Enhancing Underwater Image Quality Using Modified U-Shaped Transformer

Published: Jan. 1, 2025

Language: Английский

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Small sample learning based on probability-informed neural networks for SAR image segmentation

Neural Computing and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 8, 2025

Language: Английский

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Estimating viscosities of semiconductor-manufacturing gases

AIP Advances, Journal Year: 2025, Volume and Issue: 15(2)

Published: Feb. 1, 2025

Semiconductor manufacturing demands an accurate delivery of gases to the process chamber. To achieve this, gas viscosities are needed. Hence, this paper compares viscosity models applied pure and operating conditions relevant semiconductor develops a method design neural-network/multilayer-perceptron viscosity. Overall, perceptron give smallest root-mean-square errors in comparison with experimental data, followed closely by simplified variation well-known models. Based on these findings, uses model several semiconductor-manufacturing that unavailable gives recommendations how estimate viscosities.

Language: Английский

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To enhance sustainable development goal research, open up commercial satellite image archives

Proceedings of the National Academy of Sciences, Journal Year: 2025, Volume and Issue: 122(7)

Published: Feb. 12, 2025

The preference for simple explanations, known as the parsimony principle, has long guided development of scientific theories, hypotheses, and models. Yet recent years have seen a number successes in employing highly complex models ...

Language: Английский

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Disparities in accelerated brain aging in recent-onset and chronic schizophrenia

Psychological Medicine, Journal Year: 2025, Volume and Issue: 55

Published: Jan. 1, 2025

Abstract Background Patients with schizophrenia experience accelerated aging, accompanied by abnormalities in biomarkers such as shorter telomere length. Brain age prediction using neuroimaging data has gained attention research, consistently reported increases brain-predicted difference (brain-PAD). However, its associations clinical symptoms and illness duration remain unclear. Methods We developed brain models structural magnetic resonance imaging (MRI) from 10,938 healthy individuals. The were validated on an independent test dataset comprising 79 controls, 57 patients recent-onset schizophrenia, 71 chronic schizophrenia. Group comparisons the of brain-PAD analyzed multiple linear regression. SHapley Additive exPlanations (SHAP) values estimated feature contributions to model, between-group differences SHAP group-by-SHAP value interactions also examined. Results exhibited increased 1.2 0.9 years, respectively. Between-group identified right lateral prefrontal area (false discovery rate [FDR] p = 0.022), observed left (FDR 0.049). A negative association between Full-scale Intelligence Quotient scores was noted, which did not significant after correction for comparisons. Conclusions Brain-PAD pronounced early phase Regional contributing likely vary duration. Future longitudinal studies are required overcome limitations related sample size, heterogeneity, cross-sectional design this study.

Language: Английский

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EQLC-EC: An Efficient Voting Classifier for 1D Mass Spectrometry Data Classification

Electronics, Journal Year: 2025, Volume and Issue: 14(5), P. 968 - 968

Published: Feb. 28, 2025

Mass spectrometry (MS) data present challenges for machine learning (ML) classification due to their high dimensionality, complex feature distributions, batch effects, and intensity discrepancies, often hindering model generalization efficiency. To address these issues, this study introduces the Efficient Quick 1D Lite Convolutional Neural Network (CNN) Ensemble Classifier (EQLC-EC), integrating convolutional networks with reshape layers dual voting mechanisms enhanced representation performance. Validation was performed on five publicly available MS datasets, each featured in high-impact publications. EQLC-EC underwent comprehensive evaluation against classical models (e.g., support vector (SVM), random forest) leading deep methods reported studies. demonstrated dataset-specific improvements, including accuracy (1–5% increase) reduced standard deviation (1–10% reduction). Performance differences between soft hard were negligible (<1% variation deviation). presents a powerful efficient tool analysis potential applications across metabolomics proteomics.

Language: Английский

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Generalization of Peanut Yield Prediction Models Using Artificial Neural Networks and Vegetation Indices

Smart Agricultural Technology, Journal Year: 2025, Volume and Issue: unknown, P. 100873 - 100873

Published: March 1, 2025

Language: Английский

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Few-shot SAR image classification via multiple prototypes ensemble

Neurocomputing, Journal Year: 2025, Volume and Issue: unknown, P. 129989 - 129989

Published: March 1, 2025

Language: Английский

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Forecasting particulate matter concentration in Shanghai using a small-scale long-term dataset

Environmental Sciences Europe, Journal Year: 2025, Volume and Issue: 37(1)

Published: March 18, 2025

Language: Английский

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Erosion-SAM: Semantic segmentation of soil erosion by water

CATENA, Journal Year: 2025, Volume and Issue: 254, P. 108954 - 108954

Published: March 23, 2025

Language: Английский

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