The Use of Machine Learning to Assess the Impact of the Ozonation Process on Selected Mechanical Properties of Japanese Quince Fruits by Józef Gorzelany, Piotr Kuźniar, Miłosz Zardzewiały, Katarzyna Pentoś, Tadeusz Murawski, Wiesław Wojciechowski, Jarosław Kurek

“…In order to establish the relationship between storage conditions for various varieties and selected mechanical parameters, a novel machine learning method was employed. …”
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Machine-Learning-Assisted Identification of Steam Channeling after Cyclic Steam Stimulation in Heavy-Oil Reservoirs by Yu Li, Huiqing Liu, Peng Jiao, Qing Wang, Dong Liu, Liangyu Ma, Zhipeng Wang, Hao Peng

“…To solve the issues of steam breakthrough, it is essentially important and necessary to recognize steam channeling. In this work, a machine-learning-assisted identification model, based on a random-forest ensemble algorithm, is developed to predict the occurrence of steam channeling during steam huff-and-puff processes. …”
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A Novel Multistep Wavelet Convolutional Transfer Diagnostic Framework for Cross-Machine Bearing Fault Diagnosis by Lujia Zhao, Yuling He, Hai Zheng, Derui Dai

“…Transfer learning has emerged as a potent technique for diagnosing bearing faults in environments with fluctuating operational parameters. Nevertheless, the majority of current transfer-learning-based fault diagnosis approaches focus primarily on adapting to varying conditions within the same machine. …”
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Machine Learning-Based Evaluation of Solar Photovoltaic Panel Exergy and Efficiency Under Real Climate Conditions by Gökhan Şahin, Wilfried G. J. H. M. van Sark

“…The usefulness of the linear regression model in machine learning was validated by performance measures such as R², RMSE, MAE, and MAPE. …”
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Investigating the Effects of Ground-Transmitted Vibrations from Vehicles on Buildings and Their Occupants, with an Idea for Applying Machine Learning by Marta Mikielewicz, Anna Jakubczyk-Gałczyńska, Robert Jankowski

“…The impacts of these vibrations are complex, affected by a number of parameters, like amplitude, frequency, and duration, as well as by the properties of the soil beneath. …”
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Machining effects and multi-objective optimization in Inconel 718 turning with unitary and hybrid nanofluids under MQL by Paresh Kulkarni, Satish Chinchanikar

“…Designing tooling and cooling systems to prevent cutting tool damage is crucial while machining difficult-to-cut nickel alloys. This study investigates the machining effects during turning Inconel 718 using unitary aluminum oxide (Al2O3) and hybrid aluminum oxide+multi-walled carbon nanotube type (Al2O3+MWCNT) nanofluids under minimum quantity lubrication (NFMQL) through mathematical modeling and multi-objective optimization. …”
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Open-Loop Control System for High Precision Extrusion-Based Bioprinting Through Machine Learning Modeling by Javier Arduengo, Nicolas Hascoet, Francisco Chinesta, Jean-Yves Hascoet

“…Firstly, a method employing Logistic Regression is used to select the tests that will serve to train and test the following model. Then, using a Machine Learning Algorithm, a model that allows the optimization of printing parameters and enables process control through an open-loop system was developed. …”
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Quantifying Measurement Uncertainty in Photovoltaic Module Performance at Standard Test Conditions: A Machine-Based Approach by Amina Chahtou, El Amin Kouadri Boudjelthia, Nasreddine Belhaouas, Fateh Mehareb, Zakarya Latreche

“…This paper presents an in-depth analysis of measurement uncertainty in the output parameters of photovoltaic (PV) devices, focusing specifically on measurements conducted under standard test conditions (STC). …”
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A Hybrid Automata-Driven Machine Learning Framework for Real-Time Energy Optimization in Smart Buildings by Rikame Rajashri, Ranjan Mritunjay Kr., Jadhav Monali, Wankhede Shrushti, Bankar Sakshi, Bachhav Pranjal

“…This study presents a hybrid approach that combines Finite Automata (FA) with sophisticated Machine Learning (ML) algorithms and Artificial Intelligence (AI) to minimize energy consumption in smart buildings. …”
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Comparing the Performance of Regression and Machine Learning Models in Predicting the Usable Area of Houses with Multi-Pitched Roofs by Leszek Dawid, Anna Marta Barańska, Paweł Baran

