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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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Comparative analysis of Dimensions and Scopus bibliographic data sources: an approach to university research productivity Pachisa Kulkanjanapiban; Tipawan Silwattananusarn
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.pp706-720

Abstract

This paper shows a significant comparison of two primary bibliographic data sources at the document level of Scopus and Dimensions. The emphasis is on the differences in their document coverage by institution level of aggregation. The main objective is to assess whether Dimensions offers at the institutional level good new possibilities for bibliometric analysis as at the global level. The results of a comparative study of the citation count profiles of articles published by faculty members of Prince of Songkla University (PSU) in Dimensions and Scopus from the year the databases first included PSU-authored papers (1970 and 1978, respectively) through the end of June 2020. Descriptive statistics and correlation analysis of 19,846 articles indexed in Dimensions and 13,577 indexed in Scopus. The main finding was that the number of citations received by Dimensions was highly correlated with citation counts in Scopus. Spearman’s correlation between citation counts in Dimensions and Scopus was a high and mighty relationship. The findings mainly affect Dimensions’ possibilities as instruments for carrying out bibliometric analysis of university members’ research productivity. University researchers can use Dimensions to retrieve information, and the design policies can be used to evaluate research using scientific databases.
Power dissipation analysis of PV module under partial shading Byunggyu Yu; Seok-Cheol Ko
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.pp1029-1035

Abstract

Photovoltaic (PV) generation has been growing dramatically over the last years and it ranges from small, rooftop-mounted or building integrated systems, to large utility scale power stations. Especially for rooftop-mounted PV system, PV modules are serially connected to match with PV inverter input voltage specification. For serially connected PV system, shading is a problem since the shaded PV module reduces the output whole string of PV modules. The excess power from the unshaded PV module is dissipated in the shaded PV module. In this paper, power dissipation of PV module under partial shading is analyzed with circuit analysis for series connected PV modules. The specific current and voltage operating point of the shaded PV module are analyzed under shading. PSIM simulation tool is used to verify the power dissipation analysis. When there is no bypass diode and three solar modules are connected in series, upto 39.1% of the total maximum PV power is dissipated in the shaded PV module. On the other hand, when the bypass is attached, 0.3% of the total maximum power is generated as a loss in the shaded PV module. The proposed analysis technique of shaded PV module could be used in PV system performance analysis, especially for maximum power point tracking (MPPT) performance.
Green distributed algorithm for energy saving in IP wired networks using sleep scheduling Mohammed Hussein; Wisam Alabbasi; Ahmad Alsadeh
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp5160-5169

Abstract

Energy saving has become a critical issue and a great challenge in the past few decades, and a great effort as well is being made to reduce consumed energy. The Internet forms a major source for energy consumption. Therefore, in this work we propose an algorithm for energy saving in distributed backbone networks, the reduced energy consumption (RedCon) algorithm. In this paper, we introduce a new version for saving energy on the Internet by switching off underutilized links and switching on idle links when the network is overloaded in a distributed manner over the network nodes based on LSA messages and without any knowledge of the traffic matrix. Our algorithm is more accurate and outperforms other algorithms with its time checks and advanced learning algorithm.
Deep learning for COVID-19 diagnosis based on chest X-ray images Nashat Alrefai; Othman Ibrahim
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.pp4531-4541

Abstract

Coronavirus disease 2019 (COVID-19) is a recent global pandemic that has affected many countries around the world, causing serious health problems, especially in the lungs. Although temperature testing is suggested as a firstline test for COVID-19, it was not reliable because many diseases have the same symptoms. Thus, we propose a deep learning method based on X-ray images that used a convolutional neural network (CNN) and transfer learning (TL) for COVID-19 diagnosis, and using gradient-weighted class activation mapping (Grad-CAM) technique for producing visual explanations for the COVID-19 infection area in the lung. The low sample size of coronavirus samples was considered a challenge, thus, this issue was overridden using data augmentation techniques. The study found that the proposed (CNN) and the modified pre-trained networks VGG16 and InceptionV3 achieved a promising result for COVID-19 diagnosis by using chest X-ray images. The proposed CNN was able to differentiate 284 patients with COVID-19 or normal with 98.2 percent for training accuracy and 96.66 percent for test accuracy and 100.0 percent sensitivity. The modified VGG16 achieved the best classification result between all with 100.0 percent for training accuracy and 98.33 percent for test accuracy and 100.0 percent sensitivity, but the proposed CNN overcame the others in the side of reducing the computational complexity and training time significantly.
Research trends on CAPTCHA: A systematic literature Igbekele Emmanuel O.; Adebiyi Ayodele A.; Ibikunle Francis A.; Adebiyi Marion O.; Olugbara O. Oludayo
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.pp4300-4312

Abstract

The advent of technology has crept into virtually all sectors and this has culminated in automated processes making use of the Internet in executing various tasks and actions. Web services have now become the trend when it comes to providing solutions to mundane tasks. However, this development comes with the bottleneck of authenticity and intent of users. Providers of these Web services, whether as a platform, as a software or as an Infrastructure use various human interaction proof’s (HIPs) to validate authenticity and intent of its users. Completely automated public turing test to tell computer and human apart (CAPTCHA), a form of IDS in web services is advantageous. Research into CAPTCHA can be grouped into two -CAPTCHA development and CAPTCH recognition. Selective learning and convolutionary neural networks (CNN) as well as deep convolutionary neural network (DCNN) have become emerging trends in both the development and recognition of CAPTCHAs. This paper reviews critically over fifty article publications that shows the current trends in the area of the CAPTCHA scheme, its development and recognition mechanisms and the way forward in helping to ensure a robust and yet secure CAPTCHA development in guiding future research endeavor in the subject domain.
5G uplink interference simulations, analysis and solutions: The case of pico cells dense deployment Balboul Younes; Fattah Mohammed; Mazer Saïd; Moulhime El Bekkali
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.pp2245-2255

