Decision tree-based machine learning algorithm for prediction of acute radiation esophagitis by Mostafa Alizade-Harakiyan, Amin Khodaei, Ali Yousefi, Hamed Zamani, Asghar Mesbahi

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Temperature and relative humidity prediction in South China greenhouse based on machine learning by Xinyu Wei, Yizhi Luo, Xingxing Zhou, Junhong Zhao, Huazhong Lu, Jie Li, Jinrong Zheng, Bin Li

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Machine learning methods in the differential diagnosis of difficult-to-classify types of diabetes mellitus by N. V. Rusyaeva, I. I. Golodnikov, I. V. Kononenko, T. V. Nikonova, M. V. Shestakova

“…In this regard, various automated algorithms have been developed based on statistical methods and machine learning (deep neural networks, “decision trees”, etc.) to identify patients for whom an in-depth examination is most justified. …”
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Machine Learning-Driven Acoustic Feature Classification and Pronunciation Assessment for Mandarin Learners by Gulnur Arkin, Tangnur Abdukelim, Hankiz Yilahun, Askar Hamdulla

“…A speech corpus containing samples from advanced, intermediate, and elementary learners (N = 50) and standard speakers (N = 10) was constructed, with a total of 5880 samples. Support Vector Machine (SVM) and ID3 decision tree algorithms were employed to classify vowel formant parameters (F1-F2) patterns. …”
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Estimating Canopy Chlorophyll Content of Potato Using Machine Learning and Remote Sensing by Xiaofei Yang, Hao Zhou, Qiao Li, Xueliang Fu, Honghui Li

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Developing new machine-learning intelligent models to predict the excavation-tunnel displacements by Abdollah Tabaroei, Muhand Jawad Jasim, Ali Mohammed Al-Araji, Amir Hossein Vakili

“…In the second step, a number of three-hundred and sixty 3D simulations of an existing tunnel located directly beneath an excavation under different parameters such as excavation geometry and tunnel positions beneath the excavation were carried out. …”
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Integrating explainable artificial intelligence and light gradient boosting machine for glioma grading by Teuku Rizky Noviandy, Ghalieb Mutig Idroes, Irsan Hardi

“…Despite its importance, traditional histopathological analysis has drawbacks, spurring interest in applying machine learning (ML) techniques to improve accuracy and reliability in glioma grading. …”
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An integrated CFD and machine learning analysis on pilots in-flight thermal comfort and productivity by Xueren Li, Ziqi Chen, Yin Tang, Yihuan Yan, Bichen Shang, Shengjin Xu, Jiyuan Tu

“…A total of 27 cases, encompassing a combination of environmental and HVAC parameters were performed. This dataset was used for data analysis, employing an explainable machine, XGBoost, to highlight the significance of each variable on the pilot's thermal satisfaction. …”
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Effectiveness of three machine learning models for prediction of daily streamflow and uncertainty assessment by Luka Vinokić, Milan Dotlić, Veljko Prodanović, Slobodan Kolaković, Slobodan P. Simonovic, Milan Stojković

“…This study evaluates three Machine Learning (ML) models—Temporal Kolmogorov-Arnold Networks (TKAN), Long Short-Term Memory (LSTM), and Temporal Convolutional Networks (TCN)—focusing on their capabilities to improve prediction accuracy and efficiency in streamflow forecasting. …”
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Modeling Movement Stability of Machine-Tractor Units Based on Modular Type Tractor by Volodymyr Nadykto, Gennadii Golub, Nataliya Tsyvenkova, Volodymyr Kyurchev, Oleksandr Skliar, Radmila Skliar, Victor Golub, Vladyslav Shubenko

“…The object of the present research is a machine-tractor unit based on a tractor consisting of an energy (high-energy tractor—EM) and a technological (additional traction axle—TM) module. …”
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Solar Energy Forecasting Using Machine Learning Techniques for Enhanced Grid Stability by Attuluri R. Vijay Babu, N. Bharath Kumar, Rajanand Patnaik Narasipuram, Soundhar Periyannan, Alireza Hosseinpour, Aymen Flah

