Construction of a Surface Roughness and Burr Size Prediction Model Through the Ensemble Learning Regression Method by Ali Khosrozadeh, Seyed Ali Niknam, Fatemeh Hajizadeh

“…This study proposes an ensemble learning regression model to accurately predict burr size and surface roughness during the slot milling of aluminum alloy (AA) 6061. …”
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Quality-Aware PPG-Based Blood Pressure Classification for Energy-Efficient Trustworthy BP Monitoring Devices With Reduced False Alarms by Yalagala Sivanjaneyulu, M. Sabarimalai Manikandan, Srinivas Boppu, Linga Reddy Cenkeramaddi

“…In this paper, we present four SQA methods and nine machine learning (ML) based BP classification models, including logistic regression, decision tree, random forest, multilayer perceptron, k-nearest neighbours, XGBoost, AdaBoost, Bagged Tree, and one-dimensional convolutional neural network (1D-CNN). …”
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Histological Grade, Tumor Breadth, and Hypertension Predict Early Recurrence in Pediatric Sarcoma: A LASSO-Regularized Micro-Cohort Study by Alexander Fiedler, Mehran Dadras, Marius Drysch, Sonja Verena Schmidt, Flemming Puscz, Felix Reinkemeier, Marcus Lehnhardt, Christoph Wallner

“…This exploratory study aimed to identify clinical features associated with first tumor recurrence using a machine learning approach tailored to low-event settings. …”
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Thyroid nodule classification in ultrasound imaging using deep transfer learning by Yan Xu, Mingmin Xu, Zhe Geng, Jie Liu, Bin Meng

“…To identify the optimal model, both traditional machine learning and transfer learning approaches were employed, followed by model fusion using post-fusion techniques. …”
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Intelligent Photolithography Corrections Using Dimensionality Reductions by Parag Parashar, Chandni Akbar, Tejender S. Rawat, Sparsh Pratik, Rajat Butola, Shih H. Chen, Yung-Sung Chang, Sirapop Nuannimnoi, Albert S. Lin

“…With the shrinking of the IC technology node, optical proximity effects (OPC) and etch proximity effects (EPC) are the two major tasks in advanced photolithography patterning. Machine learning has emerged in OPC/EPC problems because conventional optical-solver-based OPC is time-consuming, and there is no physical model existing for EPC. …”
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Prediction of Shield Tunneling Attitude Based on WM-CTA Method by GAO Su, CHEN Cheng

“…Experiments were conducted on data for noise reduction and correlation analysis, followed by analysis of the model’s prediction performance and generalization ability. …”
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Detecting Falls and Slips of Wheelchair Users Using Low-Resolution Thermal Image Analysis by Shisei Nakamura, Masaaki Yamauchi, Miwa Sugita, Yoshihiro Aso, Yuichi Ohsita, Hideyuki Shimonishi

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Research on the Nonlinear Relationship Between Carbon Emissions from Residential Land and the Built Environment: A Case Study of Susong County, Anhui Province Using the XGBoost-SHA... by Congguang Xu, Wei Xiong, Simin Zhang, Hailiang Shi, Shichao Wu, Shanju Bao, Tieqiao Xiao

“…By identifying CEs from residential land through building electricity consumption, 14 built environment indicators, including land area (LA), floor area ratio (FAR), greening ratio (GA), building density (BD), gross floor area (GFA), land use mix rate (Phh), and permanent population density (PPD), were selected to establish an interpretable machine learning (ML) model based on the XGBoost-SHAP attribution analysis framework. …”
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Automated multi-model framework for malaria detection using deep learning and feature fusion by Osama R. Shahin, Hamoud H. Alshammari, Raed N. Alabdali, Ahmed M. Salaheldin, Neven Saleh

“…This study proposes an advanced, automated diagnostic framework for malaria detection using a multi-model architecture integrating deep learning and machine learning techniques. …”
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Production and optimization of affordable artificial geopolymer aggregates containing crumb rubber, plastic waste, and granulated cork based on machine learning algorithms by Mohamed Abdellatief, Abedulgader Baktheer, Mohamed Shahin, Aref A. Abadel, Ashraf M. Heniegal

“…Among the machine learning models, the GPR model demonstrated superior performance, with R² values of 0.989 for density prediction and 0.937 for CS prediction. …”
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Predicting responsiveness to fixed-dose methylene blue in adult patients with septic shock using interpretable machine learning: a retrospective study by Shasha Xue, Li Li, Zhuolun Liu, Feng Lyu, Fan Wu, Panxiao Shi, Yongmin Zhang, Lina Zhang, Zhaoxin Qian

