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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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Simulation and experimental validation of modular multilevel converters capable of producing arbitrary voltage levels using the space vector modulation method Cuong, Tran Hung; Hieu, Pham Chi; Phuong, Pham Viet
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.pp5234-5248

Abstract

Modular multilevel converters (MMC) used forDC-AC energy conversion are becoming popular to connect distributed energy systems to the power systems. There are many modulation methods that can be applied to the MMC. The space vector modulation (SVM) method can produce a maximum number of levels, i.e., 2N+1, in which N is the number of sub- modules (SMs) per branch of the MMC. The SVM method can generate rules to apply to MMCs with any number of levels. The goal of this proposal is to easily expand the number of voltage levels of the MMC when necessary while still ensuring the quality requirements of the system. The proposed SVM method only selects the three nearest vectors to generate optimal transition states, therefore making the computations simpler and more efficient. This has reduced the computational load when compared to the previously applied SVM methods. This advantage ensures an optimal switching process and harmonic quality which will significantly improve the effectiveness of the proposed method was demonstrated through simulations on MATLAB/Simulink and experimental tests on 13-levels voltage MMC converter system using a 309 field-programmable gate array (FPGA) kit.
Fractional fuzzy based static var compensator control for damping enhancement of inter-area oscillations Zabaiou, Tarik; Benayad, Khadidja
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.pp5130-5143

Abstract

Over time, the insertion of flexible alternating current transmission system (FACTS) components in the power grid became primordial to maintain the overall system stability. This paper proposed an innovative approach called hybrid auxiliary damping control based wide-area measurements for the static var compensator (SVC). The presented controller is a fractional-order fuzzy proportional integral derivative (FOFPID). Its principal task is to damp inter-area low frequency oscillations (LFOs) and to improve the power system stability over the transient dynamics. Then, a metaheuristic grey wolf optimization (GWO) method is applied to adjust the controller’s gains. The SVC-based FOFPID control scheme is implemented in a two-area four- machine test system employing the rotor speed deviations of generators as input signal. A comparative analysis of the elaborated controller with the integer PID and the fractional-order PID (FOPID) is performed to emphasize its effectiveness under a three-phase perturbation. Furthermore, a load variation effect test is completed to attest the control strategy robustness. Based on dynamic simulation results and performance indices, the suggested controller shows its robustness and provides increased efficiency for inter- area oscillations damping.
Breast cancer detection using ensemble methods Ghazy, Alaa Mohamed; Nafea, Hala Bahy; Zaki, Fayez Wanis; Amer, Hanan Mohamed
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.pp5633-5646

Abstract

Breast cancer (BC) is one of the most common cancers among women. This study's framework is divided into three phases. Firstly, a majority hard voting approach is used to apply an ensemble classification mechanism as a decision fusion technique on the level of convolutional neural networks (CNNs). Five pre-trained CNNs—visual geometry group 19 (VGG19), densely connected convolutional network 201 (DenseNet201), residual network 50 (ResNet50), mobile network version 2 (MobileNetV2), and inception version 3 (InceptionV3)—are evaluated, using a data splitting test ratio represents 30% of the total dataset. Secondly, the classification results of the five CNNs are compared to get the best-performance model. Then, seven state of art machine classifiers—decision tree (DT), histogram-based gradient boosting classifier (HGB), support vector machine (SVM), random forest (RF), logistic regression (LR), gradient boosting (GB), and extreme gradient boosting (XGB)—are used to improve system performance on the feature vector that was taken from this CNN model. Thirdly, to improve robustness, a majority hard voting technique is used at the external classifier level using the highest four classifiers selected based on their accuracy. Several experiments were conducted in this study, and the results showed that ResNet50 produced the best results in terms of precision and accuracy. The majority voting mechanism improves the system’s accuracy to 99.85% through CNNs and to 100% through traditional classifiers.
Identification types of plant using convolutional neural network Notonegoro, Radityo Hendratmojo Jati; Hustinawaty, Hustinawaty
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.pp5827-5836

