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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 111 Documents
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Search results for , issue "Vol 15, No 2: April 2025" : 111 Documents clear
Ensemble of winning tickets: pruning bidirectional encoder from the transformers attention heads for enhanced model efficiency Smarts, Nyalalani; Selvaraj, Rajalakshmi; Kuthadi, Venu Madhav
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2070-2080

Abstract

The advanced models of deep neural networks like bidirectional encoder from the transformers (BERT) and others, poses challenges in terms of computational resources and model size. In order to tackle these issues, techniques of model pruning have surfaced as the most useful methods in addressing the issues of model complexity. This research paper explores the concept of pruning BERT attention heads across the ensemble of winning tickets in order to enhance the efficiency of the model without sacrificing performance. Experimental evaluations show how effective the approach is, in achieving significant model compression while still maintaining competitive performance across different natural language processing tasks. The key findings of this study include model size that has been reduced by 36%, with our ensemble model reaching greater performance as compared to the baseline BERT model on both Stanford Sentiment Treebank v2 (SST-2) and Corpus of Linguistic Acceptability (CoLA) datasets. The results further show a F1-score of 94% and 96%, respectively, and accuracy scores of 95% and 96% on the two datasets. The findings of this research paper contribute to the ongoing efforts in enhancing the efficiency of large-scale language models.
Development and analysis of symmetric encryption algorithm Khompysh, Ardabek; Dyusenbayev, Dilmukhanbet; Maxmet, Muratkhan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1900-1911

Abstract

This paper introduces a new block encryption algorithm designed for the cryptographic protection of data. The paper introduces and explains a newly devised exponentiation modulo (EM) transform method, utilized to obtain the S-block, an essential element within the presented algorithm. A method of optimizing the choice of keys and increasing the efficiency of calculation was also used. It is proposed that incorporating characteristics of cryptographic primitives functioning within the Galois field into the algorithm can lead to favorable outcomes. To increase the encryption algorithm's speed, non-positional polynomial notation systems and a working base index table are used. The paper discusses the implementation of an encryption algorithm in C++ and examines the statistical characteristics of the resulting ciphertexts. For experimental testing of statistical safety, a set of statistical tests by National Institute of Standards and Technology (NIST) and D. Knuth was used. Furthermore, the resulting S-box was examined using linear, differential, and algebraic cryptanalysis techniques. In the future, this proposed S-box will be implemented in the encryption algorithm being developed for the preliminary encryption of confidential data.
Experimental study of performance and prototype of elliptical altitude detection based on global navigation satellite system Zulfikar, Dwi Aji; Nurkarya, Yoyok; Setiyadi, Johar; Kurniawan, Endro Sigit; Carudin, Carudin; Suhadi, Suhadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1720-1734

Abstract

Global navigation satellite system (GNSS) is a multi-satellite-based navigation system, in the GNSS positioning process involves several navigation satellites such as global positioning system (GPS) which is a navigation system to bring up more observation data so that it is very useful when determining the desired parameters in a real-time data processing. In the research, an experimental study is used to determine land subsidence which is one of the vertical deformations of the earth's crust as a consequence of crustal dynamics. The result of the analysis is raw position data with the average method of detecting the height of the ellipsoid in the XYZ location area. Data collection is done by observation using the absolute method for one hour for position and fifteen days of observation for height. While the equipment used is u-blox Neo-7M, MCU TTL RS-485 module, ESP32-S Dev Kit V1 module, memory card module and real time clock (RTC). The results of the observation validation analysis are i) GPS-1 Easting 1.09 m and Northing 1.08 m, GPS-2 Easting 1.19 m and Northing 1.32 m, GPS-3 Easting 0.54 m and Northing 0.64 m while GPS-AVG GPS Easting 0.56 m and Northing of 0.64 m, ii) The results of the GPS-1 ellipsoid height analysis are 3.76 m, GPS-2 4.28 m, GPS-3 of 3.69 m, and iii) GPS AVG of 3.01 m.
Improved colored cubes teaching kit in representing and simplifying Boolean logic functions Elewah, Ibrahim; Jalaleddine, Sara; Tbeileh, Omar A.; Yainnakou, Stavros; Kamzoul, Azzam; El-Morhabi, Sara; Tabet, Maria Gergi; Hafez, Rania
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1446-1454