“…Abstract This study aimed to develop an interpretable machine learning model to predict methylene blue (MB) responsiveness in adult patients with refractory septic shock and to identify key factors influencing MB responsiveness using the SHapley Additive exPlanations (SHAP) approach. …”
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Dynamic Machine Learning-Based Simulation for Preemptive Supply-Demand Balancing Amid EV Charging Growth in the Jamali Grid 2025–2060 by Joshua Veli Tampubolon, Rinaldy Dalimi, Budi Sudiarto

“…We introduce a novel supply–demand balance score to quantify weekly and annual deviations between projected supply and demand curves, then use this metric to guide the machine-learning model in optimizing annual growth rate (AGR) and preventing supply demand imbalance. …”
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A Three-Level Meta-Frontier Framework with Machine Learning Projections for Carbon Emission Efficiency Analysis: Heterogeneity Decomposition and Policy Implications by Xiaoxia Zhu, Tongyue Feng, Yuhe Shen, Ning Zhang, Xu Guo

“…Computational enhancements via Apache Spark achieve a 58% runtime reduction. The framework advances environmental efficiency analysis by integrating machine learning with meta-frontier theory, offering both methodological rigor (via regularization and GNN constraints) and actionable decarbonization pathways. …”
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Forecasting tuberculosis in Ethiopia using deep learning: progress toward sustainable development goal evidence from global burden of disease 1990–2021 by Zinabu Bekele Tadese, Fetlework Gubena Arage, Tigist Kifle Tsegaw, Eyob Akalewold Alemu, Tsegasilassie Gebremariam Abate, Eliyas Addisu Taye

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Algorithms for Load Balancing in Next-Generation Mobile Networks: A Systematic Literature Review by Juan Ochoa-Aldeán, Carlos Silva-Cárdenas, Renato Torres, Jorge Ivan Gonzalez, Sergio Fortes

“…Background: Machine learning methods are increasingly being used in mobile network optimization systems, especially next-generation mobile networks. …”
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Evaluating the Effect of Thermal Treatment on Phenolic Compounds in Functional Flours Using Vis–NIR–SWIR Spectroscopy: A Machine Learning Approach by Achilleas Panagiotis Zalidis, Nikolaos Tsakiridis, George Zalidis, Ioannis Mourtzinos, Konstantinos Gkatzionis

“…This study explores the thermal stability of phenolic compounds in various functional flours using visible, near and shortwave-infrared (Vis–NIR–SWIR) spectroscopy (350–2500 nm), integrated with machine learning (ML) algorithms. Random Forest models were employed to classify samples based on flour type, baking temperature, and phenolic concentration. …”
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LiDAR-Based Road Cracking Detection: Machine Learning Comparison, Intensity Normalization, and Open-Source WebGIS for Infrastructure Maintenance by Nicole Pascucci, Donatella Dominici, Ayman Habib

“…Preprocessing included noise removal, resolution reduction to 2 cm, and ground/non-ground separation using the Cloth Simulation Filter (CSF), resulting in Bare Earth (BE), Digital Terrain Model (DTM), and Above Ground (AG) point clouds. …”
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Radiomics-Based Classification of Clear Cell Renal Cell Carcinoma ISUP Grade: A Machine Learning Approach with SHAP-Enhanced Explainability by María Aymerich, Alejandra García-Baizán, Paolo Niccolò Franco, Mariña González, Pilar San Miguel Fraile, José Antonio Ortiz-Rey, Milagros Otero-García

“…This study aims to develop a radiomics-based machine learning model for the ISUP grade classification of ccRCC using nephrographic-phase CT images, with an emphasis on model interpretability through SHAP (SHapley Additive exPlanations) values. …”
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Sociodemographic inequalities in the global burden trends and machine learning-based projections of periodontitis from 1990 to 2030 across different development levels by Amr Sayed Ghanem, Róbert Bata, Nóra Kovács, Attila Csaba Nagy

“…Machine learning predicted future burden, and geospatial mapping visualized global distribution.ResultsPeriodontitis burden remains highest in low-SDI regions, with significantly greater prevalence, incidence, and DALY rates compared to higher-SDI countries (p < 0.001). …”
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Efficient Explainable Models for Alzheimer’s Disease Classification with Feature Selection and Data Balancing Approach Using Ensemble Learning by Yogita Dubey, Aditya Bhongade, Prachi Palsodkar, Punit Fulzele

“…Also, challenges such as data imbalance and high-dimensional feature sets often hinder model performance. <b>Objective:</b> This paper aims to propose a computationally efficient, reliable, and transparent machine learning-based framework for the classification of Alzheimer’s disease patients. …”
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