Data-driven machine learning with lattice distortion and thermodynamic parameters guided strength optimization of refractory high-entropy alloys by Shujian Ding, Yifan Zhang, Siyang Lei, Xiang Weng, Wenhui Li, Wei Ren, Jian Chen, Weili Wang

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Geo-Information Modeling for Determining the Movement of Human Capital by R. V. Fattakhov, P. V. Stroev, M. M. Nizamutdinov, O. V. Pivovarova, M. I. Akhmetzyanova

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Rapid detection of antibiotic resistance in Burkholderia pseudomallei using MALDI-TOF mass spectrometry by Nut Nithimongkolchai, Yothin Hinwan, Kanwara Trisakul, Lumyai Wonglakorn, Ploenchan Chetchotisakd, Auttawit Sirichoat, Arnone Nithichanon, Sorujsiri Chareonsudjai, Pisit Chareonsudjai, Jody Phelan, Taane G. Clark, Kiatichai Faksri

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Sequential biases on subjective judgments: Evidence from face attractiveness and ringtone agreeableness judgment. by Jianrui Huang, Xianyou He, Xiaojin Ma, Yian Ren, Tingting Zhao, Xin Zeng, Han Li, Yiheng Chen

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Prediction of Total Soluble Solids in Apricot Using Adaptive Boosting Ensemble Model Combined with NIR and High-Frequency UVE-Selected Variables by Feng Gao, Yage Xing, Jialong Li, Lin Guo, Yiye Sun, Wen Shi, Leiming Yuan

“…Subsequent data processes included three steps: (1) 100 continuous runs of UVE identified characteristic wavelengths, which were classified into three levels—high-frequency (≥90 times), medium-frequency (30–90 times), and low-frequency (≤30 times) subsets; (2) the development of the base optimal partial least squares regression (PLSR) models for each wavelength subset; and (3) the execution of adaptive weight optimization through the Adaboost ensemble algorithm. …”
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Physics-Based AI-Driven Surrogate Modeling for Structural Displacement Prediction in Mechanical Systems With Limited Sensor Data by Ali Hashemi, Javad Beheshti, Mahdieh Mohammadi

“…Various regression algorithms, including decision trees and deep neural networks (DNNs), were evaluated, with DNNs achieving superior accuracy (<inline-formula> <tex-math notation="LaTeX">$\text {R}^{2}$ </tex-math></inline-formula> &#x003E; 0.996, MAE &#x003C; 4%, RMSE &#x003C; 5.5%). …”
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Geospatial modeling of snow avalanches on the Šar Mountains, Balkan Peninsula by Durlević Uroš

“…The second step involves the application of the SAFI-Flow-R method and the analysis of three natural and anthropogenic factors: snow cover, terrain slope, and land use. …”
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Comparison of Machine Learning Methods (Linear Regression, Random Forest, and XGBoost) for Predicting Poverty in Central Java in 2024 by Zahwa Bunga Putri Pratama, Yani Parti Astuti

“…The data were normalized using StandardScaler and split into training (80%) and testing (20%) sets. This study compares three regression algorithms—Linear Regression, Random Forest, and XGBoost—to evaluate their effectiveness in modeling the complexity of socio-economic data. …”
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Machine learning-based model for acute asthma exacerbation detection using routine blood parameters by Youpeng Chen, Junquan Sun, Yabang Chen, Enzhong Li, Jiancai Lu, Huanhua Tang, Yifei Xie, Jiana Zhang, Lesi Peng, Haojie Wu, Zhangkai J. Cheng, Baoqing Sun

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A Simplified Gaussian Approach for Asphalt Crack Detection based on Deep Learning and RGB images by W. Darwish, W. Darwish, W. Ahmed, W. Ahmed

“…Cracks impact both the operational efficiency and safety of road pavements and significantly influence maintenance decisions. We propose a workflow to detect cracks using YOLOv9 deep learning algorithm combined with statistical analysis through principal component (PCA) and Gaussian distribution. …”
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Optimisation of Criminal Data Clustering Model using Information Gain by Prih Diantono Abda’u, Ratih Hafsarah Maharrani, Muhammad Nur Faiz, Oman Somantri

“…The results indicate that the K-Means algorithm outperforms the other two methods, achieving the best clustering quality with an optimal number of clusters (k = 6) and the lowest DBI value.…”
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Influence of the distortion type on the image quality assessment when reducing its sizes by D. G. Asatryan, M. E. Harutyunyan, Y. I. Golub, V. V. Starovoitov

