English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Imbalanced class is one of the trials in classifying materials of big data. Data disparity produces a biased output of a model regardless how recent the technology is. However, deep learning algorithms such as convolutional neural networks and deep belief networks have proven to provide promising results in many research domains, especially in image processing as well as time series forecasting, intrusion detection, and classification. Therefore, this paper will investigate the effect of imbalanced data discrepancy of classes in MNIST handwritten dataset using convolutional neural networks and deep belief networks. Based on the experiment conducted, the results show that although the algorithm is suitable for multiple domains and have shown stability, the imbalanced distribution of data still able to affect the overall performance of the models.

international journal on advanced science, engineering and information technology/international journal of advanced science, engineering and information technology

Convolutional Neural Networks and Deep Belief Networks for Analysing Imbalanced Class Issue in Handwritten Dataset