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AI-driven log reduction and storage optimization for security operations

10.11591/ijece.v16i3.pp1417-1424
Nutthakorn Chalaemwongwan
In this study, we present an AI-driven framework that integrates semantic log reduction with compliance-aware storage optimization, specifically designed for security operations center (SOC) and managed security service provider (MSSP) environments. Traditional approaches such as uniform compression, keyword filtering, and static tiering often either miss critical anomalies or preserve redundant noise, leading to excessive storage use, slower search performance, and analyst fatigue. The proposed framework addresses these challenges by combining three components: semantic reduction of repetitive entries, anomaly-focused retention supported by self-supervised models, and adaptive tiering aligned with regulatory requirements. Evaluations on HDFS, BGL, CICIDS2017, and Suricata datasets achieved 70%–80% log reduction, 55%–65% storage savings, recall rates above 95%, and a one-third reduction in query latency. These results demonstrate that pre-index reduction, together with anomaly- and compliance-aware retention, offers a scalable and regulator-ready solution for operational security environments.
Volume: 16
Issue: 3
Page: 1417-1424
Publish at: 2026-06-01

Transformer-based hybrid classification for plant leaf disease detection using vision transformer, principal component analysis, and support vector machine

10.11591/ijece.v16i3.pp1399-1406
Vijayalakshmi S. Abbigeri , Geetha D. Devanagavi
Plant diseases remain a critical challenge in agriculture, causing substantial yield losses and threatening food security. In this work, we propose a hybrid deep feature engineering framework that integrates deep learning-based feature extraction with classical machine learning for accurate plant disease detection. A pretrained vision transformer (ViT) model is employed to extract discriminative features from leaf images, effectively capturing complex spatial relationships. To address the curse of dimensionality, principal component analysis (PCA) is applied, retaining 98% of the variance while reducing feature space complexity. The refined features are then classified using a support vector machine (SVM) optimized through hyperparameter tuning. Experimental results on the bean leaf lesions dataset demonstrate strong performance, achieving 92% accuracy and a weighted F1-score of 0.92. The proposed ViT–PCA–SVM pipeline effectively balances accuracy, computational efficiency, and generalization, making it a promising solution for real-time smart farming applications.
Volume: 16
Issue: 3
Page: 1399-1406
Publish at: 2026-06-01

Analytic algebraic Riccati solution for a robust control system: application to 2-DOF arm robot

10.11591/ijece.v16i3.pp1159-1174
Menad Meriem , Ahmed Foitih Zoubir , Mokhtari Abdellah
An analytic solution to the Riccati algebraic equation has been investigated by employing eigenvalue–eigenvector techniques combined with the Gram–Schmidt orthogonality process. An analytic solution to the Riccati algebraic equation has been investigated by employing eigenvalue–eigenvector techniques combined with the Gram–Schmidt orthogonalization process. The applied method is used to improve robust control of second and third-order state-dependent systems by handling nonlinearities. An H∞ controller is designed in this context via backstepping technique to enhance robustness and reduce computational effort. The effectiveness of this method has been demonstrated on a two-degree-of-freedom (2-DOF) robotic manipulator arm. Simulation results validate the performance of the controller, showing improved tracking accuracy, disturbance rejection, and overall system stability, thereby confirming the efficiency and applicability of the combined analytic Riccati algebraic equation and H∞ backstepping approach for nonlinear robotic systems.
Volume: 16
Issue: 3
Page: 1159-1174
Publish at: 2026-06-01

Enhancing road damage detection performance using the YOLOv9 model

10.11591/ijict.v15i2.pp616-624
Muhammad Farkhan Adhitama , Sutikno Sutikno , Rismiyati Rismiyati
Roads are essential infrastructure that support community mobility, and their condition significantly impacts road user safety. However, manual road damage detection remains inefficient, time-consuming, costly, and prone to human error. To address this issue, this study proposed the YOLOv9 model for automated road damage detection and explored parameter combinations to optimize its performance. The proposed solution leverages the YOLOv9 model, which offers enhanced detection speed and accuracy compared to previous YOLO versions, due to its improved backbone and dynamic label assignment techniques. The method uses pre-trained weights and performs parameter tuning to adapt the model for identifying common road defects, including potholes, longitudinal, lateral, and alligator cracks. A publicly available dataset of road condition images was used for training and evaluation. Experimental results demonstrated that the optimized YOLOv9 model achieved a mean average precision (mAP) of 62.8%, indicating a promising ability to detect multiple types of road damage accurately. This study highlights the potential of YOLOv9 as an effective tool for road monitoring systems, contributing to proactive maintenance strategies and more efficient infrastructure management.
Volume: 15
Issue: 2
Page: 616-624
Publish at: 2026-06-01

