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M. Batumalay
INTI International University Nilai
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Bioinformatics in Sustainable Healthcare and Energy Efficiency Saif Saad Ahmed; Sumaia Ali Alal; Mina Louay Badran; Samer Saeed Issa; Ghada S. Mohammed; M. Batumalay
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.
Design of Ethical AI Frameworks for Sustainable and Adaptive Energy Management Systems Mustafa Humadi; Haider Hadi Abbas; Hassan Waryoush Hilou; Nahlah. M. A. D. Najm; Ammar Abdulkhaleq Ali; M. Batumalay
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.v5i1.1288

Abstract

The integration of Artificial Intelligence (AI) in Energy Management Systems changed completely how sustainable infrastructure operates?and is guarded. But the growing independence of AI decision-making presents some serious ethical questions about?fairness, transparency, and accountability. The article introduces a new framework with Ethical AI for Sustainable and Adaptive Energy Management Systems (EAI-SEM) that is designed to combine functional (re)configuration for operational control and ethical governance in centralized: smart buildings and?decentralized: nano-grid settings. The approach incorporates deep reinforcement learning for adaptive control, federated learning for privacy-preserving model updates, and an?integrated Ethics Verification Module for a dynamic assessment of privacy-conformance levels. In experimental simulations over 30-day operation of the smart building and 10-rounds of federated training of the nano-grid, unjust fairness deviation and explainability of the system experienced enhancements, which also indicated?the reduction of carbon dioxide emissions. The?study demonstrated that ethical protocols can be included without impacting on computational efficiency and system responsiveness. Additionally, the federated structure facilitated decentralized ethical responsibility across different actors and thus allowed for the scalable?implementation. The authors verify the possibility of integrating ethics into the computational core of?intelligent energy systems, near from auditing static policies, towards dynamic ethical choices. In the future the process innovation work could be applied to deployments in other infrastructure systems like water?systems and mobility systems, and it provides a reproducible model for the embedding of normative reasoning into AI for infrastructure.
Computer Vision for Monitoring Renewable Energy Infrastructure Ahmed Ali Hussein; Sumaia Ali Alal; Saad Abdulaziz Abdulrahman; Hanaa Hameed Merzah; Hasan Ali Abbas; M. Batumalay
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.
Cloud Computing for Optimizing Sustainable Energy Networks Lara Mwafaq; Noor Kadhim Meftin; Ali Abdulameer Rasheed; Mohammed K. H. Al-Dulaimi; Talib Kalefa Hasan; M. Batumalay
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.v5i2.1730

Abstract

The increasing integration of renewable energy sources into power systems creates significant challenges for grid stability, efficiency, and scalability. This study investigates cloud computing as a strategic control layer for optimizing these sustainable energy networks. We designed and deployed a cloud-based energy management system that utilizes intelligent data processing, real-time load balancing, and predictive analytics to enhance the performance of decentralized grids. The methodology combines virtualized monitoring with adaptive fault detection and dynamic energy routing, allowing the system to respond autonomously to fluctuations in supply and demand. Our empirical evaluation demonstrates that cloud integration significantly improves transmission efficiency, reduces system downtime, and enables higher utilization of renewable energy, thereby lowering reliance on fossil-fuel backups. Key performance metrics, including data latency and machine learning inference time, were also enhanced, accelerating overall decision-making. These findings validate the hypothesis that cloud platforms are not merely computational tools but essential instruments for the global energy transition. The study concludes by discussing limitations related to cybersecurity and interoperability and proposes future research into hybrid cloud-edge architectures for energy efficiency.
Neuromorphic Hardware Design for Energy-Aware Artificial Intelligence Computation Yaser Issam Hamodi Aljanabi; Salah Yehia Hussain; Darin Shafiq Salim; Vian S. Al-Doori; Jassim Mohamed Brieg; M. Batumalay
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.v5i1.1279

