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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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Channel fading attenuation based on rainfall rate for future 5G wireless communication system over 38-GHz Ibraheem A. Shayea; Salim A. Alkhawaldeh; Mahdi A. Nisirat; Tharek A. Rahman; Mustafa Ergen
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5104-5113

Abstract

In this paper, the effect of heavy rainfall on the propagation of a 38-GHz in a tropical region was studied and analyzed. Real measurement was collected, with a path length of 300 meters, for a (5G) radio linkage in Malaysia, installed at the Universiti Teknologi Malaysia (UTM) Johor Bahru campus. The employed system entails an Ericsson MINI-Link 38 E-0.6 mm, with a horizontal polarization (HP) antenna at the top integrated with a rain gauge and a data logger. Daily registered samples with a single minute span, for a full study period of 1 month, were collected and evaluated. The obtained rain rate was found as 56 mm/hr with a specific rain attenuation of 18.4 dB/km for 0.01% of the time. In addition to that, a calculated average rain attenuation of 5.5 dB for the transmission path of 300 meters length, was calculated. Based on these findings, a recommendation to update the International Telecommunication Union (ITU) specification of the rain attenuation for Malaysia is proposed. Based on the results, we suggest shifting the zone classification of Malaysia from zone P to zone N-P. Therefore, accurate design for future 5G systems would rely on more precise estimated attenuation levels leading to enhanced performance.
Automated information retrieval and services of graduate school using chatbot system Meennapa Rukhiran; Paniti Netinant
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5330-5338

Abstract

Automated information retrieval and servicing systems are a priority demand system in today's businesses to ensure instantaneous customer satisfaction. The chatbot system is an incredible technological application that enables communication channels to automatically respond to end-users in real-time and 24 hours a day. By providing effective services for retrieving information and electronic documents continuously and automating the information service system, the coronavirus disease (COVID-19) is challenging to promote graduate school programs, update news, and retrieve student information in this era. This article discusses automated information retrieval and services based on the architecture, components, technology, and experiment of chatbots. The chatbot system's primary functions are to deliver the course and contact information, answer frequency questions, and provide a link menu to apply for our online course platform. We manage the entire functional process of gathering course information and submitting an application for a course online. The final results compare end users' perceptions of chatbot system usage to onsite services to ensure that the chatbot system can be integrated into the university's information system, supporting university-related questions and answers. We may expand our chatbot system's connection to the university's server to provide information services to students in various informative areas for future research.
Content validity study: a gamification model to drive behavior change in defeating coronavirus disease 2019 pandemic Nurul Hidayah Mat Zain; Siti Rahayu Abdul Aziz; Nor Aiza Moketar; Norshahidatul Hasana Ishak; Heny Hendrayati
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5375-5382

Abstract

Gamification refers to transforming the environment to become more game-like to produce a positive experience. In this study, the researchers developed a gamification model, namely the GAMEBC model, to drive behavior change through a health awareness campaign in defeating the coronavirus disease 2019 (COVID-19) pandemic. The GAMEBC model was developed based on the self-determination theory (SDT) and gamification design literature. The GAMEBC model in this study involves four elements: competence, relatedness, autonomy, and engagement. Each element includes criteria that drive behavior change in health awareness campaigns. However, studies that validated the gamification model elements are limited, specifically to drive behavior change. Therefore, the content validity of the GAMEBC model instrument was carried out, and the analysis was based on thirteen expert reviews. The mean value and inter-rater agreement approach were implemented to examine the content validity ratio (CVR), item content validity index (I-CVI), and scale content validity index (S-CVI). The expert evaluation approach was implemented to review the GAMEBC model in terms of relevance and clarity. The data were analyzed using descriptive analysis. As a result of this work, we formulated an instrument that can be used to model and measure behavior change through the gamification approach in health awareness campaigns.
Optimal siting and sizing of unified power flow controller using sensitivity constrained differential evolution algorithm Karri Manoz Kumar Reddy; Kailasa A. Rao; Rayapudi Srinivasa Rao
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4680-4687

Abstract

This paper presents Sensitivity constrained placement of unified power flow controller (UPFC) considering active-power flow sensitive index (APFSI) and static voltage stability index (STATIC-VSI) to minimize active-power losses and to improve power transmission capacity. The sensitive factors are derived with respect to voltage, phase angle and current to formulate APFSI. Transmission line impedance parameters along with active and reactivepower flow measurements are considered to formulate static-VSI. Sensitivity constrained differential evolutionary (SCDE) algorithm is proposed for parameter setting through which power control and minimization of losses in system can be achieved. Testing is performed on IEEE-5, 14 and 30-bus networks in MATLAB and results indicate that SCDE is robust optimization technique compared to conventional method and genetic algorithm (GA)
Modified SEIR and machine learning prediction of the trend of the epidemic of COVID-19 in Jordan under lockdowns impact Mutasem Khalil Alsmadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5455-5466

Abstract

Susceptible exposed infectious recovered (SEIR) is a fitting model for coronavirus disease (COVID-19) spread prediction. Hence, to examine the effect of different levels of social distancing on the spreading of the disease, a variable was introduced in the SEIR equations system used in this work. We also used an artificial intelligence approach using a machine learning (ML) method known as deep neural network. This modified SEIR model was applied on the available initial spread data until June 25th, 2021 for the Hashemite Kingdom of Jordan. Without lockdown in Jordan, the analysis demonstrates potential infection to roughly 3.1 million people during the peak of spread approximately 3 months, starting from the date of lockdown (March 21st). Conversely, the present partial lockdowns strategy by the Kingdom was expected to reduce the predicted number of infections to 0.5 million in 9 months period. The analysis also demonstrates the ability of stricter lockdowns to effectively flatten the graph curve of COVID-19 in Jordan. Our modified SEIR and deep neural network (DNN) model were efficient in the prediction of COVID-19 epidemic sizes and peaks. The measures taken to control the epidemic by the government decreased the size of the COVID-19 epidemic.
Application of machine learning in cement price prediction through a web-based system Adedeji Olushola Afolabi; Oluwamayowa Abimbola
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5214-5225

