A Fused Multi-Channel Prediction Model of Pressure Injury for Adult Hospitalized Patients—The “EADB” Model by Eba’a Dasan Barghouthi, Amani Yousef Owda, Majdi Owda, Mohammad Asia

“…This study aims to construct a novel fused multi-channel prediction model of PIs in adult hospitalized patients using machine learning algorithms (MLAs). Methods: A multi-phase quantitative approach involving a case–control experimental design was used. …”
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Estimation of Ground-Level NO₂ Concentrations Over Megacities Using Sentinel-5P and Machine Learning Models: A Case Study of Istanbul by N. Yagmur Aydin, I. Aydin

“…The performance of three ML algorithms, namely multi-layer perceptron (MLP), support vector regression (SVR), and XGBoost regression (XGB), in estimating the ground level-NO₂ parameter was evaluated both quantitatively using RMSE and MAE accuracy metrics and qualitatively by visual analysis. …”
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Utilizing Circadian Heart Rate Variability Features and Machine Learning for Estimating Left Ventricular Ejection Fraction Levels in Hypertensive Patients: A Composite Multiscale E... by Nanxiang Zhang, Qi Pan, Shuo Yang, Leen Huang, Jianan Yin, Hai Lin, Xiang Huang, Chonglong Ding, Xinyan Zou, Yongjun Zheng, Jinxin Zhang

“…It has prompted us to develop a comprehensive machine learning framework for the automatic quantitative estimation of LVEF levels from electrocardiography (ECG) signals. …”
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Radiomics machine learning based on asymmetrically prominent cortical and deep medullary veins combined with clinical features to predict prognosis in acute ischemic stroke: a retr... by Hongyi Li, Cancan Chang, Bo Zhou, Yu Lan, Peizhuo Zang, Shannan Chen, Shouliang Qi, Ronghui Ju, Yang Duan

“…However, no machine learning method has been applied to investigate the correlation between the dynamic evolution of intracerebral venous collateral circulation and AIS prognosis. …”
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Prediction of the Impact of Bank Failure Risk on Micro-Credit in Iran: An Artificial Intelligence Approach by Reza Taheri Haftasiabi, Yusef Mohammadzadeh, Ameneh Naderi

“…Machine learning tools, including artificial neural networks (ANN) and support vector machine (SVM), were used to analyze macroeconomic indicators such as GDP, inflation, exchange rate, interest rate, and financial variables of banks such as investment volume, amount of loans granted, total deposits, and bankruptcy risk indicators. …”
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Pioneering CPMI framework for accurate state-of-health assessment in Lithium ion battery power management using FBG sensors by Taher M. Ghazal, Ali Q. Saeed, Mosleh M. Abualhaj, Taj-Aldeen Naser Abdali, Munir Ahmad

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Hyperspectral estimation of chlorophyll content in grapevine based on feature selection and GA-BP by YaFeng Li, XinGang Xu, WenBiao Wu, Yaohui Zhu, LuTao Gao, XiangTai Jiang, Yang Meng, GuiJun Yang, HanYu Xue

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Automatic construction of global cloud sample database based on Landsat imagery by Tao He, Guihua Huang, Lei Zhang, Daiqiang Wu, Yichuan Ma

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Automated Cough Analysis with Convolutional Recurrent Neural Network by Yiping Wang, Mustafaa Wahab, Tianqi Hong, Kyle Molinari, Gail M. Gauvreau, Ruth P. Cusack, Zhen Gao, Imran Satia, Qiyin Fang

“…A number of machine learning algorithms were studied and compared, including decision tree, support vector machine, k-nearest neighbors, logistic regression, random forest, and neural network. …”
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Construction and validation of a prediction model for central lymph node metastasis of papillary thyroid carcinoma based on contrast-enhanced venous phase CT radiomics by HE Xingyun, LIU Chen, DU Junze

