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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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An analysis of diverse computational models for predicting student achievement on e-learning platforms using machine learning Tirumanadham, Naga Satya Koti Mani Kumar; Sekhar, Thaiyalnayaki; Muthal, Sriram
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7013-7021

Abstract

Coronavirus disease 2019 (COVID-19) has led many colleges and students to use online learning. In educational databases with so much data, evaluating student development is difficult. E-learning is essential for egalitarian education since it uses technology and contemporary learning techniques. This review research found three ways for predicting online course performance: i) To choose the best features to raise student performance; ii) The most effective algorithms for transforming unbalanced data into balanced data; and iii) The best machine learning algorithms to predict online course performance. This study also offered insights into using hybrid techniques and optimization algorithms to educational data sets to improve student performance prediction. The utilization of data from independent e-learning products to enhance education today requires data processing to ensure quality. In addition to these techniques, our abstract highlights the effectiveness of hybrid feature selection methods like L2 regularization (Ridge) and recursive feature elimination (RFE) and ensemble learning models like random forest, gradient boosting, and AdaBoost. These approaches considerably improve prediction accuracy and tackle huge and sophisticated educational dataset challenges. Our work uses advanced machine-learning approaches to optimize e-learning settings and boost academic achievements in the shifting online education landscape caused by the COVID-19 pandemic.
Insights of effectivity analysis of learning-based approaches towards software defect prediction Rai, Deepti; Arcot Prashant, Jyothi
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1916-1927

Abstract

Software defect prediction is one of the essential sets of operation towards mitigating issues of risk management in software development known to contribute towards enhancing the quality of software. There is evolution of various methodologies towards resolving this issue while learning-based methodology is witnessed to be the most dominant contributor. The problem identified is that there are yet many unsolved queries associated with practical viability of such learning-based approach adoption in software quality management. Proposed approaches discussed in this paper contributes towards mitigating this challenge by introducing a simplified, compact, and crisp analysis of effectiveness associated with learning-based schemes. The paper presents its major findings of effectivity analysis of machine learning, deep learning, hybrid, and other miscellaneous approaches deployed for fault prediction followed by highlighting research trend. The major findings infer that feature selection, data imbalance, interpretability, and in adequate involvement of context are prime gaps in existing methods. The paper also contributes towards research gap as well as essential learning outcomes of present review work.
An approach toward improvement of ensemble method’s accuracy for biomedical data classification Izonin, Ivan; Muzyka, Roman; Tkachenko, Roman; Gregus, Michal; Kustra, Natalya; Mitoulis, Stergios-Aristoteles
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5949-5960

Abstract

Amidst rapid technological and healthcare advancements, biomedical data classification using machine learning (ML) is pivotal for revolutionizing medical diagnosis, treatment, and research by organizing vast healthcare-related data. Despite efforts to apply single ML models on clean datasets, satisfactory classification accuracy can still be elusive. In such cases, ML-based ensembles offer a promising solution. This paper explores cascaded ensembles as highly accurate methods. Existing cascade classifiers often partition large datasets into equal unique parts, limiting accuracy due to insufficient amount of useful information processed by weak classifiers of all levels of the cascade ensemble. To address this, we propose an improved cascaded ensemble scheme using a different data sampling approach. Our method forms larger subsamples at each cascade level, enhancing accuracy, and generalization properties during biomedical data analysis. Experimental comparisons demonstrate substantial increases in classification accuracy and generalization properties of the improved cascade ensemble.
Deep learning with filtering for defect characterization in pulsed thermography based non-destructive testing Selvan, Sethu Selvi; Delanthabettu, Sharath; Murugesan, Menaka; Balasubramaniam, Venkatraman
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp1027-1040

