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31,207 Article Results

Dual view explainability-aware log preprocessing for robust anomaly detection toward ER-CyRIS

10.11591/ijeecs.v43.i3.pp871-879
Fathoni Mahardika , Ema Utami , Kusrini Kusrini , Ferry Wahyu Wibowo
Machine learning based intrusion detection can achieve strong benchmark performance yet remain fragile under operational telemetry changes. This paper proposes a dual-view, explainability-aware log preprocessing layer for robust anomaly detection toward ER-CyRIS. The novelty is the use of dynamic-token preservation together with feature stability score (FSS), which turns SHapley additive exPlanations (SHAP)-ranking stability into a preprocessing-level evaluation criterion rather than a post-hoc explanation only. The layer preserves structural log patterns and contextual dynamic tokens, and is evaluated through detection performance, noise degradation, and SHAP-ranking stability. A leakage-controlled ablation on HDFS, BGL, CICIDS2018, and UNSW-NB15 shows that the CICIDS2018 baseline reached F1 = 0.9999 but degraded by 68.1% for XGBoost and 93.9% for random forest under small Gaussian noise. Contextual preprocessing reduced degradation to 58.7%, 54.9%, and 53.6% in selected settings. FSS reached 100% for XGBoost on HDFS and CICIDS2018. The results show that preprocessing mitigates, but does not eliminate, operational brittleness.
Volume: 43
Issue: 3
Page: 871-879
Publish at: 2026-09-01

Automated recognition of Thai fabric patterns using transfer learning with ResNet-50

10.11591/ijeecs.v43.i3.pp847-856
Kittiya Poonsilp , Pijitra Jomsri , Dulyawit Prangchumpol , Thammarat Panityakul
Traditional Thai fabric patterns are valuable cultural heritage, but expert knowledge of these patterns is declining. This study uses a convolutional neural network (CNN) with transfer learning (ResNet-50) to classify traditional Thai fabric patterns automatically. We collected 961 images across 19 pattern categories, split into training (57%), validation (11%), and test (32%) sets. Using ImageNet pre-trained weights and progressive fine-tuning, the model achieved 99.68% test accuracy with macro-averaged F1-score of 99.35%. Ablation studies validated our approach: augmentation improved accuracy by 2.26%, fine-tuning outperformed frozen backbone by 4.84%, and backbone comparison showed ResNet-50 achieves higher F1-score than MobileNetV2 while MobileNetV2 offers 90% parameter reduction for mobile deployment. Controlled stress tests demonstrated robustness under image degradations typical of smartphone photography. Unlike previous studies using controlled conditions, our dataset includes real-world variations. These results show that transfer learning with small datasets can match expert-level pattern recognition for cultural heritage preservation.
Volume: 43
Issue: 3
Page: 847-856
Publish at: 2026-09-01

Low-cost BLDC drive with PBBO-tuned FOPID controller for enhanced speed regulation and torque ripple reduction

10.11591/ijeecs.v43.i3.pp956-964
Pandi Maharajan M. , Rohini G. , Ravindran Ramkumar , Dharani Kumar Narne
Torque ripple (TR) minimization in brushless DC (BLDC) motors has become a critical research focus due to its direct impact on drive performance, efficiency, and reliability. Conventional BLDC drives typically employ large DC-link capacitors, which increase system cost and weight and are highly sensitive to operating temperature, thereby reducing lifetime. To address these limitations, this work proposes a low-cost capacitor-based BLDC drive integrated with a torque ripple compensation (TRC) technique and optimized control using the probabilistic biogeography-based optimization (PBBO) algorithm. The PBBO method is employed to tune the parameters of a fractional-order proportional-integral-derivative (FOPID) controller, ensuring effective speed regulation and TR reduction. By probabilistically refining migration and emigration rates, PBBO enhances convergence and eliminates redundant species movements, leading to superior controller parameter optimization. Simulation studies validate the proposed PBBO-FOPID approach, demonstrating significant improvements in TR reduction and speed control compared to conventional controllers such as DGOA-FOPID and spider web-based controller (SWC). Results confirm that the PBBO-FOPID controller achieves smoother torque response, reduced ripple, and enhanced speed regulation, establishing it as a cost effective and high-performance solution for BLDC motor drives.
Volume: 43
Issue: 3
Page: 956-964
Publish at: 2026-09-01

Automated smart handbag with enhanced women's safety using cutting edge technology

