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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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A deep learning framework to detect Covid-19 disease via chest X-ray and CT scan images Kamil, Mohammed Y.
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.pp844-850

Abstract

COVID-19 disease has rapidly spread all over the world at the beginning of this year. The hospitals' reports have told that low sensitivity of RT-PCR tests in the infection early stage. At which point, a rapid and accurate diagnostic technique, is needed to detect the Covid-19. CT has been demonstrated to be a successful tool in the diagnosis of disease. A deep learning framework can be developed to aid in evaluating CT exams to provide diagnosis, thus saving time for disease control. In this work, a deep learning model was modified to Covid-19 detection via features extraction from chest X-ray and CT images. Initially, many transfer-learning models have applied and comparison it, then a VGG-19 model was tuned to get the best results that can be adopted in the disease diagnosis. Diagnostic performance was assessed for all models used via the dataset that included 1000 images. The VGG-19 model achieved the highest accuracy of 99%, sensitivity of 97.4%, and specificity of 99.4%. The deep learning and image processing demonstrated high performance in early Covid-19 detection. It shows to be an auxiliary detection way for clinical doctors and thus contribute to the control of the pandemic.
Smart detection of offensive words in social media using the soundex algorithm and permuterm index Malek Z. Alksasbeh; Bassam A. Y. Alqaralleh; Tamer Abukhalil; Anas Abukaraki; Tawfiq Al Rawashdeh; Moha'med Al-Jaafreh
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.pp4431-4438

Abstract

Offensive posts in the social media that are inappropriate for a specific age, level of maturity, or impression are quite often destined more to unadult than adult participants. Nowadays, the growth in the number of the masked offensive words in the social media is one of the ethically challenging problems. Thus, there has been growing interest in development of methods that can automatically detect posts with such words. This study aimed at developing a method that can detect the masked offensive words in which partial alteration of the word may trick the conventional monitoring systems when being posted on social media. The proposed method progresses in a series of phases that can be broken down into a pre-processing phase, which includes filtering, tokenization, and stemming; offensive word extraction phase, which relies on using the soundex algorithm and permuterm index; and a post-processing phase that classifies the users’ posts in order to highlight the offensive content. Accordingly, the method detects the masked offensive words in the written text, thus forbidding certain types of offensive words from being published. Results of evaluation of performance of the proposed method indicate a 99% accuracy of detection of offensive words.
Improved control and monitor two different PLC using LabVIEW and NI-OPC server Ignatius Deradjad Pranowo; Dian Artanto
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.pp3003-3012

Abstract

This paper proposes an improved control and monitors between two different PLCs, the Mitsubishi, and Omron. The main advantage is interoperability and communication between both PLC. The use of NI OPC server as the software interface reached interoperability and communication. There were developed two field applications to test interoperability. Laboratory virtual instrument engineering workbench (LabVIEW) uses as the software application for creating the user interface to control and monitor. This improvement show OPC server technology solves data compatibility issue between different driver controller’s and reducing development cost. Regardless of whether there are more than two different PLCs, it's enough to use the NI OPC server. So the benefit of the NI OPC server is not limited to two types of PLC used right now but can also use the other manufacturers. Besides, the improvement of the previous study is the use of the LabVIEW makes data from the OPC server displayed more realistic. The use of LabVIEW allows additional monitoring functions, one of which is LabVIEW vision. Data utilization becomes more flexible, and so it can use for more complex purposes. It is envisaged that this is very useful for Integrator engineer to implement this method in industrial automation
Web document classification using topic modeling based document ranking Youngseok Lee; Jungwon Cho
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.pp2386-2392

Abstract

In this paper, we propose a web document ranking method using topic modeling for effective information collection and classification. The proposed method is applied to the document ranking technique to avoid duplicated crawling when crawling at high speed. Through the proposed document ranking technique, it is feasible to remove redundant documents, classify the documents efficiently, and confirm that the crawler service is running. The proposed method enables rapid collection of many web documents; the user can search the web pages with constant data update efficiently. In addition, the efficiency of data retrieval can be improved because new information can be automatically classified and transmitted. By expanding the scope of the method to big data based web pages and improving it for application to various websites, it is expected that more effective information retrieval will be possible.
An optimum location of on-grid bifacial based photovoltaic system in Iraq Amina Mahmoud Shakir; Siba Monther Yousif; Anas Lateef Mahmood
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.pp250-261

Abstract

Bifacial photovoltaic (PV) module can gain 30% more energy compared to monofacial if a suitable location were chosen. Iraq (a Middle East country) has a variable irradiation level according to its geographic coordinates, thus, the performance of PV systems differs. This paper an array (17 series, 13 parallel) was chosen to produce 100 kWp for an on-grid PV system. It investigates the PV system in three cities in Iraq (Mosul, Baghdad, and Basrah). Effect of albedo factor, high and pitch of the bifacial module on energy yield have been studied using PVsyst (software). It has been found that the effect is less for a pitch greater than 6 m. The energy gained from bifacial and monofacial PV system module in these cities shows that Mosul is the most suitable for installing both PV systems followed by Baghdad and lastly Basrah. However, in Basrah, the bifacial gain is 12% higher in the energy than monofacial as irradiation there is higher than the other locations, especially for elevation above 1.5 m. Moreover, the cost of bifacial array is 7.23% higher than monofacial, but this additional cost is acceptable since the bifacial gain is about 11.3% higher energy compared to the monofacial.
Visual, navigation and communication aid for visually impaired person Sagor Saha; Farhan Hossain Shakal; Mufrath Mahmood
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.pp1276-1283

