Water Quality in the Ma’an Archipelago Marine Special Protected Area: Remote Sensing Inversion Based on Machine Learning by Zhixin Wang, Zhenqi Zhang, Hailong Li, Hong Jiang, Lifei Zhuo, Huiwen Cai, Chao Chen, Sheng Zhao

“…In this paper, a remote sensing inversion model utilizing machine learning was developed to evaluate water quality variations in the Ma’an Archipelago Marine Special Protected Area (MMSPA) over a long-time series of Landsat images. …”
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Global soil moisture mapping at 5 km by combining GNSS reflectometry and machine learning in view of HydroGNSS by Emanuele Santi, Davide Comite, Laura Dente, Leila Guerriero, Nazzareno Pierdicca, Maria Paola Clarizia, Nicolas Floury

“…The potential of GNSS reflectometry (GNSS-R) for the monitoring of soil and vegetation parameters as soil moisture (SM) and forest aboveground biomass (AGB) has been largely investigated in recent years.In view of the ESA's HydroGNSS mission, planned to be launched in 2024, this study has explored the possibility to map SM at global scale and relatively high resolution of about 0.05° (corresponding approximately to 5 Km) using GNSS-R observations, by implementing and comparing two retrieval algorithms based on machine learning techniques, namely Artificial Neural Networks (ANN) and Random Forest Regressors (RF). …”
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Multiclass Fault Diagnosis in Power Transformers Using Dissolved Gas Analysis and Grid Search-Optimized Machine Learning by Andrew Adewunmi Adekunle, Issouf Fofana, Patrick Picher, Esperanza Mariela Rodriguez-Celis, Oscar Henry Arroyo-Fernandez, Hugo Simard, Marc-André Lavoie

“…The performance of six machine learning classifiers was systematically evaluated using training and testing data generated through four widely recognized gas ratio schemes. …”
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Antihypertensive Drug Recommendations for Reducing Arterial Stiffness in Patients With Hypertension: Machine Learning–Based Multicohort (RIGIPREV) Study by Iván Cavero-Redondo, Arturo Martinez-Rodrigo, Alicia Saz-Lara, Nerea Moreno-Herraiz, Veronica Casado-Vicente, Leticia Gomez-Sanchez, Luis Garcia-Ortiz, Manuel A Gomez-Marcos

“…Given the complexity of selecting the most appropriate antihypertensive medication for reducing PWV, machine learning (ML) techniques offer an opportunity to improve personalized treatment recommendations. …”
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Automated seafood freshness detection and preservation analysis using machine learning and paper-based pH sensors by B. Kumaravel, A. L. Amutha, T. P. Milintha Mary, Aryan Agrawal, Akshat Singh, S. Saran, Nagamaniammai Govindarajan

“…To address these limitations, this research presents an automated freshness detection system for refrigerated fish using machine learning and evaluates the effectiveness of different packaging techniques. …”
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A machine learning model for predicting lymph node positivity in ovarian cancer: development, validation, and clinical application by QingYong Guo, Jinji Wang, Ru Chen, LiPing Hu, Wenqiang You

“…Tumor size ≥5 cm, histological subtype, and chemotherapy were key predictive features, with SHAP analysis identifying tumor size as the most influential factor.ConclusionWe present the first machine learning model specifically developed for predicting lymph node positivity in OC, validated across large, diverse cohorts. …”
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Combined mechanistic and machine learning method for construction of oil reservoir permeability map consistent with well test measurements by Evgenii Kanin, Alsu Garipova, Sergei Boronin, Vladimir Vanovskiy, Albert Vainshtein, Andrey Afanasyev, Andrei Osiptsov, Evgeny Burnaev

“…The kernel regression parameters are optimized by minimizing the discrepancies between actual and predicted values of permeability at well locations, the integral permeability of the reservoir domain around each well, and skin factors. …”
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Urban growth simulation using cellular automata model and machine learning algorithms (case study: Tabriz metropolis) by Omid Ashkriz, Babak Mirbagheri, Ali Akbar Matkan, Alireza Shakiba

