A technique of building a value function at the stage of conceptual design of microprocessor systems by B. N. Chugaev, M. A. Shaposhnikova

“…In general, when n properties are taken into account for each of the compared systems, then the solution of the task of choosing “the best” system depends on choosing a function-criterion. Such function is called a value function in the article. …”
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Mapping changes of grassland to arable land using automatic machine learning of stacked ensembles and H2O library by Jiří Šandera, Přemysl Štych

“…The importance of several biological predictors has been tested (FAPAR, FCOVER, LAI, NDVI, etc.) with the help of a heatmap that is part of AutoML function of H2O library.…”
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Analysis on Full Automatic Operation Scenes and Related Functional Requirements of Urban Railway Transport by Pengzhe WEN, Daoliang XU, Shijie GAO, Xiangtao LI

“…Taking the automatic car wash scene and the large passenger flow operation scene as examples for analysis, the functional requirements of communication, signal, vehicle, integrated supervisory and control, platform edge door, car washing machine and other key equipments and interface relationship between various equipment of different operation scenes were pointed out. …”
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Inspired by nature, refined by numbers: formal–functional bioinspiration and intelligent computation in vehicle design by Pooya Sareh

“…For centuries, naturalist philosophers and scientists have studied the form and function of living organisms, striving to propose theories that describe the interplay between these two essential components of biological entities. …”
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The Effect of Hydraulic Partitioning on Prediction the Rate of Bed Load Transport in Gravel-bed Rivers using Support Vector Machine by Kiyoumars Roushangar, Mohammad Hosseini, Saman Shahnazi

“…This dataset covers a diverse set of streams and rivers with different topographic, morphologic, hydraulic and sedimentological characteristics. 75 percent of each river data were selected for training the models and remaining 25 percent of data were used to validate models. The RBF kernel function was used as core tool of support vector machine for all proposed models. …”
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Listener Acoustic Personalization Challenge - LAP24: Head-Related Transfer Function Upsampling by Aidan O. T. Hogg, Roberto Barumerli, Rapolas Daugintis, Katarina C. Poole, Fabian Brinkmann, Lorenzo Picinali, Michele Geronazzo

“…Head-related transfer functions (HRTFs) often play a crucial role in spatial hearing, immersive audio applications for virtual reality (VR) and augmented reality (AR), and help in improving hearing assistive devices. …”
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CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liability by Gregory W. Kyro, Matthew T. Martin, Eric D. Watt, Victor S. Batista

“…Abstract The link between in vitro hERG ion channel inhibition and subsequent in vivo QT interval prolongation, a critical risk factor for the development of arrythmias such as Torsade de Pointes, is so well established that in vitro hERG activity alone is often sufficient to end the development of an otherwise promising drug candidate. …”
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A New Paradigm in AC Drive Control: Data-Driven Control by Learning Through the High-Efficiency Data Set—Generalizations and Applications to a PMSM Drive Control System by Madalin Costin, Ion Bivol

“…Solving the regularization problem is based on building a knowledge database that contains the maximum efficiency points. Knowing a reasonable number of optimal efficiency operation points, an interpolation Radial Base Function (RBF) control was built. …”
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Machining-induced burr distribution along hole contours in unidirectional carbon fibre-reinforced polymer (UD-CFRP) composites by Norbert Geier, Gergely Magyar

“…The main aim of this study is to develop a model to determine the density and distribution functions of risky fibre cutting angles where machining-induced burrs are expected to be formed when hole-machining CFRPs. …”
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Application of causal forest double machine learning (DML) approach to assess tuberculosis preventive therapy’s impact on ART adherence by Abraham Keffale Mengistu, Kelemua Aschale Yeneakale, Nebebe Demis Baykemagn, Zelalem Yitayal Melese, Andualem Enyew Gedefaw

“…We found TPT initiation reduced adherence probability by 3.14 percentage points on average (ATE =  − 0.0314; 95% CI − 0.0373, − 0.0254; p < 0.001). …”
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ShinyGS—a graphical toolkit with a serial of genetic and machine learning models for genomic selection: application, benchmarking, and recommendations by Le Yu, Le Yu, Yifei Dai, Mingjia Zhu, Linjie Guo, Yan Ji, Huan Si, Lirui Cheng, Tao Zhao, Yanjun Zan

