The Estimation of the Remaining Useful Life of Ceramic Plates Used in Iron Ore Filtration Through a Reliability Model and Machine Learning Methods Applied to Industrial Process Var... by Robert Bento Florentino, Luiz Gustavo Lourenço Moura

“…Through the use of the CRISP-DM data analysis methodology, the fault logs of ceramic plates applied in an iron ore filtration process are coupled with sensor readings of the process variables over the time of operation to create exponential survival models via two techniques: a multiple linear regression model with averaged data and a random forest regression machine learning model with individual instant data. …”
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Mitigating malicious denial of wallet attack using attribute reduction with deep learning approach for serverless computing on next generation applications by Amal K. Alkhalifa, Mohammed Aljebreen, Rakan Alanazi, Nazir Ahmad, Sultan Alahmari, Othman Alrusaini, Ali Alqazzaz, Hassan Alkhiri

“…Serverless computing, Function-as-a-Service (FaaS), is a cloud computing (CC) system that permits developers to construct and run applications without a conventional server substructure. The deep learning (DL) model, a part of the machine learning (ML) technique, has developed as an effectual device in cybersecurity, permitting more effectual recognition of anomalous behaviour and classifying patterns indicative of threats. …”
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Optimized Ensemble Methods for Classifying Imbalanced Water Quality Index Data by Zaharaddeen Karami Lawal, Ali Aldrees, Hayati Yassin, Salisu Dan'azumi, Sujay Raghavendra Naganna, Sani I. Abba, Saad Sh. Sammen

“…This study aimed to develop an effective ensemble model for classifying river water as drinkable or polluted using advanced machine learning. …”
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Automated methane emission monitoring systems based on satellite data: Radiation transfer model analysis by K. Aghayeva, G. Krauklit

“…The findings highlight the need to integrate satellite data with ground-based measurements and radiative transfer models to improve monitoring accuracy and develop emission reduction strategies…”
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Ensemble modeling of the climate-energy nexus for renewable energy generation across multiple US states by Joy Adul, Rohini Kumar, Renee Obringer

“…This study aims to leverage machine learning models to predict renewable energy generation based on the surrounding climate. …”
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Ultrasound radiomics models improve preoperative diagnosis and reduce unnecessary biopsies in indeterminate thyroid nodules by Lu Chen, Yan Wang, Yan Wang, Haoyu Jing, Haoyu Jing, Rui Bao, Bin Sun, Mingbo Zhang, Yukun Luo

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Clinical and economic effectiveness of Schroth therapy in adolescent idiopathic scoliosis: insights from a machine learning- and active learning-based real-world study by Erdal Ayvaz, Merve Uca, Ednan Ayvaz, Zafer Yıldız

“…Recent advancements in active learning (AL) and machine learning (ML) techniques offer the potential to optimize treatment protocols by uncovering hidden predictors and enhancing model efficiency. …”
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A new geographically weighted stacked regression method for forest aboveground carbon storage estimation: A case study of bamboo forest by Jingyi Wang, Xuejian Li, Huaqiang Du, Guomo Zhou, Fangjie Mao, Mingshi Li, Enbin Liu, Weiliang Fan, Ning Han, Yanxin Xu, Zihao Huang

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Development of an electronic health record-based Human Immunodeficiency Virus (HIV) risk prediction model for women, incorporating social determinants of health by Yiyang Liu, Aokun Chen, Hwayoung Cho, Khairul A. Siddiqi, Robert L. Cook, Mattia Prosperi

“…Contextual-level SDoH were linked to EHR/claim data. Various machine learning (ML) methods were tested, and Shapley Additive Explanations (SHAP) values were used to interpret the model. …”
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Causality-Driven Feature Selection for Calibrating Low-Cost Airborne Particulate Sensors Using Machine Learning by Vinu Sooriyaarachchi, David J. Lary, Lakitha O. H. Wijeratne, John Waczak

“…However, traditional machine learning models often lack interpretability and generalizability when applied to complex, dynamic environmental data. …”
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Using machine learning and single nucleotide polymorphisms for improving rheumatoid arthritis risk Prediction in postmenopausal women. by Yingke Xu, Qing Wu