“…It is shown that the average values of correlations for all images at three types of distortions are very high, while for the other two they are unacceptably low. …”
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Assessing regional economic systems balance based on the dynamics of key social and economic indicators by A. A. Shatsky

“…The results of the analysis of the dynamics of key economic spheres development in the context of Russian regions have been presented, and the stable and unstable components of the RES have been identified. The algorithm for assessing the level of balance has been described, including the collection and analysis of statistical data, coefficients calculation and interpretation, and managerial decisions development based on the analysis results. …”
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A Convolutional Neural Network Model for Classifying Resting Tremor Amplitude in Parkinson&#x2019;s Disease by Augusto Ielo, Serena Dattola, Lilla Bonanno, Paolo De Pasquale, Alberto Cacciola, Angelo Quartarone, Maria Cristina De Cola

“…Resting data recorded during the UPDRS assessment were extracted and used to identify additional resting periods within the recordings through an automatic segmentation algorithm. At the end, for each of the selected arms, 90,000 data points were labeled based on the respective UPDRS 3.17 scores. …”
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Evaluation of multiple machine learning models predicting the results of hybrid imaging in primary hyperparathyroidism by Anna Drynda, Jacek Podlewski, Karolina Kucharczyk, Grzegorz Sokołowski, Anna Sowa-Staszczak, Alicja Hubalewska-Dydejczyk, Małgorzata Trofimiuk- Müldner

“…The aim of this study is to evaluate predictive strategies for the assessment of radiotracer uptake in pre-operative [99mTc]Tc-sestamibi scintigraphy ([99mTc] Tc-MIBI SPECT-CT) among PHP patients to identify individuals with a high probability of negative results, and to develop clinical decision-making tools. MATERIAL AND METHODS: Development and evaluation of logistic regression (LR), classification trees utilizing the classification and regression trees (CART) algorithm, random forest (RF), and boosted trees employing XGBoost (XGB) predictive models. …”
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Assessing an ovarian reserve and risk factors for premature ovarian failure as part of pre-abortion counseling for women under 40 planning to terminate own first pregnancy by L. V. Tkachenko, I. A. Gritsenko, K. Yu. Tikhaeva, N. I. Sviridova, I. S. Gavrilova, V. A. Dolgova

“…At the stage of pre-abortion counseling, it seems possible to influence a decision to keep pregnancy by identifying risk factors for premature ovarian failure (РОF), laboratory and ultrasound criteria for reducing ovarian reserve (OR).Aim: optimization of the pre-abortion counseling algorithm by introducing an assessment of OR. …”
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Interpretable machine learning for early predicting the risk of ventilator-associated pneumonia in ischemic stroke patients in the intensive care unit by Heshan Cao, Junying Wei, Ping Hua, Songran Yang, Songran Yang

“…The primary outcome was the incidence of VAP post-ICU admission. The Boruta algorithm was used to select features prior to developing 10 ML models. …”
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Predicting Stroke-Associated Pneumonia in Acute Ischemic Stroke: A Machine Learning Model Development and Validation Study with CBC-Derived Inflammatory Indices by Xie M, Liu Z, Dai F, Cao Z, Wang X

“…LightGBM demonstrated superior predictive performance (ranking score=54) without overfitting, identifying Monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), NIHSS score, age, aggregate index of systemic inflammation (AISI), and platelet-to-lymphocyte ratio (PLR) as the top predictors.Conclusion: Our findings demonstrate that machine learning models exhibit strong predictive performance for SAP, with the LightGBM algorithm outperforming other approaches. The web-based prediction tool developed from this model provides clinicians with actionable insights to support real-time clinical decision-making.Keywords: stroke-associated pneumonia, machine learning, ischemic stroke…”
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Examining Nasdaq Market Data and Presenting an Optimized Model by Extreme Gradient Boosting Regression and Artificial Bee Colony by Ali Ahmadpour

“…The study introduces an Extreme Gradient Boosting Regression (XGBR) model optimized with three distinct metaheuristic algorithms: Battle Royal Optimization (BRO), Moth Flame Optimization (MFO), and Artificial Bee Colony (ABC). …”
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Smart Management of Energy Losses in Distribution Networks Using Deep Neural Networks by Ihor Blinov, Virginijus Radziukynas, Pavlo Shymaniuk, Artur Dyczko, Kinga Stecuła, Viktoriia Sychova, Volodymyr Miroshnyk, Roman Dychkovskyi

“…Given the presence of anomalies and missing values in the operational data, a two-stage preprocessing algorithm incorporating DBSCAN clustering was applied for data cleansing and imputation. …”
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