Neuroscience-inspired artificial vision feature parallelism and deep learning models, a comparative study ii depth

This study originates a new model, the Feature Parallelism Model (FPM), and compares it to deep learning models along depth, which is the number of layers that comprises a machine learning model. It is the number of layers in the horizontal axis, in the case of FPM. We found that only 6 layers optim...

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Main Authors:	Hassan, Marwa Yousif, Khalifa, Othman Omran, Hassan Abdalla Hashim, Aisha
Format:	Article
Language:	English
Published:	AESS Publication 2019
Subjects:	TK5101 Telecommunication. Including telegraphy, radio, radar, television
Online Access:	http://irep.iium.edu.my/75336/ http://irep.iium.edu.my/75336/ http://irep.iium.edu.my/75336/ http://irep.iium.edu.my/75336/1/NEUROSCIENCE-INSPIRED%20ARTIFICIAL%20VISION%20FEATURE.pdf

id	iium-75336
recordtype	eprints
spelling	iium-753362019-12-06T01:28:49Z http://irep.iium.edu.my/75336/ Neuroscience-inspired artificial vision feature parallelism and deep learning models, a comparative study ii depth Hassan, Marwa Yousif Khalifa, Othman Omran Hassan Abdalla Hashim, Aisha TK5101 Telecommunication. Including telegraphy, radio, radar, television This study originates a new model, the Feature Parallelism Model (FPM), and compares it to deep learning models along depth, which is the number of layers that comprises a machine learning model. It is the number of layers in the horizontal axis, in the case of FPM. We found that only 6 layers optimize FPM‟s performance. FPM has been inspired by the human brain and follows some organizing principles that underlie the human visual system. We review here the standard practice in deep learning, which is opting in to the deepest model that the computational resources allow up to hundreds of layers, seeking better accuracies. We have implemented FPM using 5, 6, 7, and 8 layers and observed accuracy as well as training time for each. We show that much less depth is needed for FPM, down to 6 layers. This optimizes both accuracy and training time for the model. Moreover, in a previous study we have proposed the model and have shown that while FPM uses less computational resources proved by 21% reduction in training time, it performs as well as deep learning regarding models‟ accuracy. AESS Publication 2019 Article PeerReviewed application/pdf en http://irep.iium.edu.my/75336/1/NEUROSCIENCE-INSPIRED%20ARTIFICIAL%20VISION%20FEATURE.pdf Hassan, Marwa Yousif and Khalifa, Othman Omran and Hassan Abdalla Hashim, Aisha (2019) Neuroscience-inspired artificial vision feature parallelism and deep learning models, a comparative study ii depth. Journal of Asian Scientific Research, 9 (9). pp. 127-139. ISSN 2226-5724 E-ISSN 2223-1331 http://www.aessweb.com/pdf-files/JASR-2019-9(9)-127-139.pdf 10.18488/journal.2.2019.99.127.139
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	TK5101 Telecommunication. Including telegraphy, radio, radar, television
spellingShingle	TK5101 Telecommunication. Including telegraphy, radio, radar, television Hassan, Marwa Yousif Khalifa, Othman Omran Hassan Abdalla Hashim, Aisha Neuroscience-inspired artificial vision feature parallelism and deep learning models, a comparative study ii depth
description	This study originates a new model, the Feature Parallelism Model (FPM), and compares it to deep learning models along depth, which is the number of layers that comprises a machine learning model. It is the number of layers in the horizontal axis, in the case of FPM. We found that only 6 layers optimize FPM‟s performance. FPM has been inspired by the human brain and follows some organizing principles that underlie the human visual system. We review here the standard practice in deep learning, which is opting in to the deepest model that the computational resources allow up to hundreds of layers, seeking better accuracies. We have implemented FPM using 5, 6, 7, and 8 layers and observed accuracy as well as training time for each. We show that much less depth is needed for FPM, down to 6 layers. This optimizes both accuracy and training time for the model. Moreover, in a previous study we have proposed the model and have shown that while FPM uses less computational resources proved by 21% reduction in training time, it performs as well as deep learning regarding models‟ accuracy.
format	Article
author	Hassan, Marwa Yousif Khalifa, Othman Omran Hassan Abdalla Hashim, Aisha
author_facet	Hassan, Marwa Yousif Khalifa, Othman Omran Hassan Abdalla Hashim, Aisha
author_sort	Hassan, Marwa Yousif
title	Neuroscience-inspired artificial vision feature parallelism and deep learning models, a comparative study ii depth
title_short	Neuroscience-inspired artificial vision feature parallelism and deep learning models, a comparative study ii depth
title_full	Neuroscience-inspired artificial vision feature parallelism and deep learning models, a comparative study ii depth
title_fullStr	Neuroscience-inspired artificial vision feature parallelism and deep learning models, a comparative study ii depth
title_full_unstemmed	Neuroscience-inspired artificial vision feature parallelism and deep learning models, a comparative study ii depth
title_sort	neuroscience-inspired artificial vision feature parallelism and deep learning models, a comparative study ii depth
publisher	AESS Publication
publishDate	2019
url	http://irep.iium.edu.my/75336/ http://irep.iium.edu.my/75336/ http://irep.iium.edu.my/75336/ http://irep.iium.edu.my/75336/1/NEUROSCIENCE-INSPIRED%20ARTIFICIAL%20VISION%20FEATURE.pdf
first_indexed	2023-09-18T21:46:36Z
last_indexed	2023-09-18T21:46:36Z
_version_	1777413491608518656

Neuroscience-inspired artificial vision feature parallelism and deep learning models, a comparative study ii depth

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