“…Here, we present ShinyGS, a stand-alone R Shiny application with a user-friendly interface that allows breeders to perform genomic selection through simple point-and-click actions. This toolkit incorporates 16 methods, including linear models from maximum likelihood and Bayesian framework (BA, BB, BC, BL, and BRR), machine learning models, and a data visualization function. …”
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Physical Property Prediction of High-Temperature Nickel and Iron–Nickel Superalloys Using Direct and Inverse Composition Machine Learning Models by Jaka Fajar Fatriansyah, Dzaky Iman Ajiputro, Agrin Febrian Pradana, Rio Sudwitama Persadanta Kaban, Andreas Federico, Muhammad Anis, Dedi Priadi, Nicolas Gascoin

“…Consequently, material modifications are crucial to ensure that gas turbine components meet their required properties. Machine learning (ML) and deep learning (DL) offer promising solutions for the design of materials with tailored tensile strength, hardness, and melting point properties. …”
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Assessing land degradation in lower gangetic west bengal using GIS-based soft computing and advanced machine learning algorithms by Gopal Chowdhury, Ashis Kumar Saha

“…This study investigates land degradation in lower Gangetic West Bengal, an eastern Indian state, which has received limited attention. Two advanced machine learning models were used: the Multilayer Perceptron Neural Network (MLP-NN) and the Radial Basis Function Neural Network (RBF-NN). …”
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Thermal Error Prediction in High-Power Grinding Motorized Spindles for Computer Numerical Control Machining Based on Data-Driven Methods by Quanhui Wu, Yafeng Li, Zhengfu Lin, Baisong Pan, Dawei Gu, Hailin Luo

“…The subsequent problem of thermal error compensation can be effectively solved by a suitable thermal error model, which is crucial for improving the machining accuracy of the actual machining process. …”
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A machine learning-based approach for constructing a 3D apparent geological model using multi-resistivity data by Jordi Mahardika Puntu, Ping-Yu Chang, Haiyina Hasbia Amania, Ding-Jiun Lin, M. Syahdan Akbar Suryantara, Jui-Pin Tsai, Hwa-Lung Yu, Liang-Cheng Chang, Jun-Ru Zeng, Lingerew Nebere Kassie

“…Abstract This study presents a comprehensive approach for constructing a 3D Apparent Geological Model (AGM) by integrating multi-resistivity data using statistical methods, supervised machine learning (SML), and Python-based modeling techniques. …”
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Prescriptive Predictors of Mindfulness Ecological Momentary Intervention for Social Anxiety Disorder: Machine Learning Analysis of Randomized Controlled Trial Data by Nur Hani Zainal, Hui Han Tan, Ryan Yee Shiun Hong, Michelle Gayle Newman

“…These ML models included random forest and support vector machines (radial basis function kernel) and 10-fold nested cross-validation that separated model training, minimal tuning in inner folds, and model testing in outer folds. …”
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Modeling of working processes in the throttle-adjustable hydraulic drive of manipulation systems with separate movement of links during operation of mobile machines by Alexander V. Lagerev, Igor A. Lagerev

“…The article discusses the functional-structural scheme and the mathematical model of the working hydrodynamic processes in the throttle-adjustable hydraulic drive of the technological cranes-manipulators. …”
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Modeling of working processes in the frequency-adjustable hydraulic drive of manipulation systems with separate movement of links during operation of mobile machines by Lagerev A.V., Lagerev I.A.

“…The article discusses the functional-structural scheme and the mathematical model of the working hydrodynamic processes in the frequency-adjustable hydraulic drive of the technological cranes-manipulators. …”
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Modeling of working processes in the throttle-adjustable hydraulic drive of manipulation systems with conjoint movement of links during operation of mobile machines by Lagerev A.V., Lagerev I.A.

“…The article proposes a functional-structural scheme and a mathematical model of working hydrodynamic processes in a throttle-adjustable hydraulic drive of handling systems (cranes-manipulators) of mobile transport-technological machines during the conjoint movement of two links. …”
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On Robustness of the Explanatory Power of Machine Learning Models: Insights From a New Explainable AI Approach Using Sensitivity Analysis by Banamali Panigrahi, Saman Razavi, Lorne E. Doig, Blanchard Cordell, Hoshin V. Gupta, Karsten Liber

“…Abstract Machine learning (ML) is increasingly considered the solution to environmental problems where limited or no physico‐chemical process understanding exists. …”
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