Interpretable Prediction and Analysis of PVA Hydrogel Mechanical Behavior Using Machine Learning by Liying Xu, Siqi Liu, Anqi Lin, Zichuan Su, Daxin Liang

“…This study presents an interpretable machine learning framework for predicting PVA hydrogel tensile strain properties with emphasis on mechanistic understanding, based on a comprehensive dataset of 350 data points collected from a systematic literature review. …”
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Overcoming the challenges of data integration in ecosystem studies with machine learning workflows: an example from the Santos project by Gustavo Fonseca, Danilo Candido Vieira

“…Addressing missing values, the bagging imputation technique demonstrated superior performance compared to other methods. Both machine learning techniques discussed herein exhibit resilience against the impact of noisy data, yet the identification of noisy data remains feasible based on model outputs. …”
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Overcoming the challenges of data integration in ecosystem studies with machine learning workflows: an example from the Santos project by Gustavo Fonseca, Danilo Candido Vieira

“…Addressing missing values, the bagging imputation technique demonstrated superior performance compared to other methods. Both machine learning techniques discussed herein exhibit resilience against the impact of noisy data, yet the identification of noisy data remains feasible based on model outputs. …”
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Opinion mining in e-commerce: Evaluating machine learning approaches for sentiment analysis by L. Lakshmi, Ali B.M. Ali, K Dhana Sree Devi, Muhammad Rafiq, Iskandar Shernazarov, Nashwan Adnan Othman, M. Ijaz Khan

“…We employ three machine learning algorithms—stacking, random forest, and LogitBoost—to evaluate the performance of these approaches. …”
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Forecasting and Feature Analysis of Ship Fuel Consumption by Explainable Machine Learning Approaches by Pham Nguyen Dang Khoa, Dinh Gia Huy, Nguyen Canh Lam, Dang Hai Quoc, Pham Hoang Thai, Nguyen Quyen Tat, Tran Minh Cong

“…Generally, the employed ML approaches efficiently predicted the data; however, they are black-box methods. Hence, explainable machine learning methods like Shapley additive explanations, the DT structure, and local interpretable model-agnostic explanations (LIME) were employed to comprehend the models and perform feature analysis. …”
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Integrating SHAP analysis with machine learning to predict postpartum hemorrhage in vaginal births by Zixuan Song, Hong Lin, Mengyuan Shao, Xiaoxue Wang, Xueting Chen, Yangzi Zhou, Dandan Zhang

“…Abstract Objective This study aimed to develop a machine learning (ML) model integrated with SHapley Additive exPlanations (SHAP) analysis to predict postpartum hemorrhage (PPH) following vaginal deliveries, offering a potential tool for personalized risk assessment and prevention in clinical settings. …”
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Network-based analyses of multiomics data in biomedicine by Rachit Kumar, Joseph D. Romano, Marylyn D. Ritchie

“…This review will present various existing approaches in using network representations and analysis of data in multiomics in the framework of deep learning and machine learning approaches, subdivided into supervised and unsupervised approaches, to identify benefits and drawbacks of various approaches as well as the possible next steps for the field.…”
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Machine Learning-Based Morphological Classification and Diversity Analysis of Ornamental Pumpkin Seeds by Sıtkı Ermiş, Uğur Ercan, Aylin Kabaş, Önder Kabaş, Georgiana Moiceanu

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An integrated CFD and machine learning analysis on pilots in-flight thermal comfort and productivity by Xueren Li, Ziqi Chen, Yin Tang, Yihuan Yan, Bichen Shang, Shengjin Xu, Jiyuan Tu

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Machine learning analysis of CO2 and methane adsorption in tight reservoir rocks by Mehdi Maleki, Mohammad Rasool Dehghani, Moein Kafi, Ali Akbari, Yousef Kazemzadeh, Ali Ranjbar