Abstract

Rapid growth of the energy-efficient artificial intelligence (AI) systems has attracted substantial interest in neuromorphic computing that emulates organization and actions of a biological neural?system to support low-power, event-driven information processing. In this work, we propose a neuromorphic hardware architecture for energy-efficient AI computing that utilizes spiking neural networks and monolithic?vertical integration to improve the performance of a variety of vision tasks. The architecture is tested against three benchmark datasets— MNIST, N-MNIST, and DVS128,?representing static, spiking and dynamic input modalities, respectively. The performance metrics, such as energy efficiency, inference latency,?throughput, classification accuracy, and unified Energy Efficiency Index (EEI) are compared to characterize the generalization power of the system in different processing environments. Experimental results show that the proposed chip provides a sharply lower energy per inference with a competitively performing accuracy over conventional AI?accelerators, including GPU-based and microcontroller platforms. Additionally, the hardware achieves sub-2 ms inference latency and high throughput, indicating suitability for real-time, embedded AI applications. Comparative analysis with existing neuromorphic platforms highlights the advantage of architectural co-design in balancing energy and performance constraints. While the absence of on-chip learning presents a limitation, the system offers a scalable foundation for edge AI systems requiring efficient, continuous inference. Future directions include integrating adaptive learning mechanisms and extending evaluation to broader AI domains as a process innovation.
Context-Aware Systems for Proactive Energy Efficiency Services Maan Hameed; Nabaa Ahmed Noori; Aghaid Khudr Suleiman; Mahmood Jawad Abu-AlShaeer; Ahmed Sabah; M. Batumalay
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.v5i1.1728

Abstract

Static energy control systems are increasingly unable to meet the demands of modern built environments, where dynamic occupancy and fluctuating conditions lead to significant inefficiencies. This paper presents a context-aware system for proactive energy management that integrates real-time data acquisition, machine learning-based forecasting, and autonomous control. A multi-tiered architecture was developed and deployed across diverse settings residential, commercial, and industrial—to gather contextual data on temperature, occupancy, lighting, and equipment usage. The system uses predictive forecasting to anticipate short-term energy needs and reinforcement learning to optimize control strategies, ensuring both energy savings and user comfort. Results from the deployment demonstrate significant power reduction, high system responsiveness, and strong user satisfaction. Application-specific benchmarks revealed major efficiency gains in HVAC, lighting, and industrial machinery, while scalability tests confirmed stable performance under increasing sensor loads. This research validates the effectiveness of combining contextual intelligence with adaptive control to create sustainable, responsive, and human-centered energy systems. We provide a practical, modular framework for intelligent energy infrastructure in smart buildings and industrial parks. Future work will focus on enhancing model interpretability, integrating economic incentives, and exploring federated learning for distributed intelligence in support of energy efficiency.
Data-Driven Cloud Systems for Renewable Energy Optimization Hayder Abdulameer Yousif; Salah Yehia Hussain; Taif Sami Hassan Ali; Vian S. Al-Doori; Ahmed Sabah; M. Batumalay
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.v5i2.1729

Abstract

The growing share of renewable generation in global power systems creates operational instability due to the volatile nature of solar, wind, and hydropower. This study presents a novel cloud-edge integrated model designed to enhance the performance and efficiency of these renewable sources through a data-centric approach. The proposed architecture relies on an IoT-enabled sensor network for real-time data gathering, processed through a hybrid infrastructure combining edge-level filtration with cloud-based analytics. For energy output prediction, we compared Linear Regression, Random Forest, and Long Short-Term Memory (LSTM) models, with LSTM demonstrating superior performance. To optimize operations, a multi-objective Genetic Algorithm was implemented to simultaneously minimize energy losses and costs while improving grid utilization balance. Furthermore, exergy-based modeling was employed to evaluate the thermodynamic quality of energy transformations. The results confirmed that the system significantly improved predictive accuracy, responsiveness, and energy savings. Under varying loads, the system maintained low latency and high energy allocation efficiency, validating its real-time adaptability. In summary, this research delivers a modular and scalable solution for intelligent energy management, highlighting the power of predictive analytics and adaptive control in creating data-driven, next-generation sustainable energy efficiency systems.
Co-Authors Aghaid Khudr Suleiman Ahmed Ali Hussein Ahmed Sabah Ali Abdulameer Rasheed Ammar Abdulkhaleq Ali Darin Shafiq Salim Ghada S. Mohammed Haider Hadi Abbas Hanaa Hameed Merzah Hasan Ali Abbas Hassan Waryoush Hilou Hayder Abdulameer Yousif Jassim Mohamed Brieg Lara Mwafaq Maan Hameed Mahmood Jawad Abu-AlShaeer Mina Louay Badran Mohammed K. H. Al-Dulaimi Mustafa Humadi Nabaa Ahmed Noori Nahlah. M. A. D. Najm Noor Kadhim Meftin Saad Abdulaziz Abdulrahman Saif Saad Ahmed Salah Yehia Hussain Samer Saeed Issa Sumaia Ali Alal Taif Sami Hassan Ali Talib Kalefa Hasan Vian S. Al-Doori Yaser Issam Hamodi Aljanabi