Abstract

The launch of the new mobile network technology has paved the way for advanced and more productive industrial applications based on high-speed and low latency services offered by 5G. One of the key success points of the 5G network is the available diversity of cell deployment modes and the flexibility in radio resources allocation based on user’s needs. The concept of Pico cells will become the future of 5G as they increase the capacity and improve the network coverage at a low deployment cost. In addition, the short-range wireless transmission of this type of cells uses little energy and will allow dense applications for the internet of things. In this contribution, we present the advantages of using Pico cells and the characteristics of this type of cells in 5G networks. Then, we will do a simulation study of the interferences impact in uplink transmission in the case of PICO cells densified deployment. Finally, we will propose a solution for interference avoidance between pico cells that also allows flexible management of bands allocated to the users in uplink according to user’s density and bandwidth demand.
Analysis of electromagnetic pollution by means of geographic information system Vladimir Henao-Cespedes; Yeison Alberto Garcés-Gómez
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp5099-5106

Abstract

Currently, telecommunications systems have become more widespread and there is still a discrepancy between whether or not non-ionizing radiation produces health problems in living beings at cellular level. From an experimental point of view, it is interesting to raise the correlation of high levels of electromagnetic pollution with health problems in urban populations which would make it possible to clearly determine the effects of this type of radiation on human health and the environment. By means of remote sensing, a geographic information system (GIS) has been developed for the analysis of electromagnetic pollution levels generated by emissions from non-ionizing radiation (NIR) sources in a city. A method for measuring electromagnetic pollution was applied, which allows the generation of a table of attributes of the GIS that is the input to generate by inverse distance weighting (IDW), the layer of electromagnetic pollution. The method, as a case study, was applied in the city of Manizales, located in Colombia, obtaining as a result a layer that allows evidence that the highest levels of electromagnetic pollution are concentrated in the most central area of the city. In this way, the effects of NIR on public health can be analyzed by means of correlations.
Parameters estimation of solar photovoltaic module using camel behavior search algorithm Hassan, Kadhim H.; Rashid, Abdulmuttalib T.; Jasim, Basil H.
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i1.pp788-793

Abstract

Finding accurate mathematical model of electrical equivalent circuit of solar photovoltaic (PV) cell is crucial to achieve and improve maximum power point, simulation design and efficiency computations for solar energy system. Due to the nonlinearity of the characteristic of solar PV cell, optimization methods are the best for estimating the electrical model parameters which lead to accurate estimating I-V curve. In this paper, camel behavior search algorithm is proposed as a new method for estimating the five different parameters for single diode model of PV solar module. This is tested on multicrystalline KC 200GT PV module. A measurement data of the module is used to verify and test the consistency of accurately estimating the set of parameters that govern the characteristics I-V relationship of solar cell. The simulation results show that the current-voltage characteristic and power-voltage curve obtained are matching to the measured experimental data set. For performance evaluation, the proposed method is simple, fast in convergence response and has an acceptable accuracy in obtaining the five estimated parameters.
Elastic hybrid MAC protocol for wireless sensor networks Jamila Bhar; Imen Bouazzi
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.pp4174-4182

Abstract

The future is moving towards offering multiples services based on the same technology. Then, billions of sensors will be needed to satisfy the diversity of these services. Such considerable amount of connected devices must insure efficient data transmission for diverse applications. Wireless sensor network (WSN) represents the most preferred technology for the majority of applications. Researches in medium access control (MAC) mechanism have been of significant impact to the application growth because the MAC layer plays a major role in resource allocation in WSNs. We propose to enhance a MAC protocol of WSN to overcome traffic changes constraints. To achieve focused goal, we use elastic hybrid MAC scheme. The main interest of the developed MAC protocol is to design a medium access scheme that respect different quality of services (QoS) parameters needed by various established traffic. Simulation results show good improvement in measured parameters compared to typical protocol.
Breast cancer diagnosis system using hybrid support vector machine-artificial neural network Tze Sheng Lim; Kim Gaik Tay; Audrey Huong; Xiang Yang Lim
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.pp3059-3069

Abstract

Breast cancer is the second most common cancer occurring in women. Early detection through mammogram screening can save more women’s lives. However, even senior radiologists may over-diagnose the clinical condition. Machine learning (ML) is the most used technique in the diagnosis of cancer to help reduce human errors. This study is aimed to develop a computer-aided detection (CAD) system using ML for classification purposes. In this work, 80 digital mammograms of normal breasts, 40 of benign and 40 of malignant cases were chosen from the mini MIAS dataset. These images were denoised using median filter after they were segmented to obtain a region of interest (ROI) and enhanced using histogram equalization. This work compared the performance of artificial neural network (ANN), support vector machine (SVM), reduced features of SVM and the hybrid SVM-ANN for classification process using the statistical and gray level co-occurrence matrix (GLCM) features extracted from the enhanced images. It is found that the hybrid SVM-ANN gives the best accuracy of 99.4% and 100% in differentiating normal from abnormal, and benign from malignant cases, respectively. This hybrid SVM-ANN model was deployed in developing the CAD system which showed relatively good accuracy of 98%.

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