Physics-aware machine learning for glacier ice thickness estimation: a case study for Svalbard by V. Steidl, J. L. Bamber, J. L. Bamber, X. X. Zhu, X. X. Zhu

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Machine learning techniques for independent gait recovery prediction in acute anterior circulation ischemic stroke by Jiangping Ma, Yuanjie Xie

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Unconfined Compressive Strength Prediction of Rocks Using a Novel Hybrid Machine Learning Algorithm by Rafiqul Islam, Md. Arif Hossain

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Retracted: Predicting the Link between Stock Prices and Indices with Machine Learning in R Programming Language by Journal of Mathematics

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Integrating multiomics analysis and machine learning to refine the molecular subtyping and prognostic analysis of stomach adenocarcinoma by Miaodong Wang, Qin He, Zeshan Chen, Yijue Qin

“…We synthesized the multiomics data of patients with STAD using 10 clustering methods, construct a consensus machine learning-driven signature (CMLS)-related prognostic models by combining 10 machine learning methods, and evaluated the prognosis models using the C-index. …”
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Machine Learning-Based Mobile Device Resale Price Estimator: A Predictive Analytics Approach by Umm-e-Laila Mehdi, Khalid Mahboob, Samar Raza Talpur, Khaliq Ahmed, Mohammad Khurram

Subjects: “…Machine Learning…”
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Estimation of PM10 and PM2.5 Using Backscatter Coefficient of Ceilometer and Machine Learning by Bu-Yo Kim, Joo Wan Cha, Yong Hee Lee

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A Collection of Machine Learning Models for Improved Airport Operations Amidst Adverse Weather Conditions by Ramon Dalmau, Jonathan Attia

Subjects: “…Machine Learning…”
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Intelligent prediction and understanding of self-shrinkage in ultra-high performance concrete based on machine learning by Ji Hao, Wenbin Jiao, Xinpo Xie, Dula Man, Shengwei Huang

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Detection of COVID-19 Using Protein Sequence Data via Machine Learning Classification Approach by Siti Aminah, Gianinna Ardaneswari, Mufarrido Husnah, Ghani Deori, Handi Bagus Prasetyo

“…This study is aimed at developing an efficient classification model for coronavirus protein sequences using machine learning algorithms and feature selection techniques to aid in the early detection and prediction of novel viruses. …”
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Improving molecular subtypes and prognosis of pancreatic cancer through multi group analysis and machine learning by Xue-Jian Zhang, Fang-Fang Lin, Ya-Qing Wen, Kun-Ping Guan

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Identification and Immunological Characterization of Cuproptosis Related Genes in Preeclampsia Using Bioinformatics Analysis and Machine Learning by Tiantian Yu, Guiying Wang, Xia Xu, Jianying Yan

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Integrated Machine Learning Approaches for Landslide Susceptibility Mapping Along the Pakistan–China Karakoram Highway by Mohib Ullah, Haijun Qiu, Wenchao Huangfu, Dongdong Yang, Yingdong Wei, Bingzhe Tang

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Applying in machine learning and deep learning in finance industry: A case study on repayment prediction by Nguyễn Phát Đạt, Hồ Mai Minh Nhật, Trương Công Vinh, Lê Quang Chấn Phong, Lê Hoành Sử

“…The present inquiry advocates for the adoption of sophisticated computational methodologies, including machine learning and deep learning, to analyze borrowers’ behavioral patterns, demographic profiles, and credit histories, thus facilitating the prognostication of loan repayment likelihood. …”
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An Integrated Microfluidic Microwave Array Sensor with Machine Learning for Enrichment and Detection of Mixed Biological Solution by Sen Yang, Yanxiong Wang, Yanfeng Jiang, Tian Qiang

“…Furthermore, the measurement data are processed using machine learning. Specifically, the WBC measurement data are subjected to a further linear fitting. …”
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Harnessing synergy of machine learning and nature-inspired optimization for enhanced compressive strength prediction in concrete by Abba Bashir, Esar Ahmad, Shashivendra Dulawat, Sani I. Abba

“…Given the nonlinear characteristics of supplementary cement material concrete (SCMC) mixtures, researchers are increasingly turning to machine learning methods. This study assesses nine machine learning models, integrating conventional AI algorithms, such as artificial neural network (ANN), support vector regression (SVR), and random forest (RF) with nature-inspired optimization techniques including chicken swarm optimization (CSO), moth flame optimization algorithm (MFO), and whale optimization algorithm (WOA). …”
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Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements by Brady Metherall, Anna K. Berryman, Georgia S. Brennan

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A Comprehensive Investigation of Anomaly Detection Methods in Deep Learning and Machine Learning: 2019–2023 by Shalini Kumari, Chander Prabha, Asif Karim, Md. Mehedi Hassan, Sami Azam

“…These techniques are categorized further into machine learning (ML), deep learning (DL), and federated learning (FL). …”
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A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method by S. M. Nuruzzaman Nobel, S. M. Masfequier Rahman Swapno, Md. Rajibul Islam, Mejdl Safran, Sultan Alfarhood, M. F. Mridha

“…We utilized two effective ensemble machine learning methods: ensemble EfficientNetV2L-LGBM and ensemble UNet-BiGRU. …”
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A machine learning-based model for predicting paroxysmal and persistent atrial fibrillation based on EHR by Yuqi Zhang, Sijin Li, Peibiao Mai, Yanqi Yang, Niansang Luo, Chao Tong, Kuan Zeng, Kun Zhang

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