Improving Neural Network Using Jaya Algorithm with Opposite Learning for Air Quality Prediction

Iyad Abu Doush; Khalid Sultan; Ahmad Alsaber; Dhari Alkandari; Afsah Abdullah

doi:10.1007/978-3-031-47721-8_40

Improving Neural Network Using Jaya Algorithm with Opposite Learning for Air Quality Prediction

Iyad Abu Doush
, Khalid Sultan
, Ahmad Alsaber
, Dhari Alkandari
, Afsah Abdullah

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

The Multi-Layer Perceptron Neural Network (MLP) is the commonly used Feedforward Neural Network (FNN) for tackling classification and prediction problems. The efficiency of MLP relies on the appropriate selection of its weights and biases. Usually, a gradient-based technique is used for tuning the selection of these parameters during the learning process. This technique suffers from its slow convergence and being stuck in local optima. Predicting urban air quality is vital to prevent urban air pollution and improve the life of residents. The air quality index (AQI) is a quantitative air quality tool. In this paper, an enhanced Jaya optimization algorithm is used to improve the MLP outcome (called EOL-Jaya-MLP). The opposite-learning method is used to improve the algorithm search space exploration. A three-year dataset from air quality monitoring stations is used in this study. The proposed technique is compared against the original Jaya and six machine learning techniques. Interestingly, the EOL-Jaya-MLP outperforms other techniques when predicting the AQI.

Original language	English
Title of host publication	Intelligent Systems and Applications - Proceedings of the 2023 Intelligent Systems Conference IntelliSys Volume 1
Editors	Kohei Arai
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	597-606
Number of pages	10
ISBN (Print)	9783031477201
DOIs	https://doi.org/10.1007/978-3-031-47721-8_40
State	Published - Jan 2024
Event	Intelligent Systems Conference, IntelliSys 2023 - Amsterdam, Netherlands Duration: 7 Sep 2023 → 8 Sep 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	822
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	Intelligent Systems Conference, IntelliSys 2023
Country/Territory	Netherlands
City	Amsterdam
Period	7/09/23 → 8/09/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

Feedforward Neural Networks
Jaya Optimization Algorithm
Multi-Layer Perceptron
Optimization
Swarm Intelligence

Access to Document

10.1007/978-3-031-47721-8_40

Cite this

Doush, I. A., Sultan, K., Alsaber, A., Alkandari, D., & Abdullah, A. (2024). Improving Neural Network Using Jaya Algorithm with Opposite Learning for Air Quality Prediction. In K. Arai (Ed.), Intelligent Systems and Applications - Proceedings of the 2023 Intelligent Systems Conference IntelliSys Volume 1 (pp. 597-606). (Lecture Notes in Networks and Systems; Vol. 822). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-47721-8_40

Doush, Iyad Abu ; Sultan, Khalid ; Alsaber, Ahmad et al. / Improving Neural Network Using Jaya Algorithm with Opposite Learning for Air Quality Prediction. Intelligent Systems and Applications - Proceedings of the 2023 Intelligent Systems Conference IntelliSys Volume 1. editor / Kohei Arai. Springer Science and Business Media Deutschland GmbH, 2024. pp. 597-606 (Lecture Notes in Networks and Systems).

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title = "Improving Neural Network Using Jaya Algorithm with Opposite Learning for Air Quality Prediction",

abstract = "The Multi-Layer Perceptron Neural Network (MLP) is the commonly used Feedforward Neural Network (FNN) for tackling classification and prediction problems. The efficiency of MLP relies on the appropriate selection of its weights and biases. Usually, a gradient-based technique is used for tuning the selection of these parameters during the learning process. This technique suffers from its slow convergence and being stuck in local optima. Predicting urban air quality is vital to prevent urban air pollution and improve the life of residents. The air quality index (AQI) is a quantitative air quality tool. In this paper, an enhanced Jaya optimization algorithm is used to improve the MLP outcome (called EOL-Jaya-MLP). The opposite-learning method is used to improve the algorithm search space exploration. A three-year dataset from air quality monitoring stations is used in this study. The proposed technique is compared against the original Jaya and six machine learning techniques. Interestingly, the EOL-Jaya-MLP outperforms other techniques when predicting the AQI.",

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Doush, IA, Sultan, K , Alsaber, A, Alkandari, D & Abdullah, A 2024, Improving Neural Network Using Jaya Algorithm with Opposite Learning for Air Quality Prediction. in K Arai (ed.), Intelligent Systems and Applications - Proceedings of the 2023 Intelligent Systems Conference IntelliSys Volume 1. Lecture Notes in Networks and Systems, vol. 822, Springer Science and Business Media Deutschland GmbH, pp. 597-606, Intelligent Systems Conference, IntelliSys 2023, Amsterdam, Netherlands, 7/09/23. https://doi.org/10.1007/978-3-031-47721-8_40

Improving Neural Network Using Jaya Algorithm with Opposite Learning for Air Quality Prediction. / Doush, Iyad Abu; Sultan, Khalid ; Alsaber, Ahmad et al.
Intelligent Systems and Applications - Proceedings of the 2023 Intelligent Systems Conference IntelliSys Volume 1. ed. / Kohei Arai. Springer Science and Business Media Deutschland GmbH, 2024. p. 597-606 (Lecture Notes in Networks and Systems; Vol. 822).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Doush IA, Sultan K , Alsaber A, Alkandari D, Abdullah A. Improving Neural Network Using Jaya Algorithm with Opposite Learning for Air Quality Prediction. In Arai K, editor, Intelligent Systems and Applications - Proceedings of the 2023 Intelligent Systems Conference IntelliSys Volume 1. Springer Science and Business Media Deutschland GmbH. 2024. p. 597-606. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-47721-8_40