Assessment of flood vulnerability in a coastal metropolitan city for sustainable environmental using machine learning methods by Rana Alabdan, C. Sharmila, Nuha Alruwais, Haya Mesfer Alshahrani, S. Anbukkarasi, M. Sujatha, S. Vivek

“…The primary aim of this research is to develop a robust methodology for assessing flood vulnerability in Chennai, Tamil Nadu, India, using advanced machine learning techniques. To achieve this, we employed two ensemble models—artificial neural network (ANN) and random forest (RF)—within a GIS framework, analyzing data from 280 historical flood sites and twelve flood-related parameters. …”
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Retinal imaging based glaucoma detection using modified pelican optimization based extreme learning machine by Debendra Muduli, Rani Kumari, Adnan Akhunzada, Korhan Cengiz, Santosh Kumar Sharma, Rakesh Ranjan Kumar, Dinesh Kumar Sah

“…Lastly, a newly improved learning algorithm encompasses a modified pelican optimization algorithm (MOD-POA) and an extreme learning machine (ELM) for classification tasks. In this MOD-POA+ELM algorithm, the modified pelican optimization algorithm (MOD-POA) has been utilized to optimize the parameters of ELM’s hidden neurons. …”
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Diagnostic Accuracy of a Machine Learning-Derived Appendicitis Score in Children: A Multicenter Validation Study by Emrah Aydın, Taha Eren Sarnıç, İnan Utku Türkmen, Narmina Khanmammadova, Ufuk Ateş, Mustafa Onur Öztan, Tamer Sekmenli, Necip Fazıl Aras, Tülin Öztaş, Ali Yalçınkaya, Murat Özbek, Deniz Gökçe, Hatice Sonay Yalçın Cömert, Osman Uzunlu, Aliye Kandırıcı, Nazile Ertürk, Alev Süzen, Fatih Akova, Mehmet Paşaoğlu, Egemen Eroğlu, Gülnur Göllü Bahadır, Ahmet Murat Çakmak, Salim Bilici, Ramazan Karabulut, Mustafa İmamoğlu, Haluk Sarıhan, Süleyman Cüneyt Karakuş

“…<b>Methods</b>: This prospective, multicenter study included 8586 pediatric patients to develop a machine learning-based diagnostic model using routinely available clinical and hematological parameters. …”
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Machine learning identifies lipid-associated genes and constructs diagnostic and prognostic models for idiopathic pulmonary fibrosis by Xingren Liu, Junmei Song, Shujin Guo, Yi Liao, Jun Zou, Liqing Yang, Caiyu Jiang

“…Results Two distinct clusters were identified, showing significant differences in lung function parameters and fibrosis-related gene expression. WGCNA revealed that the blue module was strongly associated with IPF and served as the core module. …”
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Leveraging Machine Learning Regression Algorithms to Predict Mechanical Properties of Evaporitic Rocks From Their Physical Attributes by Ayham Zaitouny, Hasan Arman, Anusuya Krishnan, Alaa Ahmed, Ahmed Gad

“…Evaluating the geotechnical properties of evaporitic rocks is crucial for infrastructure stability; however, traditional methods are costly and labour-intensive. In this study, machine learning (ML) regression algorithms were applied to predict four key mechanical parameters, namely, uniaxial compressive strength (UCS), point load index (PLI), indirect tensile strength (ITS), and Schmidt hardness value (SHV), based on the physical attributes of evaporitic rocks. …”
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A novel perspective on survival prediction for AML patients: Integration of machine learning in SEER database applications by Zheng-yi Jia, Maierbiya Abulimiti, Yun Wu, Li-na Ma, Xiao-yu Li, Jie Wang

“…We also used five-fold cross-validation with 20 cycles to obtain the optimal parameters for each model, in order to improve the accuracy of predictions. …”
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The impact of multipollutant exposure on hepatic steatosis: a machine learning-based investigation into multipollutant synergistic effects by Chunying Yan, Zhanfang Zhu, Xueyan Guo, Wei Zong, Guisheng Liu, Yan Jin, Shiyuan Cui, Fuqiang Liu, Shujuan Gao

“…The EPEI demonstrated strong associations with obesity-related parameters (PLF: 7.02 vs. 3.41 in high/low-exposure groups, p &lt; 0.0001) and hyperlipidemia (OR = 2.28 vs. 1.08, p = 2.7e-06). …”
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Machine Learning Models Informed by Connected Mixture Components for Short- and Medium-Term Time Series Forecasting by Andrey K. Gorshenin, Anton L. Vilyaev

“…The introduced probability-informed approach allows us to outperform the results of both transformer NN architectures and classical statistical and machine learning methods.…”
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Assessing the Impact of Aviation Emissions on Air Quality at a Regional Greek Airport Using Machine Learning by Christos Stefanis, Ioannis Manisalidis, Elisavet Stavropoulou, Agathangelos Stavropoulos, Christina Tsigalou, Chrysoula (Chrysa) Voidarou, Theodoros C. Constantinidis, Eugenia Bezirtzoglou

