Open Access

ARTICLE

Design of ANN Based Non-Linear Network Using Interconnection of Parallel Processor

by Anjani Kumar Singha¹, Swaleha Zubair¹, Areej Malibari², Nitish Pathak³, Shabana Urooj^4,*, Neelam Sharma⁵

1 Department of Computer Science, Aligarh Muslim University, Aligarh, 202002, India
2 Department of Industrial and Systems Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh, 11671, Saudi Arabia
3 Department of Information Technology, Bhagwan Parshuram Institute of Technology (BPIT), GGSIPU, New Delhi, 110078, India
4 Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh, 11671, Saudi Arabia
5 Department of Artificial Intelligence & Machine Learning, Maharaja Agrasen Institute of Technology (MAIT), GGSIPU, New Delhi, 110086, India

* Corresponding Author: Shabana Urooj. Email:

Computer Systems Science and Engineering 2023, 46(3), 3491-3508. https://doi.org/10.32604/csse.2023.029165

Received 26 February 2022; Accepted 12 April 2022; Issue published 03 April 2023

Abstract

Suspicious mass traffic constantly evolves, making network behaviour tracing and structure more complex. Neural networks yield promising results by considering a sufficient number of processing elements with strong interconnections between them. They offer efficient computational Hopfield neural networks models and optimization constraints used by undergoing a good amount of parallelism to yield optimal results. Artificial neural network (ANN) offers optimal solutions in classifying and clustering the various reels of data, and the results obtained purely depend on identifying a problem. In this research work, the design of optimized applications is presented in an organized manner. In addition, this research work examines theoretical approaches to achieving optimized results using ANN. It mainly focuses on designing rules. The optimizing design approach of neural networks analyzes the internal process of the neural networks. Practices in developing the network are based on the interconnections among the hidden nodes and their learning parameters. The methodology is proven best for nonlinear resource allocation problems with a suitable design and complex issues. The ANN proposed here considers more or less 46k nodes hidden inside 49 million connections employed on full-fledged parallel processors. The proposed ANN offered optimal results in real-world application problems, and the results were obtained using MATLAB.

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Keywords

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