Abstract

Cement is one of the most common building materials in the construction industry. Simultaneously, its price fluctuation can affect the success or failure of the construction project's performance. The study aimed to develop a web-based platform that uses machine learning algorithms on historical data of cement prices, petrol prices, diesel prices, interest rate, and exchange rate to predict future prices of cement products. The web-based learning platform was developed using hypertext markup language (HTML), cascading style sheet (CSS), MySQL, and hypertext preprocessor (PHP). For building a reliable machine learning model, python language was used to train the system. The front end, the back end, and the machine learning model were integrated with a flask python framework. A system block diagram was designed to show the web-based learning platform's interfaces. The web-based learning platform's system implementation led to the login page, the home page, database page, and cement price analytics interface. In training the machine learning model to make reliable cement price predictions, the study obtained an 80% fitted model in the linear regression. The web-based machine learning platform was able to predict the prices of cement. The rationale behind the machine learning prediction shown by the scatter plot diagram revealed that the cement increases by 250 naira biannually.
Deep convolutional network based real time fatigue detection and drowsiness alertness system Vijay Prakash Sharma; Jitendra Singh Yadav; Vivek Sharma
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5493-5500

Abstract

Fatigue and drowsiness detection techniques based on the external features are under progress, and the methods of facial feature extraction require further development. This paper discusses the innovative processes, efficient methods, and recent advancements in the field of drowsiness and fatigue detection. In this proposed model, a wide application is planned in the field of artificial intelligence by defining the fundamentals of human-computer interaction, facial expression recognition and driver fatigue-sleepiness determination. This research outlines an efficient and effective three-phase strategy for detecting drowsiness. Viola Jones is used to detect facial traits in these three phases. Detection of yawning and tracking once the face has been identified, the segmenting the skin, the system becomes lighting invariant portion by itself, focusing on the chromatic components based on skin, and to reject most of non-face image backdrops. The color eye tracking and yawning detection are carried out by template matching with the correlation coefficient. The vectors of features based on each of the above phases is concatenated, and a binary result is obtained. The analysis of sound and successive frames into fatigue and non-fatigue states has been classified. If the time in fatigue state exceeds the threshold, the system will sound an alarm. 
Real-time remote monitoring and control system for underground pipelines Noor Hazim Abdulwahab; ‪Ali Ahmed Abed‬‏; Mohammed Ali Jaber
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4892-4902

Abstract

Underground pipelines suffer from corrosion in soil layers, and this corrosion is accelerated with the increasing of soil thickness due to more water contained. Cathodic protection (CP) is one of the most common methods for controlling corrosion of metals. Its popularity returns to the fact that CP system is simple, cheap, and suitable for many industrial applications. The drawback of the available CP systems is the need to go to the site for gathering data using classical instruments and methods, which is tedious, dangerous, uneconomic, and inaccurate. The main objective of this paper is to present a real-time remote monitoring and control (RT-RMC) system for any CP platform. The work started with implementation of an industrial-like CP prototype to realize the desired task. The implemented CP system consists of two famous CP methods, the sacrificial anodes (SACP) and the impressed current (ICCP). After that, the RT-RMC system is implemented with two techniques, global system for mobile communications(GSM), and web of things (WoT) to facilitate monitoring and control tasks. Experimental results are obtained for voltage and current measurements with different environments, disturbance, and pipe coatings.
Real time hardware implementation of discrete sliding mode fuzzy controlled buck converter using digital signal processor Viji Karthikeyan; Anil Kumar Tiwari; Chitra Kandasamy
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp4801-4807

Abstract

This paper deals with the real time hardware implementation of discrete sliding mode fuzzy control (DSMFC) for buck converter using digital signal processor (DSP). Applications like electric vehicle suspension control, flight dynamic control, robot position control and engine throttle position control; sliding mode control (SMC) plays a major role. Hardware realization is difficult with SMC strategy due to the continuous gain change results in chattering problem and actuator or contact may break. To resolve this problem the fuzzy logic (FL) approach has combined with the robust technique discrete sliding mode control (DSMC) to develop a new strategy for DSMFC. The mathematical modeling of the controller is done using MATLAB/Simulink software and practical design of the converter is also realized. The robustness of the controller is proved by introducing sudden change in input voltage as well as load with the help of switching circuit in hardware realization. The obtained practical results are verified by comparing with the simulation output and reference value.
Predicting the mental health of rural Bangladeshi children in coronavirus disease 2019 Nazmun Nessa Moon; Refath Ara Hossain; Israt Jahan; Shahriar Shakil; Shihab Uddin; Mahmudul Hassan; Fernaz Narin Nur
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5501-5510

Abstract

The novel coronavirus disease 2019 (COVID-19) current pandemic is a worldwide health emergency like no other. It is not the only COVID-19 infection in infants, children, and adolescents that is causing concern among their families and professionals; there are also other serious issues that must be carefully detected and addressed. Major things are identified due to COVID-19, some elements are affecting children’s healthcare in direct or indirect ways, affecting them not just from a medical standpoint but also from social, psychological, economic, and educational perspectives. All these factors may have affected children’s mental development, particularly in rural settings. As Bangladesh faces a major challenge such as a lack of public mental health facilities, especially in rural areas. So, we discovered a method to predict the mental development condition of rural children that they are facing at this time of COVID-19 using machine learning technology. This research work can predict whether a rural child is mentally developed or mentally hampered in Bangladesh and this prediction gives nice feedback.

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