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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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Classification of arecanut using machine learning techniques Shabari Shedthi Billadi; Madappa Siddappa; Surendra Shetty; Vidyasagar Shetty
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.pp1914-1921

Abstract

In agricultural domain research, image processing and machine learning techniques play an important role. This paper provides a unique solution for classifying the good and defective arecanuts based on their color, texture, and density value. In the market different varieties of arecanut are available. Usually, qualitative sorting is done manually, and this can be replaced by applying machine vision techniques to grade the arecanut. Classification of arecanut based on quality is done using various machine learning techniques and it is observed that artificial neural networks give good results compared to other classifiers like logistic regression, k-nearest neighbor, naive Bayes classifiers, and support vector machine. A unique density feature is considered here for better classification. The result of classifiers without considering the density feature is compared with respect to the density feature and it is observed that artificial neural networks work better than the others. The proposed method works effectively for classifying arecanut with an accuracy of 98.8%.
Smart internet of things kindergarten garbage observation system using Arduino uno Ali Abdulameer Aldujaili; Mohammed Dauwed; Ahmed Meri; Safa Sami Abduljabbar
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.pp6820-6828

Abstract

Increase the in population and kindergarten number, especially in urban areas made it difficult to properly manage waste. Thus, this paper proposed a system dedicated to kindergartens to manage to dispose of waste, the system can be called smart garbage based on internet of things (SGI). To ensure a healthy environment and an intelligent waste in the kindergarten management system in an integrated manner and supported by the internet of things (IoT), we presented it in detail identification, the SGI system includes details like a display system, an automatic lid system, and a communication system. This system supplied capabilities to monitor the status of waste continuously and on IoT website can show the percentage of waste placed inside the bin. And by using a Wi-Fi communication system, between the system unit and the monitoring body, to collect waste when the trash is full. The smart system proposed in this paper is the most efficient system of traditional waste management systems because it reduces the use of manpower and significantly limits the spread of waste and fully controls it. Additionally, it can be linked via the IoT to the mobile, thus forming an integrated monitoring system.
Modelling base electricity tariff under the Malaysia incentive-based regulation framework using system dynamics Norlee Husnafeza Ahmad; Nofri Yenita Dahlan; Nor Erne Nazira Bazin; Yusrina Yusof; Arni Munira Markom
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.pp1231-1240

Abstract

In the context of a single buyer (SB) electricity market, this study provides an electricity tariff model developed using system dynamics (SD). Using data from the Malaysian electricity supply industry (MESI), the model was developed with the intent of evaluating the influence of load variation on Malaysia’s base electricity tariff. Given that Malaysia’s electricity demand has increased significantly over the past few years in unison with the country’s economic growth and modernization, this model is developed to investigate the relationship between the two. Moreover, the lack of a comprehensive MESI upstream market model that can monitor this issue was the impetus for this research. This study employed an SD approach, as it is a well-known technique for simulating complex systems and analyzing the existing dynamism between each variable and each system. This model can be a valuable tool for developing an electrical tariff model. Findings revealed that the base electricity pricing on the MESI upstream market is affected by load growth variation during the 30-year time. Since new power sources are needed to meet demand, the tariff becomes more expensive as the load increases. This model may benefit the utility or generating company plan for future generation.
Predicting reaction based on customer's transaction using machine learning approaches Israa M. Hayder; Ghazwan Abdul Nabi Al Ali; Hussain A. Younis
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.pp1086-1096

Abstract

Banking advertisements are important because they help target specific customers on subscribing to their packages or other deals by giving their current customers more fixed-term deposit offers. This is done through promotional advertisements on the Internet or media pages, and this task is the responsibility of the shopping department. In order to build a relationship with them, offer them the best deals, and be appropriate for the client with the company's assurance to recover these deposits, many banks or telecommunications firms store the data of their customers. The Portuguese bank increases its sales by establishing a relationship with its customers. This study proposes creating a prediction model using machine learning algorithms, to see how the customer reacts to subscribe to those fixed-term deposits or offers made with the aid of their past record. This classification is binary, i.e., the prediction of whether or not a customer will embrace these offers. Four classifiers that include k-nearest neighbor (k-NN) algorithm, decision tree, naive Bayes, and support vector machines (SVM) were used, and the best result was obtained from the classifier decision tree with an accuracy of 91% and the other classifier SVM with an accuracy of 89%.
Intrusion detection method for internet of things based on the spiking neural network and decision tree method Ahmed R. Zarzoor; Nadia Adnan Shiltagh Al-Jamali; Dina A. Abdul Qader
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.pp2278-2288

Abstract

The prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices’ power usage. Also, a rand order code (ROC) technique is used with SNN to detect cyber-attacks. The proposed method is evaluated by comparing its performance with two other methods: IDS-DNN and IDS-SNNTLF by using three performance metrics: detection accuracy, latency, and energy usage. The simulation results have shown that IDS-SNNDT attained low power usage and less latency in comparison with IDS-DNN and IDS-SNNTLF methods. Also, IDS-SNNDT has achieved high detection accuracy for cyber-attacks in contrast with IDS-SNNTLF.
International Journal of Electrical and Computer Engineering: a bibliometric analysis Yeison Alberto Garcés-Gómez; Vladimir Henao-Céspedes
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.pp5667-5673

