learning-based image data-enhancement methods can adapt to different tasks and data distributions, perform image data enhancement on diverse image data and are more effective under large-scale data. In recent years, deep learning has made breakthrough progress in crop image data enhancement (CIDE),...
In addition, a data enhancement method is also employed in DEDGO to address the dependence on a large amount of training data. The accuracy and effectiveness of the DEDGO algorithm are confirmed to be much higher than those of the random forest algorithm and deep neural network,...
Zhu ZX, Ong YS, Dash M (2007) Markov blanket-embedded genetic algorithm for gene selection. Pattern Recogn 40(11):3236–3248 Article MATH Google Scholar Genuer R, Poggi JM, Tuleau-Malot C (2010) Variable selection using random forests. Pattern Recogn Lett 31(14):2225–2236 Article Googl...
Recently, Deep Learning based automatic generation of treatment recommendation has been attracting much attention. However, medical datasets are usually small, which may lead to over-fitting and inferior performances of deep learning models. In this paper, we propose multi-objective data enhancement meth...
Adaptive feature interaction enhancement network for text classification Rui Su , Shangbing Gao & Junqiang Zhang Article 29 March 2025 | Open Access An automated parallel genetic algorithm with parametric adaptation for distributed data analysis Laila Al-Terkawi & Matteo Migliavacca Article 28...
Based on the comparison of the GMM with respect to K-means, it is observed that GMM is performing better than the K-means clustering algorithm. The results show that KCGWO produced results with more accuracy than GMM and K-means. One way to look at this enhancement is due to the ...
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This study proposed a data enhancement method based on a self-attention mechanism to optimize the VAE-GAN method and solve the problem of the lack of partial discharge samples and the unbalanced distribution between different defects. First, the non-subsampled contourlet transform (NSCT) algorithm ...
In this method, testing for statistical significance of coefficients, and final choice of model using an information criterion, is built into the model generation algorithm. Using simulated data generated from large random sparse VAR models, we test the performance our methods for different lengths ...
To that end, we start by applying Hierarchical Temporal Memory (HTM) algorithm for detecting anomalous segments in the ECG dataset and then a classification procedure using Faust–a Python cluster computing platform for data stream processing. The chapter discusses the trade-off among various ...