“…These findings emphasize the advantage of combining in-depth genomic data with advanced machine learning for RA risk prediction. The most robust performance of the XGBoost model, which integrated both conventional risk factors and individual SNPs, demonstrates its potential as a tool in personalized medicine for complex diseases like RA. …”
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System-Level Optimization in Switched Reluctance Machine Design—Current Trends, Methodologies, and Future Directions by Aristotelis Tzouvaras, Georgios Falekas, Athanasios Karlis

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Visual/Near-Infrared Spectroscopy Combined with Linear Discriminant Analysis and Machine Learning for Classification of Apple Damage by ZHANG Yu, ZHANG Chongyang, DUAN Xinxin, MA Shaoge, ZHAO Fu, WANG Juxia

“…The Vis-NIR spectral data of apples with different degrees of damage were collected, and the effect of different spectral preprocessing methods on the support vector machine (SVM) classification model was analyzed. LDA was used to reduce the dimensionality of the preprocessed spectral data, and five machine learning models including SVM, random forest (RF), K-nearest neighbor (KNN), decision tree (DT) and extreme gradient boosting (XGBoost) were constructed and compared for the classification of apple damage. …”
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Machine Learning-Based Seismic Response Prediction for Nuclear Power Plant Structures Considering Aging Deterioration by Hyunsu Kim, Soyeon Lee, Junsu Jang, Sihyeon An

“…Given that aging deterioration significantly influences the structural behavior of reinforced concrete (RC) nuclear power plant (NPP) structures, it is crucial to incorporate changes in the material properties of NPPs for accurate prediction of seismic responses. In this study, machine learning (ML) models for predicting the seismic response of RC NPP structures were developed by considering aging deterioration. …”
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Hybrid machine learning-enabled multivariate bridge-specific seismic vulnerability and resilience assessment of UHPC bridges by Tadesse G. Wakjira, M. Shahria Alam

“…Thus, this study proposes a hybrid machine learning (ML)-enabled multivariate bridge-specific seismic vulnerability and resilience assessment framework for UHPC bridges. …”
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Integrated approach of extreme learning machines and locally weighted linear regression for improved discharge coefficient prediction by Mohammed Majeed Hameed, Mohamed Khalid Alomar, Siti Fatin Mohd Razali, Ali Salem

“…Overall, the findings demonstrate that the ELM-LWLR model is a practical and robust tool for Cd modeling, offering significant advantages in cost reduction and enhanced hydraulic modeling for complex engineering applications.…”
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Machine learning for forecasting factory concentrations of nitrogen oxides from univariate data exploiting trend attributes by Jiaxin Liu, Shuo Yang, Qichao Li, Leiming Ji, Xuefeng Hou, Liudong Hou, Jing Ma

“…Therefore, this study presents the outcomes of predictive activities for NOx emissions using machine learning. We employed a vector autoregression (VAR) model that considers the influence of other pollutants on NOx emissions. …”
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Machine-Learning-Assisted Identification of Steam Channeling after Cyclic Steam Stimulation in Heavy-Oil Reservoirs by Yu Li, Huiqing Liu, Peng Jiao, Qing Wang, Dong Liu, Liangyu Ma, Zhipeng Wang, Hao Peng

“…To solve the issues of steam breakthrough, it is essentially important and necessary to recognize steam channeling. In this work, a machine-learning-assisted identification model, based on a random-forest ensemble algorithm, is developed to predict the occurrence of steam channeling during steam huff-and-puff processes. …”
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Machine Learning Techniques for Predicting Typhoon‐Induced Storm Surge Using a Hybrid Wind Field by Changyu Su, Bishnupriya Sahoo, Miaohua Mao, Meng Xia

“…Since there have been limited typhoon‐induced storm surges in the Bohai Sea, an innovative prediction system is warranted to address frequent and intense typhoon‐induced impacts. Four Machine Learning (ML) models (Long Short‐Term Memory (LSTM), Convolutional Neural Networks (CNN), CNN‐LSTM, and ConvLSTM) were built to predict storm surges and significantly improve prediction when combined with a three‐dimensional Finite Volume Community Ocean Model (FVCOM), that is, FVCOM‐ML. …”
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Solving Data Overlapping Problem Using A Class‐Separable Extreme Learning Machine Auto‐Encoder by Ekkarat Boonchieng, Wanchaloem Nadda

“…Data overlapping and imbalanced data are significant challenges in data classification. Extreme learning machine auto‐encoding (ELM‐AE) is a feature reduction method that transforms original features into a new set of features capturing essential information in the data. …”
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