Boosting-Based Machine Learning Applications in Polymer Science: A Review

Polymers, Год журнала: 2025, Номер 17(4), С. 499 - 499

Опубликована: Фев. 14, 2025

The increasing complexity of polymer systems in both experimental and computational studies has led to an expanding interest machine learning (ML) methods aid data analysis, material design, predictive modeling. Among the various ML approaches, boosting methods, including AdaBoost, Gradient Boosting, XGBoost, CatBoost LightGBM, have emerged as powerful tools for tackling high-dimensional complex problems science. This paper provides overview applications science, highlighting their contributions areas such structure-property relationships, synthesis, performance prediction, characterization. By examining recent case on techniques this review aims highlight potential advancing characterization, optimization materials.

Язык: Английский

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Assessment of environmental and socioeconomic drivers of urban stormwater microplastics using machine learning

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 21, 2025

Язык: Английский

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A machine learning approach to feature selection and uncertainty analysis for biogas production in wastewater treatment plants

Waste Management, Год журнала: 2025, Номер 197, С. 14 - 24

Опубликована: Фев. 21, 2025

Язык: Английский

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Machine learning methods for predicting residual strength in corroded oil and gas steel pipes

npj Materials Degradation, Год журнала: 2025, Номер 9(1)

Опубликована: Март 24, 2025

Язык: Английский

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Study on the pitting corrosion behavior of X65 steel in supercritical and dense-phase CO2 based on in-situ electrochemical noise measurement

Process Safety and Environmental Protection, Год журнала: 2025, Номер unknown, С. 107060 - 107060

Опубликована: Март 1, 2025

Язык: Английский

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A machine learning approach for corrosion rate modeling in Patna water distribution network of Bihar

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 5, 2025

Corrosion can affect water taste, color, and odor, making it crucial to monitor control corrosion in the distribution network maintain quality standards. This study used machine learning approaches such as MARS, GMDH, MPMR model rate networks. An experimental setup was established running for data collection, where several test coupons were inserted into pipeline. A coupon weight loss method employed calculate rate. The selected site continuously monitored 315 days observe (WDN). physicochemical parameters regularly tested at Environmental Engineering Laboratory NIT Patna. Machine analyses, including multivariate adaptive regression splines (MARS), group of handling (GMDH), polynomial (MPMR), consider 13 features, pH, temperature, conductivity, total dissolved solids, alkalinity, hardness, calcium magnesium chloride, sulfate, nitrate, oxygen, time, input parameters, with output parameter. Energy dispersive X-ray (EDX) analysis revealed changes composition before after exposure: carbon content decreased from 4 3%, oxygen increased 20 31%, iron 21 60%, sulfur 3 2%, manganese 1%, zinc 49 1% by weight. performance developed assessed via metrics, error characteristic (REC) curves, comprehensive measurement (COM), ranking techniques. On basis models, proposed MARS is most accurate model, R2 = 0.9872 training 0.9741 testing phase, followed GMDH models. REC curve also demonstrates superiority lower area-over-the-curve (AOC) values (training: 0.010, testing: 0.015), 0.028, 0.024) 0.054, 0.074) With lowest COM value (0.172), outperforms indicating its superior predictive capability generalizability.

Язык: Английский

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Deep Learning Approach for Pitting Corrosion Detection in Gas Pipelines

Sensors, Год журнала: 2024, Номер 24(11), С. 3563 - 3563

Опубликована: Май 31, 2024

The paper introduces a computer vision methodology for detecting pitting corrosion in gas pipelines. To achieve this, dataset comprising 576,000 images of pipelines with and without was curated. A custom-designed optimized convolutional neural network (CNN) employed binary classification, distinguishing between corroded non-corroded images. This CNN architecture, despite having relatively few parameters compared to existing classifiers, achieved notably high classification accuracy 98.44%. proposed outperformed many contemporary classifiers its efficacy. By leveraging deep learning, this approach effectively eliminates the need manual inspection corrosion, thus streamlining what previously time-consuming cost-ineffective process.

Язык: Английский

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Machine learning-based maximum pipeline pitting corrosion depth prediction using hybrid FVIM-BNN-XGB model

Engineering Failure Analysis, Год журнала: 2025, Номер unknown, С. 109603 - 109603

Опубликована: Апрель 1, 2025

Язык: Английский

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Machine learning-driven design of wide-angle impedance matching structures for wide-angle scanning arrays

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 13, 2025

Язык: Английский

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Time-to-Failure Based Deterioration Factors of Water Networks: Systematic Review and Prioritization

Reliability Engineering & System Safety, Год журнала: 2025, Номер unknown, С. 111246 - 111246

Опубликована: Май 1, 2025

Язык: Английский

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