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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 111 Documents
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Search results for , issue "Vol 13, No 4: August 2023" : 111 Documents clear
Energy generation by crystalline silicon photovoltaic network per meter square in Iraq Tariq Emad Ali; Mohammed A. Abdala; Ameer Al-Khaykan; Dhulfiqar A. Alwahab; Counsell M. Counsell
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp3606-3612

Abstract

Iraqi people have been without energy for nearly two decades, even though their geographic position provides a high intensity of radiation appropriate for the construction of solar plants capable of producing significant quantities of electricity. Also, the annual sunny hours in Iraq are between 3,600 to 4,300 hours which makes it perfect to use the photovoltaics arrays to generate electricity with very high efficiency compared to many countries, especially in Europe. This paper shows the amount of electric energy generated by the meter square of crystalline silicon in the photovoltaic (PV) array that already installed in 18 states in Iraq for each month of the year. The results of the meter-square of PV array in three tracking positions are presented in this paper. This paper shows that the average electricity generated in North cities (Dohuk, Al-Sulaymaniyah, and Erbil) are less than the southern cities in the winter season (three positions) by about 40-50%. Iraq has a stable PV electrical generation during all the year in all regions except the North cities while the highest cities in electricity generation are (Najaf and Al-Anbar).
A proposed optimized equivalent circuit and performance analysis of dielectric barrier discharge ozone generator Mohamed El_Hanash; Mohamed I. Youssef; Magdy M. Zaky
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp3876-3884

Abstract

Traditionally, low-frequency power supplies are used in dielectric barrier discharge (DBD) ozone generators. These generators require a very high output voltage. This may limit ozone production due to limitations imposed by the dielectric strength of the insulating material. Low-frequency generators also present low efficiency, large volumes, and difficulty in controlling ozone production. On the other hand, the advantages of high frequency DBD ozone generators are the increased power density applied to the chamber electrodes, and the voltage applied to the ozone chamber decreases, allowing for higher ozone production efficiency. From this point of view, in order to enhance and control the DBD ozone generator operating at high frequency, it is necessary to determine all parameter values and optimize the equivalent model for this type of generator. This work presents and proposes the practical methodologies used to extract all parameters of the high voltage high frequency (HVHF) transformer which can be used in these systems. Resonant frequency control techniques are presented in this paper. Elimination of the stray capacitance effect will also be implemented in this paper.
Application of improved you only look once model in road traffic monitoring system Shridevi Jeevan Kamble; Manjunath R Kounte
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4612-4622

Abstract

The present research focuses on developing an intelligent traffic management solution for tracking the vehicles on roads. Our proposed work focuses on a much better you only look once (YOLOv4) traffic monitoring system that uses the CSPDarknet53 architecture as its foundation. Deep-sort learning methodology for vehicle multi-target detection from traffic video is also part of our research study. We have included features like the Kalman filter, which estimates unknown objects and can track moving targets. Hungarian techniques identify the correct frame for the object. We are using enhanced object detection network design and new data augmentation techniques with YOLOv4, which ultimately aids in traffic monitoring. Until recently, object identification models could either perform quickly or draw conclusions quickly. This was a big improvement, as YOLOv4 has an astoundingly good performance for a very high frames per second (FPS). The current study is focused on developing an intelligent video surveillance-based vehicle tracking system that tracks the vehicles using a neural network, image-based tracking, and YOLOv4. Real video sequences of road traffic are used to test the effectiveness of the method that has been suggested in the research. Through simulations, it is demonstrated that the suggested technique significantly increases graphics processing unit (GPU) speed and FSP as compared to baseline algorithms.
Assimilating sense into disaster recovery databases and judgement framing proceedings for the fastest recovery Vaheedbasha Shaik; Natarajan Kalyanasundaram
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4234-4245

