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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 locality-sensitive hashing approach for achieving optimal ‎image retrieval satisfaction Hanen Karamti; Hadil Shaiba; Abeer M. Mahmoud
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2526-2538

Abstract

Efficient methods that enable high and rapid image retrieval are continuously needed, especially with the large mass of images that are generated from different sectors and domains like business, communication media, and entertainment. Recently, deep neural networks are extensively proved higher-performing models compared to other traditional models. Besides, combining hashing methods with a deep learning architecture improves the image retrieval time and accuracy. In this paper, we propose a novel image retrieval method that employs locality-sensitive hashing with convolutional neural networks (CNN) to extract different types of features from different model layers. The aim of this hybrid framework is focusing on both the high-level information that provides semantic content and the low-level information that provides visual content of the images. Hash tables are constructed from the extracted features and trained to achieve fast image retrieval. To verify the effectiveness of the proposed framework, a variety of experiments and computational performance analysis are carried out on the CIFRA-10 and NUS-WIDE datasets. The experimental results show that the proposed method surpasses most existing hash-based image retrieval methods.
Load forecasting with support vector regression: influence of data normalization on grid search algorithm Thanh Ngoc Tran; Binh Minh Lam; Anh Tuan Nguyen; Quang Binh Le
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.pp3410-3420

Abstract

In recent years, support vector regression (SVR) models have been widely applied in short-term electricity load forecasting. A critical challenge when applying the SVR model is to determine the model for optimal hyperparameters, which can be solved using several optimization methods as the grid search algorithm. Another challenge that affects the response time and the precision of the SVR model is the normalization process of input data. In this paper, the grid search algorithm will be suggested based on data normalization methods including Z-score, min-max, max, decimal, sigmoidal, softmax; and then utilized to evaluate both the response time and precision. To verify the proposed methods, the actual electricity load demand data of two cities, including Queensland of Australia and Ho Chi Minh City of Vietnam, were utilized in this study.
Six skin diseases classification using deep convolutional neural network Ramzi Saifan; Fahed Jubair
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp3072-3082

Abstract

Smart imaging-based medical classification systems help the human diagnose the diseases and make better decisions about patient health. Recently, computer-aided classification of skin diseases has been a popular research area due to its importance in the early detection of skin diseases. This paper presents at its core, a system that exploits convolutional neural networks to classify color images of skin lesions. It relies on a pre-trained deep convolutional neural network to classify between six skin diseases: acne, athlete’s foot, chickenpox, eczema, skin cancer, and vitiligo. Additionally, we constructed a dataset of 3000 colored images from several online datasets and the Internet. Experimental results are encouraging, where the proposed model achieved an accuracy of 81.75%, which is higher than the state of the art researches in this field. This accuracy was calculated using the holdout method, where 90% of the images were used for training, and 10% of the images were used for out-of-sample accuracy testing.
The susceptible-infected-recovered-dead model for long-term identification of key epidemiological parameters of COVID-19 in Indonesia Muhammad Achirul Nanda; Anifatul Faricha; Siti Maghfirotul Ulyah; Ni'matut Tamimah; Enny Indasyah; Muhammad Falahudin Malich Salaz; Qurrotun 'Ayun Mawadatur Rohmah; Ulfah Abqari
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2900-2910

Abstract

The COVID-19 epidemic has spread massively to almost all countries including Indonesia, in just a few months. An important step to overcoming the spread of the COVID-19 is understanding its epidemiology through mathematical modeling intervention. Knowledge of epidemic dynamics patterns is an important part of making timely decisions and preparing hospitals for the outbreak peak. In this study, we developed the susceptible-infected-recovered-dead (SIRD) model, which incorporates the key epidemiological parameters to model and estimate the long-term spread of the COVID-19. The proposed model formulation is data-based analysis using public COVID-19 data from March 2, 2020 to May 15, 2021. Based on numerical analysis, the spread of the pandemic will begin to fade out after November 5, 2021. As a consequence of this virus attack, the cumulative number of infected, recovered, and dead people were estimated at ≈ 3,200,000, ≈ 3,437,000 and ≈ 63,000 people, respectively. Besides, the key epidemiological parameter indicates that the average reproduction number value of COVID-19 in Indonesia is 7.32. The long-term prediction of COVID-19 in Indonesia and its epidemiology can be well described using the SIRD model. The model can be applied in specific regions or cities in understanding the epidemic pattern of COVID-19.
Optimum consultation for serial distributed detection systems Nedal Al-Ababneh; Hasan Aldiabat
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2636-2644

