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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 47 Documents
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Search results for , issue "vol 16, no 5: october 2026" : 47 Documents clear
Design/optimization of a CSRR-incorporated microstrip patch antenna using fuzzy logic-based a self-adaptative multi-objective genetic algorithm Mohamed Hamza Boulaich; Mohamed Ali Enassar; Abdellatif El Afia
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2698-2708

Abstract

Multi-objective evolutionary algorithms have become the most efficient ap- proach for solving real-world problems, where performance depends on the convergence speed, balance between exploitation and exploration, and popu- lation diversity. Self-adaptive techniques are effective for dynamically adjust- ing hyperparameter algorithms. This paper presents an automated methodology for designing and optimizing a complementary split-ring resonator (CSRR)- incorporated microstrip patch antenna (MPA) using a fuzzy adaptive multi- objective genetic algorithm (FAMOGA). A double-ring CSRR structure etched into the ground plane introduces multi-band resonance frequencies, extending antenna coverage to the multi-bands frequencies used in internet of things (IoT) and Wi-Fi applications. The algorithm simultaneously determines the optimal CSRR ring positions, minimizes the antenna size, and satisfies design con- straints. Fuzzy logic controllers evaluate the fitness function and dynamically adapt the crossover and mutation rates, guiding the genetic algorithm toward feasible, high-performance solutions. The FAMOGA converges to the 7th gen- eration with a population of four individuals. At the three target frequencies, the optimized antenna achieves reflection coefficients of −11.08 dB, −39.25 dB, and −30.61 dB , and realized gains of 3.53 dBi, 4.16 dBi, and 6.64 dBi at 2.422 GHz , 3.646 GHz , and 5.259 GHz , correspondingly. The simulation demonstrated that FAMOGA provides a computationally efficient approach for multi-band antenna design without manual parameter tuning.
Impact of optimal power flow on power quality in a low voltage three-phase network: application to a real case in Lubumbashi (DR Congo) David Milambo Kasumba; Guy Nkulu Wa Ngoie; Hyacinthe Tungadio Diambomba; Jean-Paul Katond Mbay; Bonaventure Banza WA Banza
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2304-2320

Abstract

Low-voltage distribution systems (LVDS) in rapidly growing Sub-Saharan African cities frequently experience severe phase imbalance, voltage deviations, and high technical losses due to overloaded and poorly balanced feeders. Despite the increasing availability of advanced optimization techniques, their application to real low-voltage networks in developing countries remains limited. This study investigates the impact of an unbalanced three-phase optimal power flow (OPF) framework on the power quality and operational performance of a real low-voltage distribution network located in Kamalondo, Lubumbashi (Democratic Republic of the Congo). The network model was parameterized using field measurements collected between September and December 2024. The optimization problem was formulated as a mixed-integer nonlinear programming (MINLP) model and solved using the interior point method implemented in Pandapower. The proposed framework simultaneously minimizes active power losses and mitigates phase imbalance while respecting voltage and thermal operating constraints. Simulation results demonstrate significant improvements in network performance. The minimum phase-to-neutral voltage increased from 198 V to 210 V, while the maximum voltage decreased from 232 V to 226 V, improving compliance with power quality standards. The maximum current phase was reduced by 15%, and total active power losses decreased by 30%. Furthermore, the voltage unbalance factor (VUF) was reduced from 8% to 3% through optimized phase allocation and power redistribution. These results demonstrate that unbalanced three-phase OPF constitutes an effective and practical solution for improving power quality, reducing technical losses, and enhancing the operational reliability of heavily loaded low-voltage networks in developing urban environments.
PMGHWO: A particle-guided adaptive grey wolf optimizer for large-scale traveling salesman problems Hanadi A. Alshawabkah; Ayman Algafaan
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2819-2835

Abstract

The traveling salesman problem (TSP) remains challenging for metaheuristic algorithms, especially as the number of cities increases. This study proposes the particle-guided adaptive grey wolf optimizer (PMGHWO), which combines grey wolf optimizer (GWO)-based leadership guidance with crossover, particle-guided perturbation, and adaptive local refinement. PMGHWO was evaluated on ten TSPLIB instances and compared with grey wolf optimizer (GWO), genetic algorithms (GA), particle swarm optimization (PSO), whale optimization algorithm (WOA), and Harris Hawks optimization (HHO) under the same experimental conditions. The proposed method achieved shorter average tours on all tested instances, with reductions of 18.75% compared with GWO, 19.77% with HHO, 23.30% with GA, 39.08% with WOA, and 57.51% with PSO. Friedman and Wilcoxon tests confirmed significant differences between PMGHWO and the compared methods. The ablation study showed that adaptive refinement had the strongest effect on solution quality, while the influence of particle-guided perturbation and crossover varied across the benchmark instances. Although PMGHWO required more computation than the original GWO, its runtime remained competitive with several of the other methods. Overall, the results show that PMGHWO is a promising approach for TSP optimization and warrants further evaluation on larger and more complex combinatorial problems.
A comparative study of standard PSO and adaptive fuzzy PSO for optimization Paul Olujide Adebayo; Waheed Babatunde Yahya; Rasheed Gbenga Jimoh
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2720-2730