10.11591/ijra.v15i3.pp669-677
Vijayaraja Loganathan , Dhanasekar Ravikumar , Ashish Ragavendra Nattamai Uthayakumar , Arulmurugan Nagarajan Renukadevi , Rishikeshwaran Balamurugan Rani , Rupa Kesavan
Women’s safety has been an area of concern, especially in public places where timely assistance cannot be provided. To mitigate this problem, this paper proposes an intelligent handbag-based women’s safety system that utilizes the concept of biometric identification, location tracking, and edge computing-based AI threat verification. The proposed system, unlike other traditional women’s safety devices that rely on GPS-GSM for emergency alerts and are more likely to send false alarms, utilizes fingerprint identification for secure and authorized use, along with YOLO v3 vision model on an ESP32-CAM for threat verification. Upon failure in the authentication process or threat detection, the system sends an SOS message with the current location via GSM with the help of GPS coordinates. The system achieves an emergency response time of 33 seconds, primarily limited by GPS acquisition delay. The results confirm the effectiveness and applicability of the proposed system in providing an intelligent emergency response system for women’s safety.
Volume: 15
Issue: 3
Page: 669-677
Publish at: 2026-09-01

URL-based phishing detection using XGBoost with engineered features

10.11591/ijeecs.v43.i3.pp908-927
Jawaher Alharbi , Manal Bayousef , Hind Almisbahi
URL-based phishing involves fake uniform resource locators (URLs) created by attackers to trick users into believing they are visiting a legitimate website and thereby steal their confidential information. While several powerful machine learning (ML) and deep learning (DL) studies exist to detect phishing, they still face limitations. Many studies rely on third-party intervention to extract features, which introduces delays that make them unsuitable for fast detection. Another limitation is that existing studies often use small datasets, and traditional features hinder models' ability to learn new phishing techniques, resulting in poor generalization. Therefore, developing new features is crucial to ensure that anti-phishing tools can keep pace with evolving phishing tactics. In addition, the existing studies do not report detection time, which is important for fast detection, and reduces methodological clarity. This paper aims to address these limitations by applying a neural network model and traditional ML classification algorithms to support browser-based phishing detection that balances high accuracy with fast detection. Our XGBoost model achieved 98% accuracy on the test set, utilizing 40 third-party-independent features. Additionally, we achieved an average response time of 0.026225 seconds and an average computation time of 1.6977×10⁻6 seconds per URL, which demonstrates competitive speed. We provided a table of features from recent studies, together with their documented sources, to support future research and analyze key URL-based features.
Volume: 43
Issue: 3
Page: 908-927
Publish at: 2026-09-01

Optimal robust control for self-balancing robot-based feedback linearization and Atom Search Optimization

10.11591/ijra.v15i3.pp519-528
Alaa Jumaah Al-Maiahy , Yahya Ghufran Khidhir , Adnan Jabbar Attiya , Hisham H. Jasim
In this paper, a robust control method is suggested for attitude control of the two-wheeled self-balancing robot by combining feedback linearization with sliding mode control techniques. The proposed method takes into account important challenges such as external disturbance and system uncertainty. Feedback linearization cancels the nonlinearities in the dynamics of the robotic system, while the sliding mode control handles the uncertainties and the external disturbance. The parameters of the proposed controller are selected by tuning the controller with the Atom Search Optimization algorithm. MATLAB is used to simulate the proposed controller. Simulation results indicate a good performance of the presented controller with high robustness compared with the proportional-integral-derivative (PID) controller. Moreover, the proposed method reduces the rise time by approximately 40% and 50% with respect to PID. These results illustrate the feasibility of the presented control method to be used for real-time implementation in autonomous robotic balancing systems.
Volume: 15
Issue: 3
Page: 519-528
Publish at: 2026-09-01

Design development and techno-economic assessment of a solar-powered three-row chickpea leaf nipping and collecting machine for sustainable farming