“…This study aims to assess the impact of aviation-related emissions on air quality at Alexandroupolis Regional Airport, Greece, and evaluate the role of meteorological factors in pollution dispersion. Using machine learning models, we analyzed emissions data, including CO₂, NOx, CO, HC, SOx, PM_2.5, fuel consumption, and meteorological parameters from 2019–2020. …”
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Evaluation of a Rubber Roller One‐Pass Rice Milling Machine for Improving Milled Rice Quality by Dessye B. Tikuneh, Melese A. Mihiretu, Asnakew D. Molla, Tigist A. Haile, Mersha A. Fetene

“…ABSTRACT A comprehensive evaluation of the SB‐10D milling machine focused on two widely cultivated rice varieties: the long‐grain Nerica‐4 and the short‐grain Shaga. …”
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Prediction of heart failure risk factors from retinal optical imaging via explainable machine learning by Sona M. Al Younis, Samit Kumar Ghosh, Hina Raja, Feryal A. Alskafi, Siamak Yousefi, Siamak Yousefi, Ahsan H. Khandoker

“…Feature importance analysis highlighted key retinal parameters, such as inner segment-outer segment to retinal pigment epithelium (ISOS-RPE) and inner nuclear layer to the external limiting membrane (INL-ELM) thickness, as crucial indicators for heart failure detection. …”
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The application of risk models based on machine learning to predict endometriosis‐associated ovarian cancer in patients with endometriosis by Xiaopei Chao, Shu Wang, Jinghe Lang, Jinhua Leng, Qingbo Fan

“…The machine learning‐based risk model performed better than the logistic regression model in DeLong's test (p = 0.036). …”
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Optimizing HVAC&R System Efficiency and Comfort Levels Using Machine Learning-Based Control Methods by Suroor M. Dawood, Raad Z. Homod, Alireza Hatami

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Prediction of birthweight with early and mid-pregnancy antenatal markers utilising machine learning and explainable artificial intelligence by Manohar Pavanya, Krishnaraj Chadaga, Vennila J, Akhila Vasudeva, Bhamini Krishna Rao, Srikanth Prabhu, Shashikala K Bhat

“…Prediction of birthweight using machine learning (ML) models with antenatal data may help in better clinical management. …”
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Machine learning model for differentiating malignant from benign thyroid nodules based on the thyroid function data by Quan Zhou, Lihua Zhang, Nan Xiang, Lele Zhang, Fuqiang Ma, Fengchang Yu, Shenhui Lv, Zhilin Lu, He-Rong Mao

“…For the first time, it validated the clinical predictive value of TF parameters (FT4, FT3) and TPOAB as key biomarkers.…”
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A systematic review on machine learning-aided design of engineered biochar for soil and water contaminant removal by Yunpeng Ge, Kaiyang Ying, Guo Yu, Muhammad Ubaid Ali, Abubakr M. Idris, Abubakr M. Idris, Asfandyar Shahab, Habib Ullah, Habib Ullah

“…These instruments expose parameters including nitrogen-to-carbon (N/C) ratios and pyrolysis temperature in adsorption efficiency. …”
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Hybrid Machine Learning-Driven Automated Quality Prediction and Classification of Silicon Solar Modules in Production Lines by Yuxiang Liu, Xinzhong Xia, Jingyang Zhang, Kun Wang, Bo Yu, Mengmeng Wu, Jinchao Shi, Chao Ma, Ying Liu, Boyang Hu, Xinying Wang, Bo Wang, Ruzhi Wang, Bing Wang

“…This research introduces a novel hybrid machine learning framework for automated quality prediction and classification of silicon solar modules in production lines. …”
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Predicting the risk of postoperative avascular necrosis in patients with talar fractures based on an interpretable machine learning model by Jian Zhang, Jian Zhang, Jian Zhang, Jihai Xu, Jihai Xu, Jiapei Yu, Jiapei Yu, Jiapei Yu, Hong Chen, Hong Chen, Xin Hong, Songou Zhang, Xin Wang, Xin Wang, Chengchun Shen, Chengchun Shen, Chengchun Shen

“…Multidimensional data encompassing demographic characteristics, fracture-related variables, surgery-related parameters, and follow-up information were collected. …”
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Integrating CT radiomics and clinical features using machine learning to predict post-COVID pulmonary fibrosis by Qianqian Zhao, Yijie Li, Chunliu Zhao, Ran Dong, Jiaxin Tian, Ze Zhang, Lin Huang, Jingwen Huang, Junhai Yan, Zhitao Yang, Jiangnan Ruan, Ping Wang, Li Yu, Jieming Qu, Min Zhou

“…Clinical data and laboratory parameters were obtained from electronic health records. …”
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Automatic Extraction and Compensation of P-Bit Device Variations in Large Array Utilizing Boltzmann Machine Training by Bolin Zhang, Yu Liu, Tianqi Gao, Jialiang Yin, Zhenyu Guan, Deming Zhang, Lang Zeng

“…In this work, a behavioral model which attributes P-Bit variations to two parameters, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>Δ</mo><mi>V</mi></mrow></semantics></math></inline-formula>, is proposed. …”
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