Abstract

In engineering, complex optimization problems often require efficient and reliable solution methods. An example of a metaheuristic algorithm is particle swarm optimization (PSO), which is widely used for its simplicity and adaptability. However, PSO performance is highly sensitive to parameter settings. To tackle this issue, researchers have developed adaptive variants, such as adaptive fuzzy particle swarm optimization (AF-PSO), that adjust parameters in real time during operation. In this study, standard PSO and AF-PSO are compared using several benchmark functions, Sphere, Rosenbrock, Rastrigin, Ackley, Griewank, and Schwefel, along with the multi-objective test problems ZDT1–3 and DTLZ1–2. Standard PSO was run with fixed parameters values (w=0.729, c₁=c₂=1.494) for the computation. The AF-PSO variant of PSO parameters are adaptively tune with a fuzzy inference concept. Each algorithm is evaluated across multiple independent runs to evaluate performance consistency. Across the test set, AF-PSO achieved superior performance in terms of solution quality and convergence. Among the benchmark functions, Griewank (+74.1%), Ackley (+65.4%), and Sphere (+63.0%) each have performances more than the others. Generally, the findings suggest that adapting parameters during the search improves convergence and solution quality, particularly for complex multimodal optimization problems.
An impact of activation functions on CNN-Bi-LSTM SoC estimation for Li-Ion batteries Monica K. M.; Abhay A. Deshpande
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2357-2364

Abstract

Estimation of state of charge (SoC) in Li-ion batteries has been accomplished by many methods over couple of years. Every research in this domain majorly focusses on improving accuracy of estimation and some in exploring new algorithms. In this regard, we worked in analyzing the accuracy of estimation of SoC of sophisticatedly accurate techniques. A data-driven SoC estimation using deep neural network architectures (CNN) were collaborated with different activation functions like global pooling, exponential linear unit, Leaky rectified linear unit on convolutional neural network (CNN) and bidirectional-long short-term memory (Bi-LSTM) models. The performance of all these models was evaluated using quantitative error analysis and qualitative signal tracking studies to compare the estimated and actual SoC trajectory. This work consolidates the performances of different models and helps realize how best a fusion model works on the best accuracy in estimation.
Real-time multimodal fatigue detection using facial vision and alert integration via ESP32 for occupational health applications Andrés Enrique Rojas Primo; Alfredo Lazaro Gutierrez; Felix Pucuhuayla-Revatta
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2750-2768

Abstract

Early detection of work fatigue is a major challenge in industrial settings due to the lack of non-invasive, accessible, and low-cost systems capable of operating in real time. In this context, this research proposes a multimodal real-time fatigue detection system using facial vision and artificial intelligence, aimed at risk prevention and promoting occupational health. The system integrates geometric and behavioral parameters, such as eye aspect ratio (EAR), head tilt, and mouth opening, processed on a Raspberry Pi 5 using MediaPipe and a hybrid convolutional neural network (CNN) MobileViT model. Visual and audible alerts are managed by an ESP32 microcontroller using the message queuing telemetry transport (MQTT) protocol, while a graphical interface developed in Tkinter allows real-time monitoring of operator status. Experimental results, evaluated in a simulated work environment using AI-generated synthetic videos, show an accuracy greater than 97% and a latency of less than 250 ms, confirming the system's effectiveness in the early detection of signs of drowsiness and attention deficit. In conclusion, the proposal represents a non-invasive, scalable, and efficient solution that combines computer vision, deep learning, and the Internet of Things (IoT) to strengthen workplace safety and well-being.
Robust resource allocation in multi-cell UE-specific RIS-assisted D2D relay networks under imperfect CSI Kayode Popoola; Ayodeji Ajani; Stuart Nicholson; Muheeb Ahmed; Srilatha Narayangari Pamuri; Ibrahim Bala Alhassan
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2575-2594

