loading
Documents

Research.Publish.Connect.

Paper

Authors: Fatima Zahrae Ait Omar and Najib Belkhayat

Affiliation: Cady Ayad University, Marrakech and Morocco

ISBN: 978-989-758-330-8

Keyword(s): Artificial Neural Networks, Architectures, Classification, Artificial Intelligence, Prediction, Deep Learning.

Abstract: Artificial neural networks (ANN) have become the state-of-the-art technique to tackle highly complex problems in AI due to their high prediction and features’ extraction ability. The recent development in this technology has broadened the jungle of the existing ANNs architectures and caused the field to be less accessible to novices. It is increasingly difficult for new beginners to categorize the architectures and pick the best and well-suited ones for their study case, which makes the need to summarize and classify them undeniable. Many previous classifications tried to meet this aim but failed to clear up the use case for each architecture. The aim of this paper is to provide guidance and a clear overview to beginners and non-experts and help them choose the right architecture for their research without having to dig deeper in the field. The classification suggested for this purpose is performed according to two dimensions inspired of the brain’s perception of the outside world: th e time scale upon which the data is collected and the signal nature. (More)

PDF ImageFull Text

Download
Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.208.22.127

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Omar, F. and Belkhayat, N. (2018). TS Artificial Neural Networks Classification: A Classification Approach based on Time & Signal.In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, ISBN 978-989-758-330-8, pages 178-185. DOI: 10.5220/0006927701780185

@conference{kdir18,
author={Fatima Zahrae Ait Omar. and Najib Belkhayat.},
title={TS Artificial Neural Networks Classification: A Classification Approach based on Time & Signal},
booktitle={Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,},
year={2018},
pages={178-185},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006927701780185},
isbn={978-989-758-330-8},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,
TI - TS Artificial Neural Networks Classification: A Classification Approach based on Time & Signal
SN - 978-989-758-330-8
AU - Omar, F.
AU - Belkhayat, N.
PY - 2018
SP - 178
EP - 185
DO - 10.5220/0006927701780185

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.