IIUM Repository

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

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

[img] PDF - Published Version
Restricted to Registered users only

Download (1MB) | Request a copy


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.

Item Type: Article (Journal)
Additional Information: 4119/75336
Uncontrolled Keywords: Feature parallelism model; Deep learning; Machine learning; Neuroscience cortical column; Laminar organization computer vision; Visual system depth analysis FPM.
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101 Telecommunication. Including telegraphy, radio, radar, television
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User: Prof. Dr Othman O. Khalifa
Date Deposited: 06 Dec 2019 09:28
Last Modified: 06 Dec 2019 09:28
URI: http://irep.iium.edu.my/id/eprint/75336

Actions (login required)

View Item View Item


Downloads per month over past year