Abstract

The replication between the primary and secondary (standby) databases can be configured in either synchronous or asynchronous mode. It is referred to as out-of-sync in either mode if there is any lag between the primary and standby databases. In the previous research, the advantages of the asynchronous method were demonstrated over the synchronous method on highly transactional databases. The asynchronous method requires human intervention and a great deal of manual effort to configure disaster recovery database setups. Moreover, in existing setups there was no accurate calculation process for estimating the lag between the primary and standby databases in terms of sequences and time factors with intelligence. To address these research gaps, the current work has implemented a self-image looping database link process and provided decision-making capabilities at standby databases. Those decisions from standby are always in favor of selecting the most efficient data retrieval method and being in sync with the primary database. The purpose of this paper is to add intelligence and automation to the standby database to begin taking decisions based on the rate of concurrency in transactions at primary and out-of-sync status at standby.
Proposed energy efficient clustering and routing for wireless sensor network Gundeboyina Srinivasalu; Hanumanthappa Umadevi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4127-4135

Abstract

Wireless sensor network (WSN) is considered a growing research field that includes numerous sensor nodes used to gather, process, and broadcast information. Energy efficiency is considered one of the challenging tasks in the WSN. The clustering and routing are considered capable approaches to solve the issues of energy efficiency and enhance the network’s lifetime. In this research, the multi-objective-energy based black widow optimization algorithm (M-EBWOA) is proposed to perform the cluster-based routing over the WSN. The M-EBWOA-based optimal cluster head discovery is used to assure an energy-aware routing over the WSN. The main goal of this M-EBWOA is to minimize the energy consumed by the nodes while improving the data delivery of the WSN. The performance of the M-EBWOA is analyzed as alive and dead nodes, dissipated energy, packets sent to base station, and life expectancy. The existing research such as low-energy adaptive clustering hierarchy (LEACH), hybrid grey wolf optimizer-based sunflower optimization (HGWSFO), genetic algorithm-particle swarm optimization (GA-PSO), and energy-centric multi-objective Salp Swarm algorithm (ECMOSSA) are used to evaluate the efficiency of M-EBWOA. The alive nodes of the M-EBWOA are 100 for 2,500 rounds, which is higher than the LEACH, HGWSFO, GA-PSO, and ECMOSSA.
The role of neural network for estimating real estate prices value in post COVID-19: a case of the middle east market Laith T. khrais; Osman Saad Shidwan
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4516-4525

Abstract

The main goal of this paper was to explore the use of an artificial neural network (ANN) model in predicting real estate prices in the Middle East market. Although conventional modeling approaches such as regression can be used in prediction, they have a weakness of a predetermined relationship between input and output. In this regard, using the ANN model was expected to reduce the bias and ensure non-linear relationships are also covered in the prediction process for more accurate results. The ANN model was created using Python v.3.10 program. The model exhibited a high correlation between predicted and actual house price data (R=0.658). In this respect, it was realized that the model could be effectively used in appraising real estate by investors. However, a major limitation of the model was realized to be a limited dataset for large and luxurious houses, which were not accurately predicted as data distribution between actual and predicted values became sparse for high house prices. A key recommendation made is that future research should include more variables related to luxurious houses and macroeconomic factors to increase the ANN model accuracy.
Ecological impact due to the implementation of a modeled and optimized hybrid system Othmane Echarradi; Abdessamad Benlafkih; Abdelkader Hadjoudja; Mounir Fahoume
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp3706-3719

Abstract

This paper presents a very alarming forecasts about our future and particularly for a medium and long-term future. That is to say, several actions are being carried out by different civil and state parties to deal with these very concrete threats. And it is within this framework that this paper fits, and its objective is to highlight the hybrid systems and more precisely the photovoltaic-wind hybrid systems coupled with storage batteries, as an efficient alternative to the classical means of electricity production. This work will adopt a method of obtaining results called performance evaluation, so this manuscript will present firstly the mathematical model of this hybrid system to best conceive what it is about, then as second part will determine the exact figures of the largest amount of carbon dioxide (CO2) that can be avoided using this technology and following a precise methodology, this can be applied to our situation, i.e., a simple house or to any other type of installation.
An ontology-based spatial group decision support system for site selection application Aicha Benelhadj Djelloul; Djamila Hamdadou
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4488-4503

