Design and Test of a Bionic Auxiliary Soil-Crushing Device for Strip-Tillage Machines by Kui Zhang, Yong-Ying Zhang, Xinliang Zhao, Yun Zhao, Xin Feng, Qi Wang, Jinwu Wang

“…Suitable strip-tillage effectively enhances crop productivity and soil quality in Northeast China, yet conventional strip-tillage machines suffer from inadequate soil fragmentation. …”
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Machine-Learning-Driven Analysis of Wear Loss and Frictional Behavior in Magnesium Hybrid Composites by Barun Haldar, Hillol Joardar, Arpan Kumar Mondal, Nashmi H. Alrasheedi, Rashid Khan, Murugesan P. Papathi

“…Five distinct machine learning algorithms, Artificial Neural Network (ANN), Random Forest (RF), K-Nearest Neighbor (KNN), Gradient Boosting Machine (GBM), and Support Vector Machine (SVM), were employed to analyze experimental tribological data for predicting wear loss and coefficients of friction (COFs). …”
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Homogeneity Equation for the Effective Elastic Properties of Heterogenous Rocks Developed From Machine Learning by E. Maalouf, H. Kareem, H. Chebli

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Machine learning based risk analysis and predictive modeling of structure fire related casualties by Andres Schmidt, Eric Gemmil, Russ Hoskins

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Evaluating soiling effects to optimize solar photovoltaic performance using machine learning algorithms by Muhammad Faizan Tahir, Anthony Tzes, Tarek H.M. El-Fouly, Mohamed Shawky El Moursi, Nauman Ali Larik

“…Additionally, machine learning algorithms such as artificial neural networks, support vector machines, regression trees, ensemble of regression trees, Gaussian process regression, efficient linear regression, and kernel methods are employed to predict power reduction due to soiling and soiling losses across various soiling percentages. …”
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Design and Experiment of the <i>Codonopsis pilosula</i> Outcrop Film-Laying and Transplanting Machine by Jiajia Bai, Wei Sun, Ming Zhao, Luhai Zhang, Juanling Wang, Petru Aurelian Simionescu

“…A <i>Codonopsis pilosula</i> film-laying and outcrop transplantation machine is developed to solve problems, such as unstable quality of transplanted seedlings, high intensity of manual work, and low efficiency of work in the seedling transplantation of <i>Codonopsis pilosula</i>. …”
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Assessing HMM and SVM for Condition-Based Monitoring and Fault Detection in HEV Electrical Machines by Riham Ginzarly, Nazih Moubayed, Ghaleb Hoblos, Hassan Kanj, Mouhammad Alakkoumi, Alaa Mawas

“…The permanent magnet machine (PMM) is the most used electric machine in the electric propulsion system of HEVs, as well as the most expensive. …”
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Application of Machine Learning Techniques for Predicting Students’ Acoustic Evaluation in a University Library by Dadi Zhang, Kwok-Wai Mui, Massimiliano Masullo, Ling-Tim Wong

“…Using the collected personal information, room-related parameters, and sound pressure levels as input, six machine learning models (Support Vector Machine–Radial Basis Function (SVM (RBF)), Support Vector Machine–Sigmoid (SVM (Sigmoid)), Gradient Boosting Machine (GBM), Logistic Regression (LR), Random Forest (RF), and Naïve Bayes (NB)) were trained to predict students’ acoustic acceptance/satisfaction. …”
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Advanced evaluation of performance of machine learning models for soapstock splitting optimisation under uncertainty by Bartosz Szeląg, Krzysztof Barbusiński, Michał Stachura, Przemysław Kowal, Adam Kiczko, Eldon R. Rene

“…The objective was to evaluate and select the modeling approaches based on (i) data availability, (ii) model complexity, (iii) predictive accuracy, and (iv) sensitivity to input uncertainty. Machine learning algorithms—Extreme Gradient Boosting (XGBoost) and Support Vector Machines (SVM)—were assessed in comparison with Response Surface Methodology (RSM). …”
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Machine learning for detoxification of aflatoxin M1 by Lactococcus lactis probiotic in kashk production by Maryam Jafari, Roshanak Rafiei Nazari, Mohammad Rezaei, Mojtaba Moazzen, Nabi Shariatifar

