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All Journal JOIV : International Journal on Informatics Visualization International Journal of Renewable Energy Development
Le, Thi Thai
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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.
Artificial intelligence applications in solar energy Le, Thanh Tuan; Le, Thi Thai; Le, Huu Cuong; Dong, Van Huong; Paramasivam, Prabhu; Chung, Nghia
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2686

Abstract

Renewable energy research has become significant in the modern period owing to escalating prices of fossil fuels and the pressing need to reduce greenhouse gas emissions. Solar energy stands out among these sources due to its abundance and global accessibility. However, its weather-dependent and cyclical nature add inherent risks, making effective planning and management difficult. Soft computing technologies provide attractive solutions for modeling such systems, while machine learning and optimization techniques are gaining popularity in the solar energy industry. The current literature highlights the growing use of soft computing technologies, emphasizing their potential to address difficult challenges in solar energy systems. To effectively reap the benefits, these strategies must be seamlessly connected with emerging technologies like the Internet of Things (IoT), big data analytics, and cloud computing. This integration provides a unique opportunity to improve the scalability, flexibility, and efficiency of solar energy systems. Researchers can use these synergies to create intelligent, linked solar energy ecosystems capable of real-time optimization of energy production, delivery, and consumption. These technologies have the potential to transform the renewable energy environment, allowing for more resilient and sustainable energy infrastructures. Furthermore, as these technologies improve, there is a growing demand for trained experts to address associated cybersecurity problems, assuring the integrity and security of these sophisticated systems. Researchers may pave the road for a more sustainable and energy-efficient future by working collaboratively and using interdisciplinary methodologies.
Orchestrating green ports: An integrated BWM–Fuzzy DEMATEL–ANP–TOPSIS framework for techno-economic prioritization Do, Hoang Dat; Le, Do Duc Anh; Le, Thi Thai; Nguyen, Thi Kim Tin; Le, Thanh Tam; Truong, Thi Hoang Oanh
International Journal of Renewable Energy Development Vol 15, No 1 (2026): January 2026
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2026.61886

Abstract

This study introduces a comprehensive multi-criteria decision-making framework that integrates the Best–Worst Method (BWM), fuzzy DEMATEL, the Analytic Network Process (ANP), and TOPSIS to prioritize green port electrification and operational enhancements. The model reflects complex trade-offs that shape decarbonization plans by asking experts about 20 important techno-economic, environmental, and organizational factors. The most important results show that emission abatement, fuel savings, and pollution reduction had the highest BWM weights. This shows that environmental goals are the most important. Fuzzy DEMATEL research showed that lifecycle replacement risk and labor preparedness were the main factors that affected tariff exposure, operational dependability, and digital integration results. ANP adjusted the weights of the criteria to take into consideration interdependencies, making economic risk and human capital the most important factors in decision-making. The TOPSIS rating found that a hybrid phased deployment option was the best choice for meeting goals for cost, emissions reduction, and operational readiness. It did better than both electric and traditional methods. These results show that the framework may combine expert knowledge, causal structure, and network feedback to make green port techniques more important. The concept goes beyond linear weighing by using cause-and-effect maps and feedback loops. This gives decision-makers a better understanding and more confidence when it comes to allocating resources. The results encourage a balanced growth of capital investments, environmental protection, and the ability of the workforce. This flexible strategy is helpful in  gradually combining the renewables, tariff dynamics, and operational data to create strong, low-carbon marine logistics centers.
Co-Authors Chau, Thanh Hieu Chung, Nghia Do, Hoang Dat Do, Thi Mai Thom Dong, Van Huong Le, Do Duc Anh Le, Huu Cuong Le, Phuoc Tai Le, Thanh Tam Le, Thanh Tuan Nguyen, Thi Kim Tin Nguyen, Thi Thuy Hieu Paramasivam, Prabhu Tran, Nguyen Bao Minh Truong, Thi Hoang Oanh Vu, Van Vien