Abstract

This paper considers a distributed detection system which consists of  sensors that are connected in series. The observations of each sensor in this system design are considered to be statistically independent of all other sensors. In contrast to the popular serial decision fusion systems, we assume that consultations are allowed in a serial manner between successive sensors that make up the system. In addition, the paper demonstrates the similarity between the proposed consulting serial system and the optimal serial one in terms of detection probabilities for a give probability of false alarm. However, it should be emphasized that the proposed system has the benefit of conditional nonrandom consultation among the sensors. Consequently, its survivability is higher than that of serial systems. Numerical evaluations for the cases of two and three sensors are provided and compared with those of the serial as well as the centralized schemes.
A deep learning approach for COVID-19 and pneumonia detection from chest X-ray images Musha, Ahmmad; Al Mamun, Abdullah; Tahabilder, Anik; Hossen, Md. Jakir; Hossen, Busrat; Ranjbari, Sima
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.pp3655-3664

Abstract

There has been a surge in biomedical imaging technologies with the recent advancement of deep learning. It is being used for diagnosis from X-ray, computed tomography (CT) scan, electrocardiogram (ECG), and electroencephalography (EEG) images. However, most of them are solely for particular disease detection. In this research, a computer-aided deep learning model named COVID-CXDNetV2 has been presented to detect two separate diseases, coronavirus disease 2019 (COVID-19) and pneumonia, from the X-ray images in real-time. The proposed model is made based on you only look once (YOLOv2) with residual neural network (ResNet) and trained by a vast X-ray images dataset containing 3788 samples of three classes named COVID-19 pneumonia and normal. The model has obtained the maximum overall classification accuracy of 97.9% with a loss of 0.052 for multiclass classification (COVID-19, pneumonia, and normal) and 99.8% accuracy, 99.52% sensitivity, 100% specificity with a loss of 0.001 for binary classification (COVID-19 and normal), which beats some current state-of-the-art results. Authors believe that this method will be applicable in the medical domain for the diagnosis and will significantly contribute to real life.
A novel risk management model in the Scrum and extreme programming hybrid methodology Afshari, Mahnaz; Javdani Gandomani, Taghi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2911-2921

Abstract

Risk management in software development has always been one of the necessities of software project management. The logical nature of software projects and products has caused several challenges and risks in these projects. On the other hand, with the emergence of Agile methodologies, especially Scrum, and extreme programming (XP) methodologies, in recent years, this issue has become more serious. This is mainly because emphasizing limited documentation in these methodologies has caused these methods to pay little attention to some aspects of project management, particularly risk management. Concentrating on this challenge, the current study has proposed a risk management model in the hybrid methodology, combining Scrum and XP. Using this model in a case study shows this model's success in achieving risk management purposes. The results of this study indicate an appropriate reduction in the number of reworks, change requests, identified risks, and occurred risks. Moreover, the number of eliminated risks and team productivity have increased.
Enhancing hybrid renewable energy performance through deep Q-learning networks improved by fuzzy reward control Ameur, Chahinaze; Faquir, Sanaa; Yahyaouy, Ali; Abdelouahed, Sabri
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.pp4302-4314

Abstract

In a stand-alone system, the use of renewable energies, load changes, and interruptions to transmission lines can cause voltage drops, impacting its reliability. A way to offset a change in the nature of hybrid renewable energy immediately is to utilize energy storage without needing to turn on other plants. Photovoltaic panels, a wind turbine, and a wallbox unit (responsible for providing the vehicle’s electrical need) are the components of the proposed system; in addition to being a power source, batteries also serve as a storage unit. Taking advantage of deep learning, particularly convolutional neural networks, and this new system will take advantage of recent advances in machine learning. By employing algorithms for deep Q-learning, the agent learns from the data of the various elements of the system to create the optimal policy for enhancing performance. To increase the learning efficiency, the reward function is implemented using a fuzzy Mamdani system. Our proposed experimental results shows that the new system with fuzzy reward using deep Q-learning networks (DQN) keeps the battery and the wallbox unit optimally charged and less discharged. Moreover confirms the economic advantages of the proposed approach performs better approximate to +25% Moreover, it has dynamic response capabilities and is more efficient over the existing optimization approach using deep learning without fuzzy logic.
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.
Monitoring of power transformers using thermal model and permission time of overload Huthaifa Ahmad Al_Issa; Mohamed Qawaqzeh; Serhii Kurashkin; Serhii Halko; Serhii Kvitka; Oleksandr Vovk; Oleksandr Miroshnyk
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i3.pp2323-2334

Abstract

This paper presents the problem of increasing the reliability of electricity supply to consumers. Uninterrupted power supply to electricity consumers depends on the reliability of power supply system in general and power transformers in particular, the accident rate of which is quite high. The causes of the problem are the location of transformer substations at a considerable distance from the service centers, their spreading out over a large area, missing information about the current modes of their operation and so on. One of the ways to solve this problem is development and implementation a system for continuous diagnostics of power transformers. Failure analysis of power transformer based on fault tree is considered, the diagnostic parameters are determined. The insulation wear rate and permission operating time under overload have been defined with help of equivalent heat circuit. It is proposed to use a permission time as a parameter to diagnose the operation mode and increase the efficiency of maintenance of substations through remote monitoring based on the global service mobile (GSM) network. Remote diagnostics allows to receive an information about emergency situation timely. It helps to reduce operating costs, to ensure the reliability and quality of electricity supply for consumers.

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