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All Journal Journal of Intelligent Computing and Health Informatics (JICHI)
AN, Dorojatun
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Computer Science & IT Dentistry Electrical & Electronics Engineering Medicine & Pharmacology Public Health

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High Precision Cascaded Spatio Temporal Deep Inference for Real Time Histamine Risk Prediction: A Health Informatics Approach Nugroho, Hanityo A; AN, Dorojatun; Pribadi, Rubijanto Juni; Raharjo, Samsudi
Journal of Intelligent Computing & Health Informatics Vol 7, No 1 (2026): March
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v7i1.19802

Abstract

Rapid histamine accumulation in tropical fisheries constitutes a substantial public health hazard, particularly via scombroid poisoning, and underscores the need for rigorous, data-driven cold-chain surveillance. Artisanal vessels (≤ 30 GT), however, predominantly depend on ice-based cooling strategies that are thermally unstable and lack real-time diagnostic functionality, thereby failing to sufficiently suppress microbial growth kinetics under ambient conditions that frequently exceed 30°C. To address this gap, we propose a Cascaded Spatio-Temporal Deep Inference Architecture that couples a Convolutional Neural Network (CNN) for spatial feature denoising with a Long Short-Term Memory (LSTM) network for temporal kinetic modeling. This hybrid architecture assimilates high-frequency thermal measurements from an optimized R404A vapor-compression refrigeration system and predicts histamine risk indices under Arrhenius-based kinetic constraints. Field deployment on a 10 GT vessel demonstrated that the system maintained a highly stable storage temperature of -20.1 ± 0.5°C. The proposed model exhibited high predictive accuracy with an R2 of 0.97 and an RMSE of 0.45°C, significantly outperforming a Linear Regression baseline (RMSE = 1.85°C, p < 0.01). Importantly, the system extended the prime-quality shelf life by more than 52 hours while keeping histamine concentrations well below the U.S. FDA limit of 50 mg/kg. Collectively, these findings support a scalable health informatics framework and indicate that AI-driven predictive certification can substantially reduce food safety risks in resource-limited maritime supply chains.
Co-Authors Nugroho, Hanityo A Rubijanto Juni Pribadi SAMSUDI RAHARJO