Open Access

ARTICLE

Intelligent Model Of Ecosystem For Smart Cities Using Artificial Neural Networks

by Tooba Batool¹, Sagheer Abbas¹, Yousef Alhwaiti², Muhammad Saleem¹, Munir Ahmad¹, Muhammad Asif^1,*, Nouh Sabri Elmitwally^2,3

1 School of Computer Science, National College of Business administration and Economics, Lahore, 54000, Pakistan
2 College of Computer and Information Sciences, Jouf University, Sakaka, 72341, Saudi Arabia
3 Department of Computer Science, Faculty of Computers and Artificial Intelligence, Cairo University, 12613, Egypt

* Corresponding Author: Muhammad Asif. Email:

Intelligent Automation & Soft Computing 2021, 30(2), 513-525. https://doi.org/10.32604/iasc.2021.018770

Received 20 March 2021; Accepted 21 April 2021; Issue published 11 August 2021

Abstract

A Smart City understands the infrastructure, facilities, and schemes open to its citizens. According to the UN report, at the end of 2050, more than half of the rural population will be moved to urban areas. With such an increase, urban areas will face new health, education, Transport, and ecological issues. To overcome such kinds of issues, the world is moving towards smart cities. Cities cannot be smart without using Cloud computing platforms, the Internet of Things (IoT). The world has seen such incredible and brilliant ideas for rural areas and smart cities. While considering the Ecosystem in Smart Cities, there is a considerable requirement to improve the model to make life better. This proposed research integrates a city into a smart city using the Internet of Things (IoT) which focuses on the smart ecosystem. In this research work, a model is proposed to overcome an ecosystem’s IoT and Machine Learning techniques issues. The Levenberg-Marquardt (LM), Bayesian Regularization (BR), and the Scaled Conjugate Gradient (SCG) algorithms are implemented with an ANN-based approach named to empower the ecosystem of the smart city while developing an efficient and smart ecosystem model. The proposed method’s evaluation indicates that the BR algorithm achieves promising results concerning accuracy and miss rates. The predicted accuracy of the proposed model shows 91.55% performance of the ecosystem on the given factors.

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