Mixed Data through Multiple Input for Price Prediction with Multilayer Perception and Mini VGG Net
Satyasangram Sahoo1, Prem Kumar. B2, R. Lakshmi3
1Satyasangram Sahoo, Research Scholar, Department of Computer Science, Pondicherry University, Pondicherry, India.
2Prem Kumar Borugadda, Research Scholar, Department of Computer Science, Pondicherry University, Pondicherry, India.
3Dr R Lakshmi, Assistant Professor, Dept. of Computer Science, Pondicherry University, Pondicherry, India.
Manuscript received on 10 March 2019 | Revised Manuscript received on 18 March 2019 | Manuscript published on 30 July 2019 | PP: 6317-6320 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3597078219/2019©BEIESP | DOI: 10.35940/ijrte.B3597.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The multi-input with mixed data modality of the model is based on three folded structure. The first input model is structured by Convolution Network that accepts the images related to the house. The implementation of the network is miniVGGNet design. The network is tested among, which gives a better outcome. The output valued is concatenated eventually with numerical value entry of the same set which is trained and processed by multi-layer perceptron for review the house price of the building. The linear activation is helped to evaluate the predicted value of price after equal dimension merging of convolutional and multi-layer perceptron network.
Index Terms: Multi-Layer Perceptron, Mini VGG Net, Mixed Data, Multi-Input, House Attributes.
Scope of the Article: Perception and Semantic Interpretation