“…The framework incorporates advanced feature engineering using high-resolution meteorological and solar geometric parameters-such as relative humidity, temperature, cloud cover, zenith angle, azimuth, and angle of incidence-to enhance model accuracy. …”
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Early Childhood Anemia in Ghana: Prevalence and Predictors Using Machine Learning Techniques by Maryam Siddiqa, Gulzar Shah, Mahnoor Shahid Butt, Asifa Kamal, Samuel T. Opoku

“…We used discrimination and calibration parameters to evaluate the performance of each machine learning algorithm. …”
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The Influence of Axle Load Distribution of Machine-Tractor Aggregates on the Dip of the Wheel-Track by I. N. Shyla, N. N. Ramaniuk, A. N. Orda, S. O. Nukeshev

“…The regularity obtained makes possible to define the soil deformation depending on the different operating conditions and the drive system parameters of machine-tractor aggregates on different types of soil.…”
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Machine-Learning-Driven Approaches for Assessment, Delegation, and Optimization of Multi-Floor Building by Abtin Baghdadi, Harald Kloft

“…This study presents a novel integrated framework for the structural analysis and optimization of multi-floor buildings by combining validated theoretical models with machine learning and evolutionary algorithms. The proposed Process–Action–Response System (PARS-Solution) accurately computes key structural responses—such as deformations, shear forces, and bending moments—based on eleven critical design parameters (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>P</mi><mn>1</mn></msub></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>P</mi><mn>11</mn></msub></semantics></math></inline-formula>). …”
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Predicting in-hospital mortality in ICU patients with lymphoma using machine learning models. by Ling Xu, Guang Tu, Zhonglan Cai, Tianbi Lan

“…Traditional risk stratification tools like SOFA and APACHE scores struggle to capture complex clinical interactions. Machine learning (ML) models offer a more accurate alternative for predicting outcomes by analyzing large datasets. …”
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Multispectral Sensors and Machine Learning as Modern Tools for Nutrient Content Prediction in Soil by Rafael Felippe Ratke, Paulo Roberto Nunes Viana, Larissa Pereira Ribeiro Teodoro, Fábio Henrique Rojo Baio, Paulo Eduardo Teodoro, Dthenifer Cordeiro Santana, Carlos Eduardo da Silva Santos, Alan Mario Zuffo, Jorge González Aguilera

“…The combination of multispectral data and machine learning provides effective and flexible monitoring of the soil nutrient content, which consequently positively impacts plant productivity and food security, and ultimately promotes sustainable agricultural development overall. …”
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The Geometry of Flow: Advancing Predictions of River Geometry With Multi‐Model Machine Learning by Shuyu Y. Chang, Zahra Ghahremani, Laura Manuel, Seyed Mohammad Hassan Erfani, Chaopeng Shen, Sagy Cohen, Kimberly J. Van Meter, Jennifer L. Pierce, Ehab A. Meselhe, Erfan Goharian

“…Abstract Hydraulic geometry parameters describing river hydrogeomorphic relationships are critical for determining a channel's capacity to convey water and sediment which is important for flood forecasting. …”
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Recent advances in machine learning for defects detection and prediction in laser cladding process by X.C. Ji, R.S. Chen, C.X. Lu, J. Zhou, M.Q. Zhang, T. Zhang, H.L. Yu, Y.L. Yin, P.J. Shi, W. Zhang

“…As a fundamental component of artificial intelligence, machine learning has gained considerable prominence within the domain of laser cladding in recent years. …”
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GENDER-SPECIFIC PREDICTORS OF VAULT PERFORMANCE IN GYMNASTICS: A MACHINE LEARNING APPROACH by Dušan Đorđević, Janez Vodičar, Robi Kreft, Edvard Kolar, Miloš Paunović, Saša Veličković, Miha Marinšek

“… This study investigated gender-specific predictors of vault performance in gymnastics by applying machine learning techniques to analyse body composition and run-up dynamics. …”
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Innovative Approach Integrating Machine Learning Models for Coiled Tubing Fatigue Modeling by Khalil Moulay Brahim, Ahmed Hadjadj, Aissa Abidi Saad, Elfakeur Abidi Saad, Hichem Horra

“…We incorporated the impact of different parameters such as CT grades, wall thickness, CT diameter, internal pressure, and welding types. …”
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