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31,291 Article Results

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

ROVAA: Offline attendance automation using a voice–OCR-based 3-DOF robotic arm with Raspberry Pi

10.11591/ijra.v15i3.pp561-576
Rajanikanth Kashi Nagaraj , Archana Harihara Ranganatha , Surendra Hanumanthaiah Honnamachanahalli , Venu Manighatta Gopalakrishnappa
Conventional classroom attendance systems suffer from limitations in accuracy, hygiene, data privacy, and reliability in low-connectivity environments, whether they are manual, cloud-dependent, or single-modality systems. To address these gaps, this paper presents ROVAA, a low-cost, fully offline, AI-driven robotic attendance system in the classroom environment that uniquely integrates three complementary modalities: offline voice recognition, optical character recognition (OCR), and a 3- degrees of freedom (DOF) robotic arm controlled via inverse kinematics, an integration not demonstrated in prior work. The system operates on a Raspberry Pi 4 model B and employs the Vosk speech recognition model and Tesseract OCR for accurate offline processing. Audio and visual inputs are matched in real time to enable the arm to mark attendance at pre-calibrated positions on a touchscreen. Experimental validation under varied lighting and acoustic conditions yielded 96.2% speech recognition accuracy, 95.8% OCR accuracy, and 97.6% robotic arm precision, producing an overall system success rate of 92.8%, demonstrating that high reliability is achievable without cloud infrastructure. The system is designed for cost-effectiveness, data privacy, and scalability, making it suitable for resource-constrained environments such as rural schools and institutions with limited network access. It additionally serves as an educational platform for human–robot collaboration.
Volume: 15
Issue: 3
Page: 561-576
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

THD reduction in a 13-level multilevel inverter using the angle control technique

10.11591/ijpeds.v17.i3.pp1982-1993
Dewan Ashikur Rahaman , Aive Alamgir , Md Ashraf Hossain , Tapan Kumar Chakraborty , Raisul Islam Rafi , Raiyan Rahman Zihad , Md. Abdullah Al Mahmud , Samia Sarkar
When compared to a traditional multilevel inverter, multilevel inverters can produce switched waveforms with lower amounts of harmonic distortion. Due to their capacity to provide excellent output waveforms at lower switching frequencies and without distortion, multilevel inverters have recently attracted more attention. The dynamic voltage restorer's multilevel topology reduces the output waveform's harmonic distortion without causing inverter power output losses. This study examines the most widely used topologies to determine how the sinusoidal switching angle affects total harmonic distortion (THD%). Among the crucial multilevel topologies, the cascaded H-bridge was selected. Because it needed fewer parts than the others. Asymmetric was selected for observation and simulations of output voltages up to Fifteen levels for equal and sinusoidal angles using PSIM software after a review of the literature. Appropriate switching angles were maintained in this research. With fewer H-bridges, higher-level multilevel inverters have been manufactured.
Volume: 17
Issue: 3
Page: 1982-1993
Publish at: 2026-09-01

Novel bat algorithm for short-term peak load forecasting in Sulselrabar electricity system

10.11591/ijaas.v15.i3.pp1334-1346
Muhammad Rais , Rosihan Aminudin , Asnefi Asnefi , Andi Nur Putri , Irwan Syarif , Muhammad Ruswandi Djalal
This study addresses short-term load forecasting (STLF) in the South, Southeast, and West Sulawesi (Sulselrabar) power system, Indonesia, using interval type-2 fuzzy logic (IT2FL) optimized by the proposed novel bat algorithm (NBA). The NBA is employed to optimize the footprint of uncertainty (FOU) of the fuzzy membership functions for both antecedent (X and Y) and consequent (Z) variables. The forecasting model utilizes daily peak load data from the previous four days (d−4 to d−1) to predict the peak load of the forecast day (d). To evaluate the effectiveness of the proposed approach, NBA is benchmarked against particle swarm optimization (PSO), firefly algorithm (FA), and cuckoo search algorithm (CSA). The results demonstrate that the proposed IT2FL–NBA model provides the highest forecasting accuracy among the evaluated methods, achieving a mean absolute percentage error (MAPE) of 1.5650%. In comparison, IT2FL–PSO, IT2FL–FA, and IT2FL–CSA achieve MAPEs of 1.6353%, 1.6451%, and 1.6353%, respectively. For type-1 fuzzy logic (IT1FL), the NBA, PSO, FA, and CSA optimization methods produce MAPEs of 1.6668%, 1.6842%, 1.6805%, and 1.6768%, respectively. These findings demonstrate that optimizing the FOU of IT2FL using the proposed NBA significantly improves forecasting accuracy and provides a robust and reliable approach for STLF in power systems.
Volume: 15
Issue: 3
Page: 1334-1346
Publish at: 2026-09-01

