Flood Forecasting with Sentinel-2 Images Using Machine Learning by V. Hnatushenko, V. Hnatushenko, V. Y. Kashtan, V. V. Hnatushenko, C. Heipke

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Machine-learning driven strategies for adapting immunotherapy in metastatic NSCLC by Maliazurina B. Saad, Qasem Al-Tashi, Lingzhi Hong, Vivek Verma, Wentao Li, Daniel Boiarsky, Shenduo Li, Milena Petranovic, Carol C. Wu, Brett W. Carter, Girish S. Shroff, Tina Cascone, Xiuning Le, Yasir Y. Elamin, Mehmet Altan, Simon Heeke, Ajay Sheshadri, Joe Y. Chang, Percy P. Lee, Zhongxing Liao, Don L. Gibbons, Ara A. Vaporciyan, J. Jack Lee, Ignacio I. Wistuba, Cara Haymaker, Seyedali Mirjalili, David Jaffray, Justin F. Gainor, Yanyan Lou, Alessandro Di Federico, Federica Pecci, Mark Awad, Biagio Ricciuti, John V. Heymach, Natalie I. Vokes, Jianjun Zhang, Jia Wu

“…In this study, we demonstrate how machine learning can fill critical gaps in immunotherapy selection for NSCLC, by modeling treatment heterogeneity with real-world clinicogenomic data, driving precision medicine beyond conventional biomarker boundaries.…”
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Estimation of soil properties using Hyperspectral imaging and Machine learning by Eirini Chlouveraki, Nikolaos Katsenios, Aspasia Efthimiadou, Erato Lazarou, Kalliopi Kounani, Eleni Papakonstantinou, Dimitrios Vlachakis, Aikaterini Kasimati, Ioannis Zafeiriou, Borja Espejo-Garcia, Spyros Fountas

“…Hyperspectral sensors generate vast arrays of spectral bands, offering unprecedented opportunities to estimate soil properties quickly and cost-effectively when integrated into the appropriate machine learning (ML) pipeline. However, the high dimensionality and collinearity inherent to these spectra pose challenges for precise property detection, often leading to poor generalization. …”
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Machine learning approaches to dissect hybrid and vaccine-induced immunity by Giorgio Montesi, Simone Costagli, Simone Lucchesi, Jacopo Polvere, Fabio Fiorino, Gabiria Pastore, Margherita Sambo, Mario Tumbarello, Massimiliano Fabbiani, Francesca Montagnani, Donata Medaglini, Elena Pettini, Annalisa Ciabattini

“…Blood samples were collected before and six months after third vaccine dose. Machine Learning analysis, involving dimensionality reduction techniques, unsupervised clustering methods and classification models, were applied to serological data including antibody responses specific for wild type SARS-CoV-2 strain as well as Delta, Omicron BA.1 and Omicron BA.2 variants. …”
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Multi-objective artificial-intelligence-based parameter tuning of antennas using variable-fidelity machine learning by Slawomir Koziel, Anna Pietrenko-Dabrowska, Stanislaw Szczepanski

“…Our algorithm is a machine learning (ML) procedure employing artificial neural network models. …”
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Leveraging machine learning in nursing: innovations, challenges, and ethical insights by Sophie So Wan Yip, Sheng Ning, Niki Yan Ki Wong, Jeffrey Chan, Kei Shing Ng, Bernadette Oi Ting Kwok, Robert L. Anders, Simon Ching Lam

“…For example, the COMPOSER deep learning model for early sepsis prediction was associated with a 1.9% absolute reduction (17% relative decrease) in in-hospital sepsis mortality and a 5.0% absolute increase (10% relative increase) in sepsis bundle compliance. …”
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Radiogenomics and machine learning predict oncogenic signaling pathways in glioblastoma by Abdul Basit Ahanger, Syed Wajid Aalam, Tariq Ahmad Masoodi, Asma Shah, Meraj Alam Khan, Ajaz A. Bhat, Assif Assad, Muzafar Ahmad Macha, Muzafar Rasool Bhat

“…Conclusion We present a novel approach for the non-invasive prediction of deregulation in oncogenic signaling pathways in glioblastoma (GBM) by integrating radiogenomic data with machine learning models. This research contributes to advancing precision medicine in GBM management, highlighting the importance of integrating radiomics with genomic data to understand tumor behavior and treatment response better.…”
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A scalable machine learning strategy for resource allocation in database by Fady Nashat Manhary, Marghny H. Mohamed, Mamdouh Farouk

