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Maidin, Siti Sarah
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering 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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Journal : International Journal of Engineering, Science and Information Technology

 1 
Predictive Data Analytics for Fault Diagnosis and Energy Optimization in Industrial IoT Environments Fallah, Dina; Abdul-Kareem, Bushra Jabbar; Murad, Nada Mohammed; Mahdi, Ammar Falih; Janan, Ola; 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.v5i2.1392

Abstract

The fusion of predictive maintenance with energy optimization represents a critical advance for intelligent Industrial Internet of Things (IIoT) systems. In response to the growing industrial demand for highly reliable and efficient operations, this study introduces and validates a unified framework that couples fault diagnosis via deep learning with energy management via reinforcement learning. We utilize a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture for multivariate fault detection, which demonstrates superior classification accuracy and robustness against data incompleteness. Simultaneously, a Deep Q-Network (DQN) performs dynamic energy scheduling based on predicted system health, achieving substantial energy reductions without compromising task deadlines. Extensive experimental results from real-world industrial datasets and simulations confirm the integrated framework's superiority over conventional approaches in both diagnostic precision and energy efficiency. Key performance indicators, including inference speed and cross-validation, affirm its suitability for real-time industrial applications. This work demonstrates that integrating predictive analytics into intelligent control paradigms is crucial for improving the reliability and sustainability of modern IIoT systems and offers a replicable blueprint for developing next-generation smart manufacturing solutions.
Edge Computing Frameworks for Real-Time Optimisation in Autonomous Electric Vehicle Networks Ismail, Laith S.; Jamil, Abeer Salim; Ali, Taghreed Alaa Mohammed; Al-Dosari, Ibraheem Hatem Mohammed; Salman, Khdier; Maidin, Siti Sarah
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.1397

Abstract

Autonomous electric vehicles (AEVs) require real-time decision-making, low-latency computation, and energy-aware coordination to operate effectively. Traditional centralised cloud computing struggles to meet these demands due to inherent delays and scalability issues in large-scale AEV networks. This paper proposes a novel hybrid edge–fog computing architecture to address these challenges. Our framework utilises a three-tier system (vehicle-edge, roadside-fog, and cloud) governed by a deep reinforcement learning agent that manages energy-aware task offloading. Extensive simulations demonstrate the framework's effectiveness, achieving significant end-to-end latency reductions of up to 56% during urban peak hours and decreasing energy consumption by 20% under high-load conditions. The deep reinforcement learning agent successfully adapts control policies to dynamic road conditions, while the architecture proves highly scalable and resilient, maintaining high task success rates and recovering from node failures in seconds. These findings confirm that a hybrid edge–fog architecture, guided by reinforcement learning, is a highly effective solution for scalable, adaptive, and energy-efficient AEV operations. This study's primary contribution is an empirically validated framework that uniquely integrates predictive control and energy-aware scheduling at the edge, providing a deployable model for next-generation intelligent transportation systems.
Design and Deployment of a Secure Cyber-Physical System for Energy Monitoring in Smart Agriculture Fallah, Dina; Abbas, Elaf Sabah; Ahmed, Mohsen Ali; Sajid, Wafaa Adnan; Al Hilfi, Thamer Kadum Yousif; Maidin, Siti Sarah
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.1398

Abstract

The growing need for sustainable agricultural practices has spurred the integration of cyber-physical systems (CPS) into modern farming. This paper presents the design, deployment, and evaluation of a modular CPS architecture for adaptive energy monitoring and control in smart agriculture. The system integrates environmental sensing, predictive modelling, and optimisation-guided actuation to enhance energy efficiency and operational resilience. Field tests on a 3-hectare site across six crop environments demonstrated significant performance gains, achieving energy savings of up to 25.8% and peak demand reductions of up to 19.8%. Our multi-layer architecture, featuring STM32 microcontrollers, LoRaWAN communication, and a cloud analytics dashboard, enables proactive control by anticipating energy demand using an LSTM-NARX predictive model. This approach reduced control actuation delay to 1.8 seconds and proved robust against cyber-physical faults, recovering from communication failures and data anomalies in under 15 seconds. The results validate that embedding energy-aware, predictive logic into CPS infrastructure creates scalable, efficient, and reliable agricultural solutions. We acknowledge limitations related to predictive model complexity and communication latency, and we propose future work focused on distributed CPS coordination, federated learning, and full lifecycle sustainability analysis to further advance intelligent, resource-efficient agriculture.
Swarm Intelligence Algorithms for Resource Allocation in Renewable-Powered Smart City Infrastructures Nazar, Mustafa; Majeed, Adil Abbas; Abdul Radhi, Rafah Hassan; Jafar, Qusay Mohammed; Khalil, Baker Mohammed; 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.v5i1.1355

