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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 comprehensive fuzzy-based scheme for online detection of operational and topological changes Amin Damanjani; Mohamad Hosseini Abardeh; Azita Azarfar; Mehrdad Hojjat
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.pp3396-3409

Abstract

Operational modes and topological changes affect power flow in the power systems. As a result, a broad spectrum of protection issues may happen in the power system. So, both the operational and topological changes should be detected fast to prevent blackouts. On the other hand, the existing detection schemes are complex in analyzing and implementation. Therefore, there is a need for an online scheme to identify the network's topology and operation mode simultaneously without complex computations and additional communication infrastructures. To this end, a comprehensive scheme is proposed in which the changes are detected by analyzing the power flow obtained from the network. For this purpose, line outage contingencies and operation modes are defined in rules to be used in a fuzzy inference system (FIS) as a decision-making tool. The proposed scheme can be implemented on existing lines as a communication infrastructure and determines the network’s status in an online manner. Also, in comparison to the existing schemes, the proposed scheme reduces the complexity and the computational burden. The proposed scheme is implemented on IEEE 8-bus system and the results proved its efficiency.
4-total edge product cordial for some star related graphs Azaizeh, Almothana; Hasni, Roslan; Haddad, Firas; Alsmadi, Mutasem; Alkhasawneh, Raed; Hamad, Asma
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.pp4007-4020

Abstract

Let G = (V (G), E(G)) be a graph, define an edge labeling function ψ from E(G) to {0, 1, . . . , k − 1} where k is an integer, 2 ≤ k ≤ |E(G)|, induces a vertex labeling function ψ∗ from V (G) to {0, 1, . . . , k − 1} such that ψ∗(v) = ψ(e1) × ψ(e2) × . . . × ψ(en) mod k where e1, e2, . . . , en are all edge incident to v. This function ψ is called a k-total edge product cordial (or simply k-TEPC) labeling of G if the absolute difference between number of vertices and edges labeling with i and number of vertices and edges labeling with j no more than 1 for all i, j ∈ {0, 1, . . . , k − 1}. In this paper, 4-total edge product cordial labeling for some star related graphs are determined.
Heterogeneous computing with graphical processing unit: improvised back-propagation algorithm for water level prediction Neeru Singh; Supriya Priyabadini Panda
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.pp4090-4098

Abstract

A multitude of research has been rising for predicting the behavior of different real-world problems through machine learning models. An erratic nature occurs due to the augmented behavior and inadequacy of the prerequisite dataset for the prediction of water level over different fundamental models that show flat or low-set accuracy. In this paper, a powerful scaling strategy is proposed for improvised back-propagation algorithm using parallel computing for groundwater level prediction on graphical processing unit (GPU) for the Faridabad region, Haryana, India. This paper aims to propose the new streamlined form of a back-propagation algorithm for heterogeneous computing and to examine the coalescence of artificial neural network (ANN) with GPU for predicting the groundwater level. twenty years of data set from 2001-2020 has been taken into consideration for three input parameters namely, temperature, rainfall, and water level for predicting the groundwater level using parallelized backpropagation algorithm on compute unified device architecture (CUDA). This employs the back-propagation algorithm to be best suited to reinforce learning and performance by providing more accurate and fast results for water level predictions on GPUs as compared to sequential ones on central processing units (CPUs) alone.
Energy-efficient data-aggregation for optimizing quality of service using mobile agents in wireless sensor network Basappa, Prapulla S.; Gangadhar, Shobha; Thanuja, Tiptur Chandrashekar
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.pp3891-3899

Abstract

Quality of service (QoS) is essential for carrying out data transmission using resource-constrained sensor nodes in wireless sensor network (WSN). The introduction of mobile agent-based data aggregation is reported to offer energy efficiency; however, it has limitations, especially using a single mobile agent, where QoS optimization is not feasible. A review of existing studies showcases some dedicated attempts to use a mobile agent-based approach and address QoS enhancements. However, they were never combined studied. Therefore, this paper introduces a unique concept of retaining maximum QoS performance during data aggregation using a single mobile agent. The model introduces a unique communication framework, transmission provisioning using exceptional routine management, and simplified energy modeling. The proposed model has aimed for a lower delay and faster data aggregation speed with lower consumption of transmittance energy. The implementation and assessment of the model are carried out considering the challenging environment of WSN with multiple scales of data priority. The proposed model also contributes to evolving out with simplified communication vectors in a highly decentralized method. MATLAB's simulation outcome shows that the proposed system offers better delay performance, optimal energy management, and faster response time than existing schemes.
An efficient enhanced k-means clustering algorithm for best offer prediction in telecom Fraihat, Malak; Fraihat, Salam; Awad, Mohammed; AlKasassbeh, Mouhammd
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.pp2931-2943