Abstract

Device-to-device (D2D) communication enhances spectral efficiency but remains constrained by limited transmission range, underlay interference, and the half-duplex overhead of conventional relays. User equipment-specific reconfigurable intelligent surfaces (UE-RIS) offer a promising alternative by enabling passive beamforming to strengthen D2D links without additional spectrum consumption. However, existing studies typically assume perfect channel state information (CSI) and single-cell operation, limiting their applicability to practical deployments. This paper proposes a robust multi-cell resource allocation (RMRA) framework for UE-RIS-assisted D2D relay networks under imperfect CSI. A hybrid uncertainty model is adopted, combining statistical Gauss-Markov CSI errors for intra-cell links with bounded norm-ball uncertainty for inter-cell links. The joint optimisation of resource reuse, transmit power allocation, and RIS phase configuration is formulated as a stochastic mixed-integer nonlinear program that maximises network spectral efficiency while satisfying outage and quality-of-service constraints. To efficiently solve the problem, a three-stage algorithm is proposed comprising distance-pruned Hungarian assignment, robust power control using Bernstein-type inequality and S-procedure based semidefinite programming, and soft actor-critic (SAC) based passive beamforming. Simulation results show that RMRA achieves a 94% D2D access rate at light load and over 75% at full load, improves sum spectral efficiency by 34.7% and 70.2% over AF relaying and direct D2D, respectively, attains 118.5 bits/s/Hz/W energy efficiency, and maintains 30.2 bits/s/Hz under severe CSI uncertainty.
Development of a deep learning predictive model within a real-time business intelligence framework for goat milk supply sustainability Alusyanti Primawati; Nurfidah Dwitiyanti; Ari Irawan
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2641-2651

Abstract

This study aimed to develop a multidimensional data model and a prediction model for daily goat milk production within the framework of real-time business intelligence. The model was expanded to include predictions for goat milk production in Central Java, Indonesia. The Central Java daily goat milk production dataset is real data collected directly from dairy goat farms in Central Java, such as Kejar Farm in Tegal Regency, Alan’s Diary, and BK Farm in Pati Regency. The improved multidimensional data model successfully displays information hierarchically based on time, location, and farm unit dimensions through a BI dashboard. A prediction model was built by comparing the long short-term memory (LSTM) and Prophet models with several optimization scenarios. Model evaluation showed that Stacked LSTM was the best model with a 2-layer architecture. The LSTM evaluation metrics showed an RMSE value of 3.54, MAPE of 11.81%, and R2 of 0.67, while the Prophet model was unable to capture the short-term variations and non-linear fluctuations of daily milk production data. The research results confirmed that the deep learning approach is more adaptive to the characteristics of non-stationary time series data, even in limited data. The resulting model is ready to be integrated into the RTBI Framework.
Stator interturn short circuit fault identification in doubly-fed induction generator and its analysis using artificial neural network Vivek Kushwaha; Sanjay Kumar Maurya; Arvind Kumar Yadav
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2379-2392

Abstract

Inter-turn short circuit (ITSC) issues are a common electrical failure mainly caused by the deterioration of winding insulation in the machine over time. Failing to detect such issues early can lead to catastrophic consequences. This article investigates the interturn fault in the stator winding of a doubly-fed induction generator (DFIG) used in wind turbines. A flux linkage difference vector (FLDV) model is introduced in this study for fault detection. Additionally, an artificial neural network (ANN) model is proposed to classify these faults. Specifically, a short-circuit fault is induced in each phase of the stator winding, and the faults are classified by assigning different magnitudes to the respective phases. The ANN is trained to identify which phase contains an interturn fault, with output waveform amplitudes of 1, 2, or 3 corresponding to faults in phases "a," "b," and "c." If no fault is present, the waveform magnitude is designated as "0." This approach enables early fault diagnosis by analyzing waveform patterns, thereby preventing overheating caused by short circuits and avoiding severe, irreversible damage to the windings.
Calibration-guided score fusion for robust multimodal traffic anomaly detection Quang Hiep Do; Thien Tan Nguyen
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i5.pp2516-2525

Abstract

Multimodal traffic anomaly detection is affected by differences in visual and audio score ranges, temporal fluctuations, and unstable decision thresholds. This paper proposes a calibration-guided score fusion (CGSF) framework that processes video frames and audio spectrograms through separate reconstruction-based models. The resulting anomaly scores are temporally smoothed, normalized using validation data, and combined at the score level. A percentile estimated from normal validation samples is then used as the decision threshold. The framework was evaluated on the MAVD and DADA2000 datasets. On MAVD, CGSF achiev,,,,,,ed a ROC-AUC of 0.553, a PR-AUC of 0.082, and an F1-score of 0.129. It outperformed direct fusion in precision, recall, and F1-score, although the gain in ROC-AUC was small. Analysis on DADA2000 showed smoother temporal score behaviour after calibration and smoothing. The results indicate that CGSF mainly improves score comparability and threshold consistency rather than producing a large increase in detection accuracy. Its modular design also allows the visual and audio branches to be trained and updated independently.

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