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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 14, No 2: April 2024" : 111 Documents clear
Data generation using generative adversarial networks to increase data volume Aitimova, Ulzada; Aitimov, Murat; Mukhametzhanova, Bigul; Issakulova, Zhanat; Kassymova, Akmaral; Ismailova, Aisulu; Kadirkulov, Kuanysh; Zhumabayeva, Assel
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.pp2369-2376

Abstract

The article is an in-depth analysis of two leading approaches in the field of generative modeling: generative adversarial networks (GANs) and the pixel-to-pixel (Pix2Pix) image translation model. Given the growing interest in automation and improved image processing, the authors focus on the key operating principles of each model, analyzing their unique characteristics and features. The article also explores in detail the various applications of these approaches, highlighting their impact on modern research in computer vision and artificial intelligence. The purpose of the study is to provide readers with a scientific understanding of the effectiveness and potential of each of the models, and to highlight the opportunities and limitations of their application. The authors strive not only to cover the technical aspects of the models, but also to provide a broad overview of their impact on various industries, including medicine, the arts, and solving real-world problems in image processing. In addition, we have identified prospects for the use of these technologies in various fields, such as medicine, design, art, entertainment, and in unmanned aerial vehicle systems. The ability of GANs and Pix2Pix to adapt to a variety of tasks and produce high-quality results opens up broad prospects for industry and research.
Three layer hybrid learning to improve intrusion detection system performance Harwahyu, Ruki; Erasmus Ndolu, Fajar Henri; Overbeek, Marlinda Vasty
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.pp1691-1699

Abstract

In imbalanced network traffic, malicious cyberattacks can be hidden in a large amount of normal traffic, making it difficult for intrusion detection systems (IDS) to detect them. Therefore, anomaly-based IDS with machine learning is the solution. However, a single machine learning cannot accurately detect all types of attacks. Therefore, a hybrid model that combines long short-term memory (LSTM) and random forest (RF) in three layers is proposed. Building the hybrid model starts with Nearmiss-2 class balancing, which reduces normal samples without increasing minority samples. Then, feature selection is performed using chi-square and RF. Next, hyperparameter tuning is performed to obtain the optimal model. In the first and second layers, LSTM and RF are used for binary classification to detect normal data and attack data. While the third layer model uses RF for multiclass classification. The hybrid model verified using the CSE-CIC-IDS2018 dataset, showed better performance compared to the single algorithm. For multiclass classification, the hybrid model achieved 99.76% accuracy, 99.76% precision, 99.76% recall, and 99.75% F1-score.
Taxi-out time prediction at Mohammed V Casablanca Airport Zbakh, Douae; El Gonnouni, Amina; Benkacem, Abderrahmane; Said Kasttet, Mohammed; Lyhyaoui, Abdelouahid
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.pp2126-2134

Abstract

Airports are vital for global connectivity. However, the increasing volume of air travel has presented significant challenges in airport managing. Accurate predictions of taxi-out times (TXOT) offer potential to enhance airport performance, minimize delays, optimize airline schedules, and enhance customer satisfaction. This paper focuses on developing a machine learning model to forecast taxi-out times at Mohammed V Airport. Historical taxiing data from various airports will be analyzed to predict taxi-out times based on diverse runway-stand combinations and congestion levels. we used neural network (NN), support vector machines (SVM), and regression tree (RT) in order to create a real-time model that forecasts TXOT and congestion levels for different runway-stand combinations. The result showed that the NN model outperformed other forecasting models when their performances are compared using the mean absolute percentage error, root mean square error as accuracy measures.
Development and evaluation of a 2oo3 safety controller in FPGA using fault tree analysis and Markov models Nadir, Fatima Ezzahra; Bsiss, Mohammed; Amami, Benaissa
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.pp1496-1507

Abstract

The Safety integrity level (SIL) is a measure of the reliability and availability of a safety instrumented system. SIL determination involves qualitative and quantitative analysis based on international standards such as IEC 61508 and IEC 61511. Several techniques can be used to analyze safety instrumented systems, including reliability block diagrams, fault tree analysis, and Markov models. The aim of this paper is to design and evaluate a pressure control system for a compressed nitrogen tank using a PID controller implemented in a field programmable gate array with 2 out of 3 architecture. This architecture ensures the safety of measurements and command of the system through a voting arrangement. The availability of the system is determined by the redundancy and the one hardware failure tolerance. The quantitative analysis is performed by calculating the probability of failure on demand per hour using Markov models or a relevant probabilistic approach based on fault tree analysis. The Markov model method gives the probability of failure of the system in different states during the system life cycle. The fault tree analysis method determines the probability of failure of the system using its equivalent failure rate. Furthermore, this paper compares the SIL result obtained by each model.
A proposal and simulation analysis for a novel architecture of gate-all-around polycrystalline silicon nanowire field effect transistor El-amiri, Asseya; Demami, Fouad
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.pp1390-1397