A critical review of information retrieval techniques: current trends and challenges

10.11591/ijict.v15i2.pp456-464
Sanket D. Patil , Zahir Aalam
The realm of information retrieval is witnessing transformative advancements, driven by the integration of deep learning techniques, specialized algorithms, and domain-specific applications. Information retrieval systems play an important role in many applications including in the Artificial Intelligence powered systems that can be seen in many applications. Information Retrieval, generally, acts an important task in the knowledge discovery phase of any query based intelligent system. This paper presents a comprehensive review by conducting a detailed analysis of the technological nuances, dataset specifications, and pivotal findings. This detailed review has been done with the special emphasis on the kind of technology used to achieve accurate information retrieval, domain of the study, and the system’s ability to retain or work with tables and figures, among other parameters. Navigating through the rich tapestry of methodologies, the paper underscores the pivotal role of deep learning frameworks in revolutionizing traditional retrieval paradigms. Furthermore, it sheds light on the innovative integration of textual information, algorithmic advancements, and specialized datasets to enhance the efficacy and granularity of information retrieval mechanisms.
Volume: 15
Issue: 2
Page: 456-464
Publish at: 2026-06-01

IoT-enabled smart hydroponic system using nutrient film technique for precision agriculture

10.11591/ijict.v15i2.pp900-908
Varuna Kumara , Akshatha Naik , Fatima Tahsir , Sinchana Bommayya Devadiga , Vinitha Ramesh Naik
The study aims to develop an internet of things (IoT)-enabled automated hydroponic system using the nutrient film technique (NFT) to optimize plant growth with minimal human intervention. The system integrates sensors, microcontrollers, and cloud-based monitoring to maintain optimal conditions for crops. The system utilizes Arduino Uno, ESP8266 Wi-Fi module, and sensors including pH, TDS, DHT11 and water level sensors. Data collected from these sensors is processed in real time, allowing automated adjustments through relay-controlled water and nutrient pumps. The system transmits data to the ThingSpeak IoT platform, enabling remote monitoring and predictive analytics. The proposed hydroponic system ensures stable environmental conditions, improving plant growth efficiency. Key parameters such as pH, TDS levels and humidity are maintained within optimal ranges. The automated system reduces manual intervention, enhances water and nutrient efficiency, and increases yield consistency compared to traditional farming methods. The IoT-based NFT hydroponic system demonstrates significant potential in urban agriculture and controlled environment farming. By leveraging automation, AI-driven analytics, and cloud-based monitoring, it provides a scalable and sustainable solution for precision farming. Future advancements may include AI-based predictive analytics, solar-powered energy solutions, and robotic automation for further optimization.
Volume: 15
Issue: 2
Page: 900-908
Publish at: 2026-06-01

Ensemble windows intrusion detection system using XGBoost and deep learning

10.11591/ijict.v15i2.pp565-577
Pranitha Kedambady Shiva , Pushparaj D. Shetty
Intrusion detection systems (IDS) are critical for preserving the Windows environment from an ever-changing collection of cyber threats. Current IDS uses deep learning (DL), which are heavy models if used for detection, while others use machine learning (ML) techniques, which require external feature extraction. To resolve this challenge, this paper introduces XGBNN, a new ensemble model that combines the benefits of ML and DL to identify and mitigate attacks against Windows machines effectively. The various ML methods are trained on the publicly available dataset to classify eight types of attacks in a Windows environment. Additionally, deep neural networks (DNNs) are proposed by optimizing the layers and hyperparameters to achieve the best accuracy. Then, the DNN model and XGBoost model are integrated to detect intrusions by utilizing the feature extraction ability of DNN and providing the intermediate features extracted from the last second layer of the DNN to the XGB for classification. The Ensemble model XGBNN optimizes features and offers better decisions. The proposed model achieves an exceptional accuracy of 100%, as demonstrated by the empirical results, and outperforms the benchmark models with an improvement of 0.004%. The purpose of this study is to highlight the effectiveness of hybrid architectures in intrusion detection. These architectures offer a more robust, scalable, and effective method to improve the security of the Windows system against more sophisticated attacks.
Volume: 15
Issue: 2
Page: 565-577
Publish at: 2026-06-01