Intelligent MPC for DFIG wind turbines

10.11591/ijpeds.v17.i3.pp2047-2057
Amira Lakhdara , Tahar Bahi , Amina Azizi , Amina Benabda
This paper presents and evaluates advanced control strategies to enhance power tracking and robustness in doubly fed induction generator systems operating under realistic and perturbed wind conditions. In addition to the conventional field-oriented control, we develop a model predictive control approach that determines the optimal rotor voltage vectors by minimizing a quadratic cost function, as well as a fuzzy-weighted model predictive control in which the cost weight is adjusted online based on the tracking error and its derivative. The dynamic models used accurately represent the key behaviors of the doubly fed induction generator and its rotor-side and grid-side converters. MATLAB/Simulink simulations are carried out using two wind scenarios: a smooth sinusoidal profile and a filtered stochastic profile, while a robustness test introduces variations in rotor parameters during operation. The results demonstrate that the fuzzy-weighted model predictive control achieves faster convergence, lower steady-state error, and improved robustness, all while maintaining reasonable converter effort and acceptable power quality.
Volume: 17
Issue: 3
Page: 2047-2057
Publish at: 2026-09-01

Real-time selective harmonic elimination in multilevel inverters using embedded metaheuristic optimization on PYNQ-Z2

10.11591/ijpeds.v17.i3.pp1914-1925
Taha Ahmad Hussein , Dahaman Ishak
This paper offers a structured methodology for regulating optimal control switching angles in multilevel inverters to achieve selective harmonic elimination (SHE) using embedded optimization algorithms on the PYNQ‑Z2 FPGA operating base. The proposed approach employs metaheuristic techniques including particle swarm optimization (PSO), genetic algorithm (GA), gray wolf optimization (GWO), slime mould algorithm (SMA), and whale optimization algorithm (WOA) to generate candidate switching angles across a wide range of modulation indices. For each modulation index, the most effective solution towards harmonic reduction and waveform quality is selected and implemented on the FPGA controller, enabling reliable real‑time operation with very low delay. Experimental validation of a 31‑level single‑phase inverter confirms the effectiveness of the method in eliminating selected harmonics and refining the fundamental output. Combining comparative algorithmic selection with FPGA‑based control demonstrates a systematic and efficient strategy for advanced inverter systems. A MATLAB/Simulink model is constructed to represent the 31-level inverter to enhance the practical aspect and study the frequency spectrum, where many harmonics that contribute to obtaining THD within the IEEE standards are eliminated. Also, the different types of losses are calculated based on the governing mathematical rates.
Volume: 17
Issue: 3
Page: 1914-1925
Publish at: 2026-09-01

Digital twin driven federated multi-agent intelligence for autonomous renewable forecasting and smart grid optimization

10.11591/ijpeds.v17.i3.pp2029-2038
Pushpa Sreenivasan , K. Gattaiah , N. Hemalatha , Radhey Shyam Meena , Mallareddy Adudhodla , V. S. Bhagavan
The rapid growth of renewable energy integration has increased the complexity of smart grid operation due to the intermittent nature of distributed energy resources and continuously varying load demand. Existing approaches often rely on centralized control or combine only selected intelligent technologies, limiting scalability, data privacy, and autonomous decision-making. This paper proposes a digital twin-driven federated multi-agent intelligence (DT-FMAI) framework that integrates virtual system synchronization, privacy-preserving distributed learning, and cooperative multi-agent control within a unified architecture. Digital twins continuously mirror physical grid assets, federated learning enables collaborative forecasting without sharing raw data, and intelligent agents coordinate energy management in real time. The framework was implemented in MATLAB/Simulink with TensorFlow Federated and evaluated using renewable generation, weather, battery, and load datasets. Results demonstrate a 15-25% reduction in forecasting error, 10-18% improvement in voltage regulation, 92-96% load-matching efficiency, and 12-20% higher energy efficiency. These outcomes demonstrate the potential of the proposed framework for scalable, secure, and intelligent renewable-integrated smart grid operation.
Volume: 17
Issue: 3
Page: 2029-2038
Publish at: 2026-09-01

Toward energy-efficient AGVs: A review of mechanical design contributions and optimization framework

10.11591/ijpeds.v17.i3.pp2070-2085
Amizi Noor , Khairur Rijal Jamaludin , Wan Zuki Azman Wan Muhamad , Faizir Ramlie , Nolia Harudin
Energy optimization in automated guided vehicles (AGVs) is critical for improving efficiency and sustainability in intralogistics systems, particularly in path planning and scheduling applications. Various optimization approaches have been proposed from operational, computational, and energy supply perspectives. Although energy supply technologies offer advantages in energy storage and recovery, their integration into AGV systems remains limited due to technological maturity and implementation challenges. Consequently, control-based approaches, including artificial intelligence, have become dominant optimization strategies. While these methods improve operational performance, they also increase computational energy demand, highlighting the need for alternative approaches to reduce baseline power consumption. This paper reviews AGV energy optimization studies while emphasizing the potential of mechanical design as an alternative optimization scope. The review reveals that mobility inefficiencies such as slip, skid, and instability are commonly mitigated through control strategies rather than resolved at their mechanical source. To address this gap, a Taguchi-based mechanical optimization framework is proposed for evaluating multiple mechanical factors and parameter levels. The framework aims to reduce baseline power demand and minimize reliance on computationally intensive control strategies, contributing toward more energy-efficient AGV systems.
Volume: 17
Issue: 3
Page: 2070-2085
Publish at: 2026-09-01