“…Six machine learning classifiers, eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), k-Nearest Neighbors (KNN), and Decision Tree (DT) were implemented to construct clinical-radiomics integration models. …”
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An Explainable Multi-Model Stacked Classifier Approach for Predicting Hepatitis C Drug Candidates by Teuku Rizky Noviandy, Aga Maulana, Ghifari Maulana Idroes, Rivansyah Suhendra, Razief Perucha Fauzie Afidh, Rinaldi Idroes

“…Traditional high-throughput screening (HTS) methods are costly, time-consuming, and prone to false positives, underscoring the necessity for more efficient alternatives. Machine learning (ML), particularly quantitative structure–activity relationship (QSAR) modeling, offers a promising solution by predicting compounds’ biological activity based on chemical structures. …”
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Geological hazard susceptibility assessment under land use change: a case study of Dongchuan District, Kunming, Yunnan, China by Mengling Zhang, Ping Duan, Cheng Huang, Shunxing Peng, Shasha Feng, Kang Zhao, Jia Li

“…Land use types were extracted using the Support Vector Machine (SVM) method. Geological hazard susceptibility was assessed using machine learning, and impacts were comprehensively analyzed. …”
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Data-Driven Customer Retention Strategies in E-Commerce: A Fuzzy Z-Number Approach by Siyin Hu, An Chen

“…Our model, which uses both machine learning and fuzzy logic techniques, provides a stable solution to this important issue. …”
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Predicting Hospitalization Length in Geriatric Patients Using Artificial Intelligence and Radiomics by Lorenzo Fantechi, Federico Barbarossa, Sara Cecchini, Lorenzo Zoppi, Giulio Amabili, Mirko Di Rosa, Enrico Paci, Daniela Fornarelli, Anna Rita Bonfigli, Fabrizia Lattanzio, Elvira Maranesi, Roberta Bevilacqua

“…Radiomics, combined with machine learning (ML), offers a promising approach by extracting quantitative imaging features from CT scans. …”
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Retrieval of non-optical active water quality parameters in complex Lake environments using a novel zoning-based ensemble modeling strategy by Cheng Cai, Linlin Liu, Ziming Wang, Wei Pang, Congshuo Bai, Huanxue Zhang

“…Finally, the ZBEMS integrating four machine learning models (Random Forest Regression (RFR), Gradient Boosting Regressor (GBR), eXtreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR)) was applied across different zones for NAWQPs retrieval. …”
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Urban change detection of remote sensing images via deep-feature extraction by Haiying Wang, Mingzhong Wu

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Edge vs. Cloud: Empirical Insights into Data-Driven Condition Monitoring by Chikumbutso Christopher Walani, Wesley Doorsamy

“…The tested induction machine fault diagnosis models are developed using popular algorithms, namely support vector machines, k-nearest neighbours, and decision trees. …”
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An Innovative Neighbor Attention Mechanism Based on Coordinates for the Recognition of Facial Expressions by Cheng Peng, Bohao Li, Kun Zou, Bowen Zhang, Genan Dai, Ah Chung Tsoi

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Radiomics in pediatric brain tumors: from images to insights by Pranjal Rai, Sabha Ahmed, Abhishek Mahajan

“…Recent studies combining radiomics with machine learning algorithms — including support vector machines, random forests, and deep learning CNNs — have demonstrated promising performance, with AUCs ranging from 0.75 to 0.98 for tumor classification and 0.77 to 0.88 for molecular subgroup prediction, across cohorts from 50 to over 450 patients, with internal cross-validation and external validation in some cases. …”
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Development and validation of radiomics model for MRI-based identification of anterior talofibular ligament injuries by Tian-Xin Chen, Jun-Ying Wu, Tong-Jie Yang, Gang Chen, Yan Li, Lei Zhang

“…A dataset of 467 arthroscopically confirmed cases (276 partial tears, 191 complete tears) was analyzed, and 28 key features were selected for model construction using machine learning classifiers. The support vector machine (SVM) model achieved the best performance, with an AUC of 0.955 (95% CI: 0.931–0.980) on the training set and 0.844 (95% CI: 0.781–0.906) on the validation set. …”
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