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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 5: October 2022" : 112 Documents clear
Design and implementation of portable electrocardiogram recorder with field programmable gate arrays and IoT interface Nisha Rexline Rassal Raj; Ebenezer Priya; George Fernandez Savari
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5176-5181

Abstract

The electrical activities of the heart are used to monitor cardiovascular diseases. It can be measured using electrocardiogram (ECG), a simple, painless test that can be recorded graphically. The physician, to predict the patient’s heart conditions and recommend suitable treatments, uses electrodes placed on the patient’s skin surface, to record these signals. The P, Q, R, S, T waves in the ECG signal can be used to determine the normality and abnormality of the heart's condition. The time interval differs for each cardiovascular condition of the heart. In this work, the ECG signal is acquired real-time using an intelligent sensor module, and the recorded value is processed to find the peak values. The data is sent to the web serverusing internet of things technology at a minimal time, where the physician can view it and proper decision can be taken. The real-time ECG data acquisition is also made using the field programmable gate array kit as it is a low cost, high-speed device and the output is viewed in the computer. The developed model is validated through MATLAB software and implemented for real-time applications.
Satisfaction prediction of online education in COVID-19 situation using data mining techniques: Bangladesh perspective Lamisha Haque Poushy; Salauddin Ahmed Bhuiyan; Masuma Parvin; Refath Ara Hossain; Nazmun Nessa Moon; Jarin Nooder; Ashrarfi Mahbuba
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5553-5561

Abstract

This research focuses on the education-based online learning platform. Due to the coronavirus disease (COVID-19) epidemic, online education is gaining global popularity. It has shown how successful it is in investigating the quality of online education at the COVID-19 pandemic situation by 799 students from different academic institutions, schools, colleges, and universities. A Google web form has been utilized as the data gathering mechanism for this survey. This paper perused the prediction of online education through data mining and machine learning approaches in an online program. The data was collected through online questionnaires. To predict online education's satisfaction rate, four different types of classifiers are used e.g., logistic regression classifiers, k-nearest neighbors, support vector machine, naive Bayes classifiers. The key purpose of this research is to find out an answer to a question which is, "are the student's satisfied with starting the new online teaching system, or will it be an ambivalent effect for students in the future?".
Optimal k-means clustering using artificial bee colony algorithm with variable food sources length Sabreen Fawzi Raheem; Maytham Alabbas
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5435-5443

Abstract

Clustering is a robust machine learning task that involves dividing data points into a set of groups with similar traits. One of the widely used methods in this regard is the k-means clustering algorithm due to its simplicity and effectiveness. However, this algorithm suffers from the problem of predicting the number and coordinates of the initial clustering centers. In this paper, a method based on the first artificial bee colony algorithm with variable-length individuals is proposed to overcome the limitations of the k-means algorithm. Therefore, the proposed technique will automatically predict the clusters number (the value of k) and determine the most suitable coordinates for the initial centers of clustering instead of manually presetting them. The results were encouraging compared with the traditional k-means algorithm on three real-life clustering datasets. The proposed algorithm outperforms the traditional k-means algorithm for all tested real-life datasets.
Hybrid photovoltaic maximum power point tracking of Seagull optimizer and modified perturb and observe for complex partial shading Novie Ayub Windarko; Evi Nafiatus Sholikhah; Muhammad Nizar Habibi; Eka Prasetyono; Bambang Sumantri; Moh. Zaenal Efendi; Hazlie Mokhlis
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4571-4585

