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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 3: June 2022" : 112 Documents clear
Fish classification using extraction of appropriate feature set Usama A. Badawi
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.pp2488-2500

Abstract

The field of wild fish classification faces many challenges such as the amount of training data, pose variation and uncontrolled environmental settings. This research work introduces a hybrid genetic algorithm (GA) that integrates the simulated annealing (SA) algorithm with a back-propagation algorithm (GSB classifier) to make the classification process. The algorithm is based on determining the suitable set of extracted features using color signature and color texture features as well as shape features. Four main classes of fish images have been classified, namely, food, garden, poison, and predatory. The proposed GSB classifier has been tested using 24 fish families with different species in each. Compared to the back-propagation (BP) algorithm, the proposed classifier has achieved a rate of 87.7% while the elder rate is 82.9%.
Design and implementation of an oil leakage monitoring system based on wireless network Jamal A. Hameed; Amer T. Saeed; Mohammed M. Sultan; Musa A. Hameed
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.pp2626-2635

Abstract

Monitoring pipeline leaks is one of the recent hot studies. Leakage may occur because of time corrosion in the tube raw materials. To reduce the negative consequences of this leak, an effective leak detection system is used to prevent serious leakage accidents and damage in oil pipelines. Buildings, ecosystems, air pollution, and human life are all at risk in case of leakage occurs which could lead to fires. This paper introduces one of the research methods for the detection of pipeline leaks with a particular focus on software-based methods. The computer board interface (CBI) and wireless sensor networks have been used beside Arduino as a micro-monitor for the entire system. ZigBee is also utilized to send read data from sensors to the monitoring system displayed on the LabVIEW graphical user interface (GUI). The operator can take direct action when a leak occurs. The effectiveness of the leakage monitoring process and its practical use are demonstrated by the introduction of computerized techniques based on pressure gauge analysis on a specific pipeline in the laboratory. The result showed that the system is widely covered, accurate data transmission and robust real-time performance which reduces economic losses and environmental pollution.
Predicting the status of COVID-19 active cases using a neural network time series Almasinejad, Peyman; Golabpour, Amin; Ahouz, Fatemeh; Mollakhalili Meybodi, Mohammad Reza; Mirzaie, Kamal; Khosravi, Ahmad; Rohani-Rasaf, Marzieh; Bastani, Azadeh
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.pp3104-3117

Abstract

The design of intelligent systems for analyzing information and predicting the epidemiological trends of the disease is rapidly expanding because of the coronavirus disease (COVID-19) pandemic. The COVID-19 datasets provided by Johns Hopkins University were included in the analysis. This dataset contains some missing data that is imputed using the multi-objective particle swarm optimization method. A time series model based on nonlinear autoregressive exogenou (NARX) neural network is proposed to predict the recovered and death COVID-19 cases. This model is trained and evaluated for two modes: predicting the situation of the affected areas for the next day and the next month. After training the model based on the data from January 22 to February 27, 2020, the performance of the proposed model was evaluated in predicting the situation of the areas in the coming two weeks. The error rate was less than 5%. The prediction of the proposed model for April 9, 2020, was compared with the actual data for that day. The absolute percentage error (AE) worldwide was 12%. The lowest mean absolute error (MAE) of the model was for South America and Australia with 3 and 3.3, respectively. In this paper, we have shown that geographical areas with mortality and recovery of COVID-19 cases can be predicted using a neural network-based model.
Threshold adaptation and XOR accumulation algorithm for objects detection Hasan Thabit Rashid Kurmasha; Israa Hadi Ali
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.pp2517-2525

Abstract

Object detection, tracking and video analysis are vital and energetic tasks for intelligent video surveillance systems and computer vision applications. Object detection based on background modelling is a major technique used in dynamically objects extraction over video streams. This paper presents the threshold adaptation and XOR accumulation (TAXA) algorithm in three systematic stages throughout video sequences. First, the continuous calculation, updating and elimination of noisy background details with hybrid statistical techniques. Second, thresholds are calculated with an effective mean and gaussian for the detection of the pixels of the objects. The third is a novel step in making decisions by using XOR-accumulation to extract pixels of the objects from the thresholds accurately. Each stage was presented with practical representations and theoretical explanations. On high resolution video which has difficult scenes and lighting conditions, the proposed algorithm was used and tested. As a result, with a precision average of 0.90% memory uses of 6.56% and the use of CPU 20% as well as time performance, the result excellent overall superior to all the major used foreground object extraction algorithms. As a conclusion, in comparison to other popular OpenCV methods the proposed TAXA algorithm has excellent detection ability.
Numerical investigation of the performance of AlGaN/GaN/BGaN double-gate double-channel high electron mobility transistor Hamida Djelti
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.pp2655-2662

Abstract

In this work, we examine the direct-current (DC) behavior and the radio-frequency (RF) performance of both single-gate simple-channel (SGSC), single-gate double-channel (SGDC) and double-gate double-channel (DGDC) AlGaN/GaN/BGaN high electron mobility transistor (HEMT) with BGaN back-barriers consist of 250 nm gate length. Using Technologie Computer Aided Design (TCAD) Silvaco, our isothermal simulation results reveal that the proposed structure of double-gate double-channel HEMT with BGaN back-barriers (DGDCBB HEMT) increases electron concentration and consequently the saturation drain current, breakdown voltage, the transconductance. On the other hand, decreases the gate leakage current compared to a conventional HEMT and to a double-channel HEMT back-barriers. Furthermore, the proposed double-gate double-channel back-barrier HEMT device shows good cutoff frequency (94 GHz) and a maximum oscillation frequency (170 GHz). These results suggest that double-gate double channel HEMT back-barriers could be useful for high-frequency and high-power microwave applications.
Functions of fuzzy logic based controllers used in smart building Ali M. Baniyounes; Yazeed Yasin Ghadi; Eyad Radwan; Khalid S. Al-Olimat
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.pp3061-3071

