Gas permeability, diffusivity, and solubility in polymers: Simulation-experiment data fusion and multi-task machine learning by Brandon K. Phan, Kuan-Hsuan Shen, Rishi Gurnani, Huan Tran, Ryan Lively, Rampi Ramprasad

“…Abstract Machine learning (ML) models for predicting gas permeability through polymers have traditionally relied on experimental data. …”
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Retrieval of nicotine content in cigar leaves by remote analysis of aerial hyperspectral combining machine learning methods by Chenyu Tian, Yifei Lu, Hengduo Xie, Yufan Yu, Liming Lu

“…The output of these operations was then further processed by CARS, SPA, and UVE algorithms to determine the nicotine sensitive bands. Three machine learning algorithms were then used to analyze the data: PLS, BP, RF, and the SVM. …”
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Identifying the Roadway Infrastructure Factors Affecting Road Accidents Using Interpretable Machine Learning and Data Augmentation by Jonghak Lee, Sangyoup Kim, Tae-Young Heo, Dongwoo Lee

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Machine learning approach for predicting tramp elements in the basic oxygen furnace based on the compiled steel scrap mix by Michael Schäfer, Ulrike Faltings, Björn Glaser

“…In this paper, we present a machine learning approach based on XGBoost to predict the chemical element content in the converter. …”
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Machine learning models for predicting interaction affinity energy between human serum proteins and hemodialysis membrane materials by Simin Nazari, Amira Abdelrasoul

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Prediction of COVID-19 Pandemic in Bangladesh: Dual Application of Susceptible-Infective-Recovered (SIR) and Machine Learning Approach by Iqramul Haq, Md. Ismail Hossain, Ahmed Abdus Saleh Saleheen, Md. Iqbal Hossain Nayan, Mafruha Sultana Mila

“…Here we compare the results of the optimized SIR model and a well-known machine learning algorithm (PROPHET algorithm) for the forecasting trend of the COVID-19 pandemic. …”
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REVOLUTIONIZING LUXURY: THE ROLE OF AI AND MACHINE LEARNING IN ENHANCING MARKETING STRATEGIES WITHIN THE TOURISM AND HOSPITALITY LUXURY SECTORS by Maria Nascimento CUNHA, Manuel PEREIRA, António CARDOSO, Jorge FIGUEIREDO, Isabel OLIVEIRA

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[18F]FDG PET-Based Radiomics and Machine Learning for the Assessment of Gliomas and Glioblastomas: A Systematic Review by Francesco Dondi, Roberto Gatta, Maria Gazzilli, Pietro Bellini, Gian Luca Viganò, Cristina Ferrari, Antonio Rosario Pisani, Giuseppe Rubini, Francesco Bertagna

“…The aim of this systematic review was to assess the role of [18F]FDG PET-based radiomics and machine learning (ML) in the evaluation of these neoplasms. …”
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Towards Fault Detection in Industrial Equipment through Energy Consumption Analysis: Integrating Machine Learning and Statistical Methods by Baddou Nada, Dadda Afaf, Rzine Bouchra, Hmamed Hala

Subjects: “…machine learning…”
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Wildfire Susceptibility Mapping Using Five Boosting Machine Learning Algorithms: The Case Study of the Mediterranean Region of Turkey by Sohaib K. M. Abujayyab, Moustafa Moufid Kassem, Ashfak Ahmad Khan, Raniyah Wazirali, Mücahit Coşkun, Enes Taşoğlu, Ahmet Öztürk, Ferhat Toprak

“…Forest fires caused by different environmental and human factors are responsible for the extensive destruction of natural and economic resources. Modern machine learning techniques have become popular in developing very accurate and precise susceptibility maps of various natural disasters to help reduce the occurrence of such calamities. …”
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Correlation coefficients of vibration signals and machine learning algorithm for structural damage assessment in beams under moving load by Toan Pham-Bao, Vien Le-Ngoc

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Predicting Treatment Outcomes in Patients with Low Back Pain Using Gene Signature-Based Machine Learning Models by Youzhi Lian, Yinyu Shi, Haibin Shang, Hongsheng Zhan

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Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting by Hai Tao, Ali Omran Al-Sulttani, Ameen Mohammed Salih Ameen, Zainab Hasan Ali, Nadhir Al-Ansari, Sinan Q. Salih, Reham R. Mostafa

“…The application of machine learning (ML) models in forecasting river flow has grown rapidly. …”
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Integrating Remote Sensing and Soil Features for Enhanced Machine Learning-Based Corn Yield Prediction in the Southern US by Sayantan Sarkar, Javier M. Osorio Leyton, Efrain Noa-Yarasca, Kabindra Adhikari, Chad B. Hajda, Douglas R. Smith

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ExAIRFC-GSDC: An Advanced Machine Learning-Based Interpretable Framework for Accurate Gas Leakage Detection and Classification by B. Lalithadevi, S. Krishnaveni

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Relative Fat Mass and Physical Indices as Predictors of Gallstone Formation: Insights From Machine Learning and Logistic Regression by Deng L, Wang S, Wan D, Zhang Q, Shen W, Liu X, Zhang Y

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Leveraging machine learning for enhanced reservoir permeability estimation in geothermal hotspots: a case study of the Williston Basin by Abdul-Muaizz Koray, Emmanuel Gyimah, Mohamed Metwally, Hamid Rahnema, Olusegun Tomomewo

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Machine learning based on blood test biomarkers predicts fast progression in advanced NSCLC patients treated with immunotherapy by Jie Yang, Jian-Guo Zhou, Benjamin Frey, Hu Ma, Haitao Wang, Markus Hecht, Rainer Fietkau, Udo Gaipl, Xiaofei Chen, Ada Hang-Heng Wong, Fangya Tan, Si-Si He, Gang Shen, Yun-Jia Wang, Wenzhao Zhong

“…We aimed to develop a predictive framework based on machine learning (ML) methods to identify FP in advanced NSCLC patients using blood test biomarkers.Methods and analysis We extracted data of 1546 atezolizumab-treated patients from four multicentre clinical trials. …”
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Retracted: PLncWX: A Machine-Learning Algorithm for Plant lncRNA Identification Based on WOA-XGBoost by Journal of Chemistry

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Constructing a machine learning model for systemic infection after kidney stone surgery based on CT values by Jiaxin Li, Yao Du, Gaoming Huang, Yawei Huang, Xiaoqing Xi, Zhenfeng Ye

Subjects: “…Machine learning…”
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