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General Information
ISSN:
1798-2340 (Online)
Frequency:
Monthly
DOI:
10.12720/jait
Indexing:
ESCI (Web of Science)
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Scopus
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CNKI
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etc
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Acceptance Rate:
12%
APC:
1000 USD
Average Days to Accept:
87 days
Journal Metrics:
Impact Factor 2023: 0.9
4.2
2023
CiteScore
57th percentile
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Editor-in-Chief
Prof. Kin C. Yow
University of Regina, Saskatchewan, Canada
I'm delighted to serve as the Editor-in-Chief of
Journal of Advances in Information Technology
.
JAIT
is intended to reflect new directions of research and report latest advances in information technology. I will do my best to increase the prestige of the journal.
What's New
2024-06-26
Vol. 15, No. 6 has been published online!
2024-06-07
JAIT received the CiteScore 2023 with 4.2, ranked #169/394 in Category Computer Science: Information Systems, #174/395 in Category Computer Science: Computer Networks and Communications, #226/350 in Category Computer Science: Computer Science Applications
2024-05-28
Vol. 15, No. 5 has been published online!
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2022
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Volume 13, No. 5, October 2022
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JAIT 2022 Vol.13(5): 512-517
doi: 10.12720/jait.13.5.512-517
Parameter and Hyperparameter Optimisation of Deep Neural Network Model for Personalised Predictions of Asthma
Radiah Haque
1
, Sin-Ban Ho
1
, Ian Chai
1
, and Adina Abdullah
2
1. Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Malaysia
2. Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
Abstract
—Over the last couple of decades, numerous optimisation algorithms have been introduced to optimise machine learning models. However, until now, no evidence or framework can be found in the literature that adequately describes how to select the best algorithm for parameter and hyperparameter optimisation of the Deep Neural Network (DNN) model. In this paper, an enhanced Fragmented Grid Search (FGS) method has been introduced for tuning several hyperparameters and finding the optimal architecture of the DNN model using less computation power and time. Furthermore, several experimental models are trained on the asthma dataset using various optimisers to find the optimal parameters, which can help the DNN model converge towards the lowest loss value. The results show that the Adam optimiser provides the best accuracy rate (96%). Consequently, the optimised DNN model can be used for accurately providing personalised predictions of asthma exacerbations for effective asthma self-management.
Index Terms
—machine learning, deep neural networks, optimisation algorithm, personalization
Cite: Radiah Haque, Sin-Ban Ho, Ian Chai, and Adina Abdullah, "Parameter and Hyperparameter Optimisation of Deep Neural Network Model for Personalised Predictions of Asthma," Journal of Advances in Information Technology, Vol. 13, No. 5, pp. 512-517, October 2022.
Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License (
CC BY-NC-ND 4.0
), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.
13-LT044-Malaysia
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