Abstract

The increasing integration of renewable energy sources into urban systems necessitates the development of intelligent resource management strategies to ensure optimal and reliable power distribution. Swarm Intelligence (SI) algorithms have emerged as a promising solution for addressing the complex energy management challenges inherent in smart cities, such as generation variability, distributed loads, and the need for real-time decision-making. This paper conducts a rigorous comparative analysis of three prominent SI algorithms—Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC)—within a simulated, renewable-powered smart city environment. Our model incorporates edge computing nodes, solar and wind generation systems, and heterogeneous urban load profiles, including residential, municipal, and electric vehicle charging demands. The study evaluates each algorithm against key performance metrics, including energy efficiency, task latency, convergence behavior, load balancing, and system fault tolerance. The results unequivocally demonstrate that PSO outperforms both ACO and ABC across most performance dimensions, exhibiting faster convergence, superior energy utilization, more effective latency management, and enhanced fault recovery capabilities. While ABC demonstrates competitive performance in flexibility and fairness, ACO shows significant limitations in time-sensitive and failure-prone scenarios. This research contributes a modular simulation framework suitable for real-time edge computing applications and offers practical guidance for deploying adaptive optimization strategies in urban energy systems. Ultimately, our findings underscore the critical importance of algorithm selection in smart city energy infrastructure and highlight the potential of swarm-based intelligence to enable scalable, resilient, and efficient resource management in the sustainable cities of the future.
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.
Algorithms and Modeling for Optimizing Sustainable Energy Systems Jaleel Maktoof, Mohammed Abdul; Shaker, Alhamza Abdulsatar; Nayef, Hamdi Abdullah; Taher, Nada Adnan; Yousif Al Hilfi, Thamer Kadum; 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.v5i1.1457

Abstract

The global transition toward sustainable energy necessitates intelligent, integrated solutions to overcome the intermittency of renewable sources. This paper presents and validates a comprehensive framework for optimising Hybrid Solar-Wind Energy (HSWE) systems by integrating advanced simulation, machine learning-based forecasting, and metaheuristic optimisation. Using meteorological and operational data from three distinct climate zones, we modelled and analysed a PV-wind-lithium-ion hybrid system. A neural network was employed for precise load forecasting, while Particle Swarm Optimisation (PSO) managed real-time resource allocation and storage dispatch. Comparative analysis reveals that the optimised hybrid system significantly outperforms standalone units, increasing energy production by up to 32%, improving overall energy efficiency to 92.3%, and reducing operational costs by over 36%. The simulation models demonstrated high fidelity, with predictions matching experimental field data with less than 1% error. Furthermore, the integration of predictive fault handling and intelligent load balancing enhanced system reliability, increasing the mean time between failures (MTBF) by over 70% and achieving 97.6% system availability. This research provides a validated, replicable framework for engineers and policymakers, demonstrating a practical pathway to developing efficient, economically viable, and resilient decentralised renewable energy infrastructure to meet global sustainability goals.
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.
Artificial Intelligence, Robotics, and Automation in Renewable Energy Systems Ismail, Laith S.; Faraj, Lydia Naseer; Mohammed, Doaa Thamer; Taher, Nada Adnan; Hafedh, Milad Abdullah; 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.v5i2.1746

Abstract

The transition to clean energy requires intelligent solutions to mitigate resource intermittency, grid instability, and operational inefficiencies. This paper presents and validates an integrated framework that leverages Artificial Intelligence (AI), robotics, and automation to optimize the performance and sustainability of renewable energy assets. The study employs machine learning models (LSTM, SVM, ANN) for energy forecasting, autonomous robotic platforms for real-time inspection, and advanced algorithms (MPC, Reinforcement Learning) for grid control. The framework's transparency and ethical compliance were validated using explainability techniques (SHAP, LIME) and cybersecurity protocols. Experimental results demonstrate significant performance gains across all domains. The AI models achieved high forecasting accuracy, with the LSTM model for wind power reaching a Mean Absolute Percentage Error (MAPE) of just 2.41%. Robotic inspections improved system uptime by nearly 30% and accelerated fault detection. In grid management simulations, a Reinforcement Learning-based control strategy proved most effective, reducing energy losses by 10.6% and control costs by 17.5%. This cross-disciplinary research illustrates the powerful synergy between intelligent software and advanced hardware in creating more reliable, efficient, and ethically grounded energy systems. The findings establish a scalable and validated foundation for next-generation renewable energy operations and highlight future pathways for enhancing human-machine collaboration in the pursuit of global sustainability targets.
Big Data and Data Mining for Efficient Energy Storage and Management Nazar, Mustafa; Ali, Zaid Ghanim; Adnan, Kahtan Mohammed; Khalil, Ibraheem Mohammed; Nassar, Waleed; Maidin, Siti Sarah
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.v5i4.1759

