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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) International Journal of Applied Power Engineering (IJAPE)
Pramasivam, Prabakaran
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Computer Science & IT Electrical & Electronics Engineering

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Enhanced smart farming security with class-aware intrusion detection in fog environment Palanisamy, Selvaraj; Rajamani, Radhakrishnan; Pramasivam, Prabakaran; Sumithra, Mani; Kaliyaperumal, Prabu; Perumal, Rajakumar
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp257-266

Abstract

The adoption of the internet of things (IoT) in smart farming has enabled real-time data collection and analysis, leading to significant improvements in productivity and quality. However, incorporating diverse sensors across large-scale IoT systems creates notable security challenges, particularly in dynamic environments like Fog-to-Things architectures. Threat actors may exploit these weaknesses to disrupt communication systems and undermine their integrity. Tackling these issues necessitates an intrusion detection system (IDS) that achieves a balance between accuracy, resource optimization, compatibility, and affordability. This study introduces an innovative deep learning-driven IDS tailored for fog-assisted smart farming environments. The proposed system utilizes a class-aware autoencoder for detecting anomalies and performing initial binary classification, with a SoftMax layer subsequently employed for multi-class attack categorization. The model effectively identifies various threats, such as distributed denial of service (DDoS), ransomware, and password attacks, while enhancing security performance in environments with limited resources. By utilizing the Fog-to-Things architecture, the proposed IDS guarantees reliable and low-latency performance under extreme environmental conditions. Experimental results on the TON_IoT dataset reveal excellent performance, surpassing 98% accuracy in both binary and multi-class classification tasks. The proposed model outperforms conventional models (convolutional neural network (CNN), recurrent neural network (RNN), deep neural network (DNN), and gated recurrent unit (GRU)), highlighting its superior accuracy and effectiveness in securing smart farming networks.
Machine learning-based real-time power stability optimization for photovoltaic systems using hybrid inductor-capacitor patterns Kathirvel, Jayashree; Pushpa, S.; Kavitha, P.; Sureshkumar, Sathya; Andi, Kannan; Pramasivam, Prabakaran
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp248-256

Abstract

Photovoltaic (PV) systems often face real-time power stability challenges due to rapid fluctuations in solar irradiance and varying load conditions, which conventional control strategies struggle to manage effectively. Addressing this limitation, the present study proposes a novel machine learning-based control framework integrated with a hybrid inductor-capacitor (LC) network to enhance dynamic power regulation. The proposed system employs predictive algorithms to adjust LC parameters in real time, enabling adaptive voltage and current stabilization during transient conditions. Simulation results validate the model's effectiveness, showing a 58% reduction in power fluctuation (from 12% to 5%) and consistent improvement in voltage stability index (VSI), maintaining values above 0.95 compared to 0.88-0.93 in traditional systems. Moreover, the approach reduces computation time by 66% (150 ms versus 450 ms for PID-based systems), supporting faster and more efficient control actions. These outcomes demonstrate that the proposed intelligent control strategy significantly improves energy efficiency, voltage stability, and responsiveness in PV systems, offering a scalable solution for reliable grid integration of renewable energy sources.
Co-Authors Andi, Kannan Kaliyaperumal, Prabu Kathirvel, Jayashree Kavitha, P. Palanisamy, Selvaraj Perumal, Rajakumar Pushpa, S. Rajamani, Radhakrishnan Sumithra, Mani Sureshkumar, Sathya