Abstract

This work presents, explains, and discusses a colored variables cubes teaching kit. The cubes teaching kit is designed based on the cubes method that was developed to graphically simplify the Boolean logic functions with three, four, five, and six variables. This renewed method is developed to overcome the limitation of the conventional Karnaugh maps method in terms of simplifying Boolean functions with a maximum of four variables only. Students can use the teaching kit to place each cube in its right position. Based on the label of each cube, students will be able to figure out the function minterm number. After that, the students will sort the cubes to represent the function. Eventually, the students will develop the competency to check the cubes adjacency, and this will lead them to formulate simplified Boolean expressions. Students' engagement is expected to improve when theoretical knowledge is implemented using a three-dimensional physical cube teaching kit. The aim of this work is that both the educators and students, in engineering and engineering technology programs, can benefit from the adaptation and even more from the modification of the proposed approach to facilitate the achievement of their learning objectives.
Design and experimental validation of a single phase grid tied inverter for residential low power applications Mousmi, Ali; Schellmanns, Ambroise; Elghadhi, Salem; Desouches, Quentin
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1372-1384

Abstract

This paper presents the design and control of a single phase grid tied inverter intended for low power applications in residential sector as part of smart grid environments or solar photovoltaic source integration. The total cost of the converters used in such applications involving low power rates, generally lower than 1 kW, cannot afford expensive components or complex techniques, so, the optimization of the power circuits as well as the simplification of the control algorithms are necessary to meet the specifications. In this purpose, this work presents the design steps of a single phase grid tied inverter including the structure choice, a synchronization algorithm based on the grid voltage zero crossing method, and the algorithm to control the injected power. Different experimental tests have been carried out and show the good performance of the converter which meets the requirements in terms of total harmonic distortion and efficiency.
A new trade-off approach to photovoltaic power smoothing Galarza, Jose; Condezo, David; Calcina, Armando; Saenz, Bartolome
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1262-1273

Abstract

Renewable energy sources, such as grid-connected photovoltaic systems, present a challenge due to the intermittent nature of solar irradiance. In this research, the authors have suggested an approach for irradiance profile smoothing based on different sample sizes of measurements. The analysis has been performed using experimental measurements based on the irradiance and temperature of the series smart pyranometers (SMP3) pyranometer manufactured by Kipp & Zonen. The power smoothing uses a variable window size to compensate for a flat and intermittent profile. The moving standard deviation has been used to evaluate the window size in different ranges. The evaluation focuses on days with higher irradiance variability and the results for the suggested approach show better performance than the smoothing process with a large or small window. The suggested approach reaches an autocorrelation index of 0.988, which is more than 6% with respect to the constant window size. Furthermore, the analysis shows that more than 50% of the data have variability in the moving standard deviation within a range of 1% and 25%, as with this approach a flexible window size helps the smoothing process. An estimation of photovoltaic power between constant and variable window size has been evaluated and a difference of less than 1.5% has been obtained.
Control to compensate reactive power at medium voltage load nodes to improve performance and load voltage stabilization based on modular multilevel converter Cuong, Tran Hung; Tung, Nguyen Nhat; Anh, An Thi Hoai Thu
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1463-1472

Abstract

This paper will present a reactive power control method for medium voltage power grids based on the modular multilevel converter (MMC) structure. In particular, the MMC converter applies control algorithms to operate as a D-STATCOM device. A proportional–integral (PI) controller combined with an improved nearest level modulation (NLM) method performs the system control process. The purpose is to create sinusoidal voltage levels on the alternating current (AC) side to generate or absorb reactive power according to load requirements. This will ensure that the amount of reactive power for the load node is always within the allowable value and improve voltage quality, increasing the power factor for the load. Verifying and evaluating results are performed on MATLAB/Simulink software.
Combined-adaptive image preprocessing method based on noise detection Shamshanovna, Razakhova Bibigul; Amangeldy, Nurzada; Kassymova, Akmaral; Kudubayeva, Saule; Kurmetbek, Bekbolat; Barlybayev, Alibek; Gazizova, Nazerke; Buribayeva, Aigerim
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1584-1592