Abstract

This paper presents a new ontology-based multicriteria spatial group decision support system (GDSS) dedicated to site selection problems. Site selection is one of the most complex problems in the construction of a new building. It presents a crucial problem in terms of selecting the appropriate site among a group of decision makers with multiple alternatives (sites); in addition, the site must satisfy several criteria. To deal with this, the present paper introduces an ontology based multicriteria analysis method to solve semantic heterogeneity in vocabulary used by participants in spatial group decision support systems. The advantages of using ontology in GDSS are many: i) it enables the integration of heterogeneous sources of data available on the web and ii) it enables to facilitate meaning and sharing of data used in GDSS by participants. In order to facilitate cooperation and collaboration between participants in GDSS, our work aims to apply ontology at the model's structuration phase. The proposed system has been successfully implemented and exploited for a personalized environment.
Susceptible exposed infectious recovered-machine learning for COVID-19 prediction in Saudi Arabia Mutasem K. Alsmadi; Ghaith M. Jaradat; Sami A. Abahussain; Mohammed Fahed Tayfour; Usama A. Badawi; Hayat Alfagham; Muneerah Ebrahem Alshabanah; Daniah Abdulrahman Alrajhi; Hanouf Naif ALkhaldi; Njoud Ahmad Altuwaijri; Hany Answer ShoShan; Hayah Mohamed Abouelnaga; Ahmed Baz Mohamed Metwally
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4761-4776

Abstract

Susceptible exposed infectious recovered (SEIR) is among the epidemiological models used in forecasting the spread of disease in large populations. SEIR is a fitting model for coronavirus disease (COVID-19) spread prediction. Somehow, in its original form, SEIR could not measure the impact of lockdowns. So, in the SEIR equations system utilized in this study, a variable was included to evaluate the impact of varying levels of social distance on the transmission of COVID-19. Additionally, we applied artificial intelligence utilizing the deep neural network machine learning (ML) technique. On the initial spread data for Saudi Arabia that were available up to June 25th, 2021, this improved SEIR model was used. The study shows possible infection to around 3.1 million persons without lockdown in Saudi Arabia at the peak of spread, which lasts for about 3 months beginning from the lockdown date (March 21st). On the other hand, the Kingdom's current partial lockdown policy was estimated to cut the estimated number of infections to 0.5 million over nine months. The data shows that stricter lockdowns may successfully flatten the COVID-19 graph curve in Saudi Arabia. We successfully predicted the COVID-19 epidemic's peaks and sizes using our modified deep neural network (DNN) and SEIR model.
Vehicle positioning in urban environments using particle filtering-based global positioning system, odometry, and map data fusion Abdelkabir Lahrech; Aziz Soulhi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp3924-3938

Abstract

This article presents a new method for land vehicle navigation using global positioning system (GPS), dead reckoning sensor (DR), and digital road map information, particularly in urban environments where GPS failures can occur. The odometer sensors and map measure can be used to provide continuous navigation and correct the vehicle location in the presence of GPS masking. To solve this estimation problem for vehicle navigation, we propose to use particle filtering for GPS/odometer/map integration. The particle filter is a method based on the Bayesian estimation technique and the Monte Carlo method, which deals with non-linear models and is not limited to Gaussian statistics. When the GPS sensor cannot provide a location due to the number of satellites in view, the filter fuses the limited GPS pseudo-range data to enhance the vehicle positioning. The developed filter is then tested in a transportation network scenario in the presence of GPS failures, which shows the advantages of the proposed approach for vehicle location compared to the extended Kalman filter.

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