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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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Analysis and classification of power quality disturbances using variational mode decomposition and hybrid particle swarm optimization Idan Hussein, Husham; Majeed Ghadban, Ahmed; Rodríguez Gómez, Alejandro; Jesus Muñoz Gutierrez, Francisco
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp3768-3782

Abstract

Power quality disturbances (PQD) threaten electrical power systems, especially in distributed generation with renewable energy sources and in smart grids where PQD takes a complex form. Providing accurate information on the status and characteristics of the electrical signal facilitates the identification of practical solutions to this threat. In this paper, a variational mode decomposition (VMD) signal processing tool is proposed to analyze complex PQD. In VMD, the input signal is decomposed into different band-limited intrinsic mode functions (IMF) or non-recursively reconstructed modes. The input signal analysis by VMD, which considers the frequency values and spectral decomposition for each mode, describes the changes in the input waveform, and the IMFs help extract the behavioral patterns of these disturbances. A new hybrid particle swarm optimization-technique for order of preference by similarity to ideal solution (PSO-TOPSIS) algorithm is also proposed to classify the disturbances based on the features extracted from the signals decomposed using VMD. The performance of this method is then extensively validated by using different PQDs (including complex, stationary, and non-stationary (PQDs) and through a comparison with deep learning methods, such as convolutional and recurrent neural networks. Results show that VMD has several advantages over Fourier, wavelet, and Stockwell transforms, such as its lack of any modal aliasing effect, its capability to diagnose disturbances across four noise levels, and its ability to separate harmonics from other events. The proposed VMD in combination with PSO-TOPSIS performs more accurately than the other methods across all noise levels.
Analysis of the emotional coloring of text using machine and deep learning methods Abdykerimova, Lazzat; Abdikerimova, Gulzira; Konyrkhanova, Assem; Nurova, Gulsara; Bazarova, Madina; Bersugir, Mukhamedi; Kaldarova, Mira; Yerzhanova, Akbota
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3055-3063

Abstract

The presented scientific article is a comprehensive study of machine learning and deep learning methods in the context of emotion recognition in text data. The main goal of the study is to conduct a comprehensive analysis and comparison of various machine learning and deep learning methods to classify emotions in text. During the work, special attention was paid to the analysis of traditional machine learning algorithms, such as multinomial naive Bayes (MNB), multilayer perceptron (MLP), and support vector machine (SVM), as well as the use of deep learning methods based on long short-term memory (LSTM). The experimental part of the study involves the analysis of different data sets covering a variety of text styles and contexts. The experimental results are analyzed in detail, identifying the advantages and limitations of each method. The article provides practical recommendations for choosing the optimal method depending on the specific tasks and context of the application. The data obtained is important for the development of intelligent systems that can effectively adapt to the emotional aspects of interaction with users. Overall, this work makes a significant contribution to the field of emotion recognition in text and provides a basis for further research in this area.
Generating images using generative adversarial networks based on text descriptions Turarova, Marzhan; Bekbayeva, Roza; Abdykerimova, Lazzat; Aitimov, Murat; Bayegizova, Aigulim; Smailova, Ulmeken; Kassenova, Leila; Glazyrina, Natalya
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp2014-2023

Abstract

Modern developments in the fields of natural language processing (NLP) and computer vision (CV) emphasize the increasing importance of generating images from text descriptions. The presented article analyzes and compares two key methods in this area: generative adversarial network with conditional latent semantic analysis (GAN-CLS) and ultra-long transformer network (XLNet). The main components of GAN-CLS, including the generator, discriminator, and text encoder, are discussed in the context of their functional tasks—generating images from text inputs, assessing the realism of generated images, and converting text descriptions into latent spaces, respectively. A detailed comparative analysis of the performance of GAN-CLS and XLNet, the latter of which is widely used in the organic light-emitting diode (OEL) field, is carried out. The purpose of the study is to determine the effectiveness of each method in different scenarios and then provide valuable recommendations for selecting the best method for generating images from text descriptions, taking into account specific tasks and resources. Ultimately, our paper aims to be a valuable research resource by providing scientific guidance for NLP and CV experts.
Optimizing credit card fraud detection: a deep learning approach to imbalanced datasets Ndama, Oussama; Bensassi, Ismail; En-Naimi, El Mokhtar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4802-4814

