Unsupervised learning analysis on the proteomes of Zika virus by Edgar E. Lara-Ramírez, Gildardo Rivera, Amanda Alejandra Oliva-Hernández, Virgilio Bocanegra-Garcia, Jesús Adrián López, Xianwu Guo

“…Molecular epidemiology, supported by clustering phylogenetic gold standard studies using sequence data, has provided valuable information for tracking and controlling the spread of ZIKV. Unsupervised learning (UL), a form of machine learning algorithm, can be applied on the datasets without the need of known information for training. …”
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Optimizing flood resilience in China’s mountainous areas: Design flood estimation using advanced machine learning techniques by Xuemei Wang, Ronghua Liu, Chaoxing Sun, Xiaoyan Zhai, Liuqian Ding, Xiao Liu, Xiaolei Zhang

“…Study region: China Study focus: We developed machine learning (ML) models for design flood estimation in mountainous catchments (≤ 500 km²) across China. …”
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Learning the factors controlling mineral dissolution in three-dimensional fracture networks: applications in geologic carbon sequestration by Aleksandra A. Pachalieva, Aleksandra A. Pachalieva, Jeffrey D. Hyman, Daniel O’Malley, Gowri Srinivasan, Hari Viswanathan

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Learning from wildfires: A scalable framework to evaluate treatment effects on burn severity by Caden P. Chamberlain, Garrett W. Meigs, Derek J. Churchill, Jonathan T. Kane, Astrid Sanna, James S. Begley, Susan J. Prichard, Maureen C. Kennedy, Craig Bienz, Ryan D. Haugo, Annie C. Smith, Van R. Kane, C. Alina Cansler

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Residential Building Renovation Considering Energy, Carbon Emissions, and Cost: An Approach Integrating Machine Learning and Evolutionary Generation by Rudai Shan, Wanyu Lai, Huan Tang, Xiangyu Leng, Wei Gu

“…This study proposes an integrated artificial intelligence framework to facilitate multi-criteria energy renovation decision making by combining a surrogate-based machine learning (ML) model and an evolutionary generative algorithm to efficiently and accurately identify optimal renovation strategies. …”
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Comparison of machine learning and validation methods for high-dimensional accelerometer data to detect foot lesions in dairy cattle. by Muhammad Usman Riaz, Luke O'Grady, Conor G McAloon, Finnian Logan, Isobel Claire Gormley

“…Analyzing accelerometer data is challenging due to its wide, high-dimensional structure as it has many features and typically much fewer animals or samples, reducing the utility of many machine learning (ML) models and increasing the risk of overfitting. …”
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Geospatial digital mapping of soil organic carbon using machine learning and geostatistical methods in different land uses by Yahya Parvizi, Shahrokh Fatehi

“…The SOC changes were simulated using multivariate analysis and machine learning methods including generalized linear model (GLM), linear additive model (LAM), cubist, random forest (RF), and support vector machine (SVM) models. …”
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Intelligent brain tumor detection using hybrid finetuned deep transfer features and ensemble machine learning algorithms by Rakesh Salakapuri, Panduranga Vital Terlapu, Kishore Raju Kalidindi, Ramesh Naidu Balaka, D. Jayaram, T. Ravikumar

“…It combines deep (DL) learning and machine (ML) learning techniques. The system uses advanced models like Inception-V3, ResNet-50, and VGG-16 for feature extraction, and for dimensional reduction, it uses the PCA model. …”
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Data driven tensile strength prediction for fiber-reinforced rubberized recycled aggregate concrete using machine learning by Avijit Pal, Khondaker Sakil Ahmed, Nur Yazdani

“…To tackle this, this research examined the tensile strength behavior of fiber-reinforced rubberized recycled aggregate concrete (FR3C) using nine machine learning (ML) models. In this study, nine machine learning models—Random Forest, K-Nearest Neighbors, Support Vector Regression, Decision Tree, Artificial Neural Network, AdaBoost, Gradient Boost, CatBoost, and Extreme Gradient Boost—were trained and tested using a dataset of 346 samples representing various mix proportions. …”
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Decoding healthcare resilience for sustainable development goal 3: A machine learning analysis of global health systems by Ibrahim Alnafrah, Alexander Poroshin

“…The framework combines unsupervised learning — Principal Component Analysis (PCA) for dimensionality reduction and structural insight, and K-means for risk-level clustering — with supervised classification models, including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Random Forest (RF), Classification and Regression Trees (CART), and Linear Discriminant Analysis (LDA), for predictive analysis. …”
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A Hybrid Machine Learning Approach for Detecting and Assessing <i>Zyginidia pullula</i> Damage in Maize Leaves by Havva Esra Bakbak, Caner Balım, Aydogan Savran

