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DCPV: A taxonomy for deep learning model in computer aided system for human age detection

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dc.contributor.authorMaskey, N.
dc.contributor.authorHameedi, S.
dc.contributor.authorDawoud, A.
dc.contributor.authorJacksi, K.
dc.contributor.authorAl Sadoon, O.H.R.
dc.contributor.authorSalahuddin, A.B.E.
dc.contributor.editorDaimi, K.
dc.contributor.editorSadoon, A.
dc.date.issued2023
dc.description.abstractDeep Learning prediction techniques are widely studied and researched for their implementation in Human Age Prediction (HAP) to prevent, treat and extend life expectancy. So far most of the algorithms are based on facial images, MRI scans, and DNA methylation which is used for training and testing in the domain but rarely practiced. The lack of real-world-age HAP application is caused by several factors: no significant validation and devaluation of the system in the real-world scenario, low performance, and technical complications. This paper presents the Data, Classification technique, Prediction, and View (DCPV) taxonomy which specifies the major components of the system required for the implementation of a deep learning model to predict human age. These components are to be considered and used as validation and evaluation criteria for the introduction of the deep learning HAP model. A taxonomy of the HAP system is a step towards the development of a common baseline that will help the end users and researchers to have a clear view of the constituents of deep learning prediction approaches, providing better scope for future development of similar systems in the health domain. We assess the DCPV taxonomy by considering the performance, accuracy, robustness, and model comparisons. We demonstrate the value of the DCPV taxonomy by exploring state-of-the-art research within the domain of the HAP system.
dc.identifier.citationEvent/exhibition information: 2nd International Conference on Innovations in Computing Research, ICR 2023, Madrid, 04/09/2023-06/09/2023 Source details - Title: Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23), 2023 / Daimi, K., Sadoon, A. (ed./s), pp.64-79
dc.identifier.doi10.1007/978-3-031-35308-6_6
dc.identifier.isbn9783031353079
dc.identifier.urihttps://hdl.handle.net/11541.2/35528
dc.language.isoen
dc.publisherSpringer
dc.publisher.placeSwitzerland
dc.relation.ispartofseries721 LNNS, 2367-3370
dc.rightsCopyright 2023 The Author(s), under exclusive license to Springer Nature Switzerland
dc.source.urihttps://doi.org/10.1007/978-3-031-35308-6_6
dc.subjectdeep learning
dc.subjectclassification
dc.subjecttaxonomy
dc.subjecthuman age prediction
dc.titleDCPV: A taxonomy for deep learning model in computer aided system for human age detection
dc.typeBook chapter
pubs.publication-statusPublished
ror.mmsid9916776909301831

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