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

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

© 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,

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.

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

[1] J. Luigjes, V.Lorenzetti,S.De Haan, G.J.Youssef,C.Murawski,and Z.Sjoerds, “Defining Compulsive Behavior,” Neuro.Psychol.Rev, vol29, no.1, Pp. 4–13, 2019.
[2] M.Yücel, E.Oldenhof, S.H.Ahmed,D. Belin,J.Billieux,H.Bowden-Jones, “ A Transdiagnostic Dimensional Approach Towards A Neuropsychological Assessment for Addiction,” An International Delphi Consensus Study. Addict, vol114, no.6, Pp.1095–1109, 2019.
[3] X.Zhang, R.Wang, A.Sharma and G.G Deverajan, “Artificial Intelligence in Cognitive Psychology—Influence of Literature Based on Artificial Intelligence on Children's Mental Disorders,” Aggr.and Viol. Behav, Pp. 101590, 2021.
[4] M.Fort,A.Ichino and G.Zanella, G. “Cognitive and Noncognitive Costs of Day Care Stage 0–2 for Children in Advantaged Families,” Joul. of Politic. Eco, vol128, no.1, Pp.158–205, 2020.
[5] G.Latif,A.Shankar,J.M.Alghazo,V.Kalyanasundaram, C.S.Boopathi and M.A.Jaffar, “ I-CARES: Advancing Health Diagnosis and Medication Through Iot,” Wirel. Netw, vol 26, no.4, Pp.2379-2385, 2020.
[6] M.Oaksford, “Quantum Probability, Intuition, and Human Rationality,” Beha.L Brain Sci, vol 36, no.303, Pp.10-1017, 2013.
[7] I.Lipkus and C. Sanders, “A Pilot Study Assessing Reactions To Educational Videos on Harm of Waterpipe Among Young Adults Susceptible To Waterpipe Tobacco Smoking,” Jou. of Health Comm, vol26, no.11, Pp. 743-752, 2011.
[8] L.C.White, E.M. Pothos, and J.R.Busemeyer,” Insights From Quantum Cognitive Models for Organizational Decision Making,” Jou. of Appl. Res in Mem. and Cog, vol, no.3, Pp.229-238, 2015.
[9] M.S. Ishwarya, and A.K. Cherukuri, A.K, “Decision-Making in Cognitive Paradoxes with Contextuality and Quantum Formalism,”Appl. Soft Comp, vol95, Pp.106521, 2020.
[10] V.I.Yukalov and D.Sornette, “Processing Information in Quantum Decision Theory. Entropy,” vol11, no.4, Pp. 1073-1120, 2009.
[11] Y.Gal and Z.Ghahramani, “Dropout As A Bayesian Approximation: Representing Model Uncertainty in Deep Learning,” in International Conference on Machine Learning, Pp. 1050-1059, 2016. PMLR.
[12] J.B Hamrick, “Analogues of Mental Simulation and Imagination in Deep Learning,” Cur. Opi. in Behav. Sci, 29(2019), 8–16.
[13] S.K.Reed, “Building Bridges Between AI and Cognitive Psychology,” AI Magazine,vol40, no.2 , Pp.17–28, 2019.
[14] J.J.Wood,P.C. Kendall,K.S.Wood,C.M.Kerns,M.Seltzer,B.J.Small and E.A.Storch, “Cognitive Behavioural Treatments for Anxiety in Children with Autism Spectrum Disorder: A Randomized Clinical Trial,” JAMA Psychiatry, vol77, no.5, Pp.474–483, 2020.
[15] A.Di Nuovo and J.L.Mcclelland, “Developing the Knowledge of Number Digits in A Child-Like Robot,” Nat.Machi. Intel, vol1, no.12, Pp.594–605, 2019.
[16] A.Danese, “Annual Research Review: Rethinking Childhood Trauma-New Research Directions for Measurement, Study Design and Analytical Strategies,” Jou. of Chi. Psychol. and Psychi, vol61, no.3, Pp. 236–250, 2020.
[17] P.Beckerle, C.Castellini and B.Lenggenhager, “Robotic Interfaces for Cognitive Psychology and Embodiment Research: A Research Roadmap,” Wiley Interdis. Rev: Cog. Sci, vol10, no.2, Pp. E1486, 2019.
[18] G.W.Allport, “The Open System in Personality Theory,” The Jou.of Abno. and Soc. Psychol, vol61, no.3, Pp. 301, 1960.
[19] H.Fang, H.Shi,J.Zhang, A.K.Luhach,and S.Krishnamoorthy, “Socioeconomic Status Effects on Children's Vocabulary Brain Development,” Agg. and Vio. Beh, no.101702, 2021.
[20] J.N Marewski, U.Hoffrage and R.P , “Fisher Modeling and Aiding Intuition: Introduction To the Commentary Section,” Jou.of Appl. Mem. and Cog, vol5, no.3, Pp. 318-321, 2016.
[21] Z.Dienes and J.Perner, “A Theory of Implicit and Explicit Knowledge,” Behav. and Brain Sci, vol22, no.5, Pp.735-808, 1999.
[22] J.Bensemann and M.Witbrock, “The Effects of Implementing Phenomenology in a Deep Neural Network,” Heliyon, vol7, no.6, Pp.07246, 2021.
[23] S.S.Khemlani, M.Lotstein and P.N Johnson‐Laird, “Naive Probability: Model‐Based Estimates of Unique Events,” Cog.Sci, vol39, no.6, Pp.1216-1258, 2015.
[24] C.Kuehner,S.Huffziger and K. Liebsch,Rumination, “Distraction and Mindful Self-Focus: Effects on Mood, Dysfunctional Attitudes and Cortisol Stress Response,” Psychol. Med, vol39, no.2, Pp.219-228, 2009.
[25] M.Sharbafshaaer, R.Kumar, and M. Kord, “The Death Rumination Questionnaire 13 (DRQ-13): Factor Structure and Psychometric Properties in A Sample of Cancer Patient Population,” SSRG IJMS (SSRGIJMS), vol4, no.3, Pp.1-6, 2017.
[26] E.M.Pothos, and J.R Busemeyer, “Quantum Principles in Psychology: the Debate, the Evidence, and the Future. Behav. and Brain Sci,” vol36, no.3, Pp.310-327, 2013.