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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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Driving sleepiness detection using electrooculogram analysis and grey wolf optimizer Sarah Saadoon Jasim; Alia Karim Abdul Hassan
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6034-6044

Abstract

In modern society, providing safe and collision-free travel is essential. Therefore, detecting the drowsiness state of the driver before its ability to drive is compromised. For this purpose, an automated hybrid sleepiness classification system that combines the artificial neural network and gray wolf optimizer is proposed to distinguish human Sleepiness and fatigue. The proposed system is tested on data collected from 15 drivers (male and female) in alert and sleep-deprived conditions where physiological signals are used as sleep markers. To evaluate the performance of the proposed algorithm, k-nearest neighbors (k-NN), support vector machines (SVM), and artificial neural networks (ANN) classifiers have been used. The results show that the proposed hybrid method provides 99.6% accuracy, while the SVM classifier provides 93.0% accuracy when the kernel is (RBF) and outlier (0.1). Furthermore, the k-NN classifier provides 96.7% accuracy, whereas the standalone ANN algorithm provides 97.7% accuracy.
Three level intrusion detection system based on conditional generative adversarial network Hasan Abdulameer; Inam Musa; Noora Salim Al-Sultani
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2240-2258

Abstract

Security threat protection is important in the internet of things (IoT) applications since both the connected device and the captured data can be hacked or hijacked or both at the same time. To tackle the above-mentioned problem, we proposed three-level intrusion detection system conditional generative adversarial network (3LIDS-CGAN) model which includes four phases such as first-level intrusion detection system (IDS), second-level IDS, third-level IDS, and attack type classification. In first-level IDS, features of the incoming packets are extracted by the firewall. Based on the extracted features the packets are classified into three classes such as normal, malicious, and suspicious using support vector machine and golden eagle optimization. Suspicious packets are forwarded to the second-level IDS which classified the suspicious packets as normal or malicious. Here, signature-based intrusions are detected using attack history information, and anomaly-based intrusions are detected using event-based semantic mapping. In third-level IDS, adversary packets are detected using CGAN which automatically learns the adversarial environment and detects adversary packets accurately. Finally, proximal policy optimization is proposed to detect the attack type. Experiments are conducted using the NS-3.26 network simulator and performance is evaluated by various performance metrics which results that the proposed 3LIDS-CGAN model outperforming other existing works.
Empirical evaluation of continuous auditing system use: a systematic review Azizah Hassan; Norsaremah Salleh; Mohd Nasir Ismail; Mohammad Nazir Ahmad; Ab Razak Che Hussin
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp796-808

Abstract

For more than two decades, the concept of continuous auditing (CA) has been introduced and many large firms had taken the initiatives to apply the CA system in supporting their audit functions. Despite the benefits that the CA system is offers, it is still not widely use and the number of people using the CA system is still considered low. This research focuses on the published papers on the use of CA system within the context of auditing system addressing the quality of system implementation. This paper analyzed primary studies collected using the pre-determined search strings on nine online databases. As a result, a total of 60 articles were carefully selected to undergo further analysis based on empirical evidence of CA system use. The articles were analyzed qualitatively using ATLAS.ti 7 and the elements for the CA system use are extracted from the selected papers. A total of four elements were identified contributing to the use of CA in practice. Those elements are the participant quality, system quality, information quality and products and services quality. This study answers five research objectives to understand the current studies on CA and to determine future research works on CA.
Permutation based load balancing technique for long term evolution advanced heterogeneous networks Mohammed Jaber Alam; Abdul Gafur; Syed Zahidur Rashid; Md. Golam Sadeque; Diponkor Kundu; Rosni Syed
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6311-6319

Abstract

Traffic congestion has been one of the major performance limiting factors of heterogeneous networks (HetNets). There have been several load balancing schemes put up to solve this by balancing load among base stations (BSs), but they appear to be unfeasible due to the complexity required and other unsatisfactory performance aspects. Cell range extension (CRE) has been a promising technique to overcome this challenge. In this paper, a permutation based CRE technique is proposed to find the best possible formation of bias for BSs to achieve load balance among BSs. In comparison to the baseline scheme, results depict that the suggested method attains superior performance in terms of network load balancing and average throughput. The complexity of the suggested algorithm is considerably reduced in comparison to the proposed permutation based CRE method it is further modified from.
Investigating the relationship between knowledge management practices and organizational learning practices in the universities’ environment Zainab Amin Al-Sulami; Hayder Salah Hashim; Nor’ashikin Ali; Zaid Ameen Abduljabbar
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1680-1688

Abstract

The concept of knowledge management (KM) and organizational learning (OL) has been embraced by organizations to complement each other. Higher education institutions have embraced KM and OL as a means to improve organizational efficiency. This research explores the link between KM and OL. The target population included all the 432 academicians and administrators from 35 public universities in Iraq. The sampling was selected using a stratified random sampling technique. The correlation among the components of KM and OL was tested as well as the effect of KM components on OL. The findings were derived using smart partial least square. The findings showed that there is significant correlation between components of KM and components of OL. The regression analysis showed also that the effect of KM and its components; knowledge creation, knowledge sharing, knowledge storage, knowledge application and knowledge acquisition on OL are significant. These findings provide insights to universities management on strategies to implement KM practices that can align with OL practices to assure dynamic lifelong mechanisms for the basic daily activities such as teaching, learning, researching, and supervision.
A pre-trained model vs dedicated convolution neural networks for emotion recognition Asmaa Yaseen Nawaf; Wesam M. Jasim
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp1123-1133

