Machine-learning-based identification of patients with IgA nephropathy using a computerized medical billing database. by Ryoya Tsunoda, Keitaro Kume, Rina Kagawa, Masaru Sanuki, Hiroyuki Kitagawa, Kaori Mase, Kunihiro Yamagata

“…A manual analysis of the diagnostic accuracy and machine learning was performed. For machine learning, the datasets were preprocessed in three patterns and assigned to the XGBoost program using five-fold cross-validation. …”
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The Dynamics Affecting the Export-Import Ratio in Turkey: A Hybrid Model Proposal with Econometrics and Machine Learning Approach by Erdemalp Özden

“…In addition, a 1% increase in consumer price index will increase ratio of exports to imports by 1.9 points, while a 1% increase in producer price index will cause a -0.8 point decrease on the ratio of exports to imports. Then, the pattern between the variables was analyzed with quadratic support vector machine, a machine learning method. …”
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Leveraging Radiomics and Genetic Algorithms to Improve Lung Infection Diagnosis in X-Ray Images Using Machine Learning by A. Beena Godbin, S. Graceline Jasmine

“…Radiomics, an emerging discipline in medical imaging, focuses on extracting detailed quantitative features from images to unveil subtle patterns imperceptible to the naked eye. This study specifically employs radiomics and machine learning techniques to discern cases of viral pneumonia and COVID-19. …”
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Non-destructive assessment of hemp seed vigor using machine learning and deep learning models with hyperspectral imaging by Damrongvudhi Onwimol, Pongsan Chakranon, Kris Wonggasem, Papis Wongchaisuwat

“…Deep learning models were trained on these selected wavelengths to directly learn patterns from the raw spectral data. The performance of these deep learning models was compared to traditional machine learning approaches. …”
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Flood risk assessment with machine learning: insights from the 2022 Pakistan mega-flood and climate adaptation strategies by Peng Cui, Nazir Ahmed Bazai, Zou Qiang, Wang Jiao, Wang Yan, Qingsong Xu, Lei Yu, Zhang Bo

“…By coupling seventy years of historical flood data with advanced machine learning techniques (GeoPINS within FloodCast), this study quantifies the event’s primary drivers and projects future risk under climate change. …”
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Integrating Sentinel-1 SAR and Machine Learning Models for Optimal Soil Moisture Sensor Placement at Catchment Scale by Yi Xie, Guotao Cui, Kaifeng Zheng, Guoping Tang

“…However, effectively capturing representative soil moisture patterns across heterogeneous catchments using ground-based sensors remains a significant challenge. …”
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A Review of Recent Advances, Challenges, and Opportunities in Malicious Insider Threat Detection Using Machine Learning Methods by Fatima Rashed Alzaabi, Abid Mehmood

“…The review encompasses a broad spectrum of methodologies and techniques, with a particular focus on classical machine-learning approaches and their limitations in effectively addressing the intricacies of insider threats. …”
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Clinical Characterization of Patients with Syncope of Unclear Cause Using Unsupervised Machine-Learning Tools: A Pilot Study by María-José Muñoz-Martínez, Manuel Casal-Guisande, María Torres-Durán, Bernardo Sopeña, Alberto Fernández-Villar

“…This study aims to explore the potential of unsupervised machine learning (ML), specifically clustering algorithms, to identify clinically meaningful subgroups within a cohort of 123 patients with SUC. …”
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Advancing sedimentation modeling in large reservoir systems: Insights from multi-scale process coupling and machine learning by Yuning Tan, Huaixiang Liu, Yongjun Lu, Zhili Wang, Wenjie Li

“…Our climate scenario simulations revealed uneven sedimentation patterns and projected declining sedimentation rates over the next 30 years. …”
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Phish Fighter: Self Updating Machine Learning Shield Against Phishing Kits Based on HTML Code Analysis by Gabriela Brezeanu, Alexandru Archip, Codrut-Georgian Artene

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Development of an Optimal Machine Learning Model to Predict CO₂ Emissions at the Building Demolition Stage by Gi-Wook Cha, Choon-Wook Park

