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30,185 Article Results

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

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

Dual-mode model predictive control for non-minimum phase boost converters

10.11591/ijpeds.v17.i2.pp1211-1220
Jawhra El Hmidi , Anass Mansouri , Ali Ahaitouf
This paper aims to develop an efficient finite-set model predictive control (FS-MPC) strategy for DC-DC boost converters to improve voltage regulation while reducing computational complexity. The proposed approach introduces a split cost function that decouples voltage and current regulation, providing a simpler alternative to conventional long-horizon FS-MPC schemes used to address the converter’s non-minimum-phase (NMP) behavior. A current estimation technique is incorporated to eliminate the need for additional sensors, lowering hardware cost and improving robustness. Unlike existing FS-MPC methods that rely on horizon extension or extra measurements, the proposed strategy leverages the split cost structure to achieve comparable NMP compensation with significantly lower computational effort. The controller is implemented in real time using a hardware-in-the-loop (HIL) setup on a ZedBoard platform, with accurate data acquisition provided by an external ADC. Experimental results demonstrate that the proposed approach enhances voltage-tracking performance, eliminates overshoot and undershoot, reduces settling time by over 40%, and decreases computational effort by more than 80% compared to traditional FS-MPC methods.
Volume: 17
Issue: 2
Page: 1211-1220
Publish at: 2026-06-01

A novel single-stage high-voltage gain DC-DC boost converter for on-board PEV charging system

10.11591/ijape.v15.i2.pp610-619
Motepalli Siva Rama Ganesh , S. Sasikumar , B. Suresh Babu
Currently, the utilization of plug-in electric vehicles is quickly increasing in the vehicle industry owing to reduced costs of transportation, no need for fossil fuels, simple servicing, no fuel expense, and lower environmental effect compared to internal-combustion motor vehicles. In actuality, these motor vehicles function based on available battery energy that are charged by a utility-grid-supplied charging station. In this charging facility, a power converter defined on-board charger is generally used to charge the batteries, which improves the utility grid specifications by reducing the presence of harmonics and power factor regulation. An active two-stage load conditioning approach is commonly employed, however it doubles the conversion stages, requires larger switching components, complicated circuitry, large switching losses, and decreased efficiency, among other issues. To address these issues, a unique single-stage on-board EV charger has been used to regulate utility-grid specifications and seamless management of battery state-of-charge using a load-side DC-DC conditioning method. The major goal of this study is to propose a unique DC-DC boost converter that provides substantial voltage gain, consistent input current, minimal current ripples, and highest efficiency among numerous converters. The effectiveness of the proposed unique single-stage on-board EV charger has been evaluated through MATLAB/Simulink application, and the simulation findings have been presented.
Volume: 15
Issue: 2
Page: 610-619
Publish at: 2026-06-01

Comparison of differential evolution optimization technique with other techniques in solving multi-objective optimal power flow

10.11591/ijape.v15.i2.pp663-673
Vineeta S. Chauhan , Jaydeep Chakravorty , Siddharthsingh K. Chauhan
Optimal power flow (OPF) is a complex, non-linear optimization problem focused on determining the steady-state operating parameters of power systems for economic and secure operation. The challenge intensifies due to numerous system constraints that must be satisfied simultaneously. Although various evolutionary algorithms (EAs) have been applied to OPF in recent decades, these algorithms often use unconstrained search strategies. A common approach to handle constraint violations is the static penalty function, which penalizes infeasible solutions. However, selecting suitable penalty coefficients typically involves time-consuming trial and error, affecting overall performance. This study explores the integration of advanced constraint handling (CH) techniques within the differential evolution (DE) framework to enhance the performance of optimal power flow (OPF) solutions. In particular, it looks at three approaches: a hybrid ensemble of two CH techniques (ECHT), a self-adaptive penalty method (SP), and superiority of viable solutions (SF). The IEEE 30-bus and IEEE-57 bus benchmark systems are used to evaluate the efficacy of these techniques under a variety of OPF goals, including lowering emissions and generation costs, cutting power losses, and enhancing voltage stability. We took into consideration both weighted-sum multi-objective and single-objective formulations. The simulation outcomes indicate that the proposed CH-DE approaches deliver robust and competitive optimization results, demonstrating improved constraint handling capabilities when compared to contemporary methods in the literature.
Volume: 15
Issue: 2
Page: 663-673
Publish at: 2026-06-01

Adaptive telematics integration for enhanced EV fleet management and data acquisition

