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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Data-Driven Approaches in Antimicrobial Resistance: Machine Learning Solutions by Aikaterini Sakagianni, Christina Koufopoulou, Petros Koufopoulos, Sofia Kalantzi, Nikolaos Theodorakis, Maria Nikolaou, Evgenia Paxinou, Dimitris Kalles, Vassilios S. Verykios, Pavlos Myrianthefs, Georgios Feretzakis

“…This paper explores the capability of machine learning (ML) methods, particularly unsupervised learning methods, to enhance the understanding and prediction of AMR. …”
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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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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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Surface Ice Detection Using Hyperspectral Imaging and Machine Learning by Steve Vanlanduit, Arnaud De Vooght, Thomas De Kerf

“…This study investigates the use of hyperspectral imaging (HSI) combined with machine learning to detect and classify ice on various coated and uncoated surfaces. …”
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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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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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Evaluation of deep learning models for RGB image-based detection of potato virus y strain symptoms (O, NO, and NTN) in potato plants by Charanpreet Singh, Gurjit S. Randhawa, Aitazaz A. Farooque, Yuvraj S. Gill, Lokesh Kumar KM, Mathuresh Singh, Khalil Al-Mughrabi

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Identification of Megaconstellations in Wide-field Astronomical Images with Machine Learning by Liu Liu, Rongyu Sun, He Zhao

“…Here an automatic identification pipeline based on machine learning model ShuffleNet V2 is developed, after trained with large amount of raw data, high efficiency is achieved. …”
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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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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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Dynamic Risk Thresholds for SIEM Alerting Based on Machine Learning by Artur Kapera, Marcin Niemiec

“…In the article, a theoretical concept of a Dynamic Risk-Based Alerting model for SIEM based on machine learning has been presented. …”
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Improving the Efficiency of Photovoltaic Panels Using Machine Learning Approach by Rashmita Khilar, G. Merlin Suba, T. Sathesh Kumar, J. Samson Isaac, Santaji Krishna Shinde, S. Ramya, V. Prabhu, Kuma Gowwomsa Erko

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Applications of machine learning in decision analysis for dose management for dofetilide. by Andrew E Levy, Minakshi Biswas, Rachel Weber, Khaldoun Tarakji, Mina Chung, Peter A Noseworthy, Christopher Newton-Cheh, Michael A Rosenberg

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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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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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Genomic Selection in Alfalfa Across Multiple Ploidy Levels: A Comparative Study Using Machine Learning and Bayesian Methods by Xiaoyue Zhu, Ruixin Zhang, Tianxiang Zhang, Changhong Guo, Yongjun Shu

“…A total of 11 Bayesian and machine learning models and nine different reference genomes were used to conduct genomic selection on five traits in 385 alfalfa accessions. …”
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Machine Learning‐Driven Extraction of Hybrid Compact Models Integrating Neural Networks and Berkeley Short‐Channel Insulated‐Gate Field‐Effect Transistor Model‐Common Multigate for... by Seungjoon Eom, Seunghwan Lee, Hyeok Yun, Kyeongrae Cho, Soomin Kim, Rockhyun Baek

“…This study presents a novel machine learning–based method to accelerate and enhance the accuracy of compact model generation for multiple devices simultaneously. …”
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Optimised Extension of an Ultra-Low-Power RISC-V Processor to Support Lightweight Neural Network Models by Qiankun Liu, Sam Amiri

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Enhancing Network Security: A Study on Classification Models for Intrusion Detection Systems by Abeer Abd Alhameed Mahmood, Azhar A. Hadi, Wasan Hashim Al-Masoody

“…This study leverages AI methods to develop nine classification models using supervised machine learning classifiers. …”
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