“…Therefore, the detoxification of AFM 1 using probiotics combined with machine learning methods presents a practical, feasible, and simple method for predicting detoxification processes based on various parameters related to the probiotic application in managing aflatoxin in dairy products.…”
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An Innovative Hyperbaric Hypothermic Machine Perfusion Protects the Liver from Experimental Preservation Injury by Ferdinando A. Giannone, Davide Treré, Marco Domenicali, Ignazio Grattagliano, Alessandra Baracca, Gianluca Sgarbi, Caterina Maggioli, Pasquale Longobardi, Giancarlo Solaini, Massimo Derenzini, Mauro Bernardi, Paolo Caraceni

“…Purpose. Hypothermic machine perfusion systems seem more effective than the current static storage to prevent cold ischemic liver injury. …”
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Predictive PID Control for Automated Guided Vehicles Using Genetic Algorithm and Machine Learning by Kinza Nazir, Yong-Woon Kim, Yung-Cheol Byun

“…This study introduces a hybrid framework combining traditional Proportional-Integral-Derivative (PID) control with advanced machine learning to optimize AGV performance. A genetic algorithm (GA) was employed to generate ground truth PID parameters for diverse track configurations, ensuring superior path-tracking accuracy. …”
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Mechanical properties and machine learning analysis of concrete incorporating waste glass as coarse aggregate by Bhukya Govardhan Naik, G. Nakkeeran, Dipankar Roy, G. Uday Kiran, Kalyani Gurram, Gade Venkata Ramanjaneyulu, George Uwadiegwu Alaneme, Mutiu Shola Bakare

“…The findings correspond with suitable structural parameters, strengthening the potential of WGCA in the production of sustainable concrete. …”
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Accuracy Optimization of Robotic Machining Using Grey-Box Modeling and Simulation Planning Assistance by Minh Trinh, Michael Königs, Lukas Gründel, Marcel Beier, Oliver Petrovic, Christian Brecher

“…A simulation of the various process influences is therefore necessary to ensure stable machining during production planning in optimizing the process parameters. …”
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A Review of Machine Learning-Based Routing Protocols for Wireless Sensor Network Lifetime by Abhay R. Gaidhani, Amol D. Potgantwar

“…Machine learning is the process of acting without human involvement or reprogramming in order to learn from experiences. …”
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Epileptic Seizure Detection in EEG Signals Using Machine Learning and Deep Learning Techniques by Hepseeba Kode, Khaled Elleithy, Laiali Almazaydeh

“…This study focuses on classifying time-series data representation of EEG signals with machine learning-based classifiers by tuning parameters and deep learning-based One-Dimensional Convolutional Neural Network (1D CNN) methods. …”
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Critical Factors Governing the Frictional Coefficient in Mg Alloys—Learn From Machine Learning by Negar Bagherieh, Moslem Noori, Dongyang Li, Meisam Nouri

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Machine Learning Modeling of Disease Treatment Default: A Comparative Analysis of Classification Models by Michael Owusu-Adjei, James Ben Hayfron-Acquah, Frimpong Twum, Gaddafi Abdul-Salaam

“…The focus on contextual nonbiomedical measurements using a supervised machine learning modeling technique is aimed at creating an understanding of the reasons why treatment default occurs, including identifying important contextual parameters that contribute to treatment default. …”
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Estimating Economic Insights: A Machine Learning Method for Estimating the Shanghai Stock Exchange by Reza Seifi Majdar, Seyed Hadi Seyed Hatami

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Combining semiparametric and machine learning approaches for short-term prediction of satellite clock bias by Lihong Jin, Wanzhuo Zhao, Xiong Pan, Qingsong Ai, Mao Cai, Xiaoli Ruan

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