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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 4: August 2022" : 112 Documents clear
Local development applied to the energy scheme using the geographic information system for decision making María Rodríguez Gámez; Antonio Vázquez Pérez; Mirelys Torres Pérez; José R. Núñez Alvarez
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3343-3351

Abstract

The availability of endogenous energy resources in the province of Manabí can play an important role in achieving a diverse and sustainable territorial energy matrix. This research work shows the results of the project called geographic information system for sustainable development through the use of renewable energy sources. For the management of the project database, the geographic information system was used, and the information analysis took into account the works published in the main international databases on the use of renewable sources, planning energy, decision-making, and local development. The work allows revealing the energy potential that the territory of Manabí has in terms of the availability of renewable energy sources using the geographic information system, which can help in the decision-making process that contributes to the achievement of a diverse and sustainable territorial energy matrix.
Fuzzy optimization strategy of the maximum power point tracking for a variable wind speed system Belkacem, Belkacem; Bouhamri, Noureddine; Koridak, Lahouari Abdelhakem; Allali, Ahmed
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4264-4275

Abstract

Wind power systems are gaining more and more interests; in order to diminish dependence on fossil fuels. In this paper, we present a variable speed-wind energy global system based on a synchronous generator with permanent magnetic (PMSG). The major goal of this study is to track the maximum power that is present in the turbine. An examination of control methods to extract the MPPT point, from a wind energy conversion system (WECS) under variable speed situations is presented. An intelligent controller based on the fuzzy logic control (FLC) is proposed for regulating permanent magnetic synchronous generator (PMSG) output power, in order to improve tracking performance. The principle of this maximum power point tracking (MPPT) algorithm consists in looking for an optimal operating relation of the maximum power, then tracking this last. We simulated our system with MATLAB-Simulink software. The found results will be debated to elucidate performance of the global system.
Resource placement strategy optimization for smart grid application using 5G wireless networks Chafi, Saad-Eddine; Balboul, Younes; Mazer, Said; Fattah, Mohammed; El Bekkali, Moulhime
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3932-3942

Abstract

With the evolution of 5G-network, wireless mobile networks are growing to take a strong stand in attempts to achieve ubiquitous large-scale acquisition, connectivity and processing. Smart-grids are among the critical areas that can benefit from the capabilities of the 5G-network, especially internet of things (IoT) applications such as massive machine-type-communications or ultra-reliable low-latency communications. A distributed cloud-services use the cloud, fog and edge computing infrastructures and applications to take advantage of every available resource including network equipment and connected objects to optimize cost, energy, and latency depending on the planned optimization criteria. In this article, we present smart-grid solution based on cloud-services and 5G-network, then we study the integration of smart-grid services in the cloud based on: placement in the cloud and in the end-device, and finally we introduce our proposed solution based on Intelligent placement strategy. The scenarios are evaluated by the iFogSim simulator, and the analyzed results compare the standard cloud placement, edge placement and our intelligent placement with regard to the optimization of the energy consumption, latency, and network usage. The findings show that cloud energy consumption can be substantially reduced using Intelligent Placement while respecting the potential central processing unit (CPU) processing power-limit for each IoT-device used and network constraints in smart-grid.
A modified residual network for detection and classification of Alzheimer’s disease Faten Salim Hanoon; Abbas Hanon Hassin Alasadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4400-4407

Abstract

Alzheimer's disease (AD) is a brain disease that significantly declines a person's ability to remember and behave normally. By applying several approaches to distinguish between various stages of AD, neuroimaging data has been used to extract different patterns associated with various phases of AD. However, because the brain patterns of older adults and those in different phases are similar, researchers have had difficulty classifying them. In this paper, the 50-layer ResNet is modified by adding extra convolution layers to make the extracted features more diverse. Besides, the activation function (ReLU) was replaced with (Leaky ReLU) because ReLU takes the negative parts of its input, drops them to zero, and retains the positive parts. These negative inputs may contain useful feature information that could aid in the development of high-level discriminative features. Thus, Leaky ReLU was used instead of ReLU to prevent any potential loss of input information. In order to train the network from scratch without encountering the issue of overfitting, we added a dropout layer before the fully connected layer. The proposed method successfully classified the four stages of AD with an accuracy of 97.49 % and 98 % for precision, recall, and f1-score.
NAGA: multi-blockchain based decentralized platform architecture for cryptocurrency payment Dendej Sawarnkatat; Sucha Smanchat
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4067-4078

Abstract

Paying for electronic commerce products with cryptocurrencies is an increasingly popular method. However, the situations where a seller expects one specific cryptocurrency as a payment while a buyer only possesses another, inevitably create inconvenience to the buyer, which may lead to cancellation of purchase. In this paper, we propose a light-weighted software architecture of a payment system called NAGA platform that works with a number of crypto blockchain networks to support cross-cryptocurrency payments where buyers can pay in one currency, and the seller automatically receives another of his/her choice. This minimizes the complexity and inconvenience of the buyer, leading to an increase in sales and revenues of the electronic commerce system. With the built-in crypto exchange, crosscryptocurrency payment can be processed with real-time exchange rates that enables both buyers and sellers to receive cryptocurrency of their preferred choices.
A hybrid approach of artificial neural network-particle swarm optimization algorithm for optimal load shedding strategy Trong Le, Nghia; Trieu Phung, Tan; Huy Quyen, Anh.; Phung Nguyen, Bao Long; Ngoc Nguyen, Au
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4253-4263