10.11591/ijape.v15.i3.pp1351-1365
Prashant Kadi , Basanagouda Ronad
Agricultural mechanization enhances productivity and reduces manual labour in labour-intensive regions. Chickpea, a major pulse crop in India, is widely cultivated in semi-arid regions of North Karnataka, particularly in Vijayapura district. In 2024, 500,000 hectares of pigeonpea and 1.9 million hectares of chickpea were sown in this region. In chickpea, leaf nipping is a vital agronomic practice that encourages branching and improves yields. However, this practice is done manually, making it highly labour-intensive, time-consuming, and less feasible for small-scale farmers. To address these challenges, the current research focuses on the design, development, and techno-economic evaluation of a solar-powered, three-row leaf-nipping and collecting machine. This machine is equipped with a 150 W solar PV system, six 15 W BLDC motors, and an adjustable cutter-head assembly for efficient operation. A techno-economic analysis was conducted to evaluate cutting force, torque, power consumption, and battery discharge characteristics, as well as the payback period, cost savings, and improvements in farm income. Field trials conducted on a 9-acre farm in Vijayapura, receiving an average annual solar irradiation of approximately 1950 kWh/m², demonstrated 350-400 kg of leaf collection and a 25-30% increase in crop productivity. The machine provides a cost-effective, eco-friendly, and scalable solution for small and medium-scale farmers.
Volume: 15
Issue: 3
Page: 1351-1365
Publish at: 2026-09-01

Development of 15/33 level constant and variable DC source inverter for different loading conditions

10.11591/ijape.v15.i3.pp1051-1063
Vijayaraja Loganathan , Dhanasekar Ravikumar , Ganesh Kumar Srinivasan , Deepak Balachandran Kasthuri
In this paper, a design of symmetric and asymmetric multilevel inverter (MLI) with few quantities of switch is presented. The structure can be able to operate with both symmetric and asymmetric sources. The presented model is capable of producing output levels of 15 with symmetric structure and 33 with asymmetric structure. The tendered circuit is constructed with 14 switches and 7 sources. The presented MLI can be placed in moderate-voltage applications such as: electrical machine drives. The circuit's switching sequences are framed by a detailed discussion from its operation. In MATLAB/Simulink, the inverter is simulated for resistive, resistive-inductive, and induction motor loads, and the results are portrayed. Also, the working of the inverter is monitored in terms of harmonics presence in the load signals. Additionally, the presented MLI is developed in real time to evaluate its performances. The results obtained from the real time inverter are found satisfactory.
Volume: 15
Issue: 3
Page: 1051-1063
Publish at: 2026-09-01

Performance analysis of un-equal rotor and stator length switched reluctance motor

10.11591/ijape.v15.i3.pp1190-1199
Mohammed Moanes Ezzaldean Ali , Nadheer A. Shalash
The performance of the SRM motor is influenced by several design parameters, including the lengths of the rotor stack and stator stack. Typically, the SRM is designed with equal rotor and stator lengths. However, there are some limited exceptions where the motor has a rotor that is longer or shorter than the stator; such a motor can achieve a reduction in one or more of the following: iron loss, copper loss, weight, inertia, and cost. To evaluate the performance of the SRM when the stator and rotor lengths are unequal, a 3D-model of the 6/4 SRM was developed using Ansys Motor-CAD software. In this work, two cases were investigated: the first case involved configurations where the stator was shorter than the rotor, and the second case involved configurations where the rotor was shorter than the stator. In both cases, other motor dimensions and variables were held constant. The simulation results show that certain performance indicators of the motor are not negatively affected, where they remain almost constant or even change positively, while other performance indicators are negatively changed, these changes are not significant when the differences between the stator and rotor lengths are within 10%. The cases of unequal length of stator and rotor can be considered as a new option that is manipulated to achieve the optimal design of the SRM, especially for low cost, low inertia, and high-speed applications.
Volume: 15
Issue: 3
Page: 1190-1199
Publish at: 2026-09-01

Systematic lamp replacement for energy efficiency improvement: a comparative luminous-efficacy analysis

10.11591/ijape.v15.i3.pp1275-1286
S. Jayachandra , Shaik Rafi Kiran
Achieving energy efficiency and establishing energy conservation for driving future energy is one of the prime concerns of any nation. Besides, the lighting system, one of the major contributors of energy consumption set ahead in fixing the aforesaid concerns to the extent possible. When looking at environmental benefits and energy saving opportunities, among several available luminaries, LEDs stand far superior to standard tubular fluorescent lamps (TFL). However, the power consumption pattern differs by its manufacturing constraints which are notified to the consumers through the star ratings and energy efficiency labels. Energy savings can be achieved by systematically replacing the conventional TFLs by means of energy efficient LED lamps (EEL). This paper focuses on the practicality of use concerning the scheme of lamp replacement to achieve better energy efficiency. To carry out these isometrics, Bureau of Energy Efficiency (BEE) approved TFL and LED lamps of different brands and star ratings were used. In line with methodology discussed in the manuscript, it is obvious that by replacing 36 W and 28 W standard TFLs through appropriate EELs, the respective annual energy savings of about 11-16 kWh and 7 kWh can be achieved. The results highlight that replacing the lamps randomly can be futile; however, a strategic process in the selection and proximate replacement ensures an optimal level of efficiency. This leads to an increased lumen output, longer lamp life, or both, which ultimately saves an appreciable amount of energy. The prospective research may focus on inter EEL replacement isometrics to achieve microtic energy efficiency.
Volume: 15
Issue: 3
Page: 1275-1286
Publish at: 2026-09-01