“…Extracted features are then fused and subjected to Principal Component Analysis for dimensionality reduction. The classification task is performed using Support Vector Machines, Random Forest, and Artificial Neural Networks, ensuring robust and accurate detection. …”
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Evaluation of vascular cognitive impairment and identification of imaging markers using machine learning: a multimodal MRI study by Haoying He, Dongwei Lu, Sisi Peng, Jiu Jiang, Fan Fan, Dong Sun, Tianqi Sun, Zhipeng Xu, Ping Zhang, Xiaoxiang Peng, Ming Lei, Junjian Zhang

“…Model reduction was undertaken to simplify models without sacrificing performance. …”
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Machine learning prediction and explainability analysis of high strength glass powder concrete using SHAP PDP and ICE by Muhammad Sarmad Mahmood, Tariq Ali, Inamullah Inam, Muhammad Zeeshan Qureshi, Syed Salman Ahmad Zaidi, Muwaffaq Alqurashi, Hawreen Ahmed, Muhammad Adnan, Abdul Hakim Hotak

“…This study aims to evaluate the compressive strength (CS) of high strength glass-powder concrete (HSGPC) using machine learning (ML) models and enhance predictive accuracy through hybrid optimization techniques. …”
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Antihypertensive Drug Recommendations for Reducing Arterial Stiffness in Patients With Hypertension: Machine Learning–Based Multicohort (RIGIPREV) Study by Iván Cavero-Redondo, Arturo Martinez-Rodrigo, Alicia Saz-Lara, Nerea Moreno-Herraiz, Veronica Casado-Vicente, Leticia Gomez-Sanchez, Luis Garcia-Ortiz, Manuel A Gomez-Marcos

“…A multioutput regressor using 6 random forest models was used to predict the impact of each antihypertensive class on PWV reduction. …”
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Optimizing base fluid composition for PEMFC cooling: A machine learning approach to balance thermal and rheological performance by Praveen Kumar Kanti, Prashantha Kumar H. G, Nejla Mahjoub Said, V. Vicki Wanatasanappan, Prabhu Paramasivam, Leliso Hobicho Dabelo

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Cervical cancer screening uptake and its associated factor in Sub-Sharan Africa: a machine learning approach by Fetlework Gubena Arage, Zinabu Bekele Tadese, Eliyas Addisu Taye, Tigist Kifle Tsegaw, Tsegasilassie Gebremariam Abate, Eyob Akalewold Alemu

“…Conclusion This study demonstrates that the ensemble machine learning models, such as Extra Trees Classifier and Random Forest, are promising in predicting cervical cancer screening uptake among African women with accuracies of 94.13% and 93.87%, respectively. …”
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An explainable machine learning framework for railway predictive maintenance using data streams from the metro operator of Portugal by Silvia García-Méndez, Francisco de Arriba-Pérez, Fátima Leal, Bruno Veloso, Benedita Malheiro, Juan Carlos Burguillo-Rial

“…The proposed method implements a processing pipeline comprised of sample pre-processing, incremental classification with Machine Learning models, and outcome explanation. …”
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Energy-Efficient Prediction of Carbon Deposition in DRM Processes Through Optimized Neural Network Modeling by Rui Fang, Tuo Zhou, Zhuangzhuang Xu, Xiannan Hu, Man Zhang, Hairui Yang

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Elucidating the Prognostic and Therapeutic Implications of Insulin Resistance Genes in Breast Cancer: A Machine Learning-Powered Analysis by Lengyun Wei, Dashuai Li, Hongjin Chen, Yajing Pu, Qun Wang, Jintao Li, Meng Zhou, Chenfeng Liu, Pengpeng Long

“…In this study, we employed a suite of machine learning algorithms and statistical methods to construct a robust prognostic model for BC based on insulin resistance-related genes (IRGs). …”
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Flexural strengthening of corroded steel beams with CFRP by using the end anchorage: Experimental, numerical, and machine learning methods by Amin Shabani Ammari, Younes Nouri, Habib Ghasemi Jouneghani, Seyed Amin Hosseini, Arash Rayegani, Mehrdad Ebrahimi, Pooria Heydari

“…A new end anchorage system was developed to avoid CFRP slippage, ensuring full utilization of its tensile capacity. Numerical modeling further validated the experimental results and then numerical specimens were used for parametric and Machine Learning (ML) studies. …”
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