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Amphetamine-type stimulants (ATS) drug classification using shallow one-dimensional convolutional neural network

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Abstract

Amphetamine-type stimulants (ATS) drug analysis and identification are challenging and critical nowadays with the emergence production of new synthetic ATS drugs with sophisticated design compounds. In the present study, we proposed a one-dimensional convolutional neural network (1DCNN) model to perform ATS drug classification as an alternative method. We investigate as well as explore the classification behavior of 1DCNN with the utilization of the existing novel 3D molecular descriptors as ATS drugs representation to become the model input. The proposed 1DCNN model is composed of one convolutional layer to reduce the model complexity. Besides, pooling operation that is a standard part of traditional CNN is not applied in this architecture to have more features in the classification phase. The dropout regularization technique is employed to improve model generalization. Experiments were conducted to find the optimal values for three dominant hyper-parameters of the 1DCNN model which are the filter size, transfer function, and batch size. Our findings found that kernel size 11, exponential linear unit (ELU) transfer function and batch size 32 are optimal for the 1DCNN model. A comparison with several machine learning classifiers has shown that our proposed 1DCNN has achieved comparable performance with the Random Forest classifier and competitive performance with the others.

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References

  1. Christophersen AS (2000) Amphetamine designer drugs – an overview and epidemiology. Toxicol Lett 112–113:127–131. https://doi.org/10.1016/S0378-4274(99)00205-2

    Article  PubMed  Google Scholar 

  2. Blickman T (2009) The ATS boom in Southeast Asia. In: Withdrawal symptoms in the golden triangle - A drugs market in disarray. Transnational Institute

  3. Carroll FI, Lewin AH, Mascarella SW et al (2012) Designer drugs: a medicinal chemistry perspective. Ann N Y Acad Sci 1248:18–38. https://doi.org/10.1111/j.1749-6632.2011.06199.x

    Article  CAS  PubMed  Google Scholar 

  4. Liu L, Wheeler SE, Venkataramanan R et al (2018) Newly emerging drugs of abuse and their detection methods: an ACLPS critical review. Am J Clin Pathol 149:105–116. https://doi.org/10.1093/AJCP/AQX138

    Article  CAS  PubMed  Google Scholar 

  5. Peters FT, Martinez-Ramirez JA (2010) Analytical toxicology of emerging drugs of abuse. Ther Drug Monit 32:532–539

    Article  CAS  Google Scholar 

  6. Harper L, Powell J, Pijl EM (2017) An overview of forensic drug testing methods and their suitability for harm reduction point-of-care services. Harm Reduct J. https://doi.org/10.1186/s12954-017-0179-5

    Article  PubMed  PubMed Central  Google Scholar 

  7. Chung H, Choe S (2019) Amphetamine-type stimulants in drug testing. Mass Spectrom Lett 10:1–10. https://doi.org/10.5478/MSL.2019.10.1.1

    Article  Google Scholar 

  8. Regester LE, Chmiel DJ, Holler MJ et al (2014) Determination of designer drug cross-reactivity on five commercial immunoassay screening kits. J Anal Toxicol 39:141–151. https://doi.org/10.1093/jat/bku133

    Article  CAS  Google Scholar 

  9. Stumpfe D, Bajorath J (2011) Similarity searching. Wiley Interdiscip Rev Comput Mol Sci 1:260–282. https://doi.org/10.1002/wcms.23

    Article  CAS  Google Scholar 

  10. Bero SA, Muda AK, Choo YH et al (2017) Similarity measure for molecular structure: a brief review. J Phys Conf Ser. https://doi.org/10.1088/1742-6596/892/1/012015

    Article  Google Scholar 

  11. Krasowski MD, Ekins S (2014) Using cheminformatics to predict cross reactivity of “designer drugs” to their currently available immunoassays. J Cheminform 6:1–13. https://doi.org/10.1186/1758-2946-6-22

    Article  CAS  Google Scholar 

  12. Willett P (2020) The literature of chemoinformatics: 1978–2018. Int J Mol Sci 21:1–9. https://doi.org/10.3390/ijms21155576

    Article  Google Scholar 

  13. Shultz TR, Fahlman SE (2017) Encyclopedia of machine learning and data mining

  14. Hu M-K (1962) Visual pattern recognition by moment invariants. IRE Trans Inf Theory 8:179–187. https://doi.org/10.1109/TIT.1962.1057692

    Article  Google Scholar 

  15. Mamistvalov AG (1998) N-dimensional moment invariants and conceptual mathematical theory of recognition n-dimensional solids. IEEE Trans Pattern Anal Mach Intell 20:819–831. https://doi.org/10.1109/34.709598

