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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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An effective method for clustering-based web service recommendation Ha Huy Cuong Nguyen; Bui Thanh Khiet; Van Loi Nguyen; Thanh Thuy Nguyen
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1571-1578

Abstract

Normally web services are classified by the quality of services; however, the term quality is not absolute and defined relatively. The quality of web services is measured or derived using various parameters like reliability, scalability, flexibility, and availability. The limitation of the methods employing these parameters is that sometimes they are producing similar web services in recommendation lists. To address this research problem, the novel improved clustering-based web service recommendation method is proposed in this paper. This approach is mainly dealing with producing diversity in the results of web service recommendations. In this method, functional interest, quality of service (QoS) preference, and diversity features are combined to produce a unique recommendation list of web services to end-users. To produce the unique recommendation results, we propose a varied web service classification order that is clustering-based on web services’ functional relevance such as non-useful pertinence, recorded client intrigue importance, and potential client intrigue significance. Additionally, to further improve the performance of this approach, we designed web service graph construction, an algorithm of various widths clustering. This approach serves to enhance the exceptional quality, that is, the accuracy of web service recommendation outcomes. The performance of this method was implemented and evaluated against existing systems for precision, and f-score performance metrics, using the research datasets.
Land use/land cover classification using machine learning models Subhra Swetanisha; Amiya Ranjan Panda; Dayal Kumar Behera
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp2040-2046

Abstract

An ensemble model has been proposed in this work by combining the extreme gradient boosting classification (XGBoost) model with support vector machine (SVM) for land use and land cover classification (LULCC). We have used the multispectral Landsat-8 operational land imager sensor (OLI) data with six spectral bands in the electromagnetic spectrum (EM). The area of study is the administrative boundary of the twin cities of Odisha. Data collected in 2020 is classified into seven land use classes/labels: river, canal, pond, forest, urban, agricultural land, and sand. Comparative assessments of the results of ten machine learning models are accomplished by computing the overall accuracy, kappa coefficient, producer accuracy and user accuracy. An ensemble classifier model makes the classification more precise than the other state-of-the-art machine learning classifiers.
Medium access control protocol design for wireless communications and networks review Jaber, Zaid Hashim; Kadhim, Dheyaa Jasim; Al-Araji, Ahmed Sabah
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1711-1723

Abstract

Medium access control (MAC) protocol design plays a crucial role to increase the performance of wireless communications and networks. The channel access mechanism is provided by MAC layer to share the medium by multiple stations. Different types of wireless networks have different design requirements such as throughput, delay, power consumption, fairness, reliability, and network density, therefore, MAC protocol for these networks must satisfy their requirements. In this work, we proposed two multiplexing methods for modern wireless networks: Massive multiple-input-multiple-output (MIMO) and power domain non-orthogonal multiple access (PD-NOMA). The first research method namely Massive MIMO uses a massive number of antenna elements to improve both spectral efficiency and energy efficiency. On the other hand, the second research method (PD-NOMA) allows multiple non-orthogonal signals to share the same orthogonal resources by allocating different power level for each station. PD-NOMA has a better spectral efficiency over the orthogonal multiple access methods. A review of previous works regarding the MAC design for different wireless networks is classified based on different categories. The main contribution of this research work is to show the importance of the MAC design with added optimal functionalities to improve the spectral and energy efficiencies of the wireless networks.
Statistical modeling and optimal energy distribution of cogeneration units by genetic algorithms Othmane Maakoul; Hamid El Omari; Aziza Abid
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1131-1138

Abstract

Our main objective is to evaluate the performance of a new method to optimize the energy management of a production system composed of six cogeneration units using artificial intelligence. The optimization criterion is economic and environmental in order to minimize the total fuel cost, as well as the reduction of polluting gas emissions such as COx, NOx and SOx. First, a statistical model has been developed to determine the power that the cogeneration units can provide. Then, an economic model of operation was developed: fuel consumption and pollutant gas emissions as a function of the power produced. Finally, we studied the energy optimization of the system using genetic algorithms (GA), and contribute to the research on improving the efficiency of the studied power system. The GA has a better optimization performance, it can easily choose satisfactory solutions according to the optimization objectives, and compensate for these defects using its own characteristics. These characteristics make GA have outstanding advantages in iterative optimization. The robustness of the proposed algorithm is validated by testing six cogeneration units, and the obtained simulation results of the proposed system prove the value and effectiveness of GA for efficiency improvement as well as operating cost minimization.
Simulation and performance assessment of a modified throttled load balancing algorithm in cloud computing environment Noha G. Elnagar; Ghada F. Elkabbany; Amr A. Al-Awamry; Mohamed B. Abdelhalim
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp2087-2096

