Call For Paper Volume: V, Issue: 08 | AUGUST 2026 | International Journal of Advanced Trends in Engineering and Management (IJATEM)
Volume V | Issue 6 | 2026 | Paper ID: IJATEM-V05I06P2

ABC Based Relational Graph Network for Personality Trait Prediction From Selfie-Derived Tabular Data

, S. Prakash, A. Anish Rama

Personality Trait (PT) predictions from facial images have gained increasing attention in intelligent human-computer interaction and behavioural analysis for analysis. In this paper an Artificial Bee Colony-based Relational Graph Network (ABC-RGN) is proposed for PT prediction using selfie-derived tabular data. The data pre-processing based on data cleaning, missing-value as well as duplicate removal, followed by outlier detection and data encoding to improve data quality. The processed features are normalized and analysed through feature correlation analysis to identify the most relevant personality-related attributes. Subsequently, the RGN models interactions among different feature types and captures complex relationships within the selfie-derived tabular data. The selected features are then optimized using the ABC algorithm, which improves the selection and representation of informative feature relationships. Experimental results is demonstrated in Python software using synthetic personality dataset the proposed structure attains an accuracy, recall as well as F1-score of 97.6% precision of 97.7% outperforming existing PT methods. The proposed ABC-RGN framework provides an effective approach for learning complex feature relationships and improving the reliability of PT prediction from selfie-based data.

Personality Trait, Artificial Bee Colony, Relational Graph Network, Selfie-Derived Tabular Data.

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