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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 112 Documents
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Search results for , issue "Vol 12, No 3: June 2022" : 112 Documents clear
Energy distribution and economic analysis of a residential house with the net-energy metering scheme in Malaysia Norshahidatul Shahida Mohamed Suhaime; Shaikh Zishan Suheel; Ahmad Afif Safwan; Hasila Jarimi; Mohd Faizal; Adnan Ibrahim; Sohif Mat; Ahmad Fazlizan
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2313-2322

Abstract

Malaysia demonstrates its commitment to alleviating the negative climate and energy issues through various initiatives. One of the latest initiatives is the amendment of the net-energy metering (NEM) scheme that takes effect from 2019. This paper presents the distribution of energy to the residential house that has a grid-connected solar photovoltaic (PV) system installed. The study quantifies the percentage of energy consumed from the PV system and the grid as well as the percentage of PV generated energy that is exported to the grid. On average, 38% of generated energy was used for self-consumption that contributed to 28% of the total consumption. Economic evaluation over a 25-year lifecycle of the PV system is also conducted shows that the simple payback period for NEM 2019 and NEM 2016 is 8 years and 20 years, respectively. The latest version of NEM shows a superior advantage compared to the previous one which may attract more investments in PV generation.
Fake accounts detection system based on bidirectional gated recurrent unit neural network Faouzia Benabbou; Hanane Boukhouima; Nawal Sael
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3129-3137

Abstract

Online social networks have become the most widely used medium to interact with friends and family, share news and important events or publish daily activities. However, this growing popularity has made social networks a target for suspicious exploitation such as the spreading of misleading or malicious information, making them less reliable and less trustworthy. In this paper, a fake account detection system based on the bidirectional gated recurrent unit (BiGRU) model is proposed. The focus has been on the content of users’ tweets to classify twitter user profile as legitimate or fake. Tweets are gathered in a single file and are transformed into a vector space using the GloVe word embedding technique in order to preserve the semantic and syntax context. Compared with the baseline models such as long short-term memory (LSTM) and convolutional neural networks (CNN), the results are promising and confirm that using GloVe with BiGRU classifier outperforms with 99.44% for accuracy and 99.25% for precision. To prove the efficiency of our approach the results obtained with GloVe were compared to Word2vec under the same conditions. Results confirm that GloVe with BiGRU classifier performs the best results for detection of fake Twitter accounts using only tweets content feature.
Solar photovoltaic/thermal air collector with mirrors for optimal tilts Assia Benkaddour; Hanan Boulaich; Elhassan Aroudam
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2273-2284

Abstract

This work is the result of a study of a photovoltaic/thermal air collector that concentrates solar radiation using two mobile mirrors to enhance electrical and thermal energy. The study is made for the site of Tetouan (Morocco) (longitude=-5°, latitude=35.25°) for a daily variation during typical days in May, June, September, and December, days considered as clear sky. To prove the effectiveness of the mirrors on the production of both electrical and thermal energy by the collector, we compared their electrical and thermal efficiency in two cases, without and with mirrors at the optimal positions. We validate the obtained simulation results by comparing them to the results from experimental studies published in the literature, for which a strong agreement was obtained. The model estimates the solar energy received by the hybrid collector during the day, to optimize the performance of the fixed collector, we have searched for the values of the optimal daily tilt angles of the two mirrors which allowed us to enhance the quantity of incoming solar radiation on the collector. The tilt angles depend on the sun’s elevation angle, the azimuth angle for typical days of the year.
Enhancing multi-class web video categorization model using machine and deep learning approaches Wael M. S. Yafooz; Abdullah Alsaeedi; Reyadh Alluhaibi; Abdel-Hamid Mohamed Emara
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3176-3191

