Combining explainable machine learning, demographic and multi-omic data to inform precision medicine strategies for inflammatory bowel disease. by Laura-Jayne Gardiner, Anna Paola Carrieri, Karen Bingham, Graeme Macluskie, David Bunton, Marian McNeil, Edward O Pyzer-Knapp

“…We propose an explainable machine learning (ML) approach that combines bioinformatics and domain insight, to integrate multi-modal data and predict inter-patient variation in drug response. …”
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EUS-based intratumoral and peritumoral machine learning radiomics analysis for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer by Shuangyang Mo, Shuangyang Mo, Nan Yi, Fengyan Qin, Huaying Zhao, Huaying Zhao, Yingwei Wang, Haiyan Qin, Haixiao Wei, Haixing Jiang, Shanyu Qin

“…Six machine learning algorithms were utilized to train prediction models employing features with nonzero coefficients. …”
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A smarter approach to liquefaction risk: harnessing dynamic cone penetration test data and machine learning for safer infrastructure by Shubhendu Vikram Singh, Sufyan Ghani

“…This study establishes a threshold criterion based on the ratio of the penetration rate to the dynamic resistance (e/qd), where values exceeding four indicate high liquefaction susceptibility. ML models, including Support Vector Machine (SVM) optimized with Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Firefly Algorithm (FA), were employed to predict the e/qd ratio using key geotechnical parameters, such as fine content, peak ground acceleration, reduction factor, and penetration rate. …”
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A new geographically weighted stacked regression method for forest aboveground carbon storage estimation: A case study of bamboo forest by Jingyi Wang, Xuejian Li, Huaqiang Du, Guomo Zhou, Fangjie Mao, Mingshi Li, Enbin Liu, Weiliang Fan, Ning Han, Yanxin Xu, Zihao Huang

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Genetic diversity insights from population genomics and machine learning tools for Nordic Arctic charr (Salvelinus alpinus) populations by Christos Palaiokostas, Khrystyna Kurta, Fotis Pappas, Henrik Jeuthe, Ørjan Hagen, José Beirão, Matti Janhunen, Antti Kause

“…In addition, unsupervised machine learning models such as K-means, Gaussian and Bayesian Gaussian mixtures were assessed for their ability to detect genetic clusters. …”
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Thermal performance, entropy generation, and machine learning insights of Al₂O₃-TiO₂ hybrid nanofluids in turbulent flow by Praveen Kumar Kanti, V. Vicki Wanatasanappan, Nejla Mahjoub Said, Suman Saini, Vijayalaxmi Mishra, Prabhu Paramasivam, Mohamed Yusuf

“…Furthermore, employing machine learning techniques, highly accurate models are developed. …”
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Machine Learning Framework for Early Detection of Chronic Kidney Disease Stages Using Optimized Estimated Glomerular Filtration Rate by Samit Kumar Ghosh, Namareq Widatalla, Ahsan H. Khandoker

“…This study proposes a machine learning (ML) system that integrates regression-based eGFR estimation, metaheuristic optimization using the Grey Wolf Optimizer (GWO), and multi-class classification with various ML models to enhance CKD staging and classification. …”
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Application analysis of radiomics models based on high-resolution T2-weighted MR images for evaluating the effectiveness of neoadjuvant chemoradiotherapy in locally advanced rectal... by Liu Minglu, Shen Fu, Wang Hao, Chen Qi, Lu Jianping

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Integrated machine learning and population attributable fraction analysis of systemic inflammatory indices for mortality risk prediction in diabetes and prediabetes by Zixi Zhang, Chenyang Li, Yichao Xiao, Chan Liu, Xiaoqin Luo, Cancan Wang, Yongguo Dai, Qiuzhen Lin, Zeying Zhang, Cheng Zheng, Jiafeng Lin, Tao Tu, Qiming Liu

“…Their integration into machine learning models enhances risk prediction and may inform population-level strategies for cardiometabolic risk stratification.…”
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Reaching machine learning leverage to advance performance of electrocatalytic CO2 conversion in non-aqueous deep eutectic electrolytes by Ahmed Halilu, Mohamed Kamel Hadj-Kali, Hanee Farzana Hizaddin, Mohd Ali Hashim, Emad M. Ali, Suresh Bhargava

“…Abstract Deep eutectic electrolytes (DEEs) show promise for future electrochemical systems due to their adjustable buffer capacities. This study utilizes machine learning algorithms to analyse the carbon dioxide reduction reaction (CO2RR) in DEEs with a buffer capacity of approximately 10.21 mol/pH. …”
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Near-Infrared Spectroscopy Machine-Learning Spectral Analysis Tool for Blueberries (<i>Vaccinium corymbosum</i>) Cultivar Discrimination by Pedro Ribeiro, Maria Inês Barbosa, Clara Sousa, Pedro Miguel Rodrigues

