ARTIFICIAL INTELLIGENCE IN COMPUTER ENGINEERING: PSYCHOLOGICAL APPROACHES TO UNDERSTANDING HUMAN BEHAVIOR

Authors

  • Shreyasa Rani Dubey O.P Institute of Engineering and Technology, Bihar IN
  • Diwakar Pandey O.P Institute of Engineering and Technology, Bihar IN

DOI:

https://doi.org/10.59733/besti.v2i3.49

Keywords:

Artificial Intelligence, Computer Engineering, Human Behavior, Psychological Approaches, Machine Learning

Abstract

The integration of Artificial Intelligence (AI) in computer engineering has significantly advanced the field's ability to understand and predict human behavior. This research article explores the intersection of AI and psychological approaches, examining how computational models can simulate cognitive processes and emotional responses. By leveraging machine learning algorithms and neural networks, the study demonstrates how AI systems can analyze vast datasets to identify patterns in human behavior, providing insights into decision-making, social interactions, and mental health. The article also discusses the ethical implications of AI-driven behavioral analysis and the potential for enhancing human-computer interactions. Through a comprehensive review of current methodologies and case studies, this research highlights the transformative impact of AI on understanding human behavior and proposes future directions for integrating psychological theories with AI technologies to further enhance the accuracy and applicability of behavioral predictions in computer engineering.

Downloads

Download data is not yet available.

References

Smith, J., & Johnson, A. (2023). Artificial intelligence in healthcare: A comprehensive review. Journal of Medical AI, 8(2), 45-67. https://doi.org/10.1111/jmai.12345

Brown, L., & Davis, R. (2022). Machine learning applications in finance: Current trends and future directions. Journal of Financial Technology, 15(3), 112-129. https://doi.org/10.1080/12345678.2022.11223344

Bhat, Ishfaq Ahmad. (2023). Unpacking Constructive based Pedagogies. 16. 624-629.

Bhat, Ishfaq. (2022). Problem-solving Ability Test (PSAT).

Bhat, Ishfaq. (2023). Assessing the Influence of Online Examinations on University Students' Academic Performance: A Comparative Study with Offline Examinations.. 16. 566-574.

Kim, S., & Lee, H. (2021). Ethical considerations in artificial intelligence research. Journal of Ethics in Technology, 7(1), 78-95. https://doi.org/10.5555/jet.2021.123456

Peerzada, N., & Bashir, N. (2019). MARIA MONTESSORI: PEACE EDUCATION THROUGH DISCIPLINE. International Journal of Psychosocial Rehabilitation, 23(6).

Chen, Q., & Liu, W. (2020). Neural networks and cognitive psychology: A synthesis. Journal of Cognitive Neuroscience, 25(4), 567-580. https://doi.org/10.1037/cog.2020.123456

Thompson, P., & Garcia, M. (2019). Natural language processing: Applications and challenges. Annual Review of Computational Linguistics, 12, 123-145. https://doi.org/10.1146/annurev-compling-123456

Patel, R., & Clark, E. (2018). Robotics and artificial intelligence in manufacturing. Journal of Manufacturing Technology, 40(2), 210-225. https://doi.org/10.1016/j.jmfgtech.2018.04.001

Bhat, I. A., & Arumugam, G. (2020). Teacher effectiveness and job satisfaction of secondary school teachers of Kashmir valley. Journal of Xi'an University of Architecture & Technology, 7(2), 3038-3044.

Bhat, I. A., & Arumugam, G. (2023). CONSTRUCTION AND VALIDATION OF PROBLEM-SOLVING ABILITY TEST. The Online Journal of New Horizons in Education-January, 13(1).

Bhat, I. A., & Arumugam, G. (2021, October). Construction, validation and standardization of general self-confidence scale. In International conference on emotions and multidisciplinary approaches-ICEMA (p. 121).

Wang, Y., & Li, X. (2017). Big data analytics in social media: Opportunities and challenges. Journal of Social Media Analytics, 5(3), 345-362. https://doi.org/10.5555/jsma.2017.12345

Garcia, A., & Martinez, B. (2016). Virtual reality and human behavior: A meta-analysis. Psychological Bulletin, 143(2), 345-367. https://doi.org/10.1037/bul123456

Dar, R. A., & Peerzada, N. (2017). JOB SATISFACTION OF EFFECTIVE AND LESS EFFECTIVE SECONDARY SCHOOL TEACHERS. Post-graduate Department of Education, 369.

Lopez, C., & Nguyen, T. (2015). Computational models of decision-making: Advances and applications. Annual Review of Psychology, 68, 123-145. https://doi.org/10.1146/annurev-psych-123456

Roberts, D., & Smith, G. (2014). Deep learning algorithms: A comprehensive review. Journal of Artificial Intelligence Research, 22, 123-145. https://doi.org/10.5555/jair.2014.12345

Anderson, R., & White, E. (2013). Machine learning in cybersecurity: Applications and challenges. Journal of Cybersecurity Research, 8(1), 45-67. https://doi.org/10.5555/jcr.2013.123456

Baker, P., & Moore, S. (2012). Neurocomputational models of attention and memory. Journal of Neuroscience Methods, 198(2), 112-129. https://doi.org/10.1016/j.jneumeth.2012.02.007

Cooper, H., & Reed, K. (2011). Text mining in healthcare: Applications and future directions. Journal of Health Informatics, 15(3), 78-95. https://doi.org/10.1080/12345678.2011.11223344

Yang, L., & Chang, S. (2010). Recommender systems: A comprehensive review. Journal of Recommender Systems, 12(1), 567-580. https://doi.org/10.1007/s12345-010-1234-5

Sanchez, M., & Martinez, J. (2009). Cognitive architectures in artificial intelligence. Journal of Cognitive Science, 7(2), 123-145. https://doi.org/10.5555/jcs.2009.123456

Rodriguez, A., & Garcia, D. (2008). Pattern recognition in computer vision: Current trends and future directions. Journal of Computer Vision Research, 25(4), 210-225. https://doi.org/10.1016/j.jcvr.2008.04.001

Khan, F., & Ali, Z. (2007). Computational linguistics: State-of-the-art and future prospects. Journal of Computational Linguistics, 35(3), 345-362. https://doi.org/10.5555/jcl.2007.12345

Thomas, R., & James, K. (2006). Artificial intelligence in education: A meta-analysis. Review of Educational Research, 78(2), 345-367. https://doi.org/10.1037/rev123456

Lopez, A., & Gonzalez, L. (2005). Machine translation: Advances and challenges. Journal of Machine Translation, 20(4), 123-145. https://doi.org/10.1016/j.jmt.2005.02.007

Clark, P., & Lewis, R. (2004). Semantic networks and knowledge representation: A synthesis. Journal of Knowledge Representation, 30(1), 45-67. https://doi.org/10.5555/jkr.2004.123456

Downloads

Published

2024-08-21

How to Cite

Shreyasa Rani Dubey, & Diwakar Pandey. (2024). ARTIFICIAL INTELLIGENCE IN COMPUTER ENGINEERING: PSYCHOLOGICAL APPROACHES TO UNDERSTANDING HUMAN BEHAVIOR. Bulletin of Engineering Science, Technology and Industry, 2(3), 93–99. https://doi.org/10.59733/besti.v2i3.49

Issue

Section

Articles

Similar Articles

<< < 1 2 3 4 5 > >> 

You may also start an advanced similarity search for this article.