Utilization of BSA optimized cascade controller in a renewable energy-based AGC systems

10.11591/ijape.v15.i2.pp546-553
Rambabu Kasukurthi , R. Srinu Naik
A novel cascade controller named proportional integral derivative-tilt integral derivative (PID-TID) is proposed for a two-area thermal-wind automatic generation control (AGC) system and its gains are optimized by a novel metaheuristic bird swarm algorithm (BSA). The BSA tuned PID-TID controller enhances dynamics over PID and TID controller in terms of settling time and peak shoots. Moreover, dynamics with wind integration have shown significant improvement over thermal system alone. Further system has shown enhanced dynamics with redox flow batteries (RFB) over thermal-wind system. Furthermore, studies with automatic voltage regulation (AVR) strengthen voltage stability. Also, responses with PID-TID have shown steady dynamic profile at various loading conditions. Integrating wind energy into thermal system results in significant enhancements in dynamics showcasing greater stability. Also, improvements are evident with the RFB introduction, enhance dynamic with in hybrid system. The incorporation of AVR enhance voltage stability. The proposed PID-TID demonstrates significant robustness ensuring stable response under loading condition and effectively boost dynamic performance.
Volume: 15
Issue: 2
Page: 546-553
Publish at: 2026-06-01

Transient stability analysis of a new proposed hybrid PV-WTG microgrid for Tinghir power distribution

10.11591/ijape.v15.i2.pp449-463
Hicham Stitou , Mohamed Amine Atillah , Abdelghani Boudaoud , Mounaim Aqil
This work focuses on the transient stability of a hybrid photovoltaic and wind turbine generator (PV-WTG) system at the Tinghir 225/60/11 kV substation in Morocco. Results were obtained by evaluating the effects of the proposed configuration on power angle, frequency, voltage, and fault-clearing times in the system. The study examined key disturbances, including abrupt loss of renewable energy and major electrical faults. Analysis using ETAP demonstrated a power angle change of -55 degrees, 20 degrees greater than the normal operating point, which can be caused by the loss of PV and approaches the IEEE Std 421.5 stability limit. The maximum voltage variation was 6.1% for the PV and 2.7% for the WTG, exceeding the IEC 60034-1 limits of ±5%. Another major finding of this analysis was that WTG loss induces frequency swings of 0.8 Hz and requires 10 to 15 seconds for recovery, indicating that low-inertia systems have insufficient inertia to return to steady state quickly. Therefore, the study demonstrates that adaptive control approaches must be used to achieve stable operation of hybrid connected microgrids. Using the time domain simulation (TDS) process, we calculated the critical clearing time (CCT) of 155 ms for 3-phase faults and 464 ms for line-to-ground faults, all of which are within the CCT limit set by IEEE Std 3002.2, and this confirms the necessity of urgent clearing of faults to maintain transient stability and demonstrates the need for fast protection and adaptive control in low-inertia systems, which is of particular concern in rural grids.
Volume: 15
Issue: 2
Page: 449-463
Publish at: 2026-06-01

Optimized resonant capacitor and switching frequency for high-efficiency wireless power transfer in E-bikes using CST Studio Suite