Abstract

Telecom companies usually offer several rate plans or bundles to satisfy the customers’ different needs. Finding and recommending the best offer that perfectly matches the customer’s needs is crucial in maintaining customer loyalty and the company’s revenue in the long run. This paper presents an effective method of detecting a group of customers who have the potential to upgrade their telecom package. The used data is an actual dataset extracted from call detail records (CDRs) of a telecom operator. The method utilizes an enhanced k-means clustering model based on customer profiling. The results show that the proposed k-means-based clustering algorithm more effectively identifies potential customers willing to upgrade to a higher tier package compared to the traditional k-means algorithm. Our results showed that our proposed clustering model accuracy was over 90%, while the traditional k-means accuracy was under 70%.
Design and implementation of prepaid power billing system in smart grid environment Faizan Rashid; Saim Rasheed; Ahsan Farooq; Majid Ali; Youel Roben; Muhammad Jehanzeb; Abdul Wahab
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.pp3365-3374

Abstract

This paper presents the real-time monitoring of energy consumption utilizing the global system for mobile communication (GSM) via wireless protocols. The power supply company has the authority to change or alter the tariff rates. The method of payment is prepaid, so there will be options for the customer to request the number of units assigned to the particular meter against the money. The customer can monitor the number of units consumed with the help of GSM. If the units are ten percent of the total units, then the system will give a warning message so that the customer can recharge it before cutting off the supply. A GSM-based wireless module has remote access to the usage of electricity. Simulation is done on Proteus while the hardware is implemented using the microcontroller Arduino Uno.
Hyperparameter optimization using custom genetic algorithm for classification of benign and malicious traffic on internet of things–23 dataset Karthikayini Thavasimani; Nuggehalli Kasturirangan Srinath
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.pp4031-4041

Abstract

Hyperparameter optimization is one of the main challenges in deep learning despite its successful exploration in many areas such as image classification, speech recognition, natural language processing, and fraud detections. Hyperparameters are critical as they control the learning rate of a model and should be tuned to improve performance. Tuning the hyperparameters manually with default values is a challenging and time-intensive task. Though the time and efforts spent on tuning the hyperparameters are decreasing, it is always a burden when it comes to a new dataset or solving a new task or improving the existing model. In our paper, we propose a custom genetic algorithm to auto-tune the hyperparameters of the deep learning sequential model to classify benign and malicious traffic from internet of things-23 dataset captured by Czech Technical University, Czech Republic. The dataset is a collection of 30.85 million records of malicious and benign traffic. The experimental results show a promising outcome of 98.9% accuracy.
A new similarity-based link prediction algorithm based on combination of network topological features Hasan Saeidinezhad; Elham Parvinnia; Reza Boostani
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.pp2802-2811

Abstract

In recent years, the study of social networks and the analysis of these networks in various fields have grown significantly. One of the most widely used fields in the study of social networks is the issue of link prediction, which has recently been very popular among researchers. A link in a social network means communication between members of the network, which can include friendships, cooperation, writing a joint article or even membership in a common place such as a company or club. The main purpose of link prediction is to investigate the possibility of creating or deleting links between members in the future state of the network using the analysis of its current state. In this paper, three new similarities, degree neighbor similarity (DNS), path neighbor similarity (PNS) and degree path neighbor Similarity (DPNS) criteria are introduced using neighbor-based and path-based similarity criteria, both of which use graph structures. The results have been tested based on area under curve (AUC) and precision criteria on datasets and it shows well the superiority of the work over the criteria that only use the neighbor or the path.
Coronavirus disease 2019 detection using deep features learning A. Khalaf, Zainab; Shaheen Hammadi, Saad; Khattar Mousa, Alaa; Murtada Ali, Hanan; Ramadhan Alnajar, Hanan; Hashim Mohsin, Raghdan
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.pp4364-4372

Abstract

A Coronavirus disease 2019 (COVID-19) pandemic detection considers a critical and challenging task for the medical practitioner. The coronavirus disease spread so rapidly between people and infected more than one hundred and seventy million people worldwide. For this reason, it is necessary to detect infected people with coronavirus and take action to prevent virus spread. In this study, a COVID-19 classification methodology was adopted to detect infected people using computed tomography (CT) images. Deep learning was applied to recognize COVID-19 infected cases for different patients by employing deep features. This methodology can be beneficial for medical practitioners to diagnose infected patients. The results were based on a new data collection named BasrahDataset that includes different CT scan videos for Iraqi patients. The proposed system gave promised results with a 99% F1-score for detecting COVID-19.
Randomized QR-code scanning for a low-cost secured attendance system Imanullah, Muhammad; Reswan, Yuza
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.pp3762-3769

Abstract

Human resource management requires good documentation and good data processing. In workspaces such as offices and factories, employee salaries are determined by calculating attendance at each hour of work. Tracking and collecting attendance data for a large number of employees is a difficult thing to do. We need a secure system to facilitate the process of collecting and tracking presence data. We propose an attendance system that uses random quick response code (QR-codes) as one time password (OTP) to improve security. Employees are required to scan the QR-code within ten seconds before it is changed and randomized each time. The proposed attendance system facilitates data collection using employees’ smartphones and Mac-Address as unique identification numbers. The system is able to track employees’ arrival and departure times. We have implemented the system at the local university to collect lecturer attendance data then analyze its security and statistic in all scanning activities. The average time needed by a user to authenticate their presence in the system is 25.8877 seconds. The steps needed to sign in and out from the system are fewer than other previous researches. Those findings tell us that the approach is straightforward and more uncomplicated than other proposed methods. We conclude that randomized QR-code scanning is a relevant scheme to be applied in a secure attendance system.

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