Crack Location in Wind Turbine Blades Using Vibration Signal and Support Vector Machine by Perla Y. Sevilla-Camacho, José B. Robles-Ocampo, Juvenal Rodríguez-Resendíz, Sergio De la Cruz-Arreola, Marco A. Zuñiga-Reyes, Edwin N. Hernández-Estrada

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Building a machine learning-based risk prediction model for second-trimester miscarriage by Sangsang Qi, Shi Zheng, Mengdan Lu, Aner Chen, Yanbo Chen, Xianhu Fu

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Efficient optimal power flow learning: A deep reinforcement learning with physics-driven critic model by Ahmed Sayed, Khaled Al Jaafari, Xian Zhang, Hatem Zeineldin, Ahmed Al-Durra, Guibin Wang, Ehab Elsaadany

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Supervised Machine Learning for Real-Time Intrusion Attack Detection in Connected and Autonomous Vehicles: A Security Paradigm Shift by Ahmad Aloqaily, Emad E. Abdallah, Hiba AbuZaid, Alaa E. Abdallah, Malak Al-hassan

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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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Multi-crop plant counting and geolocation using a YOLO-Powered GUI System by Renato Herrig Furlanetto, Nathan Schawn Boyd, Ana Claudia Buzanini

“…Crop counting has traditionally relied on manual field assessments or complex machine learning algorithms, which often struggle to identify small objects or underestimate the total count of objects present in the field. …”
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Artificial intelligence driven mental health diagnosis based on physiological signals by P.R. Naregalkar, A.A. Shinde, M.V. Patil

“…The machine learning algorithms, like Decision tree and kernel support vector machine, are employed for dope classification tasks. …”
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Hyperneutron Stars from an Ab Initio Calculation by Hui Tong, Serdar Elhatisari, Ulf-G. Meißner​

“…Starting from the newly developed auxiliary field quantum Monte Carlo algorithm to simulate hyperneutron matter without any sign oscillations, we derive three distinct EOSs by employing the state-of-the-art nuclear lattice effective field theory. …”
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Development and validation of interpretable machine learning models for triage patients admitted to the intensive care unit. by Zheng Liu, Wenqi Shu, Hongyan Liu, Xuan Zhang, Wei Chong

“…In Model 3, the top four algorithms with the highest AUC were Gradient Boosting (0.81), Logistic Regression (0.81), naive Bayes (0.80), and Random Forest (0.80). …”
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A METHOD FOR ASSESSING INvESTMENT ATTRACTIvENESS OF URBAN PLANNING PROJECTS by O A. Bayuk, I. E. Denezhkina, S. A. Zadadaev

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BREAST-CAD: A Computer-Aided Diagnosis System for Breast Cancer Detection Using Machine Learning by Riyam M. Masoud, Ramadan Madi Ali Bakir, M. Sabry Saraya, Sarah M. Ayyad

“…Our approach follows a three-phase methodology: Initially, a comprehensive literature review between 2000 and 2024 informed the choice of a suitable dataset and the selection of Naive Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Decision Trees (DT) Machine Learning (ML) algorithms. …”
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Contrast-Enhanced CT Texture Analysis for Distinguishing Fat-Poor Renal Angiomyolipoma From Chromophobe Renal Cell Carcinoma by Guangjie Yang MD, Aidi Gong BS, Pei Nie MD, Lei Yan BS, Wenjie Miao BS, Yujun Zhao BS, Jie Wu MD, Jingjing Cui BS, Yan Jia BS, Zhenguang Wang MD

“…The 2D and 3D CTTA models were constructed with the least absolute shrinkage and selection operator algorithm and texture scores were calculated. …”
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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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Caffeine Content Prediction in Coffee Beans Using Hyperspectral Reflectance and Machine Learning by Dthenifer Cordeiro Santana, Rafael Felipe Ratke, Fabio Luiz Zanatta, Cid Naudi Silva Campos, Ana Carina da Silva Cândido Seron, Larissa Pereira Ribeiro Teodoro, Natielly Pereira da Silva, Gabriela Souza Oliveira, Regimar Garcia dos Santos, Rita de Cássia Félix Alvarez, Carlos Antonio da Silva Junior, Matildes Blanco, Paulo Eduardo Teodoro

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Random Forest–Based Coal Mine Roof Displacement Prediction and Application by Hongxia Li, Rong Wu, Jianan Gao

“…Based on Random Forest (RF), XGBoost, and Gradient Boosting Decision Tree (GBDT) algorithms, three kinds of roof displacement prediction models are established respectively. …”
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Figuring Digital Cascades: Issue Framing in Digital Media Ecosystems by Nathalie Casemajor, Sylvain Rocheleau

“…From an empirical point of view, we put this model to the test through a case study of the cascading data flows that emerged during this public debate. We found three key factors that constituted and shaped this information cascade: 1) the economic structure of the Canadian media market, and especially the concentration of media ownership, which is notably high in the Canadian media ecosystem; 2) data-exchange mechanisms and algorithmic filtering that drive the process of news aggregation, quickly spreading media content without being a significant source of user engagement; 3) grassroots engagement in diasporic media, which activates micro public spheres around nested interests and political standpoints regarding the public issue. …”
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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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