10.11591/ijape.v15.i2.pp514-524
Wan Muhamad Hakimi Wan Bunyamin , Rahimi Baharom
Wireless power transfer (WPT) is increasingly adopted for E-bike charging; however, its performance is often constrained by inaccurate resonant tuning, inefficient capacitor selection, and improper switching-frequency operation, which lead to significant power loss and reduced transfer efficiency. This study addresses these limitations by formulating an optimized design methodology for selecting resonant capacitors and inverter switching frequency to achieve high-efficiency energy transfer. A 40-mm air gap between the transmitter and receiver coils is modeled using CST Studio Suite, where a 3D electromagnetic circuit co-simulation framework is applied to evaluate mutual inductance, resonant behavior, magnetic-field distribution, and S-parameter characteristics. Parametric sweeps combined with a convergence-based optimization algorithm identify the optimal resonant operating point, yielding a peak resonant frequency of 38.1 kHz, a maximum simulated transfer efficiency of 99%, and a deep reflection coefficient of -21.77 dB. The optimized configuration also demonstrates stable voltage and field distribution at resonance, confirming effective impedance matching. The main contributions of this work include: i) establishing a unified EM–circuit optimization workflow for determining resonant capacitance and switching frequency, ii) providing quantitative resonance parameters and performance indicators suitable for compact E-bike WPT systems, and iii) integrating mathematical modelling to validate CST-based predictions and ensure theoretical consistency. The proposed approach significantly enhances design accuracy and efficiency, offering a scalable and high-performance solution for next-generation low-power electric vehicle (EV) and E-bike wireless charging applications.
Volume: 15
Issue: 2
Page: 514-524
Publish at: 2026-06-01

Design to optimize the location, number, and performance of dynamic voltage restorers using artificial neural networks

10.11591/ijape.v15.i2.pp793-807
Yulianta Siregar , Faizzufar Taqy , Mohd Najib Mohd Hussain , Hafizh Prihtiadi , Muldi Yuhendri
The need for electrical energy always increases from year to year. This means that the distribution system in the electric power system needs to pay attention to its level of stability and reliability. A low level of stability can cause disruption and result in losses. The system's stability and reliability can be increased by installing custom power devices (CPD) equipment such as a dynamic voltage restorer (DVR). In this research, the location, number, and performance of DVRs are optimized using an artificial neural network based on the voltage stability of the distribution network in the Sibolga Penyulang SB02 area. Based on the research results, buses 2, 12, 24, 27, and 35 are the best places to install DVRs, and the system will have five DVRs installed. A three-phase short circuit simulation was used to determine how feeder stability was impacted by DVR performance. Then, the voltage falls to 0.1770 p.u. during a disturbance and then rises to 0.8073 p.u., which is within the typical voltage limit of > 0.9 p.u. It means that DVRs restored the voltage fully to the acceptable threshold.
Volume: 15
Issue: 2
Page: 793-807
Publish at: 2026-06-01

Predictive modeling and optimization of paper mill using hybrid machine learning techniques

10.11591/ijape.v15.i2.pp692-702
Abhijit Singh Bhakuni , Sandeep Kumar Sunori , Pradeep Juneja
The paper has played a vital role in the life of humans from ancient times covering a vast range of applications such as writing, packaging, and printing. The present paper is presenting a comprehensive review of various optimization and control methodologies, ranging from conventional to advanced ones, pertaining to the paper mill. The final goal of these control strategies is to upgrade the mill’s production and quality in presence of multiple technical challenges such as nonlinear and multivariable nature of the involved processes, various disturbance parameters, and time delays. In this work, the integration of machine learning with paper mill process is illustrated. For any manufacturing process, the final product quality is the key goal. There are various traditional techniques which have already been practiced for final produced paper quality in paper mills. This paper highlights the capability of support vector machine (SVM) algorithm to assess the produced paper quality, capturing the two crucial inputs viz. the pulp consistency and the headbox level. The basic goal of this research is twofold, firstly it presents an exhaustive literature survey exploring various strategies which are practiced currently in the domain of control and optimization of various paper mill processes. Secondly, it intends to develop and evaluate various SVM and SVM-RF hybrid models using MATLAB for assessment of quality of final product on basis of two parameters- pulp consistency and head box level. Finally, genetic algorithm has been employed in MATLAB for multivariate optimization.
Volume: 15
Issue: 2
Page: 692-702
Publish at: 2026-06-01

Optimizing real-time energy control in hybrid low-voltage microgrids using a multi-agent approach

