COMPARATIVE ANALYSIS OF CLASSIFICATION MODELS FOR DETERMINING THE QUALITY OF WINE BY ITS CHEMICAL COMPOSITION by Vladimir S. Repkin, Artemy V. Li, Grigory Yu. Semenov, Nikita I. Sermavkin, Alexander S. Kovalenko, Nikolai S. Egoshin

“…Methods: machine learning methods for the formation of classification models; statistical methods for assessing the quality of classification and comparing classifiers. …”
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Machine Learning and Deep Learning Hybrid Approach Based on Muscle Imaging Features for Diagnosis of Esophageal Cancer by Yuan Hong, Hanlin Wang, Qi Zhang, Peng Zhang, Kang Cheng, Guodong Cao, Renquan Zhang, Bo Chen

“…Preoperative computed tomography (CT) images covering esophageal, stomach, and muscle (bilateral iliopsoas and erector spinae) regions were segmented automatically with manual adjustments. Diagnostic models were developed using deep learning (2D and 3D neural networks) and traditional machine learning (11 algorithms with PyRadiomics-derived features). …”
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Modality-based Modeling with Data Balancing and Dimensionality Reduction for Early Stunting Detection by Yohanes Setiawan, Mohammad Hamim Zajuli Al Faroby, Mochamad Nizar Palefi Ma’ady, I Made Wisnu Adi Sanjaya, Cisa Valentino Cahya Ramadhani

“…The main contributions of this research are the development of a comprehensive framework for modality-based analysis, the application of advanced data preprocessing techniques, and the comparison of various machine learning algorithms to identify the best model for stunting detection. …”
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Economic growth of countries in the context of military operations by Olexandr Shapurov, Oleksii Hrechanyi, Volodymyr Stoiev, Anatolii Karpelianskyi, Alina Sosnovska

“…Key factors include international aid (29.8%), investments (24.6%), and conflict reduction (19.7%). Theoretical Implications. The study adapts growth models to wartime conditions, highlighting the advantages of endogenous models and machine learning for analyzing complex economies. …”
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“Ensembled transfer learning approach for error reduction in landslide susceptibility mapping of the data scare region” by Ankit Singh, Nitesh Dhiman, K. C. Niraj, Dericks Praise Shukla

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Study of Recent Image Restoration Techniques: A Comprehensive Survey by Nikita Singhal, Anup Kadam, Pravesh Kumar, Hritik Singh, Aaryan Thakur, Pranay

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Price Forecast of Treasury Bond Market Yield: Optimize Method Based on Deep Learning Model by Weiying Ping, Yuwen Hu, Liangqing Luo

“…This paper integrates the ideas of improved multivariate time series sampling and deep learning prediction model structure optimization, and proposes an optimized deep learning model framework under the LASSO-SMLR-PCA machine learning method. …”
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Prediction of Freezing Time During Hydrogen Fueling Using Machine Learning by Ji-Ah Choi, Ji-Seong Jang, Sang-Won Ji

“…Currently, nitrogen and heating air are used for defrosting and frost reduction, which can be costly. The developed machine learning models are expected to enable prediction of both frost formation and defrosting timings, potentially allowing for more cost-effective management of defrosting and frost reduction strategies.…”
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A Comprehensive Review of Machine Learning Approaches for Flood Depth Estimation by Bo Liu, Yingbing Li, Minyuan Ma, Bojun Mao

“…This review reveals the potential of applying machine learning models in flood depth estimation, providing directions for future research and reliable support for disaster prevention and reduction efforts.…”
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Attention-driven hybrid deep learning and SVM model for early Alzheimer’s diagnosis using neuroimaging fusion by Arjun Kidavunil Paduvilan, Godlin Atlas Lawrence Livingston, Sampath Kumar Kuppuchamy, Rajesh Kumar Dhanaraj, Muthuvel Subramanian, Amal Al-Rasheed, Masresha Getahun, Ben Othman Soufiene

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Improving Climate Bias and Variability via CNN‐Based State‐Dependent Model‐Error Corrections by William E. Chapman, Judith Berner

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Delving into the Porosity Domain Continuum in Hardwood Growth Rings: What Can We Learn from Computer Vision Wood Identification Models? by Alex Wiedenhoeft, Prabu Ravindran, Adriana Costa, Rubin Shmulsky, Frank Owens

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Hybrid extreme learning machine for real-time rate of penetration prediction by Abdelhamid Kenioua, Omar Djebili, Ammar Touati Brahim

“…Abstract This study presents a comparative analysis of hybrid Extreme Learning Machine (ELM) models optimized with metaheuristic algorithms Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Grey Wolf Optimizer (GWO) for real-time Rate of Penetration (ROP) prediction in drilling operations. …”
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Enhanced slope stability prediction using ensemble machine learning techniques by Devendra Kumar Yadav, Swarup Chattopadhyay, Debi Prasad Tripathy, Pragyan Mishra, Pritiranjan Singh

“…This study presents a machine learning (ML) model for evaluating slope stability that meets high precision and speed criteria in slope engineering. …”
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Optimizing Renewable Energy Integration Using IoT and Machine Learning Algorithms by Orken Mamyrbayev, Ainur Akhmediyarova, Dina Oralbekova, Janna Alimkulova, Zhibek Alibiyeva

“…The study also implemented a reinforcement learning-based grid optimization system. Results showed significant improvements in forecasting accuracy, with the LSTM model achieving a 59.1% reduction in Mean Absolute Percentage Error compared to the persistence model. …”
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Long-term natural streamflow forecasting under drought scenarios using data-intelligence modeling by Lavínia D. Balthazar, Felix Miranda, Vinícius B.R. Cândido, Priscila Capriles, Marconi Moraes, CelsoB.M. Ribeiro, Geane Fayer, Leonardo Goliatt

“…This study significantly contributes to the progress of predicting long-term river streamflow through the application of machine learning models. Moreover, this study offers valuable insights and recommendations for hydrologists to improve future research efforts.…”
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Prediction of dialysis adequacy using data-driven machine learning algorithms by Yi-Chen Liu, Ji-Ping Qing, Rong Li, Juan Chang, Li-Xia Xu

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About the trustworthiness of physics-based machine learning – considerations for geomechanical applications by D. Degen, D. Degen, D. Degen, M. Ziegler, M. Ziegler, O. Heidbach, O. Heidbach, A. Henk, K. Reiter, F. Wellmann, F. Wellmann

“…To overcome the challenge of trustworthiness, we propose the usage of a novel hybrid machine learning method, namely the non-intrusive reduced-basis method, as a surrogate model. …”
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Enhancing landslide susceptibility modelling through a novel non-landslide sampling method and ensemble learning technique by Chao Zhou, Yue Wang, Ying Cao, Ramesh P. Singh, Bayes Ahmed, Mahdi Motagh, Yang Wang, Ling Chen, Guangchao Tan, Shanshan Li

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Advanced Methodology for Fraud Detection in Energy Using Machine Learning Algorithms by Silviu Gresoi, Grigore Stamatescu, Ioana Făgărășan

“…This approach integrates multiple machine learning models—k-nearest neighbors (kNN), decision trees, random forest, and artificial neural networks (ANNs)—to improve detection accuracy and efficiency. …”
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