Abstract

Pulsed thermography is widely used for non-destructive testing of various materials. The temperature profile obtained after pulse heating is used to characterize the underlying defects in an object. In this paper, the automation of the process of defect visualization and depth quantification in pulsed thermography through various deep learning algorithms is reported. Stainless steel plate with artificial defects is considered for analysis. The raw temperature data is smoothed using moving average, Savitzky-Golay and quadratic regression filters to reduce noise. Thermal signal reconstruction, the conventional method to eliminate noise, is also used for generating filtered datasets. Defect visualization refers to identifying and locating the defects in an image sample and Mask region convolutional neural network (Mask R-CNN) is considered for not just detecting the defects but also locating them on the image. The located defects are utilized for depth estimation using the following networks-multi-layer perceptron (MLP), long short-term memory (LSTM) and gated recurrent units (GRU). The input to the networks is the temperature contrast characteristics which symbolizes the difference in temperature over defective and non-defective areas measured over 250 time points and output of the networks is the estimated depth. The study shows that LSTM based approach provides the least percentage error of 5.5% and is a very suitable approach for automation of defect characterization in pulsed thermography.
A fully automatic curve localization method for extracted spine Xie, Aishu; Moung, Ervin Gubin; Zhou, Xu; Yang, Zhibang
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4018-4033

Abstract

The automation of scoliosis positioning presents a challenging and often understated task, yet it holds fundamental significance for the automated analysis of spinal morphological anomalies. This paper introduces a novel spinal curve localization model for precisely differentiating the spinal curves and identifying their concave centers. The proposed model contains three components: i) custom spine central line model, to define the spine central line as a combination of several secant line sequences with different polarities; ii)  custom curve model, to classify each spinal curve into one of 11 curves types and deduce each its concave centers by several custom formulas; and iii) adapted distance transform and quadratic line fitting algorithm coupled with custom secant line segment searching strategy (DTQL-LS), to search all line segments in the spine and group consecutive line segments with identical polarity into line sequence. Experimental results show that its positioning success rate is close to 99%. Furthermore, it exhibits significant time efficiency, with the average time to process a single image being less than 30 milliseconds. Moreover, even if some image boundaries are blurred, the center of the curve can still be accurately located.
Transforming clinical clerkship processes with integrated information systems Septama, Hery Dian; Hakim, Lukmanul; Muhammad, Meizano Ardhi; Rahmayani, Fidha; Anggraini, Dwi Indria; Lania, Siska; Bestak, Robert
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3036-3044

Abstract

There are still many manual administrative processes involved in clinical clerkships. However, digital technologies and software solutions can significantly improve medical education. This paper aims to utilize extreme programming (XP) as a methodology for developing the information system. A total of 23 user stories have been planned for completion. The priority of each requirement stated in the user stories is determined using the MoSCoW prioritization technique. The results indicate that the software successfully passed all tests among the 23 black box testing scenarios. Developing an integrated information system to streamline clinical clerkships has been successfully achieved. The user experience questionnaire (UEQ) is utilized to gather the perspectives of hospital management and medical faculty members regarding the information system. The average scores for each category are as follows: 1.93 for attractiveness, 2.04 for perspicuity, 2.20 for efficiency, 2.06 for dependability, 2.14 for stimulation, and 1.85 for novelty. Therefore, it can be concluded that all existing user experience scales exhibit an outstanding degree of user satisfaction.
Improved impressed current cathodic protection systems by incorporating a pulse-feeding technique integrated with internet of things capabilities Adzman, Mohd Rafi; Sallehuddin, Aiman Arif Mohd; Shamsudin, Shaiful Rizam; Husin, Nusaybah Mohd; Haniff, Nur Syakirah Mohammed; Gunasilan, Mahalaksmi; Idris, Muhd Hafizi; Amirruddin, Melaty
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7254-7265