“…Spectra were acquired from fresh blueberry leaves collected from two geographic regions and across three seasons. Machine-learning-based models, selected from a pool of 10 classifiers based on their discrimination power under a twofold stratified cross-validation process, were trained/tested with 1 to 20 components obtained by the application of data dimensionality reduction (DDR) techniques (dictionary learning, factor analysis, fast individual component analysis, and principal component analysis) to different near-infrared (NIR) spectra regions’ data, to either analyze a single spectral region and season or combine spectral regions and/or seasons for each side of the blueberry leaf. …”
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Estimation of microbial load in Ganoderma lucidum using a solar-electric hybrid dryer enhanced by machine learning and IoT by Pinit Nuangpirom, Siwasit Pitjamit, Weerin Pheerathamrongrat, Wasawat Nakkiew, Parida Jewpanya

“…The results indicated that higher temperatures, particularly 80 °C, were most effective in reducing microbial counts, achieving near-zero levels after 240 to 480 min. Machine learning (ML) models random forest regression (RFR), decision tree regression (DTR), and multiple linear regression (MLR) were trained to estimate microbial levels based on input variables such as time, temperature, and weight. …”
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Computer Science Integrations with Laser Processing for Advanced Solutions by Serguei P. Murzin

“…The role of intelligent control systems, driven by machine learning and artificial intelligence, was examined, showcasing how a real-time data analysis and adjustments lead to improved process reliability and quality. …”
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Predicting Flood Inundation after a Dike Breach Using a Long Short-Term Memory (LSTM) Neural Network by Leon S. Besseling, Anouk Bomers, Suzanne J. M. H. Hulscher

“…However, their high computational cost makes them unsuitable for real-time flood forecasting. Machine learning models are a promising alternative, as they offer reasonable accuracy at a significant reduction in computation time. …”
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Identifying and characterising asthma subgroups at high risk of severe exacerbations using machine learning and longitudinal real-world data by Patrick Long, Andres Quintero, Javier Lopez-Molina, Merina Su, Nicola Boulter, Cindy Weber, Ralica Dimitrova

“…Objectives To identify and characterise distinct subgroups of patients with asthma with severe acute exacerbations (AEs) by using a multistep clustering methodology that combines supervised and unsupervised machine learning.Methods This cohort study used anonymised, all-payer medical and prescription US claim data from October 2015 to May 2022. …”
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Revolutionizing Battery Longevity by Optimising Magnesium Alloy Anodes Performance by Bankole I. Oladapo, Mattew A. Olawumi, Francis T. Omigbodun

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Optimum Combination of Spectral Variables for Crop Mapping in Heterogeneous Landscapes based on Sentinel-2 Time Series and Machine Learning by J. G. de Oliveira Júnior, J. C. D. M. Esquerdo, J. C. D. M. Esquerdo, R. A. C. Lamparelli, R. A. C. Lamparelli

“…This article aimed to determine a workflow for more efficient large-scale crop mapping using a time series of images from the Sentinel-2 Satellite, statistical methods of attribute selection, and machine learning. The proposed methodology explores the best possible combination of spectral variables related to vegetation (16 vegetation indices in the RGB, NIR, SWIR, and Red Edge regions) to characterize different spectro-temporal profiles of Land Use and Land Cover (LULC) in spatially heterogeneous landscapes. …”
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Optimizing Academic Certificate Management With Blockchain and Machine Learning: A Novel Approach Using Optimistic Rollups and Fraud Detection by Khoa Tan-Vo, Khanh Pham, Phu Huynh, Mong-Thy Nguyen Thi, Thu-Thuy Ta, Thu Nguyen, Tu-Anh Nguyen-Hoang, Ngoc-Thanh Dinh, Hong-Tri Nguyen

“…The experimental outcomes validate the effectiveness of Optimistic Rollups in certificate revocation, showing a notable approximately 61.92% reduction in both transaction costs and latency. Moreover, the machine learning model displays impressive performance, achieving high accuracy in detecting fraudulent users, with an average F1-score of 99.42% and an AUC score nearing perfection. …”
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Optimized SVR model for predicting dissolved oxygen levels using wavelet denoising and variable reduction: Taking the Minjiang River estuary as an example by Peng Zhang, Xinyang Liu, Huiru Zhang, Chengchun Shi, Gangfu Song, Lei Tang, Ruihua Li

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Buried No longer: recent computational advances in explicit interfacial modeling of lithium-based all-solid-state battery materials by Julia H. Yang, Xinqiang Rao, Amanda Whai Shin Ooi

“…Lastly, we highlight universal machine learning potentials, challenging datasets, and opportunities for tighter integration with experiments, all of which broaden the scope of modeling. …”
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