Abstract

Due to natural randomness, partial shading conditions (PSCs) to photovoltaic (PV) power generation significantly drop the power generation. Metaheuristic based maximum power point tracking (MPPT) can handle PSCs by searching PV panels’ global maximum power point (GMPP). However, trapped at local maxima, sluggishness, continuous power oscillations around GMPP and inaccuracy are the main disadvantages of metaheuristic algorithm. Therefore, the development of algorithm under complex PSCs has been continuously attracting many researchers to yield more satisfying results. In this paper, several algorithms including conventional and metaheuristic are selected for candidate, such as perturb and observe (P&O), firefly (FF), differential evolution (DE), grey wolf optimizer (GWO) and Seagull optimizer (SO). From the preliminary study, SO has shown best performance among other candidates. Then, SO is improved for rapid global optimizer. Modified variable step sizes perturb and observe (MVSPO) is applied to enhance the accuracy tracking of SO. To evaluate the performances, high complexity multipeak partial shading is used to test the algorithms. Statistical results are also provided to analyze the trend of performances. The proposed method performances are shown better fast-tracking time and settling time, high accuracy, higher energy harvesting and low steady-state oscillations than other candidates.
Impact of carrier frequency offset and in-phase and quadrature imbalance on the performance of wireless precoded orthogonal frequency division multiplexing Suyoto Suyoto; Agus Subekti; Arief Suryadi Satyawan; Nasrullah Armi; Chaeriah Bin Ali Wael; Galih Nugraha Nurkahfi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5153-5163

Abstract

Precoding in orthogonal frequency division multiplexing (OFDM) system proved to reduce the peak-to-average power ratio (PAPR), so that it improves BER. However, from the existing literature, the effect of carrier frequency offset (CFO), in-phase and quadrature (IQ) imbalance on precoded wireless OFDM systems has not been carried out. Therefore, this study evaluated the precoded OFDM (P-OFDM) system performance by considering the impact of CFO and IQ imbalance. P-OFDM performance evaluation is expressed in signal-to-interference noise ratio (SINR) and bit error rates (BER). The communication channels used are the additive white Gaussian noise (AWGN) channel and the frequency-selective Rayleigh fading (FSRF) channel, while the channel equalization process is considered perfect. The results of the analysis and simulation show that P-OFDM is greater affected by the presence of CFO and IQ imbalance than conventional OFDM system. In AWGN channel, P-OFDM experiences different SINR for each subcarrier. This is different from conventional OFDM system, where all SINRs are the same for all subcarriers. In the FSRF channel, both the POFDM system and the OFDM system experience different SINR for each subcarrier, where the SINRs fluctuation in the P-OFDM system is much larger than in the OFDM system.
Efficient time-series forecasting of nuclear reactions using swarm intelligence algorithms Hala Shaker Mehdy; Nariman Jabbar Qasim; Haider Hadi Abbas; Israa Al_Barazanchi; Hassan Muwafaq Gheni
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5093-5103

Abstract

In this research paper, we focused on the developing a secure and efficient time-series forecasting of nuclear reactions using swarm intelligence (SI) algorithm. Nuclear radioactive management and efficient time series for casting of nuclear reactions is a problem to be addressed if nuclear power is to deliver a major part of our energy consumption. This problem explains how SI processing techniques can be used to automate accurate nuclear reaction forecasting. The goal of the study was to use swarm analysis to understand patterns and reactions in the dataset while forecasting nuclear reactions using swarm intelligence. The results obtained by training the SI algorithm for longer periods of time for predicting the efficient time series events of nuclear reactions with 94.58 percent accuracy, which is higher than the deep convolution neural networks (DCNNs) 93% accuracy for all predictions, such as the number of active reactions, to see how the results can improve. Our earliest research focused on determining the best settings and preprocessing for working with a certain nuclear reaction, such as fusion and fusion task: forecasting the time series as the reactions took 0-500 ticks being trained on 300 epochs
Game development software engineering: digital educational game promoting algorithmic thinking Kornchulee Sungkaew; Piyamas Lungban; Sirinya Lamhya
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5393-5404