Abstract

The image processing method involves several critical steps, with image preprocessing being particularly significant. Segmentation and contour extraction on digital images are essential in fields ranging from image recognition to image enhancement in various recording devices, such as photo and video cameras. This research identifies and analyzes the main drawbacks of existing segmentation and contour extraction methods, focusing on object recognition. Not all filters effectively remove noise; some may clear areas of interest, affecting gesture recognition accuracy. Therefore, studying the impact of image preprocessing on gesture recognition outcomes is crucial for improving pattern recognition performance through more efficient preprocessing methods. This study seeks to find an optimal solution by detecting specific features during the preprocessing stage that directly influence gesture recognition accuracy. This research is a key component of the AP19175452 project, funded by the ministry of science and higher education. The project aims to create automated interpretation systems for Kazakh sign language, promoting inclusivity and technological innovation in communication aids. By addressing these challenges, the study contributes to the development of more robust and adaptive image preprocessing techniques for gesture recognition systems.
Enhanced automated Alzheimer’s disease detection from MRI images by exploring handcrafted and transfer learning feature extraction methods Menad, Touati; Bentoumi, Mohamed; Larbi, Arezki; Mimi, Malika; Ahmed, Abdelmalik Taleb
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1557-1571

Abstract

The rising prevalence of Alzheimer’s disease (AD) poses a significant global health challenge. Early detection of AD enables appropriate and timely treatment to slow disease progression. In this paper, we propose an enhanced procedure for automated AD detection from magnetic resonance imaging (MRI) images, focusing on two primary tasks: feature extraction and classification. For feature extraction, we have investigated two categories of methods: handcrafted techniques and those based on pre-trained convolutional neural network (CNN) models. Handcrafted methods are preceded by a preprocessing step to improve the MRI image contrast, while the pre-trained CNN models were adapted by utilizing only a part of the models as feature extractors, incorporating a global average pooling (GAP) layer to flatten the feature vector and reduce its dimensionality. For classification, we employed three different algorithms as binary classifiers to detect AD from MRI images. Our results demonstrate that the support vector machine (SVM) classifier achieves a classification accuracy of 99.92% with Gabor features and 100% with ResNet101 CNN features, competing with existing methods. This study underscores the effectiveness of feature extraction using Gabor filters, as well as those based on the adapted pre-trained CNN models, for accurate AD detection from MRI images, offering significant advancements in early diagnosis.
Architecture of multi-agent systems for generative automatic matching among heterogeneous systems Batouta, Zouhair Ibn; Dehbi, Rachid; Talea, Mohamed
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2345-2355

Abstract

This paper presents the generative automatic matching (GAM) approach, implemented through a multi-agent system (MAS), to address the challenges of heterogeneity across meta-models. GAM integrates automatic meta-model matching with model generation, offering a comprehensive solution to complex systems involving diverse architectures. The key innovation lies in its ability to automate both the detection of correspondences and the transformation of models, improving the precision and recall of matching processes. The system's scalability and adaptability are enhanced by MAS, allowing for efficient management of diverse meta-models. The approach was evaluated through relational to big data UML meta-models (RBDU) case study. The results demonstrated high accuracy, with precision and recall metrics approaching 1, underscoring the robustness of GAM in managing heterogeneous systems. Compared to traditional methods, GAM offers significant advantages, including automated matching and generation, adaptability to various domains, and superior performance metrics. The study contributes to the field of model-driven engineering (MDE) by formalizing a method that effectively bridges the gap between heterogeneous meta-models. Future research will focus on refining matching heuristics, expanding case studies.

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