Artificial Intelligence – State of Art Convolution Neural Network Architectures in a Nutshell
F. Catherine Tamilarasi1, J. Shanmugam2
1F. Catherine Tamilarasi, Research Scholar, BIHER, Chennai, Tamil Nadu, India.
2Dr. J. Shanmugam, Research Supervisor, BIHER, Chennai, Tamil Nadu, India.
Manuscript received on 15 September 2019 | Revised Manuscript received on 23 September 2019 | Manuscript Published on 11 October 2019 | PP: 1278-1280 | Volume-8 Issue-11S September 2019 | Retrieval Number: K125709811S19/2019©BEIESP | DOI: 10.35940/ijitee.K1257.09811S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: It is a well-known fact that all the Artificial Intelligence (AI)researches happening across multiple verticals such as Neuro Imaging, Computer Vision, Deep learning etc point to one master goal of modelling the human brain function by understanding how each part of the brain works. The Convolution neural network (CNN) is one of best deep architecture suitable to handle variety of inputs. In this paper we explore the different types of input data the CNN deep architecture can process and some of the CNN configuration changes that has proved good Accuracy. We have highlighted those specialized CNN architectures along with different types of data inputs they handle including the Functional Magnetic Resonance (fMRI) Neuro Image brain data input.
Keywords: Artificial Intelligence, Deep Learning, CNN, Visual, Audio and Speech Recognition.
Scope of the Article: Artificial Intelligence