Abstract

With today’s digital revolution, many people communicate and collaborate in cyberspace. Users rely on social media platforms, such as Facebook, YouTube and Twitter, all of which exert a considerable impact on human lives. In particular, watching videos has become more preferable than simply browsing the internet because of many reasons. However, difficulties arise when searching for specific videos accurately in the same domains, such as entertainment, politics, education, video and TV shows. This problem can be solved through web video categorization (WVC) approaches that utilize video textual information, visual features, or audio approaches. However, retrieving or obtaining videos with similar content with high accuracy is challenging. Therefore, this paper proposes a novel mode for enhancing WVC that is based on user comments and weighted features from video descriptions. Specifically, this model uses supervised learning, along with machine learning classifiers (MLCs) and deep learning (DL) models. Two experiments are conducted on the proposed balanced dataset on the basis of the two proposed algorithms based on multi-classes, namely, education, politics, health and sports. The model achieves high accuracy rates of 97% and 99% by using MLCs and DL models that are based on artificial neural network (ANN) and long short-term memory (LSTM), respectively.
Data protection based neural cryptography and deoxyribonucleic acid Sahar Adill Kadum; Ali Yakoob Al-Sultan; Najlaa Adnan Hadie
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2756-2764

Abstract

The need to a robust and effective methods for secure data transferring makes the more credible. Two disciplines for data encryption presented in this paper: machine learning and deoxyribonucleic acid (DNA) to achieve the above goal and following common goals: prevent unauthorized access and eavesdropper. They used as powerful tool in cryptography. This paper grounded first on a two modified Hebbian neural network (MHNN) as a machine learning tool for message encryption in an unsupervised method. These two modified Hebbian neural nets classified as a: learning neural net (LNN) for generating optimal key ciphering and ciphering neural net CNN) for coding the plaintext using the LNN keys. The second granulation using DNA nucleated to increase data confusion and compression. Exploiting the DNA computing operations to upgrade data transmission security over the open nets. The results approved that the method is effective in protect the transferring data in a secure manner in less time
A sentiment analysis model of Agritech startup on Facebook comments using naive Bayes classifier Nawapon Kewsuwun; Siriwan Kajornkasirat
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2829-2838

Abstract

Facebook page is a tool able to generate perceptions and acceptance, and support people and investors in making business decisions. Moreover, Facebook page plays a part in engaging people in the form of a community. People share experiences and opinions toward products, services, and trends in particular periods on the Facebook page community. Regarding sentiment analysis on Facebook pages, most education and other general topics in English have only been analyzed in English. However, sentiment analysis regarding Agritech startups topics in Thai language has not been done yet. This study analyzes opinions and categorizes positive and negative comments by using naive Bayes classifier to examine the sentiments and attitudes of people and investors. The results could possibly reflect the perception rate of Agritech startups in Thailand and could be applied to explain attentiveness and assess people’s engagement opinions. Furthermore, it could be applied in studying consumer behavior, marketing analysis, spread of information, and attitudes. The study's model is generic and could be applied in other contexts to provide insightful suggestions.
Comparative and comprehensive study of linear antenna arrays’ synthesis Asem S. Al-Zoubi; Anas Atef Amaireh; Nihad I. Dib
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2645-2654

Abstract

In this paper, a comparative and comprehensive study of synthesizing linear antenna array (LAA) designs, is presented. Different desired objectives are considered in this paper; reducing the maximum sidelobe radiation pattern (i.e., pencil-beam pattern), controlling the first null beamwidth (FNBW), and imposing nulls at specific angles in some designs, which are accomplished by optimizing different array parameters (feed current amplitudes, feed current phase, and array elements positions). Three different optimization algorithms are proposed in order to achieve the wanted goals; grasshopper optimization algorithms (GOA), antlion optimization (ALO), and a new hybrid optimization algorithm based on GOA and ALO. The obtained results show the effectiveness and robustness of the proposed algorithms to achieve the wanted targets. In most experiments, the proposed algorithms outperform other well-known optimization methods, such as; Biogeography based optimization (BBO), particle swarm optimization (PSO), firefly algorithm (FA), cuckoo search (CS) algorithm, genetic algorithm (GA), Taguchi method, self-adaptive differential evolution (SADE), modified spider monkey optimization (MSMO), symbiotic organisms search (SOS), enhanced firefly algorithm (EFA), bat flower pollination (BFP) and tabu search (TS) algorithm.
Dual tuned 1H/31P quadrature microstripline-based transmit/receive switch for 7 Tesla magnetic resonance imaging Ashraf Abuelhaija; Gameel Saleh
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2177-2183

