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Machine Learning Methods for Recognizing the Emotional State of a Telecommunications System Subscriber
- Authors: Osipov A.V.1,2, Sapozhnikov A.E.1, Pleshakova E.S.1,2, Gataullin S.T.1,2,3
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Affiliations:
- Financial University under the Government of the Russian Federation
- Russian Technological University
- Central Economics and Mathematics Institute of the Russian Academy of Sciences
- Issue: No 1 (2024)
- Pages: 23-35
- Section: Information processing and data analysis
- URL: https://ogarev-online.ru/2071-8632/article/view/287288
- DOI: https://doi.org/10.14357/20718632240103
- EDN: https://elibrary.ru/IRVBHY
- ID: 287288
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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.
About the authors
Alexey V. Osipov
Financial University under the Government of the Russian Federation; Russian Technological University
Author for correspondence.
Email: a.v.osipov@mtuci.ru
Candidate of Physical and Mathematical Sciences
Russian Federation, Moscow; MoscowAnatoly E. Sapozhnikov
Financial University under the Government of the Russian Federation
Email: 007.ts@mail.ru
postgraduate student
Russian Federation, MoscowEkaterina S. Pleshakova
Financial University under the Government of the Russian Federation; Russian Technological University
Email: espleshakova@fa.ru
Candidate of Technical Sciences
Russian Federation, Moscow; MoscowSergey T. Gataullin
Financial University under the Government of the Russian Federation; Russian Technological University; Central Economics and Mathematics Institute of the Russian Academy of Sciences
Email: s.t.gataullin@mtuci.ru
Russian Federation, Moscow; Moscow; Moscow
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