Abstract

Artificial intelligence can be implemented in fields that related to environmental education by providing knowledge for taxonomy which recognize and identify plant species based on its features. The variety of plant species that inhabit in a certain area allows many plant species to be found that look similar so that difficult to distinguish and recognize a particular plant. Convolutional neural network (CNN) often used in object detection, you only look once (YOLO), one of CNN’s object detections, could identify object in real time and obtained good performance and accuracy in several researched. However, no studies have ever identified a plant from its flowers, leaves, and fruits. Therefore, the main object of this paper is identified types of plant with CNN (YOLOv8). The YOLOv8 model with 0.01 learning rate, 32 batch size, stochastic gradient descent (SGD) optimizer obtained highest precision of 69.62% and F1 score of 61.22%, recall of 54.73%, mAP50 and mAP50 – 90 on the training data of 57.61% and 42.49%.
The evolution of routing in VANET: an analysis of solutions based on artificial intelligence and software-defined networks Sánchez, Lewys Correa; Parra, Octavio José Salcedo; Gómez, Jorge
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.pp5388-5400

Abstract

This study explored the evolution of vehicular ad hoc networks (VANET) and focused on the challenges and opportunities for routing in these dynamic environments. Despite advancements in traditional protocols, a significant gap persists in the ability to adapt to highly mobile environments with variable traffic, which limits routing efficiency and quality of service. Emerging technologies, such as artificial intelligence (AI) and software- defined networks (SDN), are discussed that have the potential to revolutionize the management of VANET. Machine learning can be used to predict traffic, optimize routes, and adapt routing protocols in real-time. Furthermore, SDN can simplify routing management and enable greater flexibility in network configurations. A comprehensive overview of the convergence of AI and SDN is presented, and the potential complementarities between these technologies to address routing challenges in VANET are explored. Finally, the implications of efficient routing in VANET for road safety, traffic management, and the development of new applications are discussed, and future research lines are identified to address challenges such as scalability, data security, and computational efficiency in vehicular environments.
Optimized passive and active shielding of magnetic induction generated by ultra-high-voltage overhead power lines Houicher, Salah-Eddine; Djekidel, Rabah; Bessidek, Sid Ahmed
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.pp5144-5161

Abstract

This paper presents computational modeling to assess and limit the magnetic induction levels emitted by an extra-high-voltage (EHV) overhead transmission line of 750 kV using the fundamental principle of Biot-Savart law in magnetostatics. An optimization technique based on the grey wolf optimizer (GWO) algorithm is employed to determine the appropriate location of the passive and active loop conductors, and the associated parameters to shielding to achieve better compensation of magnetic induction in an interest zone. The resulting magnetic induction of the ultra high voltage (UHV) overhead power line exhibits a crest value of 27.78 μT at the middle of the right-of-way, which can be considered unacceptable by strict protection standards. Generally, the magnetic compensation loops optimally located under the phase conductors of the power transmission system reduce the magnetic induction levels along the transmission line corridor. The passive loop attenuates the maximum magnetic induction by a rate of 29.7%. Therefore, the performance of the active loop is better; it provides a greater reduction with a rate reaching 53.24%. The simulation results were tested with those derived by the elliptical polarization process. An excellent concordance was found, which made it possible to ensure the adopted method.
Optimization of a level shifter integrated with a gate driver using TSMC 130 nm CMOS technology Guissi, Hicham; Slaoui, Khadija
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.pp5223-5233

