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All Journal International Journal of Engineering, Science and Information Technology
Ahmed, Saif Saad
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Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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AI-Driven Text Analysis and Generation for Green Energy Applications Ahmed, Saif Saad; Mahdi, Mohammed Fadhil; Hammad, Qudama Khamis; Mahdi, Ammar Falih; Alfalahi, Saad.T.Y.; Maidin, Siti Sarah
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.1745

Abstract

The rapid growth of the green energy sector has produced a massive volume of textual data, creating significant challenges for information extraction and decision support. This study investigates the application of state-of-the-art Natural Language Processing (NLP) models, specifically BERT and GPT-4, to automate and enhance policy drafting, market analysis, and academic research clustering. We evaluated these models on a corpus of over 200,000 energy-related documents, using a structured computational workflow to measure performance on semantic coherence, factual reliability, and processing efficiency. The results demonstrate substantial improvements over manual methods. The AI-driven approach reduced policy drafting time by 39% and error rates by over 58%, while increasing semantic alignment to 93.5%. In market report synthesis, the models improved topic extraction accuracy by over 10% and reduced summary generation time by 38%. For academic literature, thematic clustering accuracy reached 92.3%, with a 44% reduction in processing time. These findings validate that fine-tuned NLP models can serve as powerful analytical tools in the sustainable energy domain, enabling institutions to navigate complex regulatory and technical information more effectively. By providing a practical demonstration of how automated NLP solutions can augment human expertise, this work contributes to the applied use of AI in achieving global green energy objectives, while also considering the associated methodological and ethical implications.
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 Alal, Sumaia Ali Alfalahi, Saad.T.Y. Badran, Mina Louay Batumalay, M. Hammad, Qudama Khamis Issa, Samer Saeed Mahdi, Ammar Falih Mahdi, Mohammed Fadhil Maidin, Siti Sarah Mohammed, Ghada S.