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Alal, Sumaia Ali
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Computer Vision for Monitoring Renewable Energy Infrastructure Hussein, Ahmed Ali; Alal, Sumaia Ali; Abdulrahman, Saad Abdulaziz; Merzah, Hanaa Hameed; Abbas, Hasan Ali; Batumalay, M.
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1727

Abstract

The operational efficiency of renewable energy installations, including solar, wind, and hydropower systems, is often hindered by the limitations of manual inspections and legacy monitoring. These methods lack the real-time, scalable fault detection necessary to prevent costly downtime. This paper proposes a comprehensive computer vision framework for automated fault detection, predictive maintenance, and inspection optimization across diverse renewable energy infrastructures. We developed a hybrid deep learning model, based on ResNet-50 with attention-based extensions, to analyze high-resolution imagery from drones and stationary cameras. The model was trained and validated on a dataset of 20,000 labeled images covering infrastructure-specific defects such as photovoltaic microcracks, wind turbine blade erosion, and hydropower sedimentation patterns. Our experiments demonstrate high-performance, with fault detection accuracy exceeding 91% for all categories and inference latencies under 70ms. The system significantly improved predictive maintenance outcomes, reducing unplanned outages by over 77% and decreasing inspection energy consumption by more than 70%. Scalability tests on a larger 50,000-image dataset confirmed the framework's robustness, maintaining high accuracy and processing speed. This work validates computer vision as a viable, cost-effective, and scalable solution for intelligent monitoring in the renewable energy sector, offering significant practical implications for autonomous diagnostic systems in smart grid and industrial applications for energy efficiency.
Bioinformatics in Sustainable Healthcare and Energy Efficiency Ahmed, Saif Saad; Alal, Sumaia Ali; Badran, Mina Louay; Issa, Samer Saeed; Mohammed, Ghada S.; Batumalay, M.
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1760

Abstract

While originating in genomics, bioinformatics is emerging as a powerful tool for optimizing complex, energy-intensive systems. This paper investigates a novel application of bioinformatics across four critical sectors—healthcare, biofuel production, renewable energy, and the Internet of Things (IoT)—to enhance energy efficiency, operational reliability, and system adaptability. Using a mixed-methods approach that combines statistical modeling, algorithm development, and institutional case studies, this research quantifies the impact of bioinformatics-driven interventions on key performance and energy metrics. The results demonstrate significant and consistent improvements across all domains. In healthcare, integrating genomic analytics and adaptive controls led to energy savings of up to 12.8%. For biofuel production, bio-inspired enzymatic and microbial process optimization reduced energy intensity by as much as 18.1% per liter. In the renewable energy sector, bioinformatics-based modeling increased the net efficiency of a solar farm by 50%. Furthermore, IoT systems with embedded bioinformatics algorithms achieved up to 15.8% improvement in energy-aware operations, confirming the methodology's cross-disciplinary value. This study positions bioinformatics not merely as a scientific tool but as a core organizing principle for fostering sustainability in digitized infrastructures. While challenges such as computational overhead and ethical governance remain, this research provides compelling evidence that bioinformatics can serve as a catalyst for cross-industrial environmental innovation. Future work should focus on integration with high-performance computing and the development of socio-ethical frameworks to ensure scalable and responsible deployment for energy efficiency.
Co-Authors Abbas, Hasan Ali Abdulrahman, Saad Abdulaziz Ahmed, Saif Saad Badran, Mina Louay Batumalay, M. Hussein, Ahmed Ali Issa, Samer Saeed Merzah, Hanaa Hameed Mohammed, Ghada S.