DL-CNN: Double Layered Convolutional Neural Networks

Lixin Fu; Rohith Rangineni

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Paper

DL-CNN: Double Layered Convolutional Neural Networks

Topics: Data Mining; Deep Learning

In Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 1: ICEIS, 281-286, 2022

Authors: Lixin Fu and Rohith Rangineni

Affiliation: Department of Computer Science, University of North Carolina at Greensboro, Greensboro, NC 27401, U.S.A.

Keyword(s): The Convolutional Layers, Double Layers, Neural Networks, Classification, Image Processing.

Abstract: We studied the traditional convolutional neural networks and developed a new model that used double layers instead of only one. In our example of this model, we used five convolutional layers and four fully connected layers. The dataset has four thousand human face images of two classes, one of them being open eyes and the other closed eyes. In this project, we dissected the original source code of the standard package into several components and changed some of the core parts to improve accuracy. In addition to using both the current layer and the prior layer to compute the next layer, we also explored whether to skip the current layer. We changed the original convolution window formula. A multiplication bias instead of originally adding bias to the linear combination was also proposed. Though it is hard to explain the rationale, the results of multiplication bias are better in our example. For our new double layer model, our simulation results showed that the accuracy was increased from 60% to 95%. (More)

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Paper citation in several formats:

Fu, L. and Rangineni, R. (2022). DL-CNN: Double Layered Convolutional Neural Networks. In Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-569-2; ISSN 2184-4992, SciTePress, pages 281-286. DOI: 10.5220/0011117000003179

@conference{iceis22,
author={Lixin Fu and Rohith Rangineni},
title={DL-CNN: Double Layered Convolutional Neural Networks},
booktitle={Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2022},
pages={281-286},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011117000003179},
isbn={978-989-758-569-2},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - DL-CNN: Double Layered Convolutional Neural Networks
SN - 978-989-758-569-2
IS - 2184-4992
AU - Fu, L.
AU - Rangineni, R.
PY - 2022
SP - 281
EP - 286
DO - 10.5220/0011117000003179
PB - SciTePress