Development of fatigue detection system using deep learning model

Darsono, Abd Majid and Kamarudin, Puteri Nur Farzanah Faghira and Ahmad Tarmizi, Nur Farah Izzati and Ja’afar, Abd Shukur and Jaafar, Anuar and Mohd Yusof, Haziezol Helmi and Hashim, Nik Mohd Zarifie and Misran, Mohamad Harris and Ahmad, Muhammad Imran (2025) Development of fatigue detection system using deep learning model. International Journal of Research and Innovation in Social Science (IJRISS), IX (VIII). pp. 7729-7740. ISSN 2454-6186

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Abstract

This paper presents a custom late fusion multimodal deep learning technique for milk quality classification by integrating visual and numerical features. Top-performing unimodal models such as MobileNet, Inception V3, and DenseNet for visual data, and LightGBM, CatBoost, and XGBoost for numerical data were identified through comparative evaluation. The proposed concatenation-with-proposed-layers fusion model achieved a peak testing accuracy of 99.77%, matching or surpassing alternative fusion techniques while employing fewer layers for improved computational efficiency. Comparative experiments demonstrated superior performance over max pooling, majority voting, and weighted average methods, with notable robustness across nine visual– numerical model pairings. A human-centered study further validated the approach, showing that combining visual and numerical inputs improved classification accuracy by up to 45.1% in certain cases. The results highlight the proposed model’s effectiveness, stability, and applicability in quality control and safety-critical domains, with potential extension to other multimodal classification tasks requiring high precision.

Item Type:	Article
Uncontrolled Keywords:	Classification, Data fusion, Late fusion technique, Milk quality, Multimodal Deep Learning
Divisions:	Faculty Of Electronics And Computer Technology And Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	11 May 2026 02:25
Last Modified:	11 May 2026 02:25
URI:	http://eprints.utem.edu.my/id/eprint/29692
Statistic Details:	View Download Statistic

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