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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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Interconnection viability of high demand isolated area through a HVDC-VSC link Juan A. Varela; Mario A. Rios
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp1002-1010

Abstract

Electrically isolated areas are separated by a great distance and, normally, have a considerably low demand; in consequence, these are non-interconnected from the main power grid or electrical national transmission systems. Great distance and low demand are the reasons why an interconnection project in high voltage AC is not feasible in most of these cases. Nevertheless, there are some isolated areas with high power demand and even though they are separated from the main grid by large distances and hard terrains; however, it is still reasonable to think about an interconnection project. This paper had developed a methodology that allows the evaluation of viability, technically and economically, of a HDVC-VSC interconnection project for great distance and high demand considering overhead and/or underground DC line. The methodology was applied to a case of study in Peru, based on the projected interconnection between Moyobamba and the isolated area of Iquitos; showing that HVDC is a feasible alternative.
Online multiclass EEG feature extraction and recognition using modified convolutional neural network method Haider Abdulkarim; Mohammed Z. Al-Faiz
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp4016-4026

Abstract

Many techniques have been introduced to improve both brain-computer interface (BCI) steps: feature extraction and classification. One of the emerging trends in this field is the implementation of deep learning algorithms. There is a limited number of studies that investigated the application of deep learning techniques in electroencephalography (EEG) feature extraction and classification. This work is intended to apply deep learning for both stages: feature extraction and classification. This paper proposes a modified convolutional neural network (CNN) feature extractorclassifier algorithm to recognize four different EEG motor imagery (MI). In addition, a four-class linear discriminant analysis (LDR) classifier model was built and compared to the proposed CNN model. The paper showed very good results with 92.8% accuracy for one EEG four-class MI set and 85.7% for another set. The results showed that the proposed CNN model outperforms multi-class linear discriminant analysis with an accuracy increase of 28.6% and 17.9% for both MI sets, respectively. Moreover, it has been shown that majority voting for five repetitions introduced an accuracy advantage of 15% and 17.2% for both EEG sets, compared with single trials. This confirms that increasing the number of trials for the same MI gesture improves the recognition accuracy
Image encryption under spatial domain based on modify 2D LSCM chaotic map via dynamic substitution-permutation network Rana Saad Mohammed; Khalid Kadhim Jabbar; Hussien Abid Hilal
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3070-3083

Abstract

Image encryption has become an important application aspect of information security. Most attempts are focused on increasing the security aspect, the quality of the resulting image, and the time consumed. On the other hand, dealing with the color image under the spatial domain in this filed is considered as another challenge added to the proposed method that make it sensitivity and difficulty. The proposed method aims to encode a color image by dealing with the main color components of the red (R), green (G), and blue (B) components of a color image to strengthen the dependence of each component by modifying a two dimensional logistic- sine coupling map (2D- LSCM). This is to satisfy the statistical features and reduce time-consumption, and benefit from a mixing step of the second of advanced encryption standard (AES) candidates (serpent block cipher) and modified it to achieve in addition of confusion and diffusion processes. The experimental results showed that our proposed method had the ability to resist against statistical attacks and differential attacks. It also had a uniform histogram, a large key space, complex and faster, closer Shannon entropy to 8, and low correlation values between two adjacent pixels compared with other methods.
DC-DC converter with 50 kHz-500 kHz range of switching frequency for passive component volume reduction Mohd Amirul Naim Kasiran; Asmarashid Ponniran; Nurul Nabilah Mad Siam; Mohd Hafizie Yatim; Nor Azmira Che Ibrahim; Asmawi Md Yunos
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp1114-1122

Abstract

This paper presents the relationship of switching frequency towards passive components volume of DC-DC boost converter. Principally, the inductor current ripple and capacitor voltage ripple must be considered in order to design the inductor and capacitor, respectively. By increasing the switching frequency, smaller size and volume of passive component can be designed. As the consequences, the switching loss increases during switching transition at turn-ON and turn-OFF conditions. This paper used soft-switching technique to reduce the switching loss at turn-ON condition. The soft-switching technique is realized by adding resonant circuit in DC-DC boost converter. The effectiveness of resonant circuit will be analysed, thus, the efficiency of the converter can be improved. The range of switching frequency considered in the experimental are 50 kHz to 500 kHz. A 100 W prototype has been developed and tested in order to verify the principle. The switching loss experimentally confirm reduced by implementing soft-switching technique with efficiency converter improved from 96.36% to 97.12% when 500 kHz of switching frequency is considered. The passive components volume reduction is achieved when high switching frequency is used where the total volume of passive component when 50 kHz and 500 kHz are 0.083 dm3 and 0.010 dm3, respectively.
Evaluation of electrical load estimation in Diyala governorate (Baaquba city) based on fuzzy inference system Siraj Manhal Hameed; Hayder Khaleel AL-Qaysi; Ali Sachit Kaittan; Mohammed Hasan Ali
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp3757-3762

