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All Journal International Journal of Engineering, Science and Information Technology
Mahdi, Mohammed Fadhil
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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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Energy-Aware Multimodal Biometric Authentication Systems for Mobile Hamodi Aljanabi, Yaser Issam; Mahdi, Mohammed Fadhil; Hadi, Shahd Imad; Shnain, Saif Kamil; Abbas, Intesar; Maidin, Siti Sarah
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.v5i1.1356

Abstract

As smartphones become central to personal identity verification, the need for secure, efficient, and power-conscious authentication methods is paramount. While multimodal biometric systems, combining features like face and fingerprint recognition, offer superior accuracy over unimodal approaches, their adoption on mobile platforms is severely hindered by high energy consumption and hardware variability. This paper introduces an energy-aware multimodal biometric authentication framework designed for Android smartphones that directly confronts this challenge. Our system features a novel adaptive fusion mechanism that intelligently balances recognition accuracy with power consumption by dynamically adjusting the weights of biometric modalities in real-time based on battery level and ambient environmental conditions. To validate our framework, we conducted an extensive experimental study involving 46 participants across 460 authentication sessions on five different smartphone models. The results demonstrate that our adaptive system significantly outperforms both unimodal and static fusion baselines. It achieves a high True Acceptance Rate (TAR) and a low Equal Error Rate (EER) while substantially reducing the Energy-Delay Product (EDP). A key feature is the system's ability to gracefully degrade to a secure, fingerprint-only mode when the battery is critically low, ensuring continuous availability without compromising security. This research proves that intelligent, context-aware modality adaptation is a viable strategy for creating robust, efficient, and sustainable biometric authentication solutions suitable for long-term use in consumer electronics.
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.
Co-Authors Abbas, Intesar Ahmed, Saif Saad Alfalahi, Saad.T.Y. Hadi, Shahd Imad Hammad, Qudama Khamis Hamodi Aljanabi, Yaser Issam Mahdi, Ammar Falih Maidin, Siti Sarah Shnain, Saif Kamil