Abstract

Facial expression recognition (FER) is one of the most important methods influencing human-machine interaction (HMI). In this paper, a comparison was made between two models, a model that was built from scratch and trained on FER dataset only, and a model previously trained on a data set containing various images, which is the VGG16 model, then the model was reset and trained using FER dataset. The FER+ data set was augmented to be used in training phases using the two proposed models. The models will be evaluated (extra validation) by using images from the internet in order to find the best model for identifying human emotions, where Dlib detector and OpenCV libraries are used for face detection. The results showed that the proposed emotion recognition convolutional neural networks (ERCNN) model dedicated to identifying human emotions significantly outperformed the pre-trained model in terms of accuracy, speed, and performance, which was 87.133% in the public test and 82.648% in the private test. While it was 71.685% in the public test and 67.338% in the private test using the proposed VGG16 pre-trained model.
Automatic generation of business process models from user stories Samia Nasiri; Amina Adadi; Mohammed Lahmer
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp809-822

Abstract

In this paper, we propose an automated approach to extract business process models from requirements, which are presented as user stories. In agile software development, the user story is a simple description of the functionality of the software. It is presented from the user's point of view and is written in natural language. Acceptance criteria are a list of specifications on how a new software feature is expected to operate. Our approach analyzes a set of acceptance criteria accompanying the user story, in order, first, to automatically generate the components of the business model, and then to produce the business model as an activity diagram which is a unified modeling language (UML) behavioral diagram. We start with the use of natural language processing (NLP) techniques to extract the elements necessary to define the rules for retrieving artifacts from the business model. These rules are then developed in Prolog language and imported into Python code. The proposed approach was evaluated on a set of use cases using different performance measures. The results indicate that our method is capable of generating correct and accurate process models.
Hydrogen storage for micro-grid application: a framework for ranking fuel cell technologies based on technical parameters John Adetunji Adebisi; Iheanacho Henry Denwigwe; Olubayo Moses Babatunde
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1221-1230

Abstract

To securely address energy shortage and various environmental issues attributed to fossil fuel, the adoption of renewable energy is growing across the globe. However, wind and solar which form the bulk of the emerging renewable energy for micro-grid applications are intermittent and need energy storage device for backup. Due to its environmentally friendly nature, the use of hydrogen as storage mechanism is now being explored for micro-grid applications. However, due to the various technical criteria attributed to various fuel cell (FC) technologies used for hydrogen production, selecting the most suitable alternative remains a challenge. This study uses evaluation based on distance from average solution, a multicriteria decision making tool to rank FC technologies that can be used to produce of hydrogen energy storage in micro-grid applications. The analysis was based on 4 FC technologies and 6 technical criteria. The results of the study show that the most preferred FC technology for micro-grid application is the polymeric electrolyte membrane while the least preferred is molten carbonate FC. It is expected that future analysis would explore the inclusion of socio-economic criteria in the evaluation of the most preferred FC technology for micro-grid application.
An optimized cost-based data allocation model for heterogeneous distributed computing systems Sashi Tarun; Mithilesh Kumar Dubey; Ranbir Singh Batth; Sukhpreet Kaur
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6373-6386

Abstract

Continuous attempts have been made to improve the flexibility and effectiveness of distributed computing systems. Extensive effort in the fields of connectivity technologies, network programs, high processing components, and storage helps to improvise results. However, concerns such as slowness in response, long execution time, and long completion time have been identified as stumbling blocks that hinder performance and require additional attention. These defects increased the total system cost and made the data allocation procedure for a geographically dispersed setup difficult. The load-based architectural model has been strengthened to improve data allocation performance. To do this, an abstract job model is employed, and a data query file containing input data is processed on a directed acyclic graph. The jobs are executed on the processing engine with the lowest execution cost, and the system's total cost is calculated. The total cost is computed by summing the costs of communication, computation, and network. The total cost of the system will be reduced using a Swarm intelligence algorithm. In heterogeneous distributed computing systems, the suggested approach attempts to reduce the system's total cost and improve data distribution. According to simulation results, the technique efficiently lowers total system cost and optimizes partitioned data allocation.
Comparative detection and fault location in underground cables using Fourier and modal transforms Vahdat Nazerian; Mohammad Esmail Zakerifar; Mahmoud Zadehbagheri; Mohammad Javad Kiani; Tole Sutikno
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp5821-5839

Abstract

In this research, we create a single-phase to ground synthetic fault by the simulation of a three-phase cable system and identify the location using mathematical techniques of Fourier and modal transforms. Current and voltage signals are measured and analyzed for fault location by the reflection of the waves between the measured point and the fault location. By simulating the network and line modeling using alternative transient programs (ATP) and MATLAB software, two single-phase to ground faults are generated at different points of the line at times of 0.3 and 0.305 s. First, the fault waveforms are displayed in the ATP software, and then this waveform is transmitted to MATLAB and presented along with its phasor view over time. In addition to the waveforms, the detection and fault location indicators are presented in different states of fault. Fault resistances of 1, 100, and 1,000 ohms are considered for fault creation and modeling with low arch strength. The results show that the proposed method has an average fault of less than 0.25% to determine the fault location, which is perfectly correct. It is varied due to changing the conditions of time, resistance, location, and type of error but does not exceed the above value.

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