Economics at your fingertips  

Neuroscience-Inspired Artificial Vision Feature Parallelism and Deep Learning Models, A Comparative Study II Depth

Marwa Yousif Hassan, Othman O. Khalifa and Aisha Hassan Abdalla
Additional contact information
Marwa Yousif Hassan: Department of Electrical and Computer Engineering, IIUM Kuala Lumpur, Malaysia.
Othman O. Khalifa: Department of Electrical and Computer Engineering, IIUM Kuala Lumpur, Malaysia.
Aisha Hassan Abdalla: Department of Electrical and Computer Engineering, IIUM Kuala Lumpur, Malaysia.

Journal of Asian Scientific Research, 2019, vol. 9, issue 9, 127-139

Abstract: 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.

Keywords: Feature Parallelism Model; Deep learning; Machine Learning; Neuroscience; Cortical Column; Laminar Organization; Computer vision; Visual system; Depth analysis; FPM. (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link) (application/pdf) (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this article

Journal of Asian Scientific Research is currently edited by Dr. RAMANI. K

More articles in Journal of Asian Scientific Research from Asian Economic and Social Society 2637 E Atlantic Blvd #43110 Pompano Beach, FL 33062, USA.
Bibliographic data for series maintained by Chan Hoi Yan ().

Page updated 2019-11-24
Handle: RePEc:asi:joasrj:2019:p:127-139