Abstract

Imbalanced datasets pose a significant challenge in credit card fraud detection, hindering the training effectiveness of models due to the scarcity of fraudulent cases. This study addresses the critical problem of data imbalance through an in-depth exploration of techniques, including cross-entropy loss minimization, weighted optimization, and synthetic minority oversampling technique-based resampling, coupled with deep neural networks (DNNs). The urgent need to combat class imbalances in credit card fraud datasets is underscored, emphasizing the creation of reliable detection models. The research method delves into the application of DNNs, strategically optimizing and resampling the dataset to enhance model performance. The study employs a dataset from October 2018, containing 284,807 transactions, with a mere 492 classified as fraudulent. Various resampling techniques, such as undersampling and SMOTE oversampling, are evaluated alongside weighted optimization. The results showcase the effectiveness of SMOTE oversampling, achieving an accuracy of 99.83% without any false negatives. The study concludes by advocating for flexible strategies, integrating cutting-edge machine learning methods, and developing adaptive defenses to safeguard against emerging financial risks in credit card fraud detection.
A machine learning model for predicting phishing websites Odette Boussi, Grace; Gupta, Himanshu; Hossain, Syed Akhter
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4228-4238

Abstract

There are various types of cybercrime, and hackers often target specific ones for different reasons, such as financial gain, recognition, or even revenge. Cybercrimes are not restricted by geographical boundaries and can occur globally. The prevalence of specific types of cybercrime can vary from country to country, influenced by factors such as economic conditions, internet usage levels, and overall development. Phishing is a common cybercrime in the financial sector across different countries, with variations in techniques between developed and developing nations. However, the impact, often leading to financial losses, remains consistent. In our analysis, we utilized a dataset featuring 48 attributes from 5,000 phishing webpages and 5,000 legitimate webpages to predict the phishing status of websites. This approach achieved an impressive 98% accuracy.
Optimal scheduling and demand response implementation for home energy management Priolkar, Jayesh; Sreeraj, ES
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1352-1368

Abstract

The optimal scheduling of the loads based on dynamic tariffs and implementation of a direct load control (DLC) based demand response program for the domestic consumer is proposed in this work. The load scheduling is carried out using binary particle swarm optimization and a newly prefaced nature-inspired discrete elephant herd optimization technique, and their effectiveness in minimization of cost and the peak-to-average ratio is analyzed. The discrete elephant herd optimization algorithm has acceptable characteristics compared to the conventional algorithms and has determined better exploring properties for multi-objective problems. A prototype hardware model for a home energy management system is developed to demonstrate and analyze the optimal load scheduling and DLC-based demand response program. The controller effectively schedules and implements DLC on consumer devices. The load scheduling optimization helps to improve PAR by a value of 2.504 and results in energy cost savings of ₹ 12.05 on the scheduled day. Implementation of DLC by 15% results in monthly savings of ₹ 204.18. The novelty of the work is the implementation of discrete elephant herd optimization for load scheduling and the development of the prototype hardware model to show effects of both optimal load scheduling and the DLC-based demand response program implementation.
Homonym and polysemy approaches with morphology extraction in weighting terms for Indonesian to English machine translation Harjo, Budi; Muljono, Muljono; Abdullah, Rachmad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7036-7045