“…To prevent over-fitting of algorithms to training samples and to obtain optimistic results, in the process of extracting optimal parameters of machine learning algorithms, the spatial cross-validation method was used to reduce the spatial correlation between training and test data.Results and discussion: The results showed that the random forest algorithm with the area under the ROC curve of 0.9228 compared to the support vector machine and multilayer perceptron neural network algorithms with 0.8951 and 0.8726, respectively, had a better performance in estimating the change potential of non-urban to urban areas. …”
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Development and Validation of Predictive Models for Inflammatory Bowel Disease Diagnosis: A Machine Learning and Nomogram-Based Approach by Dong R, Wang Y, Yao H, Chen T, Zhou Q, Zhao B, Xu J

“…Cohorts 1 and 2 were used to create predictive models. The parameters of the machine learning model established by Cohorts 1 and 2 were merged, and nomogram models were developed using Logistic regression. …”
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Application of machine learning for the analysis of peripheral blood biomarkers in oral mucosal diseases: a cross-sectional study by Huiyu Yao, Zixin Cao, Liangfu Huang, Haojie Pan, Xiaomin Xu, Fucai Sun, Xi Ding, Wan Wu

“…Additionally, it evaluated a Random Forest machine learning model for classifying various oral mucosal diseases based on peripheral blood biomarkers. …”
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Improving the performance of machine learning algorithms for detection of individual pests and beneficial insects using feature selection techniques by Rabiu Aminu, Samantha M. Cook, David Ljungberg, Oliver Hensel, Abozar Nasirahmadi

“…Results showed improved accuracy (92.62 % Random forest, 90.16 % Support vector machine, 83.61 % K-nearest neighbours, and 81.97 % Naïve Bayes) and a reduction in the number of model parameters and memory usage (7.22 × 107 Random forest, 6.23 × 103 Support vector machine, 3.64 × 104 K-nearest neighbours and 1.88 × 102 Naïve Bayes) compared to using all features. …”
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Enhanced hunger games search algorithm that incorporates the marine predator optimization algorithm for optimal extraction of parameters in PEM fuel cells by Mohamed Issa, Mohamed Abd Elaziz, Sameh I. Selem

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Analysis of the Selected Design Changes in a Wheel Hub Motor Electromagnetic Circuit on Motor Operating Parameters While Car Driving by Piotr Dukalski, Roman Krok

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Relationship Between Dynamics of Plant Biometric Parameters and Leaf Area Index of Hop (<i>Humulus lupulus</i> L.) Plants by Václav Brant, Karel Krofta, Petr Zábranský, Pavel Hamouz, Pavel Procházka, Jiří Dreksler, Milan Kroulík, Gabriela Fritschová

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Deep learning assisted non-invasive lymph node burden evaluation and CDK4/6i administration in luminal breast cancer by Yuhan Liu, Jinlin Ye, Zecheng He, Mingyue Wang, Changjun Wang, Jie Lang, Yidong Zhou, Wei Zhang

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Extended Dissipativity Analysis for Uncertain Neutral-Type Semi-Markovian Jump Systems via Two Integral Inequalities by Zihao Gao, Huaguang Zhang

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Multi-response optimization of CuZn39Pb3 brass alloy turning by implementing Grey Wolf algorithm by Nikolaos Fountas, Angelos Koutsomichalis, John Kechagias, Nikolaos Vaxevanidis

“…Two basic ma­chin­ability para­meters are the surface roughness, closely associated with the functional and tribological performance of components, and the cutting forces acting on the tool. …”
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PREDICTION OF TBM OIL TEMPERATURE BY IMPROVED CAMEL ALGORITHM ASSISTED RANDOM FOREST MODEL (MT) by REN JianJi, ZHAO RunQiu, WANG ZhenXi, LIU YuMing, YUAN YongLiang

“…Secondly, the improved camel algorithm was used to optimize the parameters of the TBM oil temperature prediction model established by the random forest to obtain the best model. …”
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Study on the Machined Surface Quality in High Speed Turning High Strength Steel under Cryogenic Minimum Quantity Lubrication by ZHANG Huiping, JIN Jie, WANG Zunjing, LIU Erliang

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Challenges in Unifying Physically Based and Machine Learning Simulations Through Differentiable Modeling: A Land Surface Case Study by Shahryar K. Ahmad, Sujay V. Kumar, Clara Draper, Rolf H. Reichle

“…Abstract Differentiable geoscientific modeling has shown promise for leveraging machine learning (ML) to unify physically based and data‐based modeling. …”
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