“…The usable floor area is one of the key parameters when appraising residential property. In Poland, valuers have to base their analysis on data from the Real Estate Price Register (RCN) in order to value a property. …”
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Geometric errors identification of the machine tool rotary axes considering R-test theoretical model constraints by Hongxu Chen, Xiaobing Hu, Yan Liu, Feng Tan, Qihao Liao

“…Abstract This study introduces a machine tool rotary axis error identification method that considers the constraints of the R-test theoretical model. …”
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Fluid-Structure Interaction Analysis of Carotid Artery Blood Flow with Machine Learning Algorithm and OpenFOAM by Banu Körbahti, Murad Kucur

“…Pulsatile velocity cycle is trained for 144 patients with different hemodynamic parameters, by machine learning algorithm using blood flow velocity measured from 337 points of the carotid artery. …”
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Spatiotemporal evaluation of irrigation groundwater quality in Hungarian agricultural sites using hydrochemical and machine learning approaches by Musaab A. A. Mohammed, Norbert P. Szabó, Viktória Mikita, Péter Szűcs

“…The research integrates hydrochemical assessment with machine learning to enhance the spatiotemporal evaluation of irrigation water quality. …”
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Design and Experiment of an Inter-Row Weeding Machine Applied in Soybean and Corn Strip Compound Planting (SCSCP) by Zihao Tang, Xiaobo Xi, Baofeng Zhang, Yangjie Shi, Yajuan Wang, Ruihong Zhang

“…This paper details the machine’s principles by analyzing the geometric relationship and mechanical model between the corresponding profiling quantities, which determine the relevant parameters for adaptive contouring to ensure stable operation on undulating ground. …”
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EXPERIMENTAL CONFIRMATION OF EQUATIONS’ ADEQUACY OF GEOMETRIC CONNECTIONS IN THE MUNICIPAL MACHINE WITH THE BRUSH WORKING BODY PARALLELOGRAPHIC SUSPENSION by S. D. Ignatov, S. I. Tsekhosh

“…In addition, the kinematic diagram of the municipal machine with the brush working body suspension is drawn up. …”
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Mathematical model of complex quality assessment of reverse-engineering of a loading and haul dump machine part by Marat R.T., Maslyakov N.S., Mametyev L.E.

“…These parameters are the most important due to their influence on the operatin time of the part, the quality of the remanufactured part of the assembly or aggregate as a whole and the suitability for measurement, especially in the conditions of repair production of mining machines. …”
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Utilization of Machine-Learning-Based model Hybridized with Meta-Heuristic Frameworks for estimation of Unconfined Compressive Strength by She Wang, Qi Zhang

“…The current study considers the RBF-based machine learning model, whose parameters have been optimized using two enhanced metaheuristic frameworks: Improved Arithmetic Optimization Algorithm (IAOA) and Flying Foxes Optimization (FFO). …”
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Energy Demand and CO2 Emission Forecast Model for Turkey with Deep Learning and Machine Learning Algorithms by Emre Bolat, Yagmur Arikan Yildiz

“…Considering Turkey’s rapidly increasing energy demand, various economic and social parameters have been used for the years 1990-2024. Both machine learning and deep learning methods have been applied, and artificial neural network (ANN), convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), and linear regression (LR) algorithms have been used for two models. …”
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Efficacy of machine learning in simulating precipitation and its extremes over the capital cities in North Indian states by Aayushi Tandon, Amit Awasthi, Kanhu Charan Pattnayak

“…This study investigates the predictability of precipitation patterns and extremes across North Indian states from 1984 to 2023 using NASA’s Prediction of Worldwide Energy Resources (POWER) datasets and machine learning (ML) models. The current ML model builds on the relationship between rainfall and key climatic parameters such as dew point temperature and relative humidity, showing a strong positive correlation (CC = 0.4) significant at the 0.05 level. …”
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Submarine Cable Vibration Signal Recognition Based on Sparrow Search Algorithm Optimized Support Vector Machine by GUO Jiaxing, QIAN Junxia, LIU Rui, MA Hongjuan

“…Then, the SSA was used to optimize the penalty factor and kernel function parameters of the SVM, thereby improving the recognition accuracy. …”
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