Identification of mango varieties with vitamin C and titratable acidity using stacking generalization from NIR spectra

Journal of Food Measurement & Characterization, Год журнала: 2025, Номер unknown

Опубликована: Апрель 22, 2025

Язык: Английский

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Generating synthetic data with variational autoencoder to address class imbalance of graph attention network prediction model for construction management

Advanced Engineering Informatics, Год журнала: 2024, Номер 62, С. 102606 - 102606

Опубликована: Май 23, 2024

Язык: Английский

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Effects of Seawater on Mechanical Performance of Composite Sandwich Structures: A Machine Learning Framework

Materials, Год журнала: 2024, Номер 17(11), С. 2549 - 2549

Опубликована: Май 25, 2024

Sandwich structures made with fibre-reinforced plastics are commonly used in maritime vessels thanks to their high strength-to-weight ratios, corrosion resistance, and buoyancy. Understanding mechanical performance after moisture uptake the implications of for structural integrity safety within out-of-plane loading regimes is vital material optimisation. The use modern methods such as acoustic emission (AE) machine learning (ML) could provide effective techniques assessment behaviour health monitoring. In this study, AE features obtained from quasi-static indentation tests on sandwich E-glass fibre face sheets polyvinyl chloride foam cores were employed. Time- frequency-domain then capture relevant information patterns data. A k-means++ algorithm was utilized clustering analysis, providing insights into principal damage modes studied structures. Three ensemble algorithms employed develop a damage-prediction model samples exposed unexposed seawater loaded indenters different geometries. developed models effectively identified all various indenter geometries under conditions accuracy scores between 86.4 95.9%. This illustrates significant potential ML prediction evolution composite marine applications.

Язык: Английский

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Multidimensional Perspective to Data Preprocessing for Model Cognition Verity

Advances in systems analysis, software engineering, and high performance computing book series, Год журнала: 2024, Номер unknown, С. 15 - 57

Опубликована: Май 14, 2024

Reliable data analysis depends on effective preparation, especially since AI-driven business intelligence unbiased and error-free for decision-making. However, developing a reliable dataset is difficult task that requires expertise. Due to the costly damage negligible error in can cause system, good understanding of processes quality transformation necessary. Data varies properties, which determines how it generated, errors it, transformations needs undergo before fed into model. Also, most used analytics sourced from public stores without means verify its or what further steps need be taken preprocessing optimal performance. This chapter provides detailed description practical scientific procedures generate develop different models scenarios. highlights tools techniques clean prepare performance prevent unreliable outcomes.

Язык: Английский

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Predicting compressive strength of pervious concrete with fly ash: a machine learning approach and analysis of fly ash compositional influence

Multiscale and Multidisciplinary Modeling Experiments and Design, Год журнала: 2024, Номер 7(6), С. 5651 - 5671

Опубликована: Июль 25, 2024

Язык: Английский

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