Intelligent fault diagnosis and protection in DG-connected systems using resistive superconducting fault current limiter and ANN-based detection

10.11591/ijape.v15.i3.pp1009-1022
Lekshmi R. Chandran , Ilango Karuppasamy , Manjula G. Nair
Ensuring reliable fault diagnosis and rapid recovery in distributed generator (DG)-connected distribution systems is critical, as the integration of DG sources significantly elevates fault current levels. This study proposes an integrated approach that combines a resistive superconducting fault current limiter (RSFCL) with an artificial neural network (ANN)-based intelligent fault diagnosis framework. The objective is to limit excessive fault currents while improving detection accuracy under varying network configurations. The RSFCL is strategically placed by analyzing fault current magnitude, voltage quality, and resistance value to achieve effective current limitation without compromising system stability. Meanwhile, the ANN employs symmetrical components of current and voltage as diagnostic features. To enhance robustness, correlated variables are identified and eliminated during feature selection, strengthening the model’s fault discrimination capability. Simulation results demonstrate that the optimal RSFCL placement reduces fault current contribution ratios by up to 82.16% under symmetrical fault conditions. The ANN-based fault detection model achieves a validation accuracy of 99.7%, outperforming conventional threshold-based methods by minimizing nuisance tripping and improving circuit breaker coordination. Overall, the combined RSFCL–ANN framework provides an effective and intelligent solution for fault diagnosis and protection in DG-integrated power systems.
Volume: 15
Issue: 3
Page: 1009-1022
Publish at: 2026-09-01

Voltage stability analysis of power transmission systems using multi-index framework of hybrid whale and particle swarm optimization technique

10.11591/ijape.v15.i3.pp1439-1457
Titus Terwase Akor , Theophilus Chukwudolue Madueme , Chibuike Peter Ohanu , Tole Sutikno
The persistent increase in grid collapse has necessitated the need for robust solutions for stability. Hybrid whale and particle swarm optimization (WAPSO) algorithm integrated with multi-index stability indices (MIS) has been applied to enhance voltage stability in this paper. The method is tested on the Nigeria 48-bus, 330 kV transmission system and simulated in MATPOWER embedded in MATLAB. The WAPSO algorithm is optimized with parameters w = 0.4, c₁ = 1.4 and c₂ = 1.5. The outcome achieved 98.84% reduction in mean MIS from 0.3959 to 0.0048 and a robustness index of 0.0051. This depicts low variability of 0.057 and 100% success rate across five dynamic scenarios explored such as gradual load growth with 98.79%, sudden spikes achieved 98.84%, cyclic fluctuations at 98.67%, renewable uncertainty at 98.74%, and N-1 contingencies at 98.84% improvement, respectively. Critical lines, 34-35 and 24-33 exhibit more than 99% MIS improvement, while weak lines, 21-22 and 24-40 achieved 85–87% enhancement, indicating areas for further reinforcement. The LSI and FVSI are reduced to 0.0048 and 0.1477 showing 56.79% improvement respectively as against the 38.9% MIS improvement obtained with the PSO-GA. This demonstrates WAPSO as a highly robust and adaptable technique offering a scalable solution for enhancing grid reliability under dynamic conditions.
Volume: 15
Issue: 3
Page: 1439-1457
Publish at: 2026-09-01

Novel approach for assessing the maximum load capacity of buses within a power transmission network