Abstract

A dual tuned transmit/receive (T/R) Switch for 7 tesla magnetic resonance imaging (MRI) that is based on concentric microstripline (MSL) coupler is introduced. The proposed switch is designed using two concentric MSL quadrature couplers on the top and bottom faces of the switch. The switch can be used to handle two frequency signals to/from two radio frequency (RF) coils. In this article, a 1H/31P atomic nuclei are excited. The two MSLs on the upper face of the switch are designed to transmit 298 and 120.6 MHz signals into RF coils, whereas each of the identical upper and lower MSLs are used to receive these signals from the RF coils. This switch can be used to transmit/receive signals from two RF coils at the same time, one work with 1H and the second with 31P atomic nuclei, and without any tuning. The proposed switch has been designed and simulated using the electromagnetic microwave studio computer simulation technology (CST). It demonstrates good matching (≈17 dB), low insertion loss (≈0.3) and high isolation (>70) for the 1H and 31P magnetic resonance signals at transmit mode. During receive mode, It demonstrates good matching (>20 dB), low insertion loss (≈0.2) and high isolation (>70) for the 1H and 31P magnetic resonance signals.
Improvement of dielectric strength and properties of cross-linked polyethylene using nano filler Sherif Essawi; Loai Nasrat; Hanafy Ismail; Jeannette Asaad
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2264-2272

Abstract

Power cables insulated with cross-linked polyethylene (XLPE) have been utilized worldwide for distribution and transmission networks. There are several advantages for this type of insulation; it has better electrical, thermal, and mechanical properties compared to other types of insulation in medium and high voltage networks. Many studies aimed to improve the XLPE characteristics through introducing nano fillers to the XLPE matrix. Therefore, this paper investigates the AC (HV) breakdown voltage (dielectric strength) of XLPE after adding nano-sized zeolite (Z) fillers with various concentrations of 1 wt%, 3 wt%, 5 wt% and 7 wt%. The dielectric strength is tested in different temperatures of 30 ⁰C and 250 ⁰C. Additionally, it was tested in low and high salty wet conditions. The dielectric strength of the XLPE has been enhanced by inducing the Z nano filler. The results of the tests were used to train the artificial neural network (ANN) to calculate the dielectric strength of XLPE composites with different concentrations of nano Z filler under different weathering conditions. Thermogravimetric analysis, tensile strength, and elongation at break tests were applied to check the thermal and mechanical characteristics of the samples. Experimental findings show that the optimum concentration of nano Z is 3.64 wt% to enhance the electrical, thermal, and mechanical properties.
Review on hypertension diagnosis using expert system and wearable devices Muhammad Izzuddin Mohd Sani; Nur Atiqah Sia Abdullah; Marshima Mohd Rosli
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3166-3175

Abstract

The popularity of smartphones and wearable devices is increasing in the global market. These devices track physical exercise records, heartbeat, medicines, and self-health diagnosis. The wearable devices can also collect personal health parameters include hypertension diagnosis. Hypertension is one of the risk factors for cardiovascular-related diseases among the Malaysian population. Many mobile applications are paired with wearable devices to monitor health conditions, but none of them able to diagnose hypertension. In this study, we reviewed research papers that focused on hypertension using expert systems and wearable devices. We performed a systematic literature review based on hypertension factors, expert systems, and wearable devices. We found 15 specific research papers after the filtering process. The key findings highlighted three main focuses, which are the factors of hypertension, the expert system techniques, and the types of sensors in wearable devices. Blood pressure is the most common factor of hypertension that can be collected by wearable devices. As for the expert system techniques, we determined the three most common techniques are machine learning, neural network, and fuzzy logic. Lastly, the wrist band is the most common sensor for wearable devices in hypertension-related research.

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