“…In this study, the adsorption behavior of CO2 and CH4 in tight reservoirs is examined using experimental data and advanced machine learning (ML) techniques. …”
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Real-Time Algal Monitoring Using Novel Machine Learning Approaches by Seyit Uguz, Yavuz Selim Sahin, Pradeep Kumar, Xufei Yang, Gary Anderson

“…To overcome these limitations, this study proposes an automated, real-time, and cost-effective solution by integrating machine learning with image-based analysis. We evaluated the performance of Decision Trees (DTS), Random Forests (RF), Gradient Boosting Machines (GBM), and K-Nearest Neighbors (k-NN) algorithms using RGB color histograms extracted from images of <i>Scenedesmus dimorphus</i> cultures. …”
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Data Fusion of Electronic Nose and Multispectral Imaging for Meat Spoilage Detection Using Machine Learning Techniques by Vassilis S. Kodogiannis, Abeer Alshejari

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Describing the performance of U.S. hospitals by applying big data analytics. by Nicholas S Downing, Alexander Cloninger, Arjun K Venkatesh, Angela Hsieh, Elizabeth E Drye, Ronald R Coifman, Harlan M Krumholz

“…Our application of a novel graph analytics method to data describing U.S. hospitals revealed nuanced differences in performance that are obscured in existing hospital rating systems.…”
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Interpretable machine learning analysis of environmental characteristics on bacillary dysentery in Sichuan Province by Yao Zhang, Qiao-Lin Wang, Wei Peng, Meng-Yuan Zhang, Yao Qin, Lun Zhang, Rong-Jie Wei, Dian-Ju Kang

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Tropospheric NO₂: Anthropogenic Influence, Global Trends, Satellite Data, and Machine Learning Application by Valeria Ojeda-Castillo, Mario Alfonso Murillo-Tovar, Leonel Hernández-Mena, Hugo Saldarriaga-Noreña, María Elena Vargas-Amado, Enrique J. Herrera-López, Jesús Díaz

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Machine learning-based modelling and analysis of carbonation depth of recycled aggregate concrete by Xuyong Chen, Xuan Liu, Shukai Cheng, Xiaoya Bian, Xixuan Bai, Xin Zheng, Xiong Xu, Zhifeng Xu

“…This paper used machine learning to model the prediction of carbonation depth and the analysis of feature parameters for recycled aggregate concrete (RAC). …”
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Identification of diagnostic biomarkers for fibromyalgia using gene expression analysis and machine learning by Fuyu Zhao, Fuyu Zhao, Fuyu Zhao, Jianan Zhao, Jianan Zhao, Jianan Zhao, Yang Li, Chenyang Song, Chenyang Song, Chenyang Song, Yaxin Cheng, Yaxin Cheng, Yaxin Cheng, Yunshen Li, Yunshen Li, Yunshen Li, Shiya Wu, Bingheng He, Juan Jiao, Cen Chang, Cen Chang

“…The diagnostic model demonstrated robust performance, with an area under the curve (AUC) of 0.8338 in the training set and 0.8178 in the validation set, indicating its potential utility in clinical settings.ConclusionThe study successfully identifies three diagnostic biomarkers for FM, supported by both bioinformatics analysis and machine learning models. These findings could significantly improve diagnostic accuracy for FM, leading to better patient management and treatment outcomes.…”
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Towards Structured Gaze Data Classification: The Gaze Data Clustering Taxonomy (GCT) by Yahdi Siradj, Kiki Maulana Adhinugraha, Eric Pardede

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Application of Artificial Intelligence to Support Design and Analysis of Steel Structures by Sina Sarfarazi, Ida Mascolo, Mariano Modano, Federico Guarracino

“…In steel structural engineering, artificial intelligence (AI) and machine learning (ML) are improving accuracy, efficiency, and automation. …”
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Sentiment Analysis using Twitter Dataset by Ibrahim Moge Noor, Metin Turan

Subjects: “…machine learning…”
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