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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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Energy management system for distribution networks integrating photovoltaic and storage units Zedak, Chaimae; Belfqih, Abdelaziz; Boukherouaa, Jamal; Lekbich, Anass; Elmariami, Faissal
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.pp3352-3364

Abstract

The concept of the optimization energy management system, developed in this work, is to determine the optimal combination of energy from several generation sources and to schedule their commitment, while optimizing the cost of energy, power losses and voltage drops. In order to achieve these objectives, the non-dominated sorting genetic Algorithm II (NSGA-II) was modified and applied to an IEEE 33-bus test network containing 10 photovoltaic power plants and 4 battery energy storage systems placed at optimal points in the network. To evaluate the system performance, the resolution was performed under several test conditions. Optimal Pareto solutions were classified using three decision-making methods, namely analytic hierarchy process (AHP), TOPSIS and entropy-TOPSIS, compared to each other for more accurate results. The simulation results obtained by NSGA-II and classified using entropy-TOPSIS showed a significant and considerable reduction in terms of energy cost, power losses and voltage drops while successfully meeting all constraints. In addition, the diversity of the results proved once again the robustness and effectiveness of the algorithm. A graphical interface was also developed to display all the decisions made by the algorithm, and all other information such as the states of power systems, voltage profiles, alarms, and history.
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.
Semantic-based visual emotion recognition in videos-a transfer learning approach Vaijayanthi Sekar; Arunnehru Jawaharlalnehru
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.pp3674-3683

Abstract

Automatic emotion recognition is active research in analyzing human’s emotional state over the past decades. It is still a challenging task in computer vision and artificial intelligence due to its high intra-class variation. The main advantage of emotion recognition is that a person’s emotion can be recognized even if he is extreme away from the surveillance monitoring since the camera is far away from the human; it is challenging to identify the emotion with facial expression alone. This scenario works better by adding visual body clues (facial actions, hand posture, body gestures). The body posture can powerfully convey the emotional state of a person in this scenario. This paper analyses the frontal view of human body movements, visual expressions, and body gestures to identify the various emotions. Initially, we extract the motion information of the body gesture using dense optical flow models. Later the high-level motion feature frames are transferred to the pre-trained convolutional neural network (CNN) models to recognize the 17 various emotions in Geneva multimodal emotion portrayals (GEMEP) dataset. In the experimental results, AlexNet exhibits the architecture's effectiveness with an overall accuracy rate of 96.63% for the GEMEP dataset is better than raw frames and 94% for visual geometry group-19 VGG-19, and 93.35% for VGG-16 respectively. This shows that the dense optical flow method performs well using transfer learning for recognizing emotions.
Breast cancer histological images nuclei segmentation and optimized classification with deep learning Fawad Salam Khan; Muhammad Inam Abbasi; Muhammad Khurram; Mohd Norzali Haji Mohd; M. Danial Khan
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.pp4099-4110

Abstract

Breast cancer incidences have grown worldwide during the previous few years. The histological images obtained from a biopsy of breast tissues are regarded as being the highest accurate approach to determine whether any cells exhibit symptoms of cancer. The visible position of nuclei inside the image is achieved through the use of instance segmentation, nevertheless, this work involves nucleus segmentation and features classification of the predicted nucleus for the achievement of best accuracy. The extracted features map using the feature pyramid network has been modified using segmenting objects by locations (SOLO) convolution with grasshopper optimization for multiclass classification. A breast cancer multiclassification technique based on a suggested deep learning algorithm was examined to achieve the accuracy of 99.2% using a huge database of ICIAR 2018, demonstrating the method’s efficacy in offering an important weapon for breast cancer multi-classification in a medical setting. The segmentation accuracy achieved is 88.46%.
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.
Advancements in energy storage technologies for smart grid development Sharma, Pankaj; Reddy Salkuti, Surender; Kim, Seong-Cheol
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.pp3421-3429

Abstract

In the modern world, the consumption of oil, coal natural gas, and nuclear energy has been causing by a serious environmental problem and an ongoing energy crisis. The generation and consumption of renewable energy sources (RESs) such as solar and wind tidal, can resolve the problem but the nature of the RESs is fluctuating and intermitted. This evolution brings a lot of challenges in the management of electrical grids. The paper reviewed the advancements in energy storage technologies for the development of a smart grid (SG). More attention was paid to the classification of energy storage technologies based on the form of energy storage and based on the form of discharge duration. The evaluation criteria for the energy storage technologies have been carried out based on technological dimensions such as storage capacity, efficiency, response time, energy density, and power density, the economic dimension such as input cost and economic benefit; and the environmental dimension such as emission and stress on ecosystem, social demission such as job creation and social acceptance were also presented in this paper.
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.
Development and validation of a tool for measuring digital library engagement Rahimi Mohamad Rosman, Mohamad; Nasir Ismail, Mohd; Noorman Masrek, Mohamad
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.pp4146-4154

Abstract

Digital library engagement can be defined as the extensive usage of the services, functions, and tools provided by the digital library (DL). Although many instruments have been developed to measure digital library usage, however, the instruments were only measuring a limited aspect of usage, such as behavioral perspective. Therefore, the aim of this study is to establish and confirm a research instrument to measure digital library engagement. The study is conducted on several empirical phases. First, a listof variables was selected. Second, an instrument was developed based on the variables selected. Third, the instrument measuring digital library engagement was validated through expert review process. Fourth, face validity was conducted, before confirming the reliability of the instrument through a pilot test. Lastly, the instrument was validated through quantitative data collection by 492 respondents. As a result, a valid instrument consisting of 14 variables underneath 5 dimensions (technological, individual, contextual, digital library engagement, perceived benefits) and 61 items were produced. The instrument could be employed to identify and assess the state of digital library engagement among practitioners, universities, government, and local communities. This study however is limited in few ways in relation to context coverage, generalization of theory, and variables selection.
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.
Dialectal Arabic sentiment analysis based on tree-based pipeline optimization tool Soukaina Mihi; Brahim Ait Ben Ali; Ismail El Bazi; Sara Arezki; Nabil Laachfoubi
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.pp4195-4205

Abstract

The heavy involvement of the Arabic internet users resulted in spreading data written in the Arabic language and creating a vast research area regarding natural language processing (NLP). Sentiment analysis is a growing field of research that is of great importance to everyone considering the high added potential for decision-making and predicting upcoming actions using the texts produced in social networks. Arabic used in microblogging websites, especially Twitter, is highly informal. It is not compliant with neither standards nor spelling regulations making it quite challenging for automatic machine-learning techniques. In this paper’s scope, we propose a new approach based on AutoML methods to improve the efficiency of the sentiment classification process for dialectal Arabic. This approach was validated through benchmarks testing on three different datasets that represent three vernacular forms of Arabic. The obtained results show that the presented framework has significantly increased accuracy than similar works in the literature.

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