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All Journal JOIV : International Journal on Informatics Visualization International Journal of Renewable Energy Development
Chau, Thanh Hieu
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Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering

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An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation Vu, Van Vien; Le, Phuoc Tai; Do, Thi Mai Thom; Nguyen, Thi Thuy Hieu; Tran, Nguyen Bao Minh; Paramasivam, Prabhu; Le, Thi Thai; Le, Huu Cuong; Chau, Thanh Hieu
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2641

Abstract

This review article looks at the developing field of artificial intelligence and machine learning in maritime and marine environment management. The marine industry is increasingly interested in applying advanced AI and ML technologies to solve sustainability, efficiency, and regulatory compliance issues. This paper examines maritime and marine AI and ML applications using a deep literature review and case study analysis. Modeling ship fuel consumption, which impacts the environment and operating expenses, is a top responsibility. The study demonstrates that ML approaches such as Random Forest and Tweedie models can estimate ship fuel use. Statistical analysis demonstrates that the Random Forest model beats the Tweedie model regarding accuracy and consistency. For the training and testing datasets, the Random Forest model has high R2 values of 0.9997 and 0.9926, indicating a solid match. Low Root Mean Square Error (RMSE) and average absolute relative deviation (AARD) suggest that the model accurately reflects fuel use variability. While still performing well, the Tweedie model has lower R2 values and higher RMSE and AARD values, suggesting reduced accuracy and precision in fuel consumption prediction. These findings provide light on the potential applications of artificial intelligence and machine learning in maritime and marine environment management. Advanced analytics enables decision-makers to analyze fuel consumption patterns better, increase operational efficiency, and decrease environmental impact, thus improving maritime sustainability.
Towards self-diagnostic solar farms: Leveraging EfficientNet and class activation mapping for predictive maintenance Nguyen, Du; Nguyen, Thi Bich Ngoc; Nguyen, Duc Chuan; Chau, Thanh Hieu; Duong, Minh Thai; Dang, Thanh Nam
International Journal of Renewable Energy Development Vol 15, No 2 (2026): March 2026
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2026.62298

Abstract

The high rate of utility photovoltaic (PV) system development has increased the demand for stable, automated, and interpretable fault diagnostic systems that can be utilised in real-world environments. Solar farms with a large size are increasingly making conventional manual inspection methods impractical, and triggering the use of intelligent data-driven solutions. This paper presents a justifiable deep learning model for automated fault classification of solar panels based on the EfficientNet-B2 architecture combined with Gradient-weighted Class Activation Mapping (Grad-CAM). A six-class image dataset made of clean panels and five prevalent fault types is used. The two stages of transfer learning used to train the model include a warm-up phase and selective fine-tuning of upper network layers. Data augmentation is also performed extensively to make it more robust to changing illumination, viewing angles, and environmental noise. The experimental findings reveal consistent convergence and excellent generalization ability, and a high level of classification accuracy of all types of faults, as it achieved high classification accuracy, macro-averaged F1-scores exceeding 0.90 for most fault classes, and a macro-averaged ROC–AUC of approximately 0.981, highlighting the robustness and reliability of the proposed diagnostic model. The suggested structure will provide a scalable, interpretable, and realistic predictive maintenance of solar farms of the next generation with self-diagnostic capabilities.
Co-Authors Dang, Thanh Nam Do, Thi Mai Thom Duong, Minh Thai Le, Huu Cuong Le, Phuoc Tai Le, Thi Thai Nguyen, Du Nguyen, Duc Chuan Nguyen, Thi Bich Ngoc Nguyen, Thi Thuy Hieu Paramasivam, Prabhu Tran, Nguyen Bao Minh Vu, Van Vien