Abstract

Homonym and polysemy features can influence some errors in translation from a source language to another target language, for example, from Indonesian to English. A lemma or a morphology factor can cause the configuration of Indonesian homonym features. For example, the word beruang can mean an animal beruang (bear) and can mean a verb alternation ber+uang (has/have money). The Indonesian polysemy feature can also impact an error in the translation process because it can have a literal meaning and a symbolic meaning. For example, the terms bunga melati (jasmine flower) and bunga hati (lover), where bunga does not only mean flower. Therefore, the development machine translation (MT) method needs to capture homonym and polysemy features in the form of a word or a phrase. This research proposes homonym and polysemy approaches with morphology extraction in weighting terms for Indonesian to English MT. First, this research uses morphology extraction to detect sentences that contain prefixes, lemma, and suffixes. Then, the word similarity measurement functions to extract homonym and polysemy in the form of uni-gram and bi-gram using bidirectional encoder representations from transformers (BERT) embedding, named entity recognition (NER), synonym-based term expansion, and semantic similarity. This research uses neural machine translation for the translation process.
Monitoring climate change effects on coral reefs using edge-based image segmentation Afreen Awalludin, Ezmahamrul; Wan Yussof, Wan Nural Jawahir Hj; Bachok, Zainudin; Firdaus Aminudin, Muhammad Afiq; Che Din, Mohd Safuan; Suzuri Hitam, Muhammad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp398-408

Abstract

Coral reefs are valuable ecosystems that face vulnerability to climate change impacts. Underwater images often encounter noise from various factors, such as water turbidity, lighting conditions, attenuation, and scattering, which can complicate edge detection and segmentation processes, leading to inaccuracies. However, image processing techniques offer a viable solution to this issue. In this study, an edge-based segmentation approach is proposed that uses multiple contrast techniques to detect and quantify changes in coral reef imagery. The proposed approach effectively identifies changes in coral reef imagery, making it a valuable tool for monitoring climate change's effects on these ecosystems. Furthermore, high-resolution images at different time points and locations were collected, and then an edge-based segmentation approach was utilized to enhance the accuracy of edge detection and segmentation. Comparing the proposed method with traditional segmentation techniques showed a significant improvement in terms of segmentation precision. Subsequently, alterations in the structure and composition of coral reefs are observed, indicating the influence of climate change on these ecosystems. This research highlights the capabilities of image processing techniques using edge-based segmentation in monitoring coral reefs. It offers an effective and precise approach to detecting changes in coral reef images, thereby contributing to conservation endeavors.
Simulation of losses in a three-phase bank of transformers in the presence of harmonic distortion Grau-Merconchin, Frank; Hernández Areu, Orestes; Nuñez Alvarez, José Ricardo; Montenegro-Romero, Michael; Quintero-Ospino, Oscar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp4827-4837

Abstract

The load losses of the transformers increase considerably with the presence of harmonic distortion in the currents. Its increase can be determined using an analytical method based on ANSI/IEEE Standard C57.110. On the other hand, when it comes to three-phase banks with transformers of different capacities, delta connections, or incomplete connections, the analytical method does not allow the losses to be accurately estimated, which is why digital simulation is necessary. This work presents an adjusted model to determine the load losses in a three-phase bank of three single-phase transformers with different connection schemes. The model allows for determining load and electrical losses and calculating total additional losses. It is also possible to decide on the load capacity of the bank's transformers, the power factor, and the efficiency with which they operate under these conditions.
An autopilot-based method for unmanned aerial vehicles trajectories control and adjustment Mochurad, Lesia; Alsayaydeh, Jamil; Yusof, Mohd Faizal
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4154-4166

Abstract

In today's world, the rapid development of aviation technologies, particularly unmanned aerial vehicles (UAVs), presents new challenges and opportunities. UAVs are utilized across various industries, including scientific research, military, robotics, surveying, logistics, and postal delivery. However, to ensure efficient and safe operation, UAVs require a reliable autopilot system that delivers precise navigation control and flight stability. This paper introduces a method for controlling and adjusting UAV trajectories, which enhances accuracy in environments and tasks corresponding to the first or second level of autonomy. It outperforms the linear-quadratic method and the unmodified predictive control method by 43% and 74%, respectively. The findings of this study can be applied to the development and modernization of new UAV, as well as the advancement of new UAV motion control systems, thereby enhancing their quality and efficiency.

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