Abstract

This study is focused on analyzing seven years of bibliometric data of the International Journal of Electrical and Computer Engineering (IJECE) from 2014 to 2020. The analysis of 2,928 papers exhibits multi-folded growth of 34.25%, rising from 109 articles in 2014 to 638 articles by 2020. In addition, the analysis of the structure of publications as well as the mapping of bibliographic data based on co-citation, bibliographic coupling, and co-occurrence showed the intellectual structure and connection between universities, countries, and contributing authors. As the journal’s first retrospective, this study not only educates and enriches IJECE’s global readership and aspiring contributors, but may also be useful to its editorial board, as it provides several inputs for navigating future research.
Early coronavirus disease detection using internet of things smart system Tabarak Ali Abdulhussein; Hamid A. Al-Falahi; Drai Ahmed Smait; Sameer Alani; Sarmad Nozad Mahmood; Mohammed Sulaiman Mustafa
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.pp1161-1168

Abstract

The internet of things (IoT) is quickly evolving, allowing for the connecting of a wide range of smart devices in a variety of applications including industry, military, education, and health. Coronavirus has recently expanded fast across the world, and there are no particular therapies available at this moment. As a result, it is critical to avoid infection and watch signs like fever and shortness of breath. This research work proposes a smart and robust system that assists patients with influenza symptoms in determining whether or not they are infected with the coronavirus disease (COVID-19). In addition to the diagnostic capabilities of the system, the system aids these patients in obtaining medical care quickly by informing medical authorities via Blynk IoT. Moreover, the global positioning system (GPS) module is used to track patient mobility in order to locate contaminated regions and analyze suspected patient behaviors. Finally, this idea might be useful in medical institutions, quarantine units, airports, and other relevant fields.
Design of an axial mode helical antenna with buffer layer for underwater applications Afiza Nur Jaafar; Hajar Ja’afar; Yoshihide Yamada; Fatemeh Sadeghikia; Idnin Pasya Ibrahim; Mohd Khairil Adzhar Mahmood
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.pp473-482

Abstract

Recently, there is an increasing demand for high-speed wireless communication network for short-range underwater communication. From previous research, most underwater antennas produced omnidirectional radiation pattern which has lower antenna gain. There are a few considerations that need to be taken if the antenna is designed to operate in water environment. This paper discusses the electromagnetic properties which affect the underwater antenna design. Physical properties such as electrical permittivity and conductivity of water contribute significant effect to the size of the antenna as it influences the behavior of electromagnetic signal that propagates in water. In this study, an axial mode helical antenna with waterproof container is presented which operates at 433 MHz. The axial mode helical antenna has circular polarization and is suitable to support wireless application which is surrounded by some obstruction. The proposed antenna produces a bidirectional radiation pattern by placing it into a waterproof casing. Good agreement between the simulation and measurement results validates the concept. However, a little discrepancy between the simulated and measured results may be attributed to the noise originated from the equipment and the environment.
A microsystem design for controlling a DC motor by pulse width modulation using MicroBlaze soft-core Abdelkarim Zemmouri; Anass Barodi; Hamad Dahou; Mohammed Alareqi; Rachid Elgouri; Laamari Hlou; Mohammed Benbrahim
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.pp1437-1448

Abstract

This paper proposes a microsystem based on the field programmable gate arrays (FPGA) electronic board. The preliminary objective is to manipulate a programming language to achieve a control part capable of controlling the speed of electric actuators, such as direct current (DC) motors. The method proposed in this work is to control the speed of the DC motor by a purely embedded architecture within the FPGA in order to reduce the space occupied by the circuit to a minimum and to ensure the reliability of the system. The implementation of this system allows the embedded MicroBlaze processor to be installed side by side with its memory blocks provided by Xilinx very high-speed integrated circuit (VHSIC) hardware description language (VHDL), Embedded C. The control signal of digital pulse-width modulation pulses is generated by an embedded block managed by the same processor. This potential application is demonstrated by experimental simulation on the Vertix5 FPGA chip.
Ameliorate the performance using soft computing approaches in wireless networks Chandrika Dadhirao; Ravi Sankar Sangam
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.pp502-510

Abstract

Wireless sensor networks are an innovative and rapidly advanced network occupying the broad spectrum of wireless networks. It works on the principle of “use with less expense, effort and with more comfort.” In these networks, routing provides efficient and effective data transmission between different sources to access points using the clustering technique. This work addresses the low-energy adaptive clustering hierarchy (LEACH) protocol’s main backdrop of choosing head nodes based on a random value. In this, the soft computing methods are used, namely the fuzzy approach, to overcome this barrier in LEACH. Our approach’s primary goal is to extend the network lifetime with efficient energy consumption and by choosing the appropriate head node in each cluster based on the fuzzy parameters. The proposed clustering algorithm focused on two fuzzy inference structures, namely Mamdani and Sugeno fuzzy logic models in two scenarios, respectively. We compared our approach with four existing works, the conventional LEACH, LEACH using the fuzzy method, multicriteria cluster head delegation, and fuzzy-based energy efficient clustering approach (FEECA) in wireless sensor network. The proposed scenario based fuzzy LEACH protocol approaches are better than the four existing methods regarding stability, network survivability, and energy consumption.

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