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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 6,301 Documents
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A new algorithm for quality-of-service improvement in mobile ad hoc networks Ali, Hanafy M.; El-Kabbany, Adel F.; Hassan, Yahia B.
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5466-5483

Abstract

The quality of service (QoS) in mobile ad hoc networks (MANETs) plays a crucial role in optimizing overall network resource utilization. MANET routing protocols, fundamental to QoS, demand adaptive and swift solutions for efficient path searching. In this context, our paper introduces a novel algorithm based on MANETs, employing a hybrid approach that combines ant colony optimization (ACO) with hybrid multipath quality of service ant (HMQAnt) routing protocols. Our algorithm emphasizes bandwidth optimization as a pivotal factor for providing effective paths. By incorporating bandwidth as a significant parameter in the MANETs algorithm, we aim to enhance its overall properties. The proposed routing protocol, focusing on bandwidth optimization, is anticipated to improve the delivery of total network traffic. Evaluation of the algorithm's performance is conducted through QoS metrics, which are overhead, end-to-end delay, and jitter, throughputs, utilizing a MATLAB simulator. Simulation results indicate that our proposed routing protocol holds a distinct advantage compared to ad hoc on-demand distance vector (AODV), destination- sequenced distance (DSDV), dynamic source routing (DSR), and hybrid ant colony optimization-based (ACO) routing protocol called (ANTMANET) algorithms.
A hybrid DMO-CNN-LSTM framework for feature selection and diabetes prediction: a deep learning perspective Alsmadi, Mutasem K.; Jaradat, Ghaith M.; Alsallak, Tariq; Alzaqebah, Malek; Jawarneh, Sana; Alfagham, Hayat; Alqurni, Jehad; Badawi, Usama A.; Almusfar, Latifa Abdullah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5555-5569

Abstract

The early and accurate prediction of diabetes mellitus remains a significant challenge in clinical decision-making due to the high dimensionality, noise, and heterogeneity of medical data. This study proposes a novel hybrid classification framework that integrates the dwarf mongoose optimization (DMO) algorithm for feature selection with a convolutional neural network–long short-term memory (CNN-LSTM) deep learning architecture for predictive modeling. The DMO algorithm is employed to intelligently select the most informative subset of features from a large-scale diabetes dataset collected from 130 U.S. hospitals over a 10-year period. These optimized features are then processed by the CNN-LSTM model, which combines spatial pattern recognition and temporal sequence learning to enhance predictive accuracy. Extensive experiments were conducted and compared against traditional machine learning models (logistic regression, random forest, XGBoost), baseline deep learning models (MLP, standalone CNN, standalone LSTM), and state-of-the-art hybrid classifiers. The proposed DMO-CNN-LSTM model achieved the highest classification performance with an accuracy of 96.1%, F1-score of 94.6%, and ROC-AUC of 0.96, significantly outperforming other models. Additional analyses, including confusion matrix, ROC curves, training convergence plots, and statistical evaluations confirm the robustness and generalizability of the approach. These findings suggest that the DMO-CNN-LSTM framework offers a powerful and interpretable tool for intelligent diabetes prediction, with strong potential for integration into real-world clinical decision-support systems.
Devanagari optical character recognition of printed text P., Malathi; Pujari, Chandrakanth G.
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5914-5923

Abstract

Hundreds of native languages and scripts are making their way on digital platform to sustain in multiple data formats. Optical character recognition (OCR) is one such dimension where the low resource languages are yet to find their stability. Devanagari OCR is one such low resource script problem to be dealt with, though it is the fourth widely used global script. Recent works carried on OCR have focused on word level approach and face challenges of spiraling complexity as language alphabet set size crosses hundreds. Most of these OCR works are done in constrained environment, with huge datasets and large computational resources. As a result, effective benchmark evaluation of the works against one another on defined metrics is scarce. Aim here is to explore character level Devanagari OCR with printed text images as input. Pattern recognition (PR) principles for diacritic classification and convolutional neural network (CNN) for base character classification are used. word error rate (WER) of 24.47% is attained. However, the training dataset complexity is reduced by 4.35 times. The ten multi class models, training time range from 45 minutes to 2.5 hours. Further the models can be trained in parallel to complete the training process in 3-4 hours. Thus, the approach used for text classification facilitates the Devanagari OCR solution to be offered in off-the-shelf computing devices.
Enhancing supply chain agility with advanced weather forecasting Zeroual, Imane; El Bouhdidi, Jaber
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5904-5913