    Article  Google Scholar 

  16. Flusser J (2006) Moment invariants in image analysis. Proc World Acad Sci Eng Technol Int J Comput Electr Autom Control Inf Eng 1:3708–3713. https://doi.org/10.5281/zenodo.1071752

    Article  Google Scholar 

  17. Murugan A, Nair SAH, Preethi AAP, Kumar KPS (2021) Diagnosis of skin cancer using machine learning techniques. Microprocess Microsyst 81:103727. https://doi.org/10.1016/j.micpro.2020.103727

    Article  Google Scholar 

  18. Zhang X, Yang J, Nguyen E (2018) Breast cancer detection via Hu moment invariant and feedforward neural network. AIP Conf Proc. https://doi.org/10.1063/1.5033394

    Article  Google Scholar 

  19. Sommer I, Müller O, Domingues FS et al (2007) Moment invariants as shape recognition technique for comparing protein binding sites. Bioinformatics 23:3139–3146. https://doi.org/10.1093/bioinformatics/btm503

    Article  CAS  PubMed  Google Scholar 

  20. Kihara D, Sael L, Chikhi R, Esquivel-Rodriguez J (2011) Molecular surface representation using 3D Zernike descriptors for protein shape comparison and docking. Curr Protein Pept Sci 12:520–530. https://doi.org/10.2174/138920311796957612

    Article  CAS  PubMed  Google Scholar 

  21. Sit A, Shin WH, Kihara D (2019) Three-dimensional Krawtchouk descriptors for protein local surface shape comparison. Pattern Recognit 93:534–545. https://doi.org/10.1016/j.patcog.2019.05.019

    Article  PubMed  PubMed Central  Google Scholar 

  22. Benouini R, Batioua I, Zenkouar K et al (2019) Fast and accurate computation of Racah moment invariants for image classification. Pattern Recognit 91:100–110. https://doi.org/10.1016/j.patcog.2019.02.014

    Article  Google Scholar 

  23. Benouini R, Batioua I, Zenkouar K et al (2018) Efficient 3D object classification by using direct Krawtchouk moment invariants. Multimed Tools Appl 77:27517–27542. https://doi.org/10.1007/s11042-018-5937-1

    Article  Google Scholar 

  24. Pratama SF, Muda AK, Choo YH et al (2017) ATS drugs molecular structure representation using refined 3D geometric moment invariants. J Math Chem 55:1951–1963. https://doi.org/10.1007/s10910-017-0775-3

    Article  CAS  Google Scholar 

  25. Pratama SF, Muda AK, Choo Y-H, et al (2018) Using 3D Hahn moments as a computational representation of ATS drugs molecular structure. arXiv Prepr arXiv180206404

  26. Kiranyaz S, Avci O, Abdeljaber O et al (2021) 1D convolutional neural networks and applications: a survey. Mech Syst Signal Process 151:107398. https://doi.org/10.1016/j.ymssp.2020.107398

    Article  Google Scholar 

  27. Pratama SF (2017) Three-dimensional exact legendre moment invariants for amphetamine-type stimulants molecular structure representation. Universiti Teknikal Malaysia Melaka

  28. Pratama SF, Muda AK, Choo YH, Abraham A (2018) Preparation of ATS drugs 3D molecular structure for 3D moment invariants-based molecular descriptors. In: Advances in intelligent systems and computing. Springer International Publishing, pp 252–261

  29. Korn F, Pagel BU, Faloutsos C (2001) On the “dimensionality curse” and the “self-similarity blessing.” IEEE Trans Knowl Data Eng 13:96–111. https://doi.org/10.1109/69.908983

    Article  Google Scholar 

  30. Xiao B, Xu Y, Bi X et al (2020) Heart sounds classification using a novel 1-D convolutional neural network with extremely low parameter consumption. Neurocomputing 392:153–159. https://doi.org/10.1016/j.neucom.2018.09.101

    Article  Google Scholar 

  31. Riese FM, Keller S (2019) Soil texture classification with 1D convolutional neural networks based on hyperspectral data. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 4:615–621. https://doi.org/10.5194/isprs-annals-IV-2-W5-615-2019

    Article  Google Scholar 

  32. Gan J, Wang W, Lu K (2019) A new perspective: recognizing online handwritten Chinese characters via 1-dimensional CNN. Inf Sci (Ny) 478:375–390. https://doi.org/10.1016/j.ins.2018.11.035

    Article  Google Scholar 

  33. Abo-Tabik M, Costen N, Darby J, Benn Y (2020) Towards a smart smoking cessation app: A 1D-CNN model predicting smoking events. Sensors (Switzerland) 20:1–18. https://doi.org/10.3390/s20041099