Abstract

The rapid expansion of decentralized and renewable energy systems necessitates intelligent strategies for energy storage and management. This paper presents a comprehensive framework that leverages big data analytics and data mining to optimize energy storage systems within smart grid architectures. By integrating high-frequency data from IoT-enabled Li-Ion batteries, flow batteries, supercapacitor arrays, and hybrid systems, our methodology enhances storage efficiency, predictive accuracy, and fault detection. The approach uniquely combines an ensemble forecasting model (Random Forest and XGBoost), which achieved a 97% R² score in predicting energy demand, with Gaussian Mixture Models for consumer pattern clustering and canonical correlation analysis to model the impact of environmental variables. Validation on real-world datasets demonstrates significant performance gains without additional hardware. For instance, algorithmic optimization improved the round-trip efficiency of a Hybrid Battery Energy Storage System from 86.7% to 93.3% and a Li-Ion battery by 7%. The study underscores the critical influence of contextual variables like temperature and humidity on state-of-charge stability. Furthermore, the analytical framework demonstrated a 50% increase in system throughput (from 34 to 51 tasks/sec) after optimization. This research provides a replicable, data-driven model for deploying intelligent analytics in both microgrid and industrial-scale settings, paving the way for more adaptive and resilient energy infrastructures. Future work will explore edge computing and reinforcement learning to further enhance scalability and autonomy.
Co-Authors Abbas, Elaf Sabah Abbas, Intesar Abdul Radhi, Rafah Hassan Abdul-Kareem, Bushra Jabbar Abdullah Abdullah Adnan, Kahtan Mohammed Ahmed, Mohsen Ali Ahmed, Saif Saad Ajitha, Ajitha Al Hilfi, Thamer Kadum Yousif Al-Dosari, Ibraheem Hatem Mohammed Alfalahi, Saad.T.Y. Ali, Taghreed Alaa Mohammed Ali, Zaid Ghanim Arthi, R. Attarbashi, Zainab S. Ayi Abdurahman Ayyasy, Yahya Bakar, Normi Sham Awang Abu Binti Abdul Rahim, Yusrina Dhilipan, J. Fallah, Dina Faraj, Lydia Naseer Fauzi, Muhammad Ashraf bin Fauri Ge, Wu Gelar Budiman Govindaraju, S Guangfa, Wu Hadi, Shahd Imad Hafedh, Milad Abdullah Hammad, Qudama Khamis Hamodi Aljanabi, Yaser Issam Hao Wu Haodic, Gao Hemalatha, M. I Dewa Gede Budi Utama I Gede Astawan Indirani, M Indrarini Dyah Irawati Ishak, Wan Hussain Wan Ismail, Laith S. Jafar, Qusay Mohammed Jaleel Maktoof, Mohammed Abdul Jamil, Abeer Salim Janan, Ola Jaya, M. Izham Jeyaboopathiraja, J. Jing Sun Khalil, Baker Mohammed Khalil, Ibraheem Mohammed Kowthalam, Vijay Rathnam Kumar, B.L. Shiva Lie, Ye Luo Jun Mahdi, Ammar Falih Mahdi, Mohammed Fadhil Mahendiran, N Majeed, Adil Abbas Mariajohn, Princess Mohammed, Doaa Thamer Murad, Nada Mohammed Nassar, Waleed Nayef, Hamdi Abdullah Nazar, Mustafa Nivetha, N. Praneesh, M. Priscilla, G Maria Priscilla, G. Maria Rahmafadilla, Rahmafadilla Rizal, Mochammad Fahru Sajid, Wafaa Adnan Salman, Khdier Samson, A Sunil Selvaraj, Poovarasan Shaker, Alhamza Abdulsatar Shanmugam, D.B. Shing, Wong Ling Shivakumar, B L Shivakumar, B. L. Shnain, Saif Kamil Subramanian, Devibala Sumathi, N Sumathi, V. Taher, Nada Adnan Thavamani, S. Triasari, Biyantika Emili Varun, S. T. Vidhya, B. Vijayalakshmi, N. Wan Ishak, Wan Hussain Wayan Eka Paramartha Wei, Jingchuan Wider, Walton Yahya, Norzariyah Yamin, Fadhilah Yang, Qingxue Yi, Ding Yilin, Li Yousif Al Hilfi, Thamer Kadum YULI SUN HARIYANI Zhang Xing Zhao, Zhong Zhaoji, Fu