10.11591/ijape.v15.i2.pp808-817
Kavitha Kumaraswamy , Pasumarthi Usha , S. Ashok Kumar , Deekshitha Arasa , Suganthi Neelagiri
Telematic control units (TCUs) and on-board diagnostics (OBD-II) systems are commonly used to monitor vehicles and enable real-time communication. However, traditional OBD-II systems provide limited data, making it difficult to accurately detect faults and analyze performance, especially in hybrid, flex-fuel, and electric vehicles. A TCU is an embedded system installed in vehicles that enables wireless communication with external networks. This paper introduces a standalone device designed to seamlessly integrate with electric vehicles (EVs) by utilizing TCU capabilities to enhance data acquisition. The TCU uses a combination of sensors to collect important real-time vehicle data, such as GPS location, battery charge level, and voltage levels. The collected data is processed to generate meaningful insights that support decision-making and system optimization. The proposed system uses the TCU as a core component to transmit real-time data to a fleet management system (FMS). By providing enhanced data to the FMS, the system improves diagnostic accuracy, strengthens EV safety monitoring, and enables more efficient fleet management across diverse vehicle types. This approach allows deeper monitoring of EVs and improves overall fleet efficiency. The framework offers a cost-effective and scalable solution for advanced monitoring and optimization of electric vehicle fleets.
Volume: 15
Issue: 2
Page: 808-817
Publish at: 2026-06-01

Intelligent gear shifting in electric and hybrid vehicles: a CAN controller-based approach using SOC%

10.11591/ijape.v15.i2.pp581-589
Kalagotla Chenchireddy , Naresh Jella , Vadthya Jagan , R. Naveena Bhargavi , Shabbier Ahmed Sydu , Nunavath Praveen
The intelligent management of gear shifting in electric and hybrid vehicles (EVs and HEVs) is essential for optimizing energy efficiency, improving fuel economy, and enhancing driving comfort. Traditional gear shifting strategies, which are designed for internal combustion engine (ICE) vehicles, do not fully accommodate the unique dynamics of electric and hybrid powertrains. This paper proposes a novel approach for gear shifting in EVs and HEVs, integrating the state of charge (SOC%) of the battery as a critical input for decision-making. The proposed algorithm utilizes real-time data from the vehicle's controller area network (CAN), enabling seamless communication between the transmission control unit, battery management system, and powertrain control module. The algorithm adjusts gear shifting based on SOC%, vehicle speed, engine RPM, and throttle position, ensuring optimal use of the electric motor and internal combustion engine. At high SOC%, the algorithm prioritizes electric motor use to conserve fuel and extend battery life, while at lower SOC%, it switches to relying more on the combustion engine. The proposed method optimizes energy usage, enhances fuel efficiency, and prolongs battery life by adapting the shifting strategy to varying driving conditions.
Volume: 15
Issue: 2
Page: 581-589
Publish at: 2026-06-01

Techno-economic assessment of gas engine power plants penetration in a power grid

10.11591/ijape.v15.i2.pp535-545
Adelhard Beni Rehiara , Frederik Haryanto Sumbung
This paper presents a techno-economic assessment of integrating engine power plants into a power grid, using the snake optimization (SO) algorithm to solve the multi-objective optimal power flow (OPF) problem. The study focuses on four key objectives: minimizing fuel costs, reducing voltage deviation, enhancing voltage stability, and minimizing active power losses. Simulations conducted on the 38-bus of Manokwari grid system demonstrate that the SO algorithm significantly improved performance in all areas. Fuel costs were reduced to 2.003 million USD/h while maintaining a stable voltage profile. Voltage deviation was reduced to 0.5577 p.u., ensuring better voltage consistency across the grid. Voltage stability was enhanced with a minimized Lmax value of 0.0200 p.u., and active power losses were reduced to 0.3423 MW, reflecting a notable increase in system efficiency. These findings demonstrate the effectiveness of integrating gas engine power plants, which led to noticeable improvements in operational efficiency and grid stability.
Volume: 15
Issue: 2
Page: 535-545
Publish at: 2026-06-01

A new modified characteristic equation for optimal coordination of directional overcurrent relays

10.11591/ijict.v15i2.pp789-796
Neelakanteshwar Rao Battu , Surender Reddy Salkuti
The integration of distributed generation (DG) into power systems is increasing to meet the requirements of the utility system. Renewable energy sources are given priority due to their clean energy and high consistency advantages. Integration of DG into the system makes the bi-directional flow of current. Directional type overcurrent relays are usually used for protection of lines associated with bidirectional power flows. The installation of DGs, (especially, inverter-based) invites challenges to the existing protection schemes. A new modified characteristic equation-based approach is proposed in this paper to obtain the faster operational time of relays. The relay coordination scheme proposed in this paper is applied to an 8-bus test system integrated with the solar-based photovoltaic integrated distributed generator (PVIDG). The comparative analysis between the conventional and proposed approaches is done.
Volume: 15
Issue: 2
Page: 789-796
Publish at: 2026-06-01
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