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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Journal of Applied Data Sciences International Journal of Engineering, Science and Information Technology
Batumalay, M.
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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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Optimized AI-IoT Solution for Real-Time Pest Identification in Smart Agriculture S, Aasha Nandhini; Manoj, R. Karthick; Batumalay, M.
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.810

Abstract

Pest detection and identification play a crucial role in reducing the damage caused by pest, insect and diseases.  Timely detection and response are essential to increase the quality and quantity of crop production. Efficient pest management strategies are important for achieving optimal crop quality and promoting sustainable agricultural practices. This research proposes a framework that can automatically detect pests and offer timely solutions to farmers. The proposed approach integrates intelligent computing methods with connected device networks to identify and classify pests in real time with high precision. The methodology focuses on efficiently segmenting the pest from the captured leaf image using a novel region growing based segmentation algorithm. The threshold for region growing based segmentation algorithm is based on the adaptive local region entropy which contributes to the efficient segmentation. Stacked Ensemble Classifier (SEC) is used for the classification. The metrics used for evaluating the performance of the pest detection framework are accuracy, Area Under the Receiver Operating Characteristic Curve, F1-Score and Mean Average Precision (mAP). The results indicate that the proposed SEC with region growing based segmentation framework achieves 98 % of classification accuracy and mAP of 0.96 proving that it is very effective in both classification and segmentation task. The comparative analysis further reveals that the SEC outperforms the existing machine learning models and ensemble learning models like majority voting and weighted average models for process innovation.
Adaptive ANFIS-based MPPT for PV-powered green ships with high gain SEPIC converter Jegadeeswari, G.; Govindaraju, Rohini; Balakumar, D.; Lakshmi, D.; Marisargunam, S.; Batumalay, M.; Kirubadurai, B.
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2768-2779

Abstract

To align with global climate goals, the International Maritime Organization (IMO) has enforced strict measures to reduce greenhouse gas emissions from the shipping industry by promoting energy efficiency and cleaner propulsion methods. Ship engines remain major contributors to environmental pollution due to their dependence on fossil fuels and inefficient propulsion systems, highlighting the need for clean and sustainable alternatives. This study aims to design a renewable energy-based marine power system that effectively stores and utilizes solar energy, improving overall efficiency and reducing emissions for process innovation. A hybrid setup was developed using photovoltaic (PV) panels, batteries, and a bidirectional DC-DC converter to enable flexible power flow during both charging and discharging cycles. An adaptive neuro-fuzzy inference system (ANFIS)-based maximum power point tracking (MPPT) algorithm was employed alongside a SEPIC converter to enhance energy extraction from the PV system under dynamic conditions. The integrated system achieved a power extraction efficiency of 97.12%, confirming the effectiveness of the ANFIS-based MPPT strategy and showcasing the viability of intelligent renewable energy solutions in maritime applications.
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.
Context-Aware Systems for Proactive Energy Efficiency Services Hameed, Maan; Noori, Nabaa Ahmed; Suleiman, Aghaid Khudr; Abu-AlShaeer, Mahmood Jawad; Sabah, Ahmed; 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.v5i1.1728

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 Yousif, Hayder Abdulameer; Hussain, Salah Yehia; Hassan Ali, Taif Sami; Al-Doori, Vian S.; Sabah, Ahmed; Batumalay, M.
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.
Cloud Computing for Optimizing Sustainable Energy Networks Mwafaq, Lara; Meftin, Noor Kadhim; Rasheed, Ali Abdulameer; Al-Dulaimi, Mohammed K. H.; Hasan, Talib Kalefa; Batumalay, M.
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.
Blockchain Technology for Renewable Energy Transactions and Grid Management Mousa, Sura Hamed; Hussain, Refat Taleb; Hassan, Zahraa Mohammed; Qasim, Nameer Hashim; Mahdi, Akram Fadhel; Batumalay, M.
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.v5i3.1731

Abstract

The transition to renewable energy sources necessitates novel solutions for decentralized energy management, secure transactions, and transparent regulatory compliance. This paper presents the design and evaluation of a blockchain-based system addressing these challenges through peer-to-peer (P2P) energy trading, dynamic smart grid coordination, and automated Renewable Energy Certificate (REC) lifecycle management. Employing a hybrid methodology that combined qualitative stakeholder interviews with a six-month quantitative simulation of 50 prosumers, our Ethereum Proof-of-Stake (PoS) platform was assessed for efficiency, latency, and stability. The results indicate superior performance over traditional models, revealing significant gains in energy transfer efficiency, marked reductions in transaction latency under various network loads, near-elimination of REC fraud, and enhanced grid frequency stability. This study empirically confirms that decentralized architectures can augment or replace centralized utility models, establishing blockchain as a viable infrastructure for future smart grids and informing policy decisions needed to create a more resilient and equitable energy market for energy efficiency.
Co-Authors Abbas, Haider Hadi Abbas, Hasan Ali Abdulkareem, Sarah Ali Abdullah, Salima Baji Abdulrahman, Saad Abdulaziz Abu-AlShaeer, Mahmood Jawad Al-Doori, Vian S. Al-Dulaimi, Mohammed K. H. Alal, Sumaia Ali Ali, Ammar Abdulkhaleq Aljanabi, Yaser Issam Hamodi Bako, Imad Matti Balakumar, D. Brieg, Jassim Mohamed Govindaraju, Rohini Hameed, Maan Hasan, Talib Kalefa Hassan Ali, Taif Sami Hassan, Zahraa Mohammed Hilou, Hassan Waryoush Humadi, Mustafa Hussain, Refat Taleb Hussain, Salah Yehia Hussein, Ahmed Ali Jegadeeswari, G. Kalaiselvi, B Kirubadurai, B. Kiruthika, S. Lakshmi, D. M. Kallow, Sabah Mahdi, Akram Fadhel Manikandaprabhu, P. Manoj, R. Karthick Marisargunam, S. Meftin, Noor Kadhim Merzah, Hanaa Hameed Mousa, Sura Hamed Mwafaq, Lara Nagarajan, R. Najm, Nahlah. M. A. D. Noori, Nabaa Ahmed Prakash, S. Priscilla, G. Maria Qasim, Nameer Hashim Rajasree, R. Rasheed, Ali Abdulameer S, Aasha Nandhini Sabah, Ahmed Salim, Darin Shafiq Suleiman, Aghaid Khudr T.Y. Alfalahi, Saad Thirumoorthi, C. Vijendran, Anna Saro Xu, Zhengrui Yousif, Hayder Abdulameer