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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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

“…The results suggest distinct autonomic and respiratory patterns in specific clusters, pointing toward possible links among sympathetic dysregulation, sleep-related disturbances, and syncope. …”
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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

“…Moreover, for a better explanation of thermal patterns in the study region, past records of LST are also taken into account. …”
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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

“…ImportanceUnderstanding heterogeneous recovery patterns in sepsis is crucial for personalizing treatment strategies and improving outcomes.ObjectiveTo identify distinct recovery trajectories in sepsis and develop a prediction model using early clinical and immunological markers.Design, setting, and participantsRetrospective cohort study using data from 28,745 adult patients admitted to 12 intensive care units (ICUs) with sepsis between January 2014 and December 2024.Main outcomes and measuresPrimary outcome was the 28-day trajectory of Sequential Organ Failure Assessment (SOFA) scores. …”
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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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Regression Analysis of Heat Release Rate for Box-Type Power Bank Based on Experimental and Machine Learning Methods by Shihan Luo, Hua Chen, Xiaobing Mao, Wenbing Zhu, Yuanyi Xie, Wenbin Wei, Mengmeng Jiang, Chenyang Zhang, Chaozhe Jiang

“…This study uses experimental testing and machine learning regression analysis to explore the heat release rate (HRR) characteristics and influencing factors of box-type power banks under fire conditions. …”
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Revealing age-related changes in the intraocular microenvironment and senescence modulators using aqueous humor proteomics and machine learning by Xiaosheng Huang, Tiansheng Chou, Tiansheng Chou, Xinhua Liu, Kun Zeng, Liangnan Sun, Zonghui Yan, Shaoyi Mei, Wenqun Xi, Zongyi Zhan, Yi Liu, Songguo Dong, Siqi Liu, Jun Zhao

“…Aging proteins (APs) and their functional enrichment were evaluated using various statistical and bioinformatics methods, while aging modulators were predicted using multiple machine-learning models.ResultsAH proteomic expression patterns exhibited various types of linear and nonlinear changes across the age groups. …”
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Enhancing soil organic carbon prediction by unraveling the role of crop residue coverage using interpretable machine learning by Yi Dong, Xinting Wang, Sheng Wang, Baoguo Li, Junming Liu, Jianxi Huang, Xuecao Li, Yelu Zeng, Wei Su

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Integration of UAV-sensed features using machine learning methods to assess species richness in wet grassland ecosystems by Clara Oliva Gonçalves Bazzo, Bahareh Kamali, Murilo dos Santos Vianna, Dominik Behrend, Hubert Hueging, Inga Schleip, Paul Mosebach, Almut Haub, Axel Behrendt, Thomas Gaiser

“…The analysis revealed that the performance of the machine learning methods varied with the feature combinations. …”
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Big Data in Leadership Studies: Automated Machine Learning Model to Predict Preferred Leader Behavior Across Cultures by Erik Lankut, Gillian Warner-Søderholm, Ilan Alon, Inga Minelgaité

“…This study proposes a comprehensive predictive model to explore significant preferred leadership factors, drawn from the Leader Behavior Description Questionnaire (LBDQXII), across cultures using automated machine learning (AML). We offer a robust empirical measurement of culturally contingent leader behavior and entrepreneurship behaviors and provide a tool for assessing the cultural predictors of preferred leader behavior to minimize predictive errors, explore patterns in the data and make predictions in an empirically robust way. …”
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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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A transferable machine learning model for real-time forecast of epidemic dynamics and pre-trigger event warning by Enpei Chen, Xiong Yu

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Image-based machine learning and cluster analysis for urban road network: employing Orange for codeless visual programming by Zuo Zhang, Mengwei Zhang, Xiangxiang Song, Zhi Li

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Effect of geographical parameters on PM10 pollution in European landscapes: a machine learning algorithm-based analysis by Seyedehmehrmanzar Sohrab, Nándor Csikós, Péter Szilassi

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Integrating Metaheuristics and Machine Learning for Enhanced Vehicle Routing: A Comparative Study of Hyperheuristic and VAE-Based Approaches by Kassem Danach, Louai Saker, Hassan Harb

“…In contrast, the VAE-based approach leverages deep learning to model historical routing patterns and autonomously generate new heuristics tailored to problem-specific characteristics. …”
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