Human Addictive Behavior Classification (ABC) using Adaptive Quantum Mean Value Approach

Human Addictive Behavior Classification (ABC) using Adaptive Quantum Mean Value Approach
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© 2022 by IJETT Journal
Volume-70 Issue-7
Year of Publication : 2022
Authors : V. Sabapathi, J. Selvin Paul Peter
DOI : 10.14445/22315381/IJETT-V70I7P219

How to Cite?

V. Sabapathi, J. Selvin Paul Peter, "Human Addictive Behavior Classification (ABC) using Adaptive Quantum Mean Value Approach" International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 180-189, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I7P219

Abstract
Making decisions with uncertainty contextually, such as human cognitive behaviors such as addictions, emotions, and skills has complex systems to understand. To analyze the addiction behavior context, there is a need for a cognitive psychometric-based decision-making classification approach as well as symptom validation tests for uncertain addiction circumstances. In Kolmogorov, probability theory (KPT) is used for low and less symptom validation, and Quantum probability theory (QPT) approaches are used for highly addiction-based symptom validation. But, most occasional triers have never been understood whether they are in addiction or not unless they have been affected by addiction behavior syndrome. The occasional context becomes an uncertain activity. Decision-making in an uncertain context has quite a challenge in analyzing and predicting the human behavior pattern. The majority of human behaviors and conscious routines become unconscious, compulsive activities. Hence the routine behaviors might be turned into addictive behaviors, which are the state of being unable to avoid acts. The main objective is to develop a non-cognitive-based classification model to help people analyze their consciousness of addictive behaviors. In this context, this paper discusses a psychometric cognitive modeling method for forecasting the amount of addiction classification system model using adaptive quantum and mean value (QMV) formulation. Multiple addiction contexts were used to test the efficacy of QMV. The adaptive QMV approach has been effectively supported for noncognitive tests and addiction symptom validation. This proposal is highly intense to make cognitive, conscious-based measures for an uncertain context, addiction prediction, and effective classification. In conclusion, the consistency of human behavior makes a huge difference between addictive and non-addictive.

Keywords
Addictive assessment, Behavior classification, Multi-addict context, Noncognitive test, Symptom validation, Quantum mean value.

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