughness Prediction with the Cutting Parameters in CNC Turning by Muammer Nalbant, Hasan Gokkaya, İhsan Toktaş

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Justifying the Operation Parameters of a Robotic Cassette Loading Device of the Carousel Type for a Selection Seeder by Andrey S. Chulkov, Mikhail E. Chaplygin, Marsel’ M. Shaykhov

“…The development and implementation of automated and robotic machines and devices for performing works on selection and seed production of grain and other crops creates conditions for increasing productivity and reducing labor intensity of work and contributes to increasing the production volume of domestic crop seeds. …”
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Optimization of magnetic compound fluid polishing process parameters for PMMA workpieces based on grey relational analysis by Youliang WANG, Xichun GAO, Wenjuan ZHANG

“…ObjectivesMCF polishing technology has become an advanced ultra-precision machining method. To address the issue of different process parameters in achieving optimal surface quality or maximum processing efficiency in MCF polishing technology, it is necessary to accurately control the range of each process parameter and deeply understand the impact of different process parameters on MCF polishing performance.MethodsThe process parameters of the MCF polishing tool are optimized based on grey relation analysis (GRA) to meet the requirements of minimum surface roughness while improving material removal efficiency. …”
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A Machine Learning Tool for Determining the Required Sample Size for GEV Fitting in Climate Applications by R. J. Matear, P. Jyoteeshkumar Reddy

“…For ECEs like heatwaves with a negative GEV shape parameter the maximum extreme temperatures of heatwaves are bounded and fewer samples are needed to estimate the return value to given uncertainty than rainfall extremes which have positive shape parameter with unbounded extreme values. …”
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Stability Evaluation of Rock Slope in Hydraulic Engineering Based on Improved Support Vector Machine Algorithm by Fei Li, Hongyun Zhang

“…In this paper, a cuckoo search algorithm-improved support vector machine (CS-SVM) method is applied to slope stability analysis and parameter inversion. …”
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Forecasting of heat and mass transfer in Casson nanofluid flow with entropy optimization: Machine learning approach by P. Kavya, K. Loganathan, K. Chellapriya, S. Eswaramoorthi, S. Divya, Krishna Prakash Arunachalam, P. Senthilkumar

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Machine learning with hyperparameter optimization applied in facies-supported permeability modeling in carbonate oil reservoirs by Watheq J. Al-Mudhafar, Alqassim A. Hasan, Mohammed A. Abbas, David A. Wood

“…There is a need to identify a suitable parameter space, especially when the target variable range is changing. …”
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CLASSIFICATION OF STUNTING IN CHILDREN UNDER FIVE YEARS IN PADANG CITY USING SUPPORT VECTOR MACHINE by Izzati Rahmi, Mega Susanti, Hazmira Yozza, Frilianda Wulandari

“…In this study, the parameter of the SVM model that must be determined is the cost value and gamma. …”
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PRINCIPLES OF EFFICIENCY ESTIMATION TECHNIQUE OF MACHINE-PROCESS UNITS BASED ON THE FIFTH-GENERATION MOBILE UTILITIES by Edward Iosifovich Lipkovich, Vladimir Vladimirovich Shchirov

“…The problem is solved through a numerically-analytical method with the parameter synthesis of the multipurpose machine-process units. …”
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Relevance vector machine and multivariate adaptive regression spline for modelling ultimate capacity of pile foundation by Pijush Samui, Mohamed A. Shahin

“…This study examines the capability of the Relevance Vector Machine (RVM) and Multivariate Adaptive Regression Spline (MARS) for prediction of ultimate capacity of driven piles and drilled shafts. …”
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A supervised machine-learning analysis of doxorubicin-loaded electrospun nanofibers and their anticancer activity capabilities by Mohammadreza Rostami, Mohammadreza Rostami, Maliheh Gharibshahian, Maliheh Gharibshahian, Mehrnaz Mostafavi, Ali Sufali, Mahsa Golmohammadi, Mohammad Reza Barati, Reza Maleki, Nima Beheshtizadeh, Nima Beheshtizadeh

“…This study employed a supervised machine-learning analysis to extract the influencing parameters of the input from quantitative data for doxorubicin-loaded electrospun nanofibers. …”
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Evolving Machine Learning Methods for Density Estimation of Liquid Alkali Metals over the Wide Ranges by Tao Lin, Amir Seraj

“…According to this analysis, the amount of lithium can be the most effective parameter on the mixture density.…”
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DESIGN OF KIP KULIAH SELECTION SYSTEM AND RECIPIENT DETERMINATION USING SUPPORT VECTOR MACHINE (SVM) by Mozart Winston Talakua, Berny Pebo Tomasouw, Venn Yan Ishak Ilwaru

“…In this study, the Support Vector Machine (SVM) method was applied to create a system for selecting and determining KIP Kuliah recipients. …”
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Deep reinforcement learning enhanced PID control for hydraulic servo systems in injection molding machines by Xiaoxi Hao, Zengmiao Xin, Weizhuo Huang, Sicheng Wan, Guangfan Qiu, Tianlei Wang, Zhu Wang

“…Meanwhile, the DDPG algorithm is utilized to adjust the PID parameters in real time based on tracking errors and system state feedback, thereby improving the controller’s adaptability to time-varying operating conditions. …”
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Evaluation of Ground Displacements Caused by Installing Jet Grouted Columns Using Machine Learning Methods by Zhi-Feng Wang, Xing-Bin Peng, Yong Liu, Wen-Chieh Cheng, Ya-Qiong Wang, Chao-Jun Wu

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STRESS, EQUIVALENT STRAIN AND DEFORMATION FOR MACHINING OPERATION UTILIZING EXPLICIT DYNAMIC ANALYSIS FOR VARIOUS MATERIALS by Anand Valiavalappil

“…During the machining process, it’s very difficult to get and accurate value of stress for the tool and raw (test) material, so Ansys FEA analysis method is adopted to predict the parameter like stress, strain and total deformation. …”
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Intelligent Diagnosis Method for Centrifugal Pump System Using Vibration Signal and Support Vector Machine by Hongtao Xue, Zhongxing Li, Huaqing Wang, Peng Chen

“…Secondly, the optimal classification hyperplane for distinguishing two states is obtained by SVM and NSPs, and its function is defined as synthetic symptom parameter (SSP) in order to increase the diagnosis’ sensitivity. …”
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Assessing wildfire susceptibility in Iran: Leveraging machine learning for geospatial analysis of climatic and anthropogenic factors by Ehsan Masoudian, Ali Mirzaei, Hossein Bagheri

“…Utilizing advanced remote sensing, geospatial information system (GIS) processing techniques such as cloud computing, and machine learning algorithms, this research analyzed the impact of climatic parameters, topographic features, and human-related factors on wildfire susceptibility assessment and prediction in Iran. …”
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A machine vision approach for classification and dimensional design of furniture panels using GMM-SVM by Yuan Tian, Li Zhao, Haoxin Li

“…The findings of this study reveal that the use of various feature parameters and kernel functions affects the support vector machine’s recognition accuracy. …”
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