Results 51 to 60 of about 17,625 (278)
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
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
doaj +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
doaj +1 more source
AN EMPIRICAL EVALUATION OF REPETITIVE UNDERSAMPLING TECHNIQUES
, 2010 Class imbalance is a fundamental problem in data mining and knowledge discovery which is encountered in a wide array of application domains. Random undersampling has been widely used to alleviate the harmful effects of imbalance, however, this technique
JASON VAN HULSE +2 more
core +1 more source
Advanced Science, EarlyView.To overcome two‐dimensional modulation bottlenecks, Hadamard Matrix Slicing Single‐Pixel Imaging (HMS‐SPI) establishes an efficient one‐dimensional imaging paradigm. By slicing the traditional Hadamard matrix into one‐dimensional encoding vectors and spatially expanding them, the required measurement patterns decrease by a factor of N.
Xiaoxue Li +8 more
wiley +1 more source
From Chaos to Randomness via Geometric Undersampling
, 2014 Editors: Witold Jarczyk, Daniele Fournier-Prunaret, João Manuel Goncalves CabralWe propose a new mechanism for undersampling chaotic numbers obtained by the ring coupling of one-dimensional maps.
Lozi, René +3 more
core +2 more sources
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
doaj +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
doaj +1 more source
undersampling of artificial communities
, 2017 R file to investigate how the accuracy and precision of FD metrics is affected by undersampling, and how this depends on the species richness, number of individuals and species abundance distribution of the 'region,' 'species pool' or ...
Peter Manning (26576) +9 more
core +1 more source
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