Abstract

This article presents a solution that leverages artificial intelligence techniques to enhance urban freight transportation planning and organization through the integration of weather forecasting data. We identify key challenges in the current urban logistics landscape and introduce a range of machine learning models designed to predict delivery delays. Logistic regression serves as the foundational model, analyzing historical delivery data in conjunction with weather conditions to assess the likelihood of delays, thus enabling informed decision-making for companies. Additionally, we evaluate two other machine learning models to determine the most effective approach for our specific context, assessing their accuracy and capacity to deliver actionable insights. By improving the predictive capabilities of urban freight systems, this research aims to streamline operations, reduce costs, and enhance overall service reliability, contributing to more efficient and resilient urban transportation networks.
Improving electrical load forecasting by integrating a weighted forecast model with the artificial bee colony algorithm Shabri, Ani; Samsudin, Ruhaidah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5854-5862

Abstract

Nonlinear and seasonal fluctuations present significant challenges in predicting electricity load. To address this, a combination weighted forecast model (CWFM) based on individual prediction models is proposed. The artificial bee colony (ABC) algorithm is used to optimize the weighted coefficients. To evaluate the model’s performance, the novel CWFM and three benchmark models are applied to forecast electricity load in Malaysia and Thailand. Performance is assessed using mean absolute percentage error (MAPE) and root mean square error (RMSE). The experimental results indicate that the proposed combined model outperforms the single models, demonstrating improved accuracy and better capturing seasonal variations in electricity load. The ABC algorithm helps in finding the optimal combination of weights, ensuring that the model adapts effectively to different forecasting scenarios.
Nonlinear backstepping and model predictive control for grid-connected permanent magnet synchronous generator wind turbines Kassoumi, Adil El; Lamhamdi, Mohamed; Mouhsen, Ahmed; Fdaili, Mohammed; Aboudrar, Imad; Mouhsen, Azeddine
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5091-5105

Abstract

This research investigates and compares two nonlinear current-control strategies, backstepping control (BSC) and finite control set model predictive control (FCS-MPC) for machine-side and grid-side converters in grid-connected direct-drive permanent magnet synchronous generator (DD-PMSG) wind turbines. Addressing the control challenges in wind energy systems with varying speeds, the study aims to determine which strategy offers superior performance under identical operating conditions. The nonlinear BSC regulates stator and grid currents using Lyapunov-based techniques, while FCS-MPC leverages model predictions to select optimal switching states based on a cost function. A comprehensive simulation using MATLAB/Simulink is conducted, analyzing each controller’s transient behavior, steady-state response, torque ripple, and power quality total harmonic distortion (THD). Results show that FCS-MPC achieves faster convergence, lower overshoot, and superior power quality compared to BSC, though it requires higher computational resources. Statistical validation supports the robustness of FCS-MPC under parameter uncertainties. This work contributes a structured comparison of advanced nonlinear strategies for PMSG-based wind turbines and provides a foundation for future implementations in real-time embedded control systems. Future directions include experimental validation and hybrid model predictive controller- artificial intelligence (MPC-AI) control frameworks.
Faster R-CNN implementation for hand sign recognition of the Indonesian sign language system (SIBI) Adhiatma, Paulus Lestyo; Prakisya, Nurcahya Pradana Taufik; Ariyuana, Rosihan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5759-5769

Abstract

The Indonesian sign language system (SIBI) is the authorized sign system in Indonesia that the deaf society uses to convey in Indonesian. However, its use still needs to be expanded and more widespread in the community, causing difficulties in communication for hard-of-hearing people. The product of deep learning technologies such as faster region-based convolutional neural network (Faster R-CNN) in object recognition has the potential to help improve communication between deaf people and the general public. This research will implement the Faster R-CNN algorithm with three different residual network (ResNet) architectures (50, 101, and 152) for SIBI recognition. The comparison of the faster R-CNN algorithm with different architectures is also conducted to identify the best architecture for SIBI recognition, and the results are evaluated using accuracy, precision, recall, and F1-score metrics from confusion matrix calculation and execution time. Faster R-CNN model with ResNet-50 architecture showed the best and most efficient performance with accuracy, recall, precision, and F1-score metrics of 96.15%, 95%, 93%, and 94%, respectively, and an execution time of 36.84 seconds in the testing process compared to models with ResNet-101 and ResNet-152 architectures.
Enhancing Segway scooter optimization for adaptive stability with proportional derivative control system Artanto, Dian; Pranowo, Ignatius Deradjad; Sutyasadi, Petrus
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5266-5275