10.11591/ijape.v15.i3.pp1105-1116
Moussa Gonda , Arouna Oloulade , Richard Gilles Agbokpanzo , Maurel Richy Aza-Gnandji , Hassane Ousseyni Ibrahim , Cossi Télesphore Nounangnonhou , François-Xavier Fifatin , Adolphe Moukengue Imano
Achieving a balance between the satisfaction of energy requirements and adherence to environmental and social regulations stipulated in international agreements necessitates the rigorous management of existing electricity systems by electricity network operators, thereby ensuring that thermal and stability limits are not exceeded. Consequently, the assessment of load bus maximum capacity (LBMC) at any given load bus (LB) becomes imperative for ensuring the reliability and efficiency of electricity transmission networks. The extant literature on the subject of assessing LBMC in electrical networks either fails to take into account the combined effects on such networks of their various load points or, if it does, it is computationally intensive. The approach proposed in this paper involves a simplified method for assessing the LBMC for each of the LBs in the network. This is achieved to ensure that the combined effect of increasing the load on each of these buses does not compromise the load planning determined by a given network performance index, either during normal operation or in the event of a malfunction. The findings substantiate the efficacy of the proposed methodology, which facilitates the reliable estimation of LBMC. The reliability of this approach is ensured by the selection of a reliable performance indicator, which in our case is the complex stability index for transmission lines (CSITL).
Volume: 15
Issue: 3
Page: 1105-1116
Publish at: 2026-09-01

Explainable AI for harmonic fingerprinting and voltage sag diagnosis in decentralized power grids: trends, challenges and future directions

10.11591/ijape.v15.i3.pp1484-1498
Mohd Hatta Jopri , Tole Sutikno , Yacine Djeghader , Mohd Riduan Mohd Shariff , Wan Azlan Wan Zainal Abidin
The evolution of decentralized power grids has increased the complexity of power-quality monitoring, particularly harmonic fingerprinting and voltage sag diagnosis. Artificial intelligence improves disturbance detection and classification, yet black-box models limit transparency, engineering validation, and operator trust. This review synthesizes 108 selected studies on explainable artificial intelligence (XAI) for power-system diagnostics, focusing on SHapley Additive exPlanations (SHAP), local interpretable model-agnostic explanations (LIME), attention-based interpretability, visual analytics, and physics-informed learning. The review integrates harmonic fingerprinting with voltage sag diagnosis through their shared requirements for source attribution, temporal interpretation, physical consistency, and operator-oriented explanation. Four major deployment gaps are identified: data quality, computational latency, physical grounding, and trustworthiness. Future priorities include real-time embedded XAI, physics-informed neural networks, federated learning, standardized trustworthiness metrics, and adaptive model lifecycle management. The findings indicate that reliable autonomous diagnosis requires explainability to be integrated with predictive performance, electrical-system physics, computational efficiency, and field validation. This integration provides a stronger foundation for transparent, resilient, and trustworthy diagnostic systems in decentralized power grids.
Volume: 15
Issue: 3
Page: 1484-1498
Publish at: 2026-09-01

Multi-objective optimization and multi-criteria decision analysis of passive power filters for power quality improvement in arc furnace applications

10.11591/ijape.v15.i3.pp1200-1211
Alvaro Yassif Marca Yucra , Gastón Orlando Suvire , John Armando Morales
This article presents a multi-objective optimization methodology for the optimal tuning of passive power filters in steelmaking facilities that operate with electric arc furnaces (EAFs). These industrial loads are well-known for introducing severe harmonic distortion, voltage unbalance, and flicker into the electrical network, significantly degrading power quality and equipment performance. To address these challenges, a multi-objective optimization problem is solved using the non-dominated sorting genetic algorithm II (NSGA-II), which simultaneously minimizes three key power quality indices: total harmonic distortion (THD), total demand distortion (TDD), and voltage unbalance factor (VUF). In addition, a multi-criteria decision analysis (MCDA) technique is applied to rank and select the most balanced and robust solution in different EAF operating scenarios. Unlike conventional filter design methods that prioritize a single performance criterion or rely on static harmonic assumptions, the proposed approach accounts for the nonlinear and time-varying behavior of EAFs, ensuring robust performance under diverse operating conditions. A comprehensive case study based on a Bolivian steel plant illustrates the effectiveness of the optimization strategy. Results indicate reductions of 47.6% in THD, 33.5% in TDD, and 63.6% in VUF, clearly outperforming conventional design approaches and significantly improving overall power quality. This work highlights the potential of evolutionary multi-objective algorithms for enhancing passive filter performance in complex industrial environments with highly distorted and unbalanced power conditions.
Volume: 15
Issue: 3
Page: 1200-1211
Publish at: 2026-09-01
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