Abstract

The loss of vision restrained the visually impaired people from performing their daily task. This issue has impeded their free-movement and turned them into dependent a person. People in this sector did not face technologies revamping their situations. With the advent of computer vision, artificial intelligence, the situation improved to a great extent. The propounded design is an implementation of a wearable device which is capable of performing a lot of features. It is employed to provide visual instinct by recognizing objects, identifying the face of choices. The device runs a pre-trained model to classify common objects from household items to automobiles items. Optical character recognition and Google translate were executed to read any text from image and convert speech of the user to text respectively. Besides, the user can search for an interesting topic by the command in the form of speech. Additionally, ultrasonic sensors were kept fixed at three positions to sense the obstacle during navigation. The display attached help in communication with deaf person and GPS and GSM module aid in tracing the user. All these features run by voice commands which are passed through the microphone of any earphone. The visual input is received through the camera and the computation task is processed in the raspberry pi board. However, the device seemed to be effective during the test and validation.
A new method for vehicles detection and tracking using information and image processing Mazouzi Amine; Kerfa Djoudi; Ismail Rakip Karas
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.pp4942-4949

Abstract

In this article, a new method of vehicles detecting and tracking is presented: A thresholding followed by a mathematical morphology treatment are used. The tracking phase uses the information about a vehicle. An original labeling is proposed in this article. It helps to reduce some artefacts that occur at the detection level. The main contribution of this article lies in the possibility of merging information of low level (detection) and high level (tracking). In other words, it is shown that many artefacts resulting from image processing (low level) can be detected, and eliminated thanks to the information contained in the labeling (high level). The proposed method has been tested on many video sequences and examples are given illustrating the merits of our approach.
Pine wilt disease spreading prevention system using semantic segmentation Chulhyun Hwang; Jaean Jeong; Hoekyung Jung
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.pp2666-2673

Abstract

Pine wilt disease is a disease that affects ecosystems by rapidly killing trees in a short period of time due to the close interaction between three factors such as trees, mediates, and pathogens. There is no 100% mortality infectious forest pests. According to the Korea Forest Service survey, as of April 2019, the damage of pine re-nematode disease was about 490,000 dead trees in 117 cities, counties and wards across the country. It's a fatal condition. In order to prevent this problem, this paper proposes a system that detects dead trees, early infection trees, and the like, using deep learning-based semantic segmentation. In addition, drones were used to photograph the area of the forest, and a separate pixel segmentation label could be used to identify three levels of transmission information: Suspicion, attention, and confirmation. This allows the user to grasp information such as area, location, and alarm to prevent the spread of re-nematode disease.
Deep segmentation of the liver and the hepatic tumors from abdomen tomography images Nermeen Elmenabawy; Mervat El-Seddek; Hossam El-Din Moustafa; Ahmed Elnakib
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.pp303-310

Abstract

A pipelined framework is proposed for accurate, automated, simultaneous segmentation of the liver as well as the hepatic tumors from computed tomography (CT) images. The introduced framework composed of three pipelined levels. First, two different transfers deep convolutional neural networks (CNN) are applied to get high-level compact features of CT images. Second, a pixel-wise classifier is used to obtain two output-classified maps for each CNN model. Finally, a fusion neural network (FNN) is used to integrate the two maps. Experimentations performed on the MICCAI’2017 database of the liver tumor segmentation (LITS) challenge, result in a dice similarity coefficient (DSC) of 93.5% for the segmentation of the liver and of 74.40% for the segmentation of the lesion, using a 5-fold cross-validation scheme. Comparative results with the state-of-the-art techniques on the same data show the competing performance of the proposed framework for simultaneous liver and tumor segmentation.
Growth of relational model: Interdependence and complementary to big data Sucharitha Shetty; B. Dinesh Rao; Srikanth Prabhu
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.pp1780-1795

Abstract

A database management system is a constant application of science that provides a platform for the creation, movement, and use of voluminous data. The area has witnessed a series of developments and technological advancements from its conventional structured database to the recent buzzword, bigdata. This paper aims to provide a complete model of a relational database that is still being widely used because of its well known ACID properties namely, atomicity, consistency, integrity and durability. Specifically, the objective of this paper is to highlight the adoption of relational model approaches by bigdata techniques. Towards addressing the reason for this in corporation, this paper qualitatively studied the advancements done over a while on the relational data model. First, the variations in the data storage layout are illustrated based on the needs of the application. Second, quick data retrieval techniques like indexing, query processing and concurrency control methods are revealed. The paper provides vital insights to appraise the efficiency of the structured database in the unstructured environment, particularly when both consistency and scalability become an issue in the working of the hybrid transactional and analytical database management system.

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