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3D Geometric Moment Invariants for ATS Drugs Identification: A More Precise Approximation

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Proceedings of the 16th International Conference on Hybrid Intelligent Systems (HIS 2016) (HIS 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 552))

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Abstract

National development is constantly threatened by drug abuse. The chemical composition of the drugs heavily determines the results of identification process, which becomes more unreliable due to the introduction of new, sophisticated, and increasingly complex ATS analogues. The identification of the unique characteristics of molecular structure in ATS drug is very crucial. Therefore, this paper is meant for formulating a more precise 3D geometric moment invariants to represent the molecular structure. The performance of the proposed technique was analyzed using drug molecular structures obtained from United Nations Office of Drugs and Crime and also from various sources. The evaluation shows the technique is qualified to be further explored and adapted to be fully compatible with ATS drug identification domain.

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Acknowledgements

This work was supported by UTeM Postgraduate Fellowship Scheme and PJP High Impact Research Grant (S01473-PJP/2016/FTMK/HI3) from Universiti Teknikal Malaysia Melaka (UTeM), Malaysia.

Author information

Authors and Affiliations

  1. Computational Intelligence and Technologies (CIT) Research Group, Center of Advanced Computing and Technologies, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia

    Satrya Fajri Pratama, Azah Kamilah Muda, Yun-Huoy Choo & Ajith Abraham

  2. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Auburn, Washington, USA

    Ajith Abraham

Authors
  1. Satrya Fajri Pratama
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  2. Azah Kamilah Muda
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  3. Yun-Huoy Choo
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  4. Ajith Abraham
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Corresponding author

Correspondence to Azah Kamilah Muda .

Editor information

Editors and Affiliations

  1. (MIR Labs), Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  2. Hassan 1st University, Settat, Morocco

    Abdelkrim Haqiq

  3. ENIS, University of Sfax, Sfax, Tunisia

    Adel M. Alimi

  4. Technopolis Rabat-Shore Rocade, International University of Rabat, Sala el Jadida, Morocco

    Ghita Mezzour

  5. Inst Applied Dept of Electronics, Taffala, University of Sousse, Sousse, Tunisia

    Nizar Rokbani

  6. Fakulti Teknologi Maklumat dan Komunikas, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia

    Azah Kamilah Muda

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Pratama, S.F., Muda, A.K., Choo, YH., Abraham, A. (2017). 3D Geometric Moment Invariants for ATS Drugs Identification: A More Precise Approximation. In: Abraham, A., Haqiq, A., Alimi, A., Mezzour, G., Rokbani, N., Muda, A. (eds) Proceedings of the 16th International Conference on Hybrid Intelligent Systems (HIS 2016). HIS 2016. Advances in Intelligent Systems and Computing, vol 552. Springer, Cham. https://doi.org/10.1007/978-3-319-52941-7_13

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  • DOI: https://doi.org/10.1007/978-3-319-52941-7_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52940-0

  • Online ISBN: 978-3-319-52941-7

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