Abstract

The main aim of this study is to support design and development processes of advanced fuzzy-logic-based controller for smart buildings e.g., heating, ventilation and air conditioning, heating, ventilation and air conditioning (HVAC) and indoor lighting control systems. Moreover, the proposed methodology can be used to assess systems energy and environmental performances, also compare energy usages of fuzzy control systems with the performances of conventional on/off and proportional integral derivative controller (PID). The main objective and purpose of using fuzzy-logic-based model and control is to precisely control indoor thermal comfort e.g., temperature, humidity, air quality, air velocity, thermal comfort, and energy balance. Moreover, this article present and highlight mathematical models of indoor temperature and humidity transfer matrix, uncertainties of users’ comfort preference set-points and a fuzzy algorithm.
Geological aspect analysis for micro hydro power plant site selection based on remote sensing data Yugo Kumoro; Yuliana Susilowati; Pudji Irasari; Wawan Hendriawan Nur; Yunarto Yunarto
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.pp2300-2312

Abstract

Geological characters analysis is essential for micro hydropower plant (MHP) development planning. This paper presents an analysis of the geological aspect to determine the layout of MHP components based on remote sensing data as part of a solution to addressing power shortages in Sungai Are District, South Ogan Komering Ulu Regency, South Sumatra Province. Remote sensing and topographic map were extracted to identify the potential site. The topographic map and geological analysis were used to calculate the potential of electrical energy and the geological hazard risk, particularly floods and landslides. The results of the study identified four potential sites. Site 1 (Luas River, Ulu Danau Village) and site 3 (Putih River, Gintung Village) are suitable for MHP with a low cost of construction. Site 2 (Pecah Pinggan Village) and site 4 (Simpang Luas Village) are prone to flooding and landslides that makes it suitable for MHP but with a high cost of construction. Based on the geological aspect analysis, it is possible to optimize the hydropower capacity, by adding the volume of water flow from several nearby tributaries channeled into the hydropower flow system through civil construction engineering or by making a cascade design on the tailrace water flow.
A feature selection method based on auto-encoder for internet of things intrusion detection Ahmed Fahad Alshudukhi; Saif Ahmed Jabbar; Basel Alshaikhdeeb
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.pp3265-3275

Abstract

The evolution in gadgets where various devices have become connected to the internet such as sensors, cameras, smartphones, and others, has led to the emergence of internet-of-things (IoT). As any network, security is the main issue facing IoT. Several studies addressed the intrusion detection task in IoT. The majority of these studies utilized different statistical and bio-inspired feature selection techniques. Deep learning is a family of techniques that demonstrated remarkable performance in the field of classification. The emergence of deep learning techniques has led to configure new neural network architectures that is designed for the feature selection task. This study proposes a deep learning architecture known as auto-encoder (AE) for the task of feature selection in IoT intrusion detection. A benchmark dataset for IoT intrusions has been considered in the experiments. The proposed AE has been carried out for the feature selection task along with a simple neural network (NN) architecture for the classification task. Experimental results showed that the proposed AE showed an accuracy of 99.97% with a false alarm rate (FAR) of 1.0. The comparison against the state of the art proves the efficacy of AE.
EEIT2-F: energy-efficient aware IT2-fuzzy based clustering protocol in wireless sensor networks Enaam Abd Al-Husain; Ghaida Abdulrazzaq Al-Suhail
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.pp2672-2680

Abstract

Improving the network lifetime is still a vital challenge because most wireless sensor networks (WSNs) run in an unreached environment, and offer almost impossible human access and tracking. Clustering is one of the most effective methods for ensuring that the relevant device process takes place to improve network scalability, decrease energy consumption and maintain an extended network lifetime. Many research have been developed on the numerous effective clustering algorithms to address this problem.  Such algorithms almost dominate on the cluster head (CH) selection and cluster formation; using the intelligent type1 fuzzy-logic (T1-FL) scheme. In this paper, we suggest an interval type2 FL (IT2-FL) methodology that assumes uncertain levels of a decision to be more efficient than the T1-FL model. It is the so-called energy-efficient interval type2 fuzzy (EEIT2-F) low energy adaptive clustering hierarchical (LEACH) protocol. The IT2-FL system depends on three inputs of the residual energy of each node, the node distance from the base station (sink node), and the centrality of each node. Accordingly, the simulation results show that the suggested clustering protocol outperforms the other existing proposals in terms of energy consumption and network lifetime.
Fast discrimination of fake video manipulation Wildan Jameel Hadi; Suhad Malallah Kadhem; Ayad Rodhan Abbas
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.pp2582-2587

Abstract

Deepfakes have become possible using artificial intelligence techniques, replacing one person’s face with another person’s face (primarily a public figure), making the latter do or say things he would not have done. Therefore, contributing to a solution for video credibility has become a critical goal that we will address in this paper. Our work exploits the visible artifacts (blur inconsistencies) which are generated by the manipulation process. We analyze focus quality and its ability to detect these artifacts. Focus measure operators in this paper include image Laplacian and image gradient groups, which are very fast to compute and do not need a large dataset for training. The results showed that i) the Laplacian group operators, as a value, may be lower or higher in the fake video than its value in the real video, depending on the quality of the fake video, so we cannot use them for deepfake detection and ii) the gradient-based measure (GRA7) decreases its value in the fake video in all cases, whether the fake video is of high or low quality and can help detect deepfake.

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