Results 41 to 50 of about 13,919 (263)
ProceedingsTraditional infrared thermography (IRT) techniques can only provide two-dimensional (2D) projections of surface temperatures, and it is difficult to intuitively present the surface profile of the three-dimensional (3D) structure and the spatial ...
Rongbang Wang +2 more
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ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals
Advanced Science, EarlyView.The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray +3 more
wiley +1 more source
Zero-Inflated Text Data Analysis Using Imbalanced Data Sampling and Statistical Models
ComputersText data often exhibits high sparsity and zero inflation, where a substantial proportion of entries in the document–keyword matrix are zeros. This characteristic presents challenges to traditional count-based models, which may suffer from reduced ...
Sunghae Jun
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Advanced Science, EarlyView.PhosSight is a unified deep‐learning framework for phosphoproteome identification, featured by a phosphorylation‐aware detectability predictor. It improves identification sensitivity in DDA through deep re‐localization and rescoring, accelerates DIA searches by detectability‐guided spectral library pruning, and expands phosphoproteome coverage to ...
Ben Wang +10 more
wiley +1 more source
Resampling imbalanced data for network intrusion detection datasets
Journal of Big Data, 2021 Machine learning plays an increasingly significant role in the building of Network Intrusion Detection Systems. However, machine learning models trained with imbalanced cybersecurity data cannot recognize minority data, hence attacks, effectively.
Sikha Bagui, Kunqi Li
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Random Walk-steered Majority Undersampling
2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2022 In this work, we propose Random Walk-steered Majority Undersampling (RWMaU), which undersamples the majority points of a class imbalanced dataset, in order to balance the classes. Rather than marking the majority points which belong to the neighborhood of a few minority points, we are interested to perceive the closeness of the majority points to the ...
Payel Sadhukhan +2 more
openaire +2 more sources
Advanced Science, EarlyView.ProMetNet introduces a biologically constrained deep learning framework for proteo‐metabolomic integration by embedding Reactome‐derived pathway topology into neural networks. It captures non‐linear molecular dependencies and pathway‐level metabolic reorganization, enabling interpretable discrimination.
Minghui Zhao +6 more
wiley +1 more source
Undersampling bankruptcy prediction: Taiwan bankruptcy data.
PLoS ONE, 2021 Machine learning models have increasingly been used in bankruptcy prediction. However, the observed historical data of bankrupt companies are often affected by data imbalance, which causes incorrect prediction, resulting in substantial economic losses ...
Haoming Wang, Xiangdong Liu
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Phase-synchronous undersampling in nonlinear spectroscopy [PDF]
Optics Letters, 2018 We introduce the concept of phase-synchronous undersampling in nonlinear spectroscopy. The respective theory is presented and validated experimentally in a phase-modulated quantum beat experiment by sampling high phase modulation frequencies with low laser repetition rates.
Lukas Bruder +2 more
openaire +5 more sources
Advanced Intelligent Discovery, EarlyView.This study introduces a tree‐based machine learning approach to accelerate USP8 inhibitor discovery. The best‐performing model identified 100 high‐confidence repurposable compounds, half already approved or in clinical trials, and uncovered novel scaffolds not previously studied. These findings offer a solid foundation for rapid experimental follow‐up,
Yik Kwong Ng +4 more
wiley +1 more source