|
Abstract.
Human behavior in stressful situations depends on the psychotype, socialization on a host of other factors. Phone scammers build their conversation focusing on the behavior of a certain category of people. Previously, a person is introduced into a state of acute stress, in which his further behavior to one degree or another can be manipulated. We have developed a modification of the WFT capsular neural network - 2D-CapsNet, which allowed using the photoplethysmogram (PPG) graph to identify the state of panic-stupor with an accuracy of 82%, which does not allow him to make logically sound decisions. When synchronizing a smart bracelet with a smartphone, the method allows real-time tracking of such states, which makes it possible to respond to a call from a telephone scammer during a conversation with a subscriber.
Keywords:
robotics, artificial intelligence, neural networks, engineering, CapsNet, smart bracelet, photoplethysmogram, emotional state.
DOI 10.14357/20718632240103
EDN IRVBHY References
1. SujaSreeithPanicker, Prakasam Gayathri, A survey of machine learning techniques in physiology based mental stress detection systems, Biocybernetics and Biomedical Engineering, Volume 39, Issue 2, 2019, Pages 444-469, ISSN 0208-5216, https://doi.org/10.1016/j.bbe.2019.01.004. 2. Qi Li, Yunqing Liu, Fei Yan, Qiong Zhang, Cong Liu, Emotion recognition based on multiple physiological signals, Biomedical Signal Processing and Control, Volume 85, 2023, 104989, ISSN 1746-8094, https://doi.org/10.1016/j.bspc.2023.104989. 3. Angelo Costa, Jaime A. Rincon, Carlos Carrascosa, Vicente Julian, Paulo Novais, Emotions detection on an ambient intelligent system using wearable devices, Future Generation Computer Systems, Volume 92, 2019, Pages 479-489, ISSN 0167-739X, https://doi.org/10.1016/j.future.2018.03.038. 4. Richardson J. Is there a silver bullet to stop cybercrime?ComputerFraud&Security, 2020. 5. Bojarski, K. (2015). Dealer, hacker, lawyer, spy. Modern techniques and legal boundaries of counter-cybercrime operations. TheEuropeanreviewoforganisedcrime, 2(2), 25-50. 6. ChevrotА, VernotteА, LegeardВ. CAE: Contextual autoencoder for multivariate time-series anomaly detection in air transportation. Computers & Security, 2022. 7. Al-Hashedi K, Magalingam P. Financial fraud detection applying data mining techniques: A comprehensive review from 2009 to 2019. Computer Science Review, 2021. 8. Feng W, Liu Sh, Cheng X. EagleMine: Vision-guided Micro-clusters recognition and collective anomaly detection/ Future Generation Computer Systems, 2021. 9. Shin S-Y, Kang Y-W, Kim Y-G. Android-GAN: Defending against android pattern attacks using multi-modal generative network as anomaly detector. ExpertSystemswithApplications, 2020. 10. Hilal W, Gadsden A, Yawney J. Financial Fraud: A Review of Anomaly Detection Techniques and Recen Advances. ExpertSystemswithApplications. 2022. 11. Ren, P., Xiao, Y., Chang, X., Huang, P. Y., Li, Z., Chen, X., & Wang, X. (2021). A comprehensive survey of neural architecture search: Challenges and solutions. ACM ComputingSurveys (CSUR), 54(4), 1-34. 12. Kenton JDMWC, Toutanova LK (2019) Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies (NAACL-HLT), pp. 4171–4186 13. Sun N, Lin G, Qiu J, Rimba P (2020) Near real-time twitter spam detection with machine learning techniques. Int J Comput Appl. https://doi.org/10.1080/1206212X.2020.1751387. 14. Drucker, H., Wu, D., &Vapnik, V. N. (1999). Support vector machines for spam categorization. IEEE TransactionsonNeuralnetworks, 10(5), 1048-105 15. Tong, X., Wang, J., Zhang, C., Wang, R., Ge, Z., Liu, W., & Zhao, Z. (2021). A content-based chinese spam detection method using a capsule network with long-short attention. IEEE SensorsJournal, 21(22), 25409-25420. 16. Chavez, A. (2020). TF-IDF classification based Multinomial Naïve Bayes model for spam filtering (Doctoral dissertation, Dublin, National College of Ireland). 17. Kovalchuk, M. V., &Kholodny, Y. I. (2020). Functional magnetic resonance imaging augmented with polygraph:new capabilities. In Biologically Inspired Cognitive Architectures 2019: Proceedings of the Tenth Annual Meeting of the BICA Society 10 (pp. 260-265). SpringerInternationalPublishing. 18. Cook, L. G., &Mitschow, L. C. (2019). Beyond the polygraph: Deception detection and the autonomic nervous system. FederalPractitioner, 36(7), 316. 19. Banham, M. R., Galatsanos, N. P., Gonzalez, H. L., &Katsaggelos, A. K. (1994). Multichannel restoration of single channel images using a wavelet-based subband decomposition. IEEE TransactionsonImageProcessing, 3(6), 821-833. 20. Guo, Y., & Li, B. Z. (2016). Blind image watermarking method based on linear canonical wavelet transform and QR decomposition. IET imageprocessing, 10(10), 773-786. 21. Singh, K. R., & Chaudhury, S. (2016). Efficient technique for rice grain classification using back‐propagation neural network and wavelet decomposition. IET ComputerVision, 10(8), 780-787. 22. You, N., Han, L., Zhu, D., & Song, W. (2023). Research on image denoising in edge detection based on wavelet transform. AppliedSciences, 13(3), 1837. 23. Sui, K., & Kim, H. G. (2019). Research on application of multimedia image processing technology based on wavelet transform. EURASIP JournalonImageandVideoProcessing, 2019(1), 1-9. 24. Sabour S., Frosst N., Hinton G. E. Dynamic routing between capsules //Advances in neural information pro-cessing systems. – 2017. – Т. 30. https://doi.org/10.48550/arXiv.1710.09829 25. Hinton G. E., Sabour S., Frosst N. Matrix capsules with EM routing //International conference on learning representations. – 2018. https://doi.org/10.13140/RG.2.2.27416.44800 26. Butun, E., Yildirim, O., Talo, M., Tan, R. S., & Acharya, U. R. (2020). 1D-CADCapsNet: One dimensional deep capsule networks for coronary artery disease detection usingECG signals. PhysicaMedica, 70, 39-48.
|
