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Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Predicting book sales trend using deep learning framework
Author(s): Feng, Tan Qin
Choy, Murphy
Laik, Ma Nang
Keywords: Generative adversarial network
deep learning framework
book sales forecasting
Issue Date: 2020
Citation: Feng TQ, Choy M & Laik MN (2020) Predicting book sales trend using deep learning framework. International Journal of Advanced Computer Science and Applications, 11 (2), pp. 28-39.
Abstract: A deep learning framework like Generative Adversarial Network (GAN) has gained popularity in recent years for handling many different computer visions related problems. In this research, instead of focusing on generating the near-real images using GAN, the aim is to develop a comprehensive GAN framework for book sales ranks prediction, based on the historical sales rankings and different attributes collected from the Amazon site. Different analysis stages have been conducted in the research. In this research, a comprehensive data preprocessing is required before the modeling and evaluation. Extensive predevelopment on the data, related features selections for predicting the sales rankings, and several data transformation techniques are being applied before generating the models. Later then various models are being trained and evaluated on prediction results. In the GAN architecture, the generator network that used to generate the features is being built, and the discriminator network that used to differentiate between real and fake features is being trained before the predictions. Lastly, the regression GAN model prediction results are compared against the different neural network models like multilayer perceptron, deep belief network, convolution neural network.
DOI Link: 10.14569/IJACSA.2020.0110205
Rights: This is an open access article licensed under a Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited
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