Spatiotemporal digital twin for city-scale EV charging infrastructure using LSTM-GNN fusion and resilience-driven optimization

10.11591/ijpeds.v17.i3.pp2271-2280
Deepa Somasundaram , B. Ravisankar , Chavvakula Janaki Devi , Ajay Babu Bathula , Sabarimuthu Muthusamy , K. Vinoth
The rapid growth of electric vehicles (EVs) requires intelligent and resilient planning of charging infrastructure under dynamic urban conditions. This paper proposes a city-scale spatiotemporal digital twin (SDT) that integrates LSTM-GNN fusion with resilience-driven hybrid optimization (GA-PSO-deep reinforcement learning) for adaptive EV infrastructure management. The LSTM model captures temporal variations in charging demand, while the graph neural network (GNN) learns spatial dependencies across charging stations, mobility networks, and grid components. Unlike existing approaches, the proposed framework incorporates power electronics-aware modeling, including charger power conversion system (PCS) efficiency, switching losses, and harmonic distortion constraints, ensuring realistic grid interaction. The digital twin also considers energy system metrics such as transformer loading, voltage deviation, and renewable energy variability, along with EV drive-cycle characteristics like fast charging and battery limits. Simulation results show that the proposed model improves demand prediction accuracy by 14-22%, reduces grid overload probability by 35%, lowers operational cost by 18%, and achieves improved power quality performance compared to conventional methods. The system maintains a high resilience index (>0.92) under stress scenarios. Overall, this work presents a holistic AI-driven digital twin framework that enhances grid stability, supports sustainable EV integration, and enables scalable deployment of future smart charging infrastructure.
Volume: 17
Issue: 3
Page: 2271-2280
Publish at: 2026-09-01

Deep learning-based intelligent islanding detection for grid-connected photovoltaic systems using convolutional neural networks

10.11591/ijpeds.v17.i3.pp1755-1767
Dondapati Ravi Kishore , T. Vijay Muni , K. Venkata Kishore , V. Suresh , S. Saahithi , Thandava Krishna Sai Pandraju , S. N. Chaitra , B. Logeshwary
The increasing integration of photovoltaic (PV) systems into smart grids requires fast and reliable islanding detection to maintain grid stability and operational safety. Conventional detection methods often face challenges such as delayed response, reduced accuracy, and large non-detection zones under varying operating conditions. This paper proposes an intelligent islanding detection method for grid-connected PV systems using advanced artificial intelligence and deep learning techniques. Electrical parameters including voltage, current, frequency, and power signals are analyzed using signal processing methods and classified through a convolutional neural network (CNN) model developed in MATLAB/Simulink. Simulation results demonstrate that the proposed AI-based approach achieves rapid and accurate detection of islanding events with improved sensitivity and reduced false detections compared to conventional techniques. The proposed framework enhances the reliability, safety, and protection performance of modern photovoltaic power systems integrated with smart grids.
Volume: 17
Issue: 3
Page: 1755-1767
Publish at: 2026-09-01

Short-circuit analysis and protection coordination of a 33 kV phosphate plant distribution network

10.11591/ijpeds.v17.i3.pp1768-1779
Cheikhani Abdel Kader , Bamba El Heiba , Mohamed El Mamy Mohamed Mahmoud , Beibah Gleigume , Abdel Kader Mahmoud
This study presents a short-circuit and protection coordination analysis of a 33 kV/0.4 kV industrial distribution network supplying a phosphate processing plant in Mauritania. Using ETAP software, the system was modeled to evaluate fault behaviors based on IEC 60909 and IEC 60255-151 standards. The study assesses the existing protection architecture and proposes an optimized digital protection framework. Simulation results indicate that the initial symmetrical short-circuit current at the 0.4 kV busbar reaches 96.5 kA, exceeding the 25-36 kA breaking capacity of the legacy equipment by nearly 300%. The analysis also investigates single-line-to-ground faults, revealing a fault current inversion phenomenon driven by the solid grounding configuration. To address these vulnerabilities, the study proposes upgrading to 150 kA-rated high-breaking-capacity switchgear and implementing advanced negative-sequence protection (ANSI 46) and transformer differential protection (ANSI 87T). This work provides a practical diagnostic framework for mitigating asymmetrical faults and optimizing protection coordination in heavy-duty industrial clusters.
Volume: 17
Issue: 3
Page: 1768-1779
Publish at: 2026-09-01
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