Abstract

The purpose of this study is to create a digital educational game that promotes algorithmic thinking for elementary school students. However, the processes of game development differ from traditional software development which cannot guarantee its effectiveness in terms of human-machine interfaces. In this article, we propose a new game development software engineering (GDSE) as a model for game development. This new model aims to complement and mitigate the shortcomings of traditional software development. The principles of human-computer interaction are now incorporated in the new model. The GDSE includes design, development, usability inspection, game experience evaluation, educational value evaluation and release. It was used as a research method to develop a game that promotes algorithmic thinking for children. The results of this study are not only a digital educational game that promotes algorithmic thinking for children but also a new game development life cycle that guarantees the performance of digital games in terms of usability enhancement, game experience and educational value.
Application of green-emitting ZnS:Eu2+ for boosting the spectrum of white light-emitting diode packages Nguyen Thi, Dieu An; Dang Huu, Phuc
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4765-4771

Abstract

Through utilizing a nonlinear application to acquire the best lumen efficiency (LE) for radiation (also known as LER) when color rendering index (CRI) value, especially CRI of R9 for strong red exceeds 90 with correlated color temperature (CCT) range of 2700-6500 K, the white light emitting diodes (WLED) package with adjustable CCT value and comprised of mixed-type light-emitting diodes (LEDs) can be acquired. The WLED model here contains blue and red LEDs with direct emission and a phosphorconversion blue LED or pc/B-LED (including orange and green phosphors mixed with blue LED colorant). The peak wavelengths of each LED constituent are 465 and 628 nm for LEDs in blue and red, 452 nm for the blue LED colorant, 530 and 586 nm for the phosphors exhibiting green and orange colors. Under the CCT of 2722-6464 K, the attained actual LED package, either with conversion phosphor, in red or in blue, possibly displays both CRI and R9 values measured from 90 to 96, color quality scale (CQS) values measured from 89 to 94, with LERs and LEs of 303-358 lm/W and 105-119 lm/W, respectively.
A new procedure for lung region segmentation from computed tomography images Mohd Firdaus Abdullah; Siti Noraini Sulaiman; Muhammad Khusairi Osman; Noor Khairiah Abdul Karim; Samsul Setumin; Iza Sazanita Isa
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4978-4987

Abstract

Lung cancer is the leading cause of cancer death among people worldwide. The primary aim of this research is to establish an image processing method for lung cancer detection. This paper focuses on lung region segmentation from computed tomography (CT) scan images. In this work, a new procedure for lung region segmentation is proposed. First, the lung CT scan images will undergo an image thresholding stage before going through two morphological reconstruction and masking stages. In between morphological and masking stages, object extraction, border change, and object elimination will occur. Finally, the lung field will be annotated. The outcomes of the proposed procedure and previous lung segmentation methods i.e., the modified watershed segmentation method is compared with the ground truth images for performance evaluation that will be carried out both in qualitative and quantitative manners. Based on the analyses, the new proposed procedure for lung segmentation, denotes better performance, an increment by 0.02% to 3.5% in quantitative analysis. The proposed procedure produced better-segmented images for qualitative analysis and became the most frequently selected method by the 22 experts. This study shows that the outcome from the proposed method outperforms the existing modified watershed segmentation method.
Performance evaluation of route optimization management of producer mobility in information-centric networking Xian Wee Low; Yu-Beng Leau; Zhiwei Yan; Yong-Jin Park; Mohammed Anbar
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5260-5271

Abstract

Named data networking (NDN) is a network service evolving the Internet's host-based packet delivery model. The idea of NDN is to use named data for routing, which specifies what they are looking for, instead of using location addresses that determine where they expect it to be provided. This architecture is expected to solve many issues that are currently faced by transmission control protocol/internet protocol (TCP/IP) architecture, such as scalability, robustness, mobility, security, and etcetera. One of the problems is about handling producer mobility. Considering the explosion growth rate of Internet connection in public transport vehicles, this is a challenge that needs to be overcome. Therefore, we have proposed a new scheme called route optimization management of producer mobility (ROM-P) with new features such as distributing anchor points and caching by using the same data name and com-paring our previous scheme, efficient producer mobility support (EPMS). This paper shows the analysis result between the ROM-P and EPMS by using simulation. All simulations were conducted using ndnSIM 2.4 NS-3 based. Throughout the simulation ROM-P shows a promising development in better performing compares to EPMS.

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