10.11591/ijape.v15.i2.pp505-513
Doha El Hafiane , Abdelmounime El Magri , Ilyass El Myasse , Adil Mansouri , Rachid Lajouad
This research proposes a real-time framework for energy management and control in hybrid low-voltage microgrids (LVMGs) through multi-agent systems (MAS). The proposed framework enables decentralized and autonomous coordination among renewable energy sources, energy storage systems, loads, and the utility grid to dynamically optimize power flows under varying operating conditions. Each agent adjusts its setpoints using local information while cooperating with other agents to achieve global objectives. The MAS is implemented using The Java Agent Development Framework (JADE) and co-simulated with MATLAB/Simulink to accurately represent the microgrid’s physical behavior. Simulation results under grid-connected and islanded modes demonstrate that the proposed approach increases renewable energy utilization by up to 10% and reduces total energy costs by 7.6% compared to conventional centralized control schemes. Moreover, the system exhibits strong adaptability and robustness in the presence of renewable intermittency and load fluctuations, ensuring reliable real-time operation. These results confirm that MAS-based control provides an effective, scalable, and resilient solution for real-time energy management in hybrid LVMGs.
Volume: 15
Issue: 2
Page: 505-513
Publish at: 2026-06-01

Coastline segmentation on Landsat 8 OLI images using majority voting with deep learning models

10.11591/ijict.v15i2.pp588-596
Nur Nafiiyah , Salwa Nabilah , Nur Azizah Affandy , Rifky Aisyatul Faroh , Esa Prakasa
Coastlines are highly dynamic due to both natural processes and anthropogenic factors, including global warming and sea level rise. Accurate coastline segmentation is essential for effective monitoring and management. Although previous studies have applied deep learning for coastline detection, many existing models still suffer from instability across scenes, blurred boundaries, and segmentation artifacts, indicating that model generalization remains a challenge. This study aims to develop a more robust coastline segmentation approach by introducing an automated majority voting strategy that integrates three deep learning models: ResNet50, ResNet18, and MobileNet-V2. Landsat 8 OLI imagery is used for training and testing. The Jaccard index results show that ResNet18, ResNet50, and MobileNet-V2 achieved scores of 0.96, 0.98, and 0.95 respectively, while the proposed majority voting method also achieved 0.98. Despite the producing a similar numerical score to the best individual model (ResNet50), the ensemble method improves segmentation consistency by reducing artifacts such as unwanted peripheral shapes and cracks within land areas. These findings demonstrate that combining multiple segmentation outputs yields more stable and reliable coastline detection than using single models. Future work will apply this approach to broader Indonesian coastal regions to further assess its generalizability across diverse shoreline conditions.
Volume: 15
Issue: 2
Page: 588-596
Publish at: 2026-06-01

Enhancing grid performance through coordinated SVC-TCSC operation with PV support: A case study on IEEE 30-bus system under progressive loading

10.11591/ijpeds.v17.i2.pp1254-1264
Hafidha Reriballah , Latifa Smail , Ali Abderrazak Tadjeddine , Hocine Guentri , Rim Feyrouz Abdelgoui , Fatima Zohra Boudjella
Power systems face growing challenges of voltage instability, line congestion, and increased losses under rising demand. This study proposes a coordinated approach using two flexible AC transmission system (FACTS) devices: the static var compensator (SVC) and the thyristor controlled series capacitor (TCSC), together with photovoltaic (PV) generation, to enhance grid performance. The IEEE 30 bus test system is analyzed under normal and increased load conditions (5%, 10%, 15% load growth). Results show that coordinated SVC TCSC operation improves voltage profiles, reduces critical line loading by 14%, and lowers active and reactive losses by 10% and 23.8%, respectively, in the base case. Under a 15% load increase, integrating a 25 MW PV system with the coordinated FACTS restores the minimum voltage to 0.95 p.u., reduces line congestion by 27%, and decreases active and reactive losses by 35.5% and 53.5%. The combined FACTS PV strategy proves essential for maintaining stability and efficiency under high load growth. This integrated approach provides practical guidance for transmission operators toward resilient, loss aware, and renewable integrated smart grids.
Volume: 17
Issue: 2
Page: 1254-1264
Publish at: 2026-06-01
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