Abstract

A proposal for a novel gate-all-around (GAA) polycrystalline silicon nanowire (poly-SiNW) field effect transistor (FET) is presented and discussed in this paper. The device architecture is based on the realization of poly-SiNW in a V-shaped cavity obtained by tetra methyl ammonium hydroxide (TMAH) etch of monocrystalline silicon (100). The device’s behavior is simulated using Silvaco commercial software, including the density of states (DOS) model described by the double exponential distribution of acceptor trap density within the gap. The electric field, potential, and free electron concentration are analyzed in different nanowire regions to investigate the device's performance. The results show good performance despite the high density of deep states in poly-SiNW. This can be explained by the strong electric field caused by the corner effect in the nanowire, which favors the ionization of the acceptor traps and increases the free electron concentration.
Feature selection using non-parametric correlations and important features on recursive feature elimination for stock price prediction Priyatno, Arif Mudi; Ramadhan Sudirman, Wahyu Febri; Musridho, Raja Joko
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.pp1906-1915

Abstract

Stock price prediction using machine learning is a rapidly growing area of research. However, the large number of features that can be used can complicate the learning process. The feature selection method that can be used to overcome this problem is recursive feature elimination. Standard recursive feature elimination carries the risk of producing inaccurate algorithms because the top-ranked features are not necessarily the most important features. This research proposes a feature selection method that combines important features and nonparametric correlation in recursive feature elimination for stock price prediction. The data features used are technical indicators and stock price history. The recursive feature elimination method is modified with important features and nonparametric correlation features. The strategy for combining important features and non-parametric features is average weight, 25:75% weight, 75:25% weight, maximum weight, and minimum weight. The performance evaluation results show that the proposed feature selection method succeeded in obtaining small error values. The proposed method for predicting PT Bank Rakyat Indonesia Tbk (BBRI) stock prices obtains mean squared error, root mean square error, mean absolute error, and mean absolute percentage error evaluation values of 0.0000336, 0.00577, 0.00459, and 1.78%, respectively. This shows that recursive feature elimination with feature selection that combines important features and non-parametric correlation works better than the original recursive feature elimination at predicting stock prices.
Optimal model of vehicular ad-hoc network assisted by unmanned aerial vehicles and information-centric networking to enhance network performance Houari, Abdeslam; Mazri, Tomader
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.pp1788-1796

Abstract

Vehicular ad-hoc network (VANET) is a promising project related to intelligent transportation systems (ITS), which aims at connecting vehicles and providing a set of functionalities for the efficient management of the network. However, the high mobility of the network nodes is considered a significant challenge for implementing a reliable, secure, and efficient exchange system. Furthermore, VANET faces the issue of packet delivery due to the high mobility of the nodes and packet collisions complicate the process of sending and receiving packets. We propose to combine two technologies which are unmanned aerial vehicle (UAV) and information centric networks (ICN) and apply it in VANET architecture as supporting technology. The UAV are more reliable and less affected by channel fading. And can be used in areas where we cannot install network infrastructure. The UAV has many advantages that we have cited in this article and can solve many issues of VANET. Using ICN can solve some of the problems of VANET since ICN has many strategies to capture and retrieve data. This study proposes a new VANET model based on an UAV and ICN, to reduce the overload of the vehicles, which in most cases require more resources and have a limited time to process and act especially in case of an accident or emergency.
Detecting and resolving feature envy through automated machine learning and move method refactoring Al-Fraihat, Dimah; Sharrab, Yousef; Al-Ghuwairi, Abdel-Rahman; AlElaimat, Majed; Alzaidi, Maram
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.pp2330-2343

Abstract

Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Dynamic voltage restorer performance analysis using fuzzy logic controller and battery energy storage system for voltage sagging Siregar, Yulianta; Azhari Nasution, Azrial Aziz; Suan Tial, Mai Kai; Mubarakah, Naemah; Soeharwinto, Soeharwinto
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.pp1215-1227

Abstract

Power quality is a major issue in the power transfer process. This is caused by disturbances such as voltage sags, voltage spikes, and harmonics. Voltage sag is the most common disturbance in the electric power system. However, the dynamic voltage restorer (DVR) is the most effective device for voltage sags. This research uses the DVR to overcome voltage sags using fuzzy logic controller (FLC) and battery energy storage system (BESS) to improve the performance of the DVR. The results showed that DVR using FLC improved the quality of voltage recovery compared to BESS because FLC injected a greater voltage of 0.0991 pu than BESS.
Improving cyberbullying detection through multi-level machine learning Salsabila, Salsabila; Sarno, Riyanarto; Ghozali, Imam; Sungkono, Kelly Rossa
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.pp1779-1787

Abstract

Cyberbullying is a known risk factor for mental health issues, demanding immediate attention. This study aims to detect cyberbullying on social media in alignment with the third sustainable development goal (SDG) for health and well-being. Many previous studies employ single-level classification, but this research introduces a multi-class multi-level (MCML) algorithm for a more detailed approach. The MCML approach incorporates two levels of classification: level one for cyberbullying or not cyberbullying, and level two for classifying cyberbullying by type. This study used a dataset of 47,000 tweets from Twitter with six class labels and employed an 80:20 training and testing data split. By integrating bidirectional encoder representations from transformers (BERT) and MCML at level two, we achieved a remarkable 99% accuracy, surpassing BERT-based single-level classification at 94%. In conclusion, the combination of MCML and BERT offers enhanced cyberbullying classification accuracy, contributing to the broader goal of promoting mental health and well-being.

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