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Optimization of machine learning methods for de-anonymization in social networks by Nurzhigit Smailov, Fatima Uralova, Rashida Kadyrova, Raiymbek Magazov, Akezhan Sabibolda

“…In this study, we develop a machine learning-driven de-anonymization system for social networks, with a focus on feature selection, hyperparameter tuning, and dimensionality reduction. …”
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Development and validation of a quick screening tool for predicting neck pain patients benefiting from spinal manipulation: a machine learning study by Changxiao Han, Guangyi Yang, Haibao Wen, Minrui Fu, Bochen Peng, Bo Xu, Xunlu Yin, Ping Wang, Liguo Zhu, Minshan Feng

“…This study aims to develop and validate a machine learning-based prediction model to identify NP patients most likely to benefit from spinal manipulation. …”
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Tropospheric ozone trends and attributions over East and Southeast Asia in 1995–2019: an integrated assessment using statistical methods, machine learning models, and multiple chem... by X. Lu, X. Lu, Y. Liu, Y. Liu, J. Su, J. Su, X. Weng, X. Weng, X. Weng, T. Ansari, Y. Zhang, G. He, G. He, Y. Zhu, Y. Zhu, H. Wang, H. Wang, G. Zeng, G. Zeng, J. Li, J. Li, C. He, C. He, S. Li, S. Li, T. Amnuaylojaroen, T. Butler, Q. Fan, Q. Fan, S. Fan, S. Fan, G. L. Forster, G. L. Forster, M. Gao, J. Hu, Y. Kanaya, M. T. Latif, K. Lu, P. Nédélec, P. Nowack, P. Nowack, B. Sauvage, X. Xu, L. Zhang, K. Li, J.-H. Koo, T. Nagashima

“…<p>We apply a statistical model, two machine learning models, and three chemical transport models to attribute the observed ozone increases over East and Southeast Asia (ESEA) to changes in anthropogenic emissions and climate. …”
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Dashboard‑Driven Machine Learning Analytics and Conceptual LLM Simulations for IIoT Education in Smart Steel Manufacturing by Mehdi Imani, Ali Imanifard, Babak Majidi, Abdolah Shamisa

“…Through advanced analytical models such as machine learning (ML) and, conceptually, Large Language Models (LLMs), this study explores how Industrial Internet of Things (IIoT) applications can transform educational experiences in the context of smart steel production. …”
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Limitations of XGBoost in Predicting Material Parameters for Complex Constitutive Models by Prates Pedro, Mitreiro Dário, Andrade-Campos António

“…Machine learning models, particularly Extreme Gradient Boosting, have been explored for predicting material parameters in constitutive models that describe the plastic behaviour of metal sheets. …”
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YOLO-LSM: A Lightweight UAV Target Detection Algorithm Based on Shallow and Multiscale Information Learning by Chenxing Wu, Changlong Cai, Feng Xiao, Jiahao Wang, Yulin Guo, Longhui Ma

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Interpretable Machine Learning for Multi-Energy Supply Station Revenue Forecasting: A SHAP-Driven Framework to Accelerate Urban Carbon Neutrality by Zhihui Zhao, Minjuan Wang, Jin Wei, Xiao Cen, Shengnan Du, Ziwen Wu, Huanying Liu, Weiqiang Wang

“…By leveraging real-world consumption data from Hangzhou West Lake Tanghe Station, we constructed a dataset with nine critical parameters, including energy types, transaction frequency, and temporal features. Four machine learning models—decision tree regression, random forest (RF), support vector regression, and multilayer perceptron—were evaluated using MAE, MSE, and R² metrics. …”
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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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Hybrid optimization of thermally-enhanced Zn-Fe LDH catalysts for fenton-like reactions: Integrating design of experiments with machine learning models for optimisation by Ramadhan Muhammad Naufal, Nawwal Hikmah, Dessy Ariyanti

“…This study presents a novel hybrid modeling framework that combines Response Surface Methodology (RSM) with machine learning (ML) algorithms– Support Vector Regression (SVR) and Gradient Boosting Regression (GBR)– to contribute to the predictive modeling and optimization of thermally-activated ZnFe-LDH based Fenton catalysis. …”
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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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A highly generalized federated learning algorithm for brain tumor segmentation by Jun Wen, Xiusheng Li, Xin Ye, Xiaoli Li, Hang Mao

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