Abstract

Modern electronic systems increasingly operate across multiple voltage domains, necessitating robust and efficient level shifter (LS) circuits to ensure reliable inter-domain communication. In low-power digital applications, minimizing propagation delay and transition time is critical for achieving high-speed and energy-efficient operation. This work presents a high-performance level shifter optimized for integration within Li-ion battery charger systems. The proposed design achieves a substantial reduction in propagation delays from 0.15 to 0.09062 ns while preserving signal integrity. When integrated with a gate driver, the overall structure exhibits a propagation delay of 0.20468 ns and a transition time of 0.014 ns, marking a significant improvement from the previous 0.036 ns. Furthermore, the proposed circuit occupies only 0.00039 mm² of silicon area, representing a 92% reduction compared to prior implementations (0.05 mm²). The complete design was implemented using Taiwan semiconductor manufacturing company (TSMC) 130 nm complementary metal–oxide– semiconductor (CMOS) technology, with both schematic simulation and layout carried out in the Cadence Virtuoso design environment. These results underscore the potential of the proposed solution for compact and high-efficiency system-on-chip (SoC) battery management applications.
Memoryless state-recovery cryptanalysis method for lightweight stream cipher – A5/1 Audumbar, Khedkar Aboli; Khot, Uday Pandit; Hogade, Balaji 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.pp5453-5465

Abstract

Cryptology refers to the discipline concerned with securing communication and data in transit by transforming it into an unintelligible form, thereby preventing interpretation by unauthorized entities. Cryptanalysis is the study and practice of analyzing cryptographic systems with the aim of uncovering their weaknesses, finding vulnerabilities and obtaining unauthorized access to encrypted data. A5/1 is a lightweight stream cipher used to protect GSM communications. There are two memoryless cryptanalysis techniques used for this cipher which are Golic’s Guess-and-determine attack and Zhang’s Near Collision attack. In this paper a new guessing technique called move guessing technique used to construct linear equation filter along with Golic’s guess and determine technique is studied. Two modifications in move guessing technique are proposed for recovery of internal states S0 and S1. Further, a novel algorithm is proposed to select the modification to get minimum time complexity for recovery of internal states S0 and S1. The proposed algorithm gives minimum time complexity of 229.3138 at t = 14 for recovery of S0 state and 243.246 for recovery of S1 at t = 22.
Integration of ultra-wideband elliptical antenna with frequency selective surfaces array for performance improvement in wireless communication Omar, Saleh; Baccouch, Chokri; Chibani, Rhaimi Belgacem
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.pp5515-5523

Abstract

The integration of frequency selective surfaces (FSS) with antennas has gained significant attention due to its ability to enhance key radio frequency (RF) performance parameters such as gain, directivity, and bandwidth, making it highly beneficial for modern wireless communication systems. In this work, we propose and investigate an ultra-wideband (UWB) elliptical antenna operating within the 5.2 to 10 GHz frequency range. To further improve its performance, we integrate the antenna with a 13×13 FSS array. The impact of the FSS on the antenna’s characteristics is analyzed, showing a remarkable gain enhancement from 2.6 dBi (without FSS) to 10.05 dBi (with FSS). These results confirm the effectiveness of FSS integration in optimizing UWB antenna performance, making it a promising approach for advanced wireless communication applications.
Improving network security using deep learning for intrusion detection Al-Shabi, Mohammed; Abuhamdah, Anmar; Alzaqebah, Malek
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.pp5570-5583

Abstract

As cyber threats and network complexity grow, it is crucial to implement effective intrusion detection systems (IDS) to safeguard sensitive data and infrastructure. Traditional methods often struggle to identify sophisticated attacks, necessitating advanced approaches like machine learning (ML) and deep learning (DL). This study explores the application of ML and DL algorithms in IDS. Feature selection techniques, such as correlation and variance analysis, were employed to identify key factors contributing to accurate classification. Tools like WEKA and MATLAB supported data pre-processing and model development. Using the UNSW-NB15 and NSL-KDD datasets, the study highlights the superior performance of random forest (RF) and multi-layer perceptron (MLP) algorithms. RF ensemble decision trees and MLP multi-layered architecture enable accurate attack detection, demonstrating the potential of these advanced techniques for enhanced network security.

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