Machine learning for predicting 5-year mortality risks: data from the ESSE-RF study in Primorsky Krai by V. A. Nevzorova, T. A. Brodskaya, K. I. Shakhgeldyan, B. I. Geltser, V. V. Kosterin, L. G. Priseko

“…The χ2, Fisher and MannWhitney tests, univariate logistic regression (LR) were used for data processing and analysis. To build predictive models, we used following machine learning (ML) methods: multivariate LR, Weibull regression, and stochastic gradient boosting.Results. …”
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Forecasting the Incidence of Mumps Based on the Baidu Index and Environmental Data in Yunnan, China: Deep Learning Model Study by Xin Xiong, Linghui Xiang, Litao Chang, Irene XY Wu, Shuzhen Deng

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Predictive and Explainable Machine Learning Models for Endocrine, Nutritional, and Metabolic Mortality in Italy Using Geolocalized Pollution Data by Donato Romano, Michele Magarelli, Pierfrancesco Novielli, Domenico Diacono, Pierpaolo Di Bitonto, Nicola Amoroso, Alfonso Monaco, Roberto Bellotti, Sabina Tangaro

“…By combining advanced machine learning techniques with explainability tools, this research demonstrates the potential for data-driven approaches to inform public health strategies and promote targeted interventions in the context of complex environmental and social determinants of health.…”
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An effective scheduling in data centres for efficient CPU usage and service level agreement fulfilment using machine learning by Rohit Daid, Yogesh Kumar, Yu-Chen Hu, Wu-Lin Chen

“…In this paper, efficient minimum execution and completion time scheduling are accomplished by using a machine learning approach for effectual CPU usage and service level agreement fulfilment in data centres, considered in terms of average accuracy which will reduce costs for the maintenance of the data centres in real-time scenarios. …”
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Leveraging multi-source data and teleconnection indices for enhanced runoff prediction using coupled deep learning models by Jintao Li, Ping Ai, Chuansheng Xiong, Yanhong Song

“…This study introduces two innovative coupled models—SRA-SVR and SRA-MLPR—to enhance runoff prediction by leveraging the strengths of statistical and deep learning approaches. Stepwise Regression Analysis (SRA) was employed to effectively handle high-dimensional data and multicollinearity, ensuring that only the most influential predictive variables were retained. …”
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A Data and Machine Learning-Based Approach for the Conversion of the Encounter Wave Frequency Spectrum to the Original Wave Spectrum by JeongYong Park, MooHyun Kim

“…This study introduces a data-driven and machine learning (ML)-based methodology for converting the encounter wave frequency spectrum to the original wave spectrum, a critical process for navigating vessels with forward speed in various control and adjustment missions. …”
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Individual Travel Knowledge Graph-Based Public Transport Commuter Identification: A Mixed Data Learning Approach by Song Hu, Jiancheng Weng, Quan Liang, Wei Zhou, Peizhao Wang

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Methodology for detecting anomalies in cyber attack assessment data using Random Forest and Gradient Boosting in machine learning by A. S. Kechedzhiev, O. L. Tsvetkova, A. I. Dubrovina

“…The research aims to detect anomalies in data using machine learning models, in particular random forest and gradient boosting, to analyze network activity and detect cyberattacks. …”
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Evaluating Transit-Oriented Development Performance: An Integrated Approach Using Multisource Big Data and Interpretable Machine Learning by Huadong Chen, Kai Zhao, Zhan Zhang, Haodong Zhang, Linjun Lu

“…This study suggests a multi-indicator TOD performance evaluation method based on a multi-indicator approach grounded in the analysis of multisource urban big data, revealing the role of rail transit TOD station characteristics on critical indicators of station operation through an interpretable machine learning approach. …”
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A comparative study of machine learning classifiers for intelligent fault diagnosis of electric vehicles based on FMECA data by Leila Boucerredj, Nadir Benalia

