Investigating reduced-dimensional variability in aircraft-observed aerosol–cloud parameters by Kayley M. Butler, Sam J. Silva, Armin Sorooshian, Richard H. Moore, Glenn S. Diskin, John B. Nowak, Luke Ziemba, Ewan Crosbie, Michael A. Shook, Joshua DiGangi, Edward Winstead, Claire Robinson, Yonghoon Choi

“…Here, we apply two machine learning methods to explore variability in in-situ observations from the NASA ACTIVATE mission. …”
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Machine learning method based on radiomics help differentiate posterior pituitary tumors from pituitary neuroendocrine tumors and craniopharyngioma by Yukun Liu, Yanpeng Zhou, Chunyao Zhou, Zhenmin Wang, Ziwen Fan, Kai Tang, Siyuan Chen

“…We established a group of machine learning models to noninvasively differentiate PPTs from NPPTs before surgery, which may improve the surgical plan of PPTs to better complete resection of the tumors and protection of important structures around the tumors.…”
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Machine Learning-Assisted Sustainable Mix Design of Waste Glass Powder Concrete with Strength–Cost–CO₂ Emissions Trade-Offs by Yuzhuo Zhang, Jiale Peng, Zi Wang, Meng Xi, Jinlong Liu, Lei Xu

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Comparative analysis of machine learning models for predicting river water quality: a case study of the Zayandeh Rood River by Elham Fazel Najafabadi, Paria Shojaei, Mojgan Askarizadeh

“…This study evaluated five machine learning models, i.e., Lasso Regression, Random Forest (RF), Gradient Boosting (GB), XGBoost, and Support Vector Machine (SVM) for predicting four water quality parameters—EC (Electrical Conductivity), TDS (Total Dissolved Solids), Sodium Adsorption Ratio (SAR), and TH (Total Hardness)—using data collected over a 31-year period from eight monitoring stations along the Zayandeh Rood River, a vital water source for drinking, agriculture, and industry in the arid region of central Iran. …”
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Predictive modeling of hydrogen production and methane conversion from biomass-derived methane using machine learning and optimisation techniques by Adegboyega Bolu Ehinmowo, Bright Ikechukwu Nwaneri, Joseph Oluwatobi Olaide

“…The study hence established the great opportunity of integration of machine learning models with optimisation techniques in attempts to improve the prediction of hydrogen yield and methane conversion in processes for hydrogen production.…”
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Multi-model radiomics and machine learning for differentiating lipid-poor adrenal adenomas from metastases using automatic segmentation by Shengnan Yin, Ning Ding, Shaocai Wang, Mengjuan Li, Yichi Zhang, Jiacheng Shen, Haitao Hu, Yiding Ji, Long Jin

“…Clinical and imaging features were then incorporated into an XGBoost machine learning model, and model performance was evaluated using Area Under Curve (AUC), accuracy, precision, sensitivity, specificity, and F1 score. …”
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Big data-driven corporate financial forecasting and decision support: a study of CNN-LSTM machine learning models by Aixiang Yang

“…With the rapid advancement of information technology, particularly the widespread adoption of big data and machine learning, corporate financial management is undergoing unprecedented transformation. …”
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Operational Performance Assessment of PV-Powered Street Lighting: A Comparative Study of Different Machine Learning Prediction Models by Safwan Nadweh, Nabil Mohammed, Charalambos Konstantinou, Shehab Ahmed

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Implementing partial least squares and machine learning regressive models for prediction of drug release in targeted drug delivery application by Anupam Yadav, B. Jayaprakash, Laith Hussein Jasim, Mayank Kundlas, Maan Younis Anad, Ankur Srivastava, M. Janaki Ramudu, B. Bharathi, Prabhat Kumar Sahu

“…Abstract A combined methodology was performed based on chemometrics and machine learning regressive models in estimation of polysaccharide-coated colonic drug delivery. …”
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A machine learning model for predicting postoperative complication risk in young and middle-aged patients with femoral neck fractures by Yixin Huang, Yixin Huang, Dongze Lin, Bin Chen, Xiaole Jiang, Shanglin Shangguan, Fengfei Lin

