A Pattern Recognition Model of Python Programming using Artificial Neural Network via NeMo
M.Janardhan1, M.Srilakshmi2, S Prem Kumar3

1M.Janardhan Associate Professor, Department of Computer Science and Engineering, G.Pullaiah College of Engineering and Technology (Autonomous).
2M.Srilakshmi, Assistant Professor, Department of Computer Science and Engineering, G.Pullaiah College of Engineering and Technology (Autonomous).
3Dr.S Prem Kumar, Professor & Dean, Department of Computer Science and Engineering, G.Pullaiah College of Engineering and Technology (Autonomous).
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 109-113 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6960018520/2020©BEIESP | DOI: 10.35940/ijrte.E6960.018520

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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: Background/Objectives: In the field of software development, the diversity of programming languages increases dramatically with the increase in their complexity. This leads both programmers and researchers to develop and investigate automated tools to distinguish these programming languages. Different efforts were conducted to achieve this task using keywords of source codes of these programming languages. Therefore, instead of using keywords classification for recognition, this work is conducted to investigate the ability to detect the pattern of a programming language characteristic by using NeMo(High-performance spiking neural network simulator) of neural network and testing the ability of this toolkit to provide detailed analyzable results. Methods/Statistical analysis: the method of achieving these objectives is by using a back propagation neural network via NeMo based on pattern recognition methodology. Findings: The results show that the NeMo neural network of pattern recognition can identify and recognize the pattern of python programming language with high accuracy. It also shows the ability of the NeMo toolkit to represent the analyzable results through a percentage of certainty. Improvements/Applications: it can be noticed from the results the ability of NeMo simulator to provide beneficial platform for studying and analyzing the complexity of the backpropagation neural network model.
Keywords: Nemo, Pattern Recognition, Artificial Neural Network, Backpropagation Neural Network.
Scope of the Article: Image Processing and Pattern Recognition.