Abstract

This paper introduces an innovative improvement to impressed current cathodic protection (ICCP) systems by integrating a pulse-feeding technique designed to address metal protection degradation during off-potential periods, a common issue in conventional systems. The proposed system enhances the overall effectiveness and reliability of ICCP, providing consistent corrosion protection for critical metal structures. A notable advantage of this method is its simplicity, utilizing a cost-effective microcontroller for pulse feeding. This approach simplifies integration processes and enhances cost-effectiveness, making it an attractive solution for improving cathodic protection system performance without substantial additional costs. The method addresses conventional ICCP weaknesses by applying a high-frequency pulse current during off-potential periods. This reduces excessive negative charge buildup on metal surfaces during interruptions, boosting the system’s effectiveness and stability. Research laboratory experiments were conducted using pulse width modulation (PWM) on an ATmega328P microcontroller to demonstrate the method’s effectiveness. Additionally, an IoT-monitored ICCP system was developed using an ESP32 microcontroller and the Blynk application. Results highlight the superiority of a 50 kHz pulse feeding frequency in preventing corrosion compared to lower frequencies. Overall, this advancement significantly enhances ICCP systems, providing improved corrosion protection and durability in harsh environments.
Real time performance assessment of utility grid interfaced solar photovoltaic plant Padmaja, Suragani Mohini; Kumar Varma, Sagiraju Dileep; Omkar, Koduri; Rao, Gajula Srinivasa
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1323-1333

Abstract

Continuous monitoring of large-scale solar photovoltaic (PV) installations is necessary to check the deterioration and monitor the performance of the PV plant. Fault diagnosis is crucial to ensure the PV plant operates safely and reliably. This paper presents a diagnosis methodology based on current-voltage (I-V) and PV characteristics to monitor and assess the behavior of solar PV. In this paper, I-V curve characterization using an I-V curve tracer is used to check the deterioration and diagnosis of the PV panels. The real-time performance of the 50.4 kWp rooftop solar grid interfaced PV plant is investigated and analyzed using I-V and PV curve tracers in real-time conditions. The overall performance of solar PV is assessed on a real-time test system in different scenarios such as variable climatic conditions, partial shading conditions, aging of solar panels, short circuit conditions, and dust decomposition. Furthermore, the performance assessment of solar PV is evaluated using performance indicators such as open circuit voltage index, short circuit current index, fill factor, and performance ratio.
Optimal energy management of hybrid battery/supercapacitor storage system for electric vehicle application Rezk, Hegazy; Al-Dhaifallah, Mujahed; Amrr, Syed Muhammad; Alharbi, Abdullah
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6076-6085

Abstract

A proposed approach for efficient energy management for lithium-ion battery and supercapacitor hybrid energy storage system is outlined in this study. The primary aim is to ensure the electric vehicle receives a stable and high-quality supply of electricity. The suggested management strategy focuses on maintaining the bus voltage at a consistent level while meeting the varying load demands with high-quality power across different scenarios. To achieve this, a management controller is employed, which utilizes a metaheuristics technique to define the parameters of the integral sliding mode control. The supercapacitor units are responsible for controlling the direct current (DC) bus, while the lithium-ion battery plays a role in balancing power distribution on the common line. This study evaluates the potential benefits of integrating salp swarm algorithm techniques into the controller's performance. The findings of the study suggest combining metaheuristic optimization methods with integral sliding mode control. Can lead to improvements in power quality. The proposed management algorithm not only efficiently allocates power resources but also safeguards them, ultimately ensuring a consistent and high-quality power supply for the electric vehicle.
Accuracy study of image classification for reverse vending machine waste segregation using convolutional neural network Hor Yan, Tan; Mohd Azam, Sazuan Nazrah; Md. Sani, Zamani; Azizan, Azizul
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp366-374

Abstract

This study aims to create a sorting system with high accuracy that can classify various beverage containers based on types and separate them accordingly. This reverse vending machine (RVM) provides an image classification method and allows for recycling three types of beverage containers: drink carton boxes, polyethylene terephthalate (PET) bottles, and aluminium cans. The image classification method used in this project is transfer learning with convolutional neural networks (CNN). AlexNet, GoogLeNet, DenseNet201, InceptionResNetV2, InceptionV3, MobileNetV2, XceptionNet, ShuffleNet, ResNet 18, ResNet 50, and ResNet 101 are the neural networks that used in this project. This project will compare the F1-score and computational time among the eleven networks. The F1-score and computational time of image classification differs for each neural network. In this project, the AlexNet network gave the best F1-score, 97.50% with the shortest computational time, 2229.235 s among the eleven neural networks.

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