“…Key predictors affecting postoperative complications were identified through LASSO regression and multifactorial logistic regression analyses. Several machine learning (ML) models were then integrated for comparative analysis. …”
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CECT-Based Radiomic Nomogram of Different Machine Learning Models for Differentiating Malignant and Benign Solid-Containing Renal Masses by Qian L, Fu B, He H, Liu S, Lu R

“…Radiomic features were extracted from the arterial, venous and delayed phases and further analysed by dimensionality reduction and selection. Four mainstream machine learning algorithm training models, namely, support vector machine (SVM), k-nearest neighbour (kNN), light gradient boosting (LightGBM) and logistic regression (LR), were constructed to determine the best classifier model. …”
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Construction of a prognostic model for endometrial cancer related to programmed cell death using WGCNA and machine learning algorithms by Weicheng Pan, Jinlian Cheng, Shanshan Lin, Qianxi Li, Yuanyuan Liang, Huiying Li, Xianxian Nong, Huizhen Nong

“…To isolate core prognostic PCD-DEGs, methods including consistency clustering analysis, weighted gene co-expression network analysis (WGCNA), univariate Cox regression analysis, and five machine learning techniques for dimensionality reduction were utilized. …”
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Identification and validation of an explainable machine learning model for vascular depression diagnosis in the older adults: a multicenter cohort study by Ran Zhang, Tian Li, Fan Fan, Haoying He, Liuyi Lan, Dong Sun, Zhipeng Xu, Sisi Peng, Jing Cao, Juan Xu, Xiaoxiang Peng, Ming Lei, Hao Song, Junjian Zhang

“…This study aimed to develop and validate an interpretable machine learning (ML) model for VaDep to serve as a clinical support tool. …”
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Utilizing Machine Learning Techniques for Cancer Prediction and Classification based on Gene Expression Data by Mariwan Mahmood Hama Aziz, Sozan Abdullah Mahmood

“…Lately, several studies have delved into cancer classification by leveraging data mining techniques, machine learning algorithms, and statistical methods to thoroughly analyze high-dimensional datasets. …”
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Predictive Analysis of Cardiovascular Disease Risk Factors in Romania using Machine Learning and Medical Statistics by Radu-Anton MOLDOVAN, Sebastian-Aurelian ŞTEFĂNIGĂ

“…The aim of the present study was to identify and assess the significant risk factors of CVD and develop evidence-based prevention strategies. To do this, we used machine learning algorithms such as logistic regression, random forests, support vector machines (SVM), and artificial neural networks (ANNs) to forecast cardiovascular risk factors from past medical data and epidemiology trends. …”
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Elucidating Early Radiation-Induced Cardiotoxicity Markers in Preclinical Genetic Models Through Advanced Machine Learning and Cardiac MRI by Dayeong An, El-Sayed Ibrahim

“…This study aimed to detect early markers of RIHD using machine learning (ML) techniques and cardiac MRI in a rat model. …”
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Spatiotemporal estimation of near-surface CO2 concentrations over the global continent based on hybrid machine learning model by Xinfeng Huang, Hui Yang, Senwei Qiao, Yuejing Yao, Liu Cui, Huaiwei Fan, Qingzhou Lv, Yina Qiao, Gefei Feng

“…This study proposed a hybrid machine learning model consisting of mix attention (MA) module, deep forest (DF) module and LightGBM to estimate near-surface CO2 concentrations. …”
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Mitigating Climate Risk to China's Renewable Energy Generation by Power Plants Redeployment Using a Machine‐Learning Model by Lin Lv, Miaomiao Liu, Yuli Shan, Jinghang Xu, Jianxun Yang, Wen Fang, Zongwei Ma, Jun Bi

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NitroNet – a machine learning model for the prediction of tropospheric NO₂ profiles from TROPOMI observations by L. Kuhn, L. Kuhn, S. Beirle, S. Osipov, S. Osipov, A. Pozzer, T. Wagner, T. Wagner

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Investigation of Micro-Scale Damage and Weakening Mechanisms in Rocks Induced by Microwave Radiation and Their Associated Strength Reduction Patterns: Employing Meta-Heuristic Opti... by Zhongyuan Gu, Xin Xiong, Chengye Yang, Miaocong Cao

“…This model was benchmarked against other prevalent machine learning frameworks, with Shapley additive explanatory methods employed to assess each parameter’s influence on UCSA. …”
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