Abstract

The evaluation of electrical load estimation is requisitely of any electrical power system. This manner is needed for system obligation, economical distribution and maintenance time of electrical system. In this paper, we propose electrical load estimation method based on fuzzy inference system which gives accurate results for estimated loads in Iraq (Diyala governorateBaaquba city). And it can assist the electrical generation and distribution system that depends on important parameters (temperature, humidity and the speed of the wind). By considering the parameters temperature, humidity and the speed of the wind. These parameters are applied as inputs to the fuzzy logic control system to obtain the normalize estimated load as output by electing membership functions. It is exceptionally valuable to form a choice by taking into consideration these assessed readings that come to from the proposed FIS that displayed in this paper with precision of 0.969 from the real stack request.
Forecasting number of vulnerabilities using long short-term neural memory network Mohammad Shamsul Hoque; Norziana Jamil; Nowshad Amin; Azril Azam Abdul Rahim; Razali B. Jidin
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp4381-4391

Abstract

Cyber-attacks are launched through the exploitation of some existing vulnerabilities in the software, hardware, system and/or network. Machine learning algorithms can be used to forecast the number of post release vulnerabilities. Traditional neural networks work like a black box approach; hence it is unclear how reasoning is used in utilizing past data points in inferring the subsequent data points. However, the long short-term memory network (LSTM), a variant of the recurrent neural network, is able to address this limitation by introducing a lot of loops in its network to retain and utilize past data points for future calculations. Moving on from the previous finding, we further enhance the results to predict the number of vulnerabilities by developing a time series-based sequential model using a long short-term memory neural network. Specifically, this study developed a supervised machine learning based on the non-linear sequential time series forecasting model with a long short-term memory neural network to predict the number of vulnerabilities for three vendors having the highest number of vulnerabilities published in the national vulnerability database (NVD), namely microsoft, IBM and oracle. Our proposed model outperforms the existing models with a prediction result root mean squared error (RMSE) of as low as 0.072.
Atar: Attention-based LSTM for Arabizi transliteration Bashar Talafha; Analle Abuammar; Mahmoud Al-Ayyoub
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2327-2334

Abstract

A non-standard romanization of Arabic script, known as Arbizi, is widely used in Arabic online and SMS/chat communities. However, since state-of-the-art tools and applications for Arabic NLP expects Arabic to be written in Arabic script, handling contents written in Arabizi requires a special attention either by building customized tools or by transliterating them into Arabic script. The latter approach is the more common one and this work presents two significant contributions in this direction. The first one is to collect and publicly release the first large-scale “Arabizi to Arabic script” parallel corpus focusing on the Jordanian dialect and consisting of more than 25 k pairs carefully created and inspected by native speakers to ensure highest quality. Second, we present Atar, an attention-based encoder-decoder model for Arabizi transliteration. Training and testing this model on our dataset yields impressive accuracy (79%) and BLEU score (88.49).
The optimal solution for unit commitment problem using binary hybrid grey wolf optimizer Ali Iqbal Abbas; Afaneen Anwer
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp122-130

Abstract

The aim of this work is to solve the unit commitment (UC) problem in power systems by calculating minimum production cost for the power generation and finding the best distribution of the generation among the units (units scheduling) using binary grey wolf optimizer based on particle swarm optimization (BGWOPSO) algorithm. The minimum production cost calculating is based on using the quadratic programming method and represents the global solution that must be arriving by the BGWOPSO algorithm then appearing units status (on or off). The suggested method was applied on “39 bus IEEE test systems”, the simulation results show the effectiveness of the suggested method over other algorithms in terms of minimizing of production cost and suggesting excellent scheduling of units.
Arabic tweeps dialect prediction based on machine learning approach Khaled Alrifai; Ghaida Rebdawi; Nada Ghneim
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i2.pp1627-1633

Abstract

In this paper, we present our approach for profiling Arabic authors on twitter, based on their tweets. We consider here the dialect of an Arabic author as an important trait to be predicted. For this purpose, many indicators, feature vectors and machine learning-based classifiers were implemented. The results of these classifiers were compared to find out the best dialect prediction model. The best dialect prediction model was obtained using random forest classifier with full forms and their stems as feature vector.
Learning games creation: IMIE model Ali Abarkan; Abderrahim Saaidi; Majid Ben Yakhlef
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp4373-4380

Abstract

In this article, we propose a new model for the learning games design, which aims to enrich and solve the problems of existing models. From the work carried out around the models of educational games, we carefully identify a series of steps to follow, taking into account the simplicity, ease of adaptation and the integration of several factors interest such as the integration of a set sub-models treated pedagogically define the pedagogical side in the game, also the distribution of the intervening necessary for each of the stages to ensure better collaboration between them, for the purpose to helping the creators to trace their path from the start of the creation, in order to achieve the best example of games with an adequate oscillation pedagogical-fun, that will meet the requirements of existing classical teaching methods.

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