American International Journal of Research and Innovation

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Human Behaviour Analysis Through Intelligent Computational Approaches

Author(s) Tibor Bosse
Country Netherlands
Abstract The analysis of human behaviour has become an increasingly important research area with the advancement of intelligent computational technologies. Traditional approaches to understanding human behaviour primarily relied on psychological observations, surveys, and qualitative assessments, whereas modern computational approaches enable large-scale behavioural analysis through artificial intelligence, machine learning, data analytics, and intelligent modelling techniques. This research article examines the role of intelligent computational approaches in analysing complex human behavioural patterns and explores their contribution toward developing predictive, adaptive, and data-driven behavioural understanding systems. The study adopts a conceptual research methodology based on the analysis of existing computational frameworks, behavioural science theories, and emerging artificial intelligence applications.
The analysis highlights that intelligent computational approaches can support behavioural prediction, pattern recognition, sentiment analysis, decision modelling, and personalized interaction systems across diverse domains. However, challenges related to data privacy, algorithmic bias, ethical responsibility, and interpretability remain critical concerns in human behaviour analysis. The study emphasizes that successful computational behavioural analysis requires integration between advanced technological capabilities and human-centered research principles. The findings contribute to understanding how intelligent computational systems can enhance behavioural research while maintaining ethical and responsible approaches toward human data interpretation.
Keywords Human Behaviour Analysis; Intelligent Computing; Artificial Intelligence; Machine Learning; Behavioural Modelling; Data Analytics; Computational Approaches
Field Engineering
Published In Volume 7, Issue 1, January-February 2025
Published On 2025-02-27
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