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DconnLoop: a deep learning model for predicting chromatin loops based on multi-source data integration by Junfeng Wang, Kuikui Cheng, Chaokun Yan, Huimin Luo, Junwei Luo

“…In contrast, multi-source data integration and deep learning approaches, though not yet widely applied, hold significant potential. …”
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Prediction of antibiotic resistance from antibiotic susceptibility testing results from surveillance data using machine learning by Swetha Valavarasu, Yasaswini Sangu, Tanmaya Mahapatra

“…The dataset was divided into two subsets: Phenotype-Only and Phenotype + Genotype, excluding and including 589,998 isolates with genotype data, respectively. Both subsets underwent exploratory data analysis, preprocessing, machine learning model training, validation, and optimization. …”
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Blockchain Enabled Secure Medical Data Transmission and Diagnosis Using Golden Jackal Optimization Algorithm with Deep Learning by Kiruthikadevi Kulandaivelu, Sivaraj Rajappan, Vijayakumar Murugasamy

“…Abstract The incorporation of deep learning (DL) and blockchain (BC) technologies in healthcare revolutionizes disease diagnoses and improves data security. …”
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Railway traffic characterisation data based on weigh-in-motion and machine learning: A case study in PortugalZenodo by Idilson A. Nhamage, Cláudio S. Horas, José A. Campos e Matos, João Poças Martins

“…This paper presents the outcomes from a WIM system installed on the Alcácer do Sal bridge in Portugal to capture real-time train data, which was then post-processed through an automated Machine Learning (ML) approach for train classification. …”
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A scoping review of self-supervised representation learning for clinical decision making using EHR categorical data by Yuanyuan Zheng, Adel Bensahla, Mina Bjelogrlic, Jamil Zaghir, Hugues Turbe, Lydie Bednarczyk, Christophe Gaudet-Blavignac, Julien Ehrsam, Stéphane Marchand-Maillet, Christian Lovis

“…Abstract The widespread adoption of Electronic Health Records (EHRs) and deep learning, particularly through Self-Supervised Representation Learning (SSRL) for categorical data, has transformed clinical decision-making. …”
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Developing an ensemble machine learning framework for enhanced climate projections using CMIP6 data in the Middle East by Younes Khosravi, Taha B.M.J. Ouarda, Saeid Homayouni

“…This study introduces the Stacking-EML framework, which merges five machine learning models three meta-learners to predict maximum temperature, minimum temperature, and precipitation using CMIP6 data under SSP1-2.6, SSP2-4.5, and SSP5-8.5. …”
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Innovative data augmentation strategy for deep learning on biological datasets with limited gene representations focused on chloroplast genomes by Mohammad Ali Abbasi-Vineh, Shirin Rouzbahani, Kaveh Kavousi, Masoumeh Emadpour

“…Abstract One key barrier to applying deep learning (DL) to omics and other biological datasets is data scarcity, particularly when each gene or protein is represented by a single sequence. …”
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Forest Fire Hazard Assessment using Remote Sensing Data and Machine Learning, Case Study of Jijel, Algeria by Matougui Zakaria, Zouidi Mohamed

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Petri graph neural networks advance learning higher order multimodal complex interactions in graph structured data by Alma Ademovic Tahirovic, David Angeli, Adnan Tahirovic, Goran Strbac

“…Abstract Graphs are widely used to model interconnected systems, offering powerful tools for data representation and problem-solving. However, their reliance on pairwise, single-type, and static connections limits their expressive capacity. …”
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Machine learning-based drought prediction using Palmer Drought Severity Index and TerraClimate data in Ethiopia. by Tadele Melese, Gizachew Assefa, Baye Terefe, Tatek Belay, Getachew Bayable, Abebe Senamew

“…This study investigates the classification of the Palmer Drought Severity Index (PDSI) using machine learning models trained on TerraClimate data, incorporating variables such as precipitation, temperature, soil moisture, and vapor pressure deficit. …”
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