Abstract

This paper proposes an under-frequency load shedding (UFLS) method by using the optimization technique of artificial neural network (ANN) combined with particle swarm optimization (PSO) algorithm to determine the minimum load shedding capacity. The suggested technique using a hybrid algorithm ANN-PSO focuses on 2 main goals: determine whether process shedding plan or not and the distribution of the minimum of shedding power on each demand load bus in order to restore system’s frequency back to acceptable values. In the hybrid algorithm ANN-PSO, the PSO algorithm takes responsible for searching the optimal weights in the neural network structure, which can help to optimize the network training in terms of training speed and accuracy. The distribution of shedding power at each node considering the primary control and secondary control of the generators’ unit and the phase electrical distance between the outage generators and load nodes. The effectiveness of the proposed method is experimented with multiple generators outage cases at various load levels in the IEEE-37 Bus scheme where load shedding cases are considered compared with other traditional technique.
Efficient addressing schemes for internet of things Venkatesh Thamarai Kannan; Rekha Chakravarthi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4415-4429

Abstract

The internet of things (IoT) defines the connectivity of physical devices to provide the machine to machine communication. This communication is achieved through various wireless standards for sensor node connectivity. The IoT calls from the formation of various wireless sensor nodes (WSNs) in a network. The existing neighborhood discovery method had the disadvantage of time complexity to calculate the cluster distance. Our proposed method rectifies this issue and gives accurate execution time. This paper proposed mobility management system based on proxy mobile IPv6 as distributed PMIPv6 with constrained application protocol (CoAP-DPMIP) and PMIPv6 with constrained application protocol (CoAP-PMIP). It also provides the optimized transmission path to reduce the delay handover in IoT network. The PMIPv6 described the IPv6 address of mobile sensor device for efficient mobility management. The network architecture explains three protocol layers of open systems interconnection model (OSI model). The OSI layers are data link layer, network layer and transport layer. We have proposed the distance estimation algorithm for efficient data frames transmission. This paper mainly focuses the secure data transmission with minimum loss of error. The evaluation result proved that proposed technique performance with delay, energy, throughput and packet delivery ratio (PDR). Also, it measures the computational time very effectively.
Performance evaluation of dual backhaul links RF/FSO for small cells of 5G cellular system A. Aldhaibani, Jaafar; Naser Jurn, Yaseen; Ibrahim Abdulkhaleq, Nadhir
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp3922-3931

Abstract

Radio frequency (RF) backhaul links between the wireless stations especially between small cells stations such as relay stations (RSs) are insufficient with high capacities and huge number of users for 5G cellular networks. An alternative solution is using free space optics (FSO) communications, however, there are limitations in this system. In this paper we proposed a mixed of RF link together with FSO link. Using hardselector between them and programmatically controlled according of quality of links to overcome the obstacles faced the transfer of high data. Based on the outage probability of each links an algorithm is proposed to select the optimal link and provide the power consumption with guaranteed high quality of the link. The analytical expressions for the outage probability and ergodic capacity are derived. The numerical results show the effectiveness of proposed model in terms of connectivity and capacity compared to other links.
Hybrid model in machine learning–robust regression applied for sustainability agriculture and food security Mukhtar Mukhtar; Majid Khan Majahar Ali; Mohd. Tahir Ismail; Ferdinand Murni Hamundu; Alimuddin Alimuddin; Naseem Akhtar; Ahmad Fudholi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4457-4468

Abstract

A dataset containing 1924 observations used in this study to evaluate the effect of 435 different independent variables on one dependent variable. Big data has some issues such as irrelevant variables and outliers. Therefore, this study focused on analysing and comparing the impact of three different variable selection based on machine learning techniques, including random forest (RF), support vector machines (SVM), and Boosting. Further, the M robust regression was applied to address the outliers using M–bi square, M–Hampel, and M–Huber. Random forest and M-Hampel results revealed the significant comparing from the other methods such as mean absolute error (MAE) 175.33995, mean square error (MSE) 31.8608, mean average percentage error (MAPE) 9.16091, sum of square error (SSE) 89270.45, R–square 0.829511, and R–square adjusted 0.82670. Also, these techniques indicated that the 8 selection criteria were lower than the other techniques including Akaike information criterion (AIC) 47.25915, generalized cross validation (GCV) 47.27169, hannan-quinn (HQ) 47.60351, RICE (47.2845), SCHWARZ 51.7099, sigma square (SGMASQ) 46.50605, SHIBATA 47.23489, and final prediction error (FPE) 47.25929. Therefore, the study recommended that the best random forest and M-Hampel models are helpful to show the minimum issues and efficient validation for analysing and comparing big data.
Application of optimization algorithms for classification problem Alaa Eleyan; Mohammad Shukri Salman; Bahaa Al-Sheikh
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4373-4379

Abstract

The work presented in this paper investigates the use of metaheuristic optimization algorithms for the face recognition problem. In the first setup, a face recognition system is implemented using particle swarm optimization (PSO) and firefly optimization algorithms, separately. PSO and firefly are used for forming the feature vectors in the feature selection stage. These feature vectors serve as the new representation for the face images that will be fed to the classifier. In the second setup, selected features from both PSO and firefly algorithms are fused to form one single feature vector for each face image before the classification stage. Extensive simulations are conducted using Poznan University of Technology (PUT) and face recognition technology (FERET) face databases. Optimal values for population size and maximum iterations number were selected before conducting the experiments. The effect of using different numbers of selected features on the performance is investigated for feature selection using PSO, firefly, and feature fusion of both.

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