“…In this study, research on the development of optimal machine learning (ML) models was conducted to predict CO₂ emissions at the demolition stage. …”
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Histogram-based gradient boosting machine with SHAP-driven interpretability for predicting intensity of urban heat Island effect by Nhat-Duc Hoang

“…Histogram-Based Gradient Boosting Machine (HBGBM), a state-of-the-art machine learning approach, is used to generalize a functional relationship between LST and the influencing factors. …”
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Distinct immunological signatures define three sepsis recovery trajectories: a multi-cohort machine learning study by Rui Zhang, Fang Long, Jingyi Wu, Ruoming Tan

“…Secondary outcomes included 90-day mortality and hospital length of stay.ResultsAmong 24,450 eligible patients (mean [SD] age, 64.5 [15.3] years; 54.2% male), three distinct recovery trajectories were identified: rapid recovery (42.3%), slow recovery (35.8%), and deterioration (21.9%). The machine learning model achieved an AUROC of 0.85 (95% CI, 0.83–0.87) for trajectory prediction. …”
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Influence of Ocean Current Features on the Performance of Machine Learning and Dynamic Tracking Methods in Predicting Marine Drifter Trajectories by Huan Lin, Weiye Yu, Zhan Lian

“…In general, LSTM provides a more accurate geometric pattern of trajectories at the initial stages of forecasting, while DT offers superior accuracy in predicting specific trajectory positions. …”
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Identification of biomarkers for the diagnosis in colorectal polyps and metabolic dysfunction-associated steatohepatitis (MASH) by bioinformatics analysis and machine learning by Ying Geng, Yifang Li, Ge Liu, Jian Jiao

“…The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses depicted they were mainly enriched in apoptosis, proliferation and infection pathways. Machine learning algorithms identified S100P, FOXO1, and LPAR1 were biomarkers for colorectal polyps and MASH, ROC curve and violin plot showed ideal AUC and stable expression patterns in both the discovery and validation sets. …”
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Enhancing Structural Health Monitoring of Super-Tall Buildings Using Support Vector Machines, MEMD, and Wavelet Transform by Rouzbeh Doroudi, Seyed Hossein Hosseini Lavasani, Mohsen Shahrouzi, Aref Afshar

“…SVMs efficiently identify damage patterns. However, require parameter tuning, addressed using Observer-Teacher-Learner-Based Optimization. …”
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Leveraging Social Media Data to Understand COVID-19 Prevention Measures in Construction: A Machine Learning Approach by Emmanuel B. Boateng, Daniel Oteng, Dan N. O. Bonsu, Vinod Gopaldasani

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GAINSeq: glaucoma pre-symptomatic detection using machine learning models driven by next-generation sequencing data by Muhammad Iqbal, Arshad Iqbal, Humaira Ayub, Maqbool Khan, Naveed Ahmad, Yasir Javed, Mohammed Ali Alshara

“…The findings highlight the capacity of machine learning methods to reveal complex patterns in NGS data, therefore improving the proposed comprehension of the causes of congenital glaucoma. …”
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CO2 adsorption on NaOH and acid modified montmorillonite: Response surface methodology and machine learning modeling by Pardis Mehrmohammadi, Amir Ahmadvand, Ahad Ghaemi

“…This study investigates the use of modified montmorillonite (MMT) for CO₂ adsorption through an integrated approach combining Machine Learning (ML) modeling and Response Surface Methodology (RSM). …”
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Intervention of machine learning in bladder cancer research using multi-omics datasets: systematic review on biomarker identification by Blessy Kiruba, P. S. Athul Narayan, Badhari Raj, S. Rohieth Raj, Sam George Mathew, Sudhakaran Sajitha Lulu, Vino Sundararajan

“…However, challenges such as computational complexity and data integration prevent these methods from achieving robust diagnostic capabilities. Hence, machine learning (ML), with its ability to process high-dimensional data and identify complex patterns, offers a promising patient outcome. …”
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