Abstract

This study presents a locally manufactured Segway scooter utilizing a proportional derivative (PD) control system for adaptive stability under load variations. The system employs a lookup table correlating PD parameters with user weight categories (50–60 kg, 60–70 kg, 70–80 kg). Constructed from lightweight steel and powered by a 24 V lithium-ion battery, the prototype supports up to 85 kg while maintaining energy efficiency. Experimental results confirm the PD controller’s effectiveness in achieving stability with minimal oscillation across all tested loads. It sustains a steady- state error below 0.5° (50–60 kg) and under 1° (70–80 kg), with oscillations under 7° and recovery from 35° disturbances. Compared to complex methods like genetic algorithms or fuzzy logic, the PD system offers greater simplicity and cost-efficiency. It matches fuzzy-PID stability while reducing computational overhead by 20–40% and power consumption to 10–20 W/s, outperforming conventional PID in dynamic load adaptability. The integration of PD control with locally sourced materials underscores the solution’s sustainability and practicality, providing a scalable, energy- efficient paradigm for personal transportation with robust performance across varying conditions.
Plant disease detection and classification: based on machine learning and Eig(Hess)-co-occurrence histograms of oriented gradients Mehdi, El Aroussi El; Latifa, Barakat; Hassan, Silkan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5336-5346

Abstract

Agricultural districts provide high-quality food and contribute substantially to economic growth and population support. However, plant diseases can directly reduce food production and threaten species diversity. The use of precise, automated detection techniques for early disease identification can improve food quality and mitigate economic losses. Over the past decade, numerous methods have been proposed for plant disease classification, and in recent years the focus has shifted toward deep learning approaches because of their outstanding performance. In this study, we employ the Eig(Hess)-co-occurrence histograms of oriented gradients (CoHOG) descriptor alongside pre-trained machine-learning models to accurately identify various plant diseases. We apply principal component analysis (PCA) for dimensionality reduction, thereby enhancing computational efficiency and overall model performance. Our experiments were conducted on the popular PlantVillage database, which contains 54,305 images across 38 disease classes. We evaluate model performance using classification accuracy, sensitivity, specificity, and F1-score, and we perform a comparative analysis against state-of-the-art methods. The findings indicate that the approach we proposed achieves up to 99.83% accuracy, outperforming existing models. Additionally, we test the robustness of our method under various conditions to highlight its potential for real-world agricultural applications.
Detecting lung nodules in computed tomography images based on deep learning Hien, Lam Thanh; Tu, Le Anh; Hieu, Pham Trung; Duc, Pham Minh; Nang, Nguyen Van; Toan, Do Nang
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5604-5615

Abstract

Lung cancer is currently recognized as one of the most dangerous cancers, with high mortality rate. In order to deal with lung cancer, an important task is to detect lung nodules early to improve patient survival rates, and computed tomography (CT) scans are crucial data for this. In this research, we propose a deep learning-based method for detecting lung nodules in the CT images with the goal of increasing the likelihood of nodule appearance in the input data of the network, making it easier for the model to focus on relevant areas while reducing noise from areas unrelated to the result. Specifically, we propose a simple lung region segmentation process and optimize the hyperparameters of the faster region-based convolutional neural networks (faster R-CNN) model based on the analysis of nodule characteristics in CT image data. In our experiments, to evaluate the effectiveness of our proposals, we conducted tests on the standard LUNA16 dataset with different backbone configurations for the model, namely ResNet50, ResNet50v2, and MobileNet. The best results achieved were 0.86 mAP50 and 0.91 Recall for the Resnet50, and 0.84 mAP50 and 0.94 Recall for the ResNet50v2. These impressive outcomes underscore the success of our method and establish a robust basis for future studies to further integrate AI into healthcare solutions.

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