Abstract

Load balancing is crucial to ensure scalability, reliability, minimize response time, and processing time and maximize resource utilization in cloud computing. However, the load fluctuation accompanied with the distribution of a huge number of requests among a set of virtual machines (VMs) is challenging and needs effective and practical load balancers. In this work, a two listed throttled load balancer (TLT-LB) algorithm is proposed and further simulated using the CloudAnalyst simulator. The TLT-LB algorithm is based on the modification of the conventional TLB algorithm to improve the distribution of the tasks between different VMs. The performance of the TLT-LB algorithm compared to the TLB, round robin (RR), and active monitoring load balancer (AMLB) algorithms has been evaluated using two different configurations. Interestingly, the TLT-LB significantly balances the load between the VMs by reducing the loading gap between the heaviest loaded and the lightest loaded VMs to be 6.45% compared to 68.55% for the TLB and AMLB algorithms. Furthermore, the TLT-LB algorithm considerably reduces the average response time and processing time compared to the TLB, RR, and AMLB algorithms.
Improving collaborative filtering using lexicon-based sentiment analysis Rouhia Mohammed Sallam; Mahmoud Hussein; Hamdy M. Mousa
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1744-1753

Abstract

Since data is available increasingly on the Internet, efforts are needed to develop and improve recommender systems to produce a list of possible favorite items. In this paper, we expand our work to enhance the accuracy of Arabic collaborative filtering by applying sentiment analysis to user reviews, we also addressed major problems of the current work by applying effective techniques to handle the scalability and sparsity problems. The proposed approach consists of two phases: the sentiment analysis and the recommendation phase. The sentiment analysis phase estimates sentiment scores using a special lexicon for the Arabic dataset. The item-based and singular value decomposition-based collaborative filtering are used in the second phase. Overall, our proposed approach improves the experiments’ results by reducing average of mean absolute and root mean squared errors using a large Arabic dataset consisting of 63,000 book reviews.
A data mining analysis of COVID-19 cases in states of United States of America Özerk Yavuz
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1754-1758

Abstract

Epidemic diseases can be extremely dangerous with its hazarding influences. They may have negative effects on economies, businesses, environment, humans, and workforce. In this paper, some of the factors that are interrelated with COVID-19 pandemic have been examined using data mining methodologies and approaches. As a result of the analysis some rules and insights have been discovered and performances of the data mining algorithms have been evaluated. According to the analysis results, JRip algorithmic technique had the most correct classification rate and the lowest root mean squared error (RMSE). Considering classification rate and RMSE measure, JRip can be considered as an effective method in understanding factors that are related with corona virus caused deaths.
A new high speed charge and high efficiency Li-Ion battery charger interface using pulse control technique Mustapha El Alaoui; Karim El Khadiri; Rachid El Alami; Ahmed Tahiri; Ahmed Lakhssassi; Hassan Qjidaa
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1168-1179

Abstract

A new Li-Ion battery charger interface (BCI) using pulse control (PC) technique is designed and analyzed in this paper. Thanks to the use of PC technique, the main standards of the Li-Ion battery charger, i.e. fast charge, small surface area and high efficiency, are achieved. The proposed charger achieves full charge in forty-one minutes passing by the constant current (CC) charging mode which also included the start-up and the constant voltage mode (CV) charging mode. It designed, simulated and layouted which occupies a small size area 0.1 mm2 by using Taiwan Semiconductor Manufacturing Company 180 nm complementary metal oxide semi-conductor technology (TSMC 180 nm CMOS) technology in Cadence Virtuoso software. The battery voltage VBAT varies between 2.9 V to 4.35 V and the maximum battery current IBAT is 2.1 A in CC charging mode, according to a maximum input voltage VIN equal 5 V. The maximum charging efficiency reaches 98%.
An innovative method of fault detection in power transformers Vladimir Mikhailovich Levin; Ammar Abdulazez Yahya
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1123-1130

Abstract

The Bayesian classifier is a priori the optimal solution for minimizing the total error in problems of statistical pattern recognition. The article suggests using the classifier as a regular tool to increase the reliability of defect recognition in power oil-filled transformers based on the results of the analysis of gases dissolved in oil. The wide application of the Bayesian method for solving tasks of technical diagnostics of electrical equipment is limited by the problem of the multidimensional distribution of random parameters (features) and the nonlinearity of classification. The application of a generalized feature of a defect in the form of a nonlinear function of the transformer state parameters is proposed. This simultaneously reduces the dimension of the initial space of the controlled parameters and significantly improves the stochastic properties of the random distribution of the generalized feature. A special algorithm has been developed to perform statistical calculations and the procedure for recognizing the current technical condition of the transformer using the generated decision rule. The presented research results illustrate the possibility of the practical application of the developed method in the conditions of real operation of power transformers.
Expansion dataset COVID-19 chest X-ray using data augmentation and histogram equalization Farah Flayeh Alkhalid; Abdulhakeem Qusay Albayati; Ahmed Ali Alhammad
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1904-1909

Abstract

The main important factor that plays vital role in success the deep learning is the deep training by many and many images, if neural networks are getting bigger and bigger but the training datasets are not, then it sounds like going to hit an accuracy wall. Briefly, this paper investigates the current state of the art of approaches used for a data augmentation for expansion the corona virus disease 2019 (COVID-19) chest X-ray images using different data augmentation methods (transformation and enhancement) the dataset expansion helps to rise numbers of images from 138 to 5520, the increasing rate is 3,900%, this proposed model can be used to expand any type of image dataset, in addition, the dataset have used with convolutional neural network (CNN) model to make classification if detected infection with COVID-19 in X-ray, the results have gotten high training accuracy=99%

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