Abstract :

In Recent Years, Understanding Player Behavior And Emotional States During Gameplay Has Become A Key Focus In Game Analytics And Human–Computer Interaction (HCI). Emotional State Detection Helps Game Designers Analyze Player Engagement, Enhance User Experience, And Build Adaptive Gaming Environments. Traditional Methods Such As Questionnaires, Player Observation, And Basic Statistical Analysis Were Time-consuming, Less Accurate, And Lacked Real-time Capabilities. To Overcome These Limitations, This Research Proposes A Hybrid Fuzzy Boosting Architecture For Accurate Emotional State Detection In Real-time Gameplay. The System Is Implemented As A Web-based Application Using The Django Web Framework. Machine Learning (ML) Techniques Are Applied To Analyze Gameplay Behavior Data And Predict Emotional States Efficiently. The Study Utilizes Multiple ML Algorithms, Including KNearest Neighbors (KNN), Random Forest (RF), And Support Vector Machine (SVM), Along With A Proposed Hybrid Model Combining A Fuzzy Neural Network (FNN) And Histogram Gradient Boosting (HGB), Referred To As FNN-HGB. These Models Are Trained And Evaluated Using Classification And Regression Tree (CART) Techniques To Classify Player Behavior (play_behavior) And Predict Engagement Intensity (activity_level). Experimental Results Show That The Proposed FNN-HGB Model Achieves Higher Prediction Accuracy Compared To Traditional Classifiers, Effectively Handling Complex And Noisy Gameplay Data. The System Also Includes Modules Such As User Authentication, Exploratory Data Analysis (EDA), Model Training, Performance Comparison, And Real-time Prediction. By Integrating ML Models With A Django-based Interface, The System Provides An Efficient Platform For Gameplay Behavior Analysis And Emotional State Prediction, Enabling Improved Decision-making And Enhanced Player Experience In Modern Gaming Environments.

Published:

22-4-1-2026

Issue:

Vol. 26 No. 4-1 (2026)

Page Nos:

743-751

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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