    Article  Google Scholar 

  34. Sharma A, Malacaria P, Khouzani MHR (2019) Malware detection using 1-dimensional convolutional neural networks. Proc - 4th IEEE Eur Symp Secur Priv Work EUROS PW 2019 247–256. https://doi.org/10.1109/EuroSPW.2019.00034

  35. Yunita Dewi F, Faza A, Prajitno P, Kusuma Wijaya S (2020) Stroke severity classification based on EEG signals using 1D convolutional neural network. J Phys Conf Ser. https://doi.org/10.1088/1742-6596/1528/1/012006

    Article  Google Scholar 

  36. Srivastava N, Hinton G, Krizhevsky A et al (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929–1958. https://doi.org/10.1016/0370-2693(93)90272-J

    Article  Google Scholar 

  37. Kingma DP, Ba JL (2015) Adam: A method for stochastic optimization. 3rd Int Conf Learn Represent ICLR 2015 - Conf Track Proc 1–15

  38. Nair V, Hinton GE (2010) Rectified linear units improve restricted Boltzmann machines vinod. In: 27th International Conference on Machine Learning. pp 807–814

  39. Clevert DA, Unterthiner T, Hochreiter S (2016) Fast and accurate deep network learning by exponential linear units (ELUs). 4th Int Conf Learn Represent ICLR 2016 - Conf Track Proc 1–14

  40. Mesbah A, Berrahou A, Hammouchi H, et al (2018) Non-rigid 3D model classification using 3D hahn moment convolutional neural networks. Eurographics Work 3D Object Retrieval, EG 3DOR 2018-April:79–85. https://doi.org/10.2312/3dor.20181056

  41. Kim P (2017) MATLAB deep learning: with machine learning, neural networks and artificial intelligence

  42. Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. J Mach Learn Res 9:249–256

    Google Scholar 

  43. Learning M (2014) Simple guide to confusion matrix terminology. 1–9

  44. Kandel I, Castelli M (2020) The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset. ICT Express. https://doi.org/10.1016/j.icte.2020.04.010

    Article  Google Scholar 

  45. Radiuk PM (2018) Impact of training set batch size on the performance of convolutional neural networks for diverse datasets. Inf Technol Manag Sci 20:20–24. https://doi.org/10.1515/itms-2017-0003

    Article  Google Scholar 

  46. Masters D, Luschi C (2018) Revisiting Small Batch Training for Deep Neural Networks. 1–18

  47. Srivastava S (2014) Weka: a tool for data preprocessing, classification, ensemble, clustering and association rule mining. Int J Comput Appl 88:26–29. https://doi.org/10.5120/15389-3809

    Article  Google Scholar 

  48. Ribani R, Marengoni M (2019) A survey of transfer learning for convolutional neural networks. Proc - 32nd Conf Graph Patterns Images Tutorials, SIBGRAPI-T 2019 47–57. https://doi.org/10.1109/SIBGRAPI-T.2019.00010

  49. Ijjina EP, Chalavadi KM (2016) Human action recognition using genetic algorithms and convolutional neural networks. Pattern Recognit. https://doi.org/10.1016/j.patcog.2016.01.012

    Article  Google Scholar 

  50. Zatarain Cabada R, Rodriguez Rangel H, Barron Estrada ML, Cardenas Lopez HM (2020) Hyperparameter optimization in CNN for learning-centered emotion recognition for intelligent tutoring systems. Soft Comput 24:7593–7602. https://doi.org/10.1007/s00500-019-04387-4

    Article  Google Scholar 

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Acknowledgements

This work was supported by Fundamental Research Grant Scheme [FRGS/1/2020/FTMK-CACT/F00461] from the Ministry of Higher Education, Malaysia.

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Authors and Affiliations

  1. Fakulti Teknologi Kejuruteraan Elektrik dan Elektronik, Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, 76100, Melaka, Malaysia

    Norfadzlia Mohd Yusof

  2. Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, 76100, Melaka, Malaysia

    Azah Kamilah Muda & Satrya Fajri Pratama

  3. Institut de Qu´ımica Computacional i Cata`lisi, Universitat de Girona, 17071, Girona, Catalonia, Spain

    Ramon Carbo-Dorca

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  1. Norfadzlia Mohd Yusof
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  4. Ramon Carbo-Dorca
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Correspondence to Azah Kamilah Muda.

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Mohd Yusof, N., Muda, A.K., Pratama, S.F. et al. Amphetamine-type stimulants (ATS) drug classification using shallow one-dimensional convolutional neural network. Mol Divers 26, 1609–1619 (2022). https://doi.org/10.1007/s11030-021-10289-1

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