Results 11 to 20 of about 83,179 (266)
Revealing Traces of Image Resampling and Resampling Antiforensics [PDF]
Advances in Multimedia, 2017 Image resampling is a common manipulation in image processing. The forensics of resampling plays an important role in image tampering detection, steganography, and steganalysis.
Anjie Peng, Yadong Wu, Xiangui Kang
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The Megopolis resampler: Memory coalesced resampling on GPUs [PDF]
Digital Signal Processing, 2022 The resampling process employed in widely used methods such as Importance Sampling (IS), with its adaptive extension (AIS), are used to solve challenging problems requiring approximate inference; for example, non-linear, non-Gaussian state estimation problems.
Joshua A. Chesser +2 more
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Fingerprint resampling: A generic method for efficient resampling [PDF]
Scientific Reports, 2015 AbstractIn resampling methods, such as bootstrapping or cross validation, a very similar computational problem (usually an optimization procedure) is solved over and over again for a set of very similar data sets. If it is computationally burdensome to solve this computational problem once, the whole resampling method can become unfeasible.
Mestdagh, Merijn +3 more
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The Chopthin Algorithm for Resampling [PDF]
IEEE Transactions on Signal Processing, 2016 14 pages, 4 ...
Axel Gandy, F. Din-Houn Lau
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Frontiers in Applied Mathematics and Statistics, 2023 When initial sample information falls short of enabling industrial engineers to confidently make decisions about lot quality assessment, repetitive sampling emerges as a solution.
Jose J. Muñoz +2 more
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Arrhythmia detection using resampling and deep learning methods on unbalanced data
Компьютерная оптика, 2022 Due to cardiovascular diseases millions of people die around the world. One way to detect abnormality in the heart condition is with the help of electrocardiogram signal (ECG) analysis.
E.Y. Shchetinin, A.G. Glushkova
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A Novel Ensemble Framework Based on K-Means and Resampling for Imbalanced Data
Applied Sciences, 2020 Imbalanced classification is one of the most important problems of machine learning and data mining, existing in many real datasets. In the past, many basic classifiers such as SVM, KNN, and so on have been used for imbalanced datasets in which the ...
Huajuan Duan +3 more
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Sample size for estimation of the Pearson correlation coefficient in cherry tomato tests
Ciência Rural, 2017 : The aim of this study was to determine the required sample size for estimation of the Pearson coefficient of correlation between cherry tomato variables. Two uniformity tests were set up in a protected environment in the spring/summer of 2014.
Bruno Giacomini Sari +5 more
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Techno.Com, 2021 Penderita diabetes di seluruh dunia terus mengalami peningkatan dengan angka kematian sebesar 4,6 juta pada tahun 2011 dan diperkirakan akan terus meningkat secara global menjadi 552 juta pada tahun 2030.
Wahyu Nugraha, Raja Sabaruddin
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IEEE Access, 2019 In recent years, machine learning and deep learning based fault diagnosis methods have been studied, however, most of them remain at the experimental stage mainly because of two obstacles, briefly, a) inadequate faulty examples and b) various working ...
Tianhao Hu, Tang Tang, Ming Chen
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