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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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Pairwise test case generation with harmony search, one-parameter-at-at-time, seeding, and constraint mechanism integration Aminu Muazu, Aminu; Hashim, Ahmad Sobri; Maiwada, Umar Danjuma; Isma'ila, Umar Audi; Yakubu, Muhammad Muntasir; Abubakar Ibrahim, Muhammad
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.pp3137-3149

Abstract

Pairwise testing is a method for identifying defects through combinatorial analysis. It involves testing all possible combinations of input parameters in pairs within a system, ensuring that each pair is tested at least once. The field of test case generation is highly active in the realm of combinatorial interaction testing. Research in this area is particularly encouraged, as it falls under the category of non-deterministic polynomial-time hardness. A big challenge in this field is the combinatorial explosion problem. It is about finding the best test suite that covers all possible combinations of interaction strength. In this paper, we present the task of discovering a pairwise test set as a search problem and introduce an innovative testing tool referred to as pairwise test case generation in harmony search algorithm with seeding and constraint mechanism (PHOSC). Experimental results show that PHOSC performs better compared to some existing pairwise strategies in terms of test suite size. Additionally, PHOSC provides a comprehensive framework and serves as a research platform for the generation of pairwise test sets employing the harmony search algorithm. It adopts an approach that focuses on one parameter at a time (OPAT) and incorporates seeding and constraint mechanisms at the same time, thereby enhancing the efficiency and effectiveness of the testing process.
Feasibility and sustainability analysis of a hybrid microgrid in Bangladesh Chowdhury, Aditta; Miskat, Monirul Islam; Ahmed, Tofael; Ahmad, Shameem; Hazari, Md. Rifat; Awalin, Lilik Jamilatul; Mekhilef, Saad
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.pp1334-1351

Abstract

The demand for renewable sources-based micro-grid systems is increasing all over the world to address the United Nation’s (UN) sustainable development goal 7 (SDG7) “affordable and clean energy”. However, without proper viability analysis, these micro-grid systems might lead to economic losses to both customers and investors. Therefore, this paper aims to explore the feasibility and sustainability of a hybrid micro-grid system based on available renewable resources in remote hill tracts region of Bangladesh. Nine different scenarios are analyzed here, and a combination of solar, hydro, biogas, and diesel generator systems are found to be the best feasible solution in regard to the least cost of electricity and emission. The optimized result shows that with a renewable fraction of 0.995, the unit levelized cost of energy of the micro-grid system is $0.182 and it emits 54 and 117 times less CO2 compared to grid-based and diesel-based systems. Further, the fuel share of the system being 0.5% and greenhouse gas per energy being 0.06425 kg/KWh, validate the system as highly sustainable and eco-friendly. With the ability to fulfill load demands without interrupting supply, and reducing the emissions of greenhouse gases, the designed microgrid can provide sustainable energy solutions to any hill-tracts of Bangladesh.
Estimation of kernel density function using Kapur entropy Chawla, Leena; Kumar, Vijay; Saxena, Arti
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.pp6016-6022

Abstract

Information-theoretic measures play a vital role in training learning systems. Many researchers proposed non-parametric entropy estimators that have applications in adaptive systems. In this work, a kernel density estimator using Kapur entropy of order α and type β has been proposed and discussed with the help of theorems and properties. From the results, it has been observed that the proposed density measure is consistent, minimum, and smooth for the probability density function (PDF) underlying given conditions and validated with the help of theorems and properties. The objective of the paper is to understand the theoretical viewpoint behind the underlying concept.
Best sum-throughput evaluation of cooperative downlink transmission nonorthogonal multiple access system Albdairat, Ahmad; Wanis Zaki, Fayez; Ashour, Mohammed Mahmoud
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.pp509-519

Abstract

In cooperative simultaneous wireless information and power transfer (SWIPT) nonorthogonal multiple access (NOMA) downlink situations, the current research investigates the total throughput of users in center and edge of cell. We focus on creating ways to solve these problems because the fair transmission rate of users located in cell edge and outage performance are significant hurdles at NOMA schemes. To enhance the functionality of cell-edge users, we examine a two-user NOMA scheme whereby the cell-center user functions as a SWIPT relay using power splitting (PS) with a multiple-input single-output. We calculated the probability of an outage for both center and edge cell users, using closed-form approximation formulas and evaluate the system efficacy. The usability of cell edge users is maximized by downlink transmission NOMA (CDT-NOMA) employing a SWIPT relay that employs PS. The suggested approach calculates the ideal value of the PS coefficient to optimize the sum throughput. Compared to the noncooperative and single-input single-output NOMA systems, the best SWIPT-NOMA system provides the cell-edge user with a significant throughput gain. Applying SWIPT-based relaying transmission has no impact on the framework’s overall throughput.
Non-binary codes approach on the performance of short-packet full-duplex transmissions Vuong, Bao Quoc; Trang, Kien; Nguyen, An Hoang; Do, Hung Ngoc
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.pp1683-1690

Abstract

This paper illustrates the enhancement of the performance of short-packet full-duplex (FD) transmission by taking the approach of non-binary low density parity check (NB-LDPC) codes over higher Galois field. For the purpose of reducing the impacts of self-interference (SI), high order of modulation, complexity, and latency decoder, a blind feedback process composed of channels estimation and decoding algorithm is implemented. In particular, this method uses an iterative process to simultaneously suppress SI component of FD transmission, estimate intended channel, and decode messages. The results indicate that the proposed technique provides a better solution than both the NB-LDPC without feedback and the binary LDPC feedback algorithms. Indeed, it can significantly improve the performance of overall system in two important factors, which are bit-error-rate (BER) and mean square error (MSE), especially in high order of modulation. The suggested algorithm also shows a robustness in reliability and power consumption for both short-packet FD transmissions and high order modulation communications.
Strategic plant maintenance planning in agriculture by integrating lean principles and optimization Simarmata, Gayus; Suwilo, Saib; Sitompul, Opim Salim; Sutarman, Sutarman
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.pp6279-6286

Abstract

Operational planning within agricultural production systems plays a pivotal role in facilitating farmers' decision-making processes. This study introduces a novel mathematical model aimed at optimizing plant maintenance planning through the efficient allocation of labor, optimal utilization of machinery, and strategic scheduling. Utilizing mixed integer non-linear programming (MINLP), the model integrates lean principles to minimize waste and improve operational efficiency. The primary contributions of this study include the development of a comprehensive maintenance planning model, the application of advanced mathematical techniques in agriculture, and the enhancement of resource allocation strategies. The results demonstrate significant improvements in maintenance task scheduling, reduced downtime, and enhanced productivity, ultimately contributing to sustainable farming practices and food security. This model serves as a strategic decision-support tool for farmers, enabling data-driven planning and resource utilization to achieve both short-term efficiency and long-term agricultural viability.
Noisy image enhancements using deep learning techniques Daurenbekov, Kuanysh; Aitimova, Ulzada; Dauitbayeva, Aigul; Sankibayev, Arman; Tulegenova, Elmira; Yerzhan, Assel; Yerzhanova, Akbota; Mukhamedrakhimova, Galiya
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.pp811-818

Abstract

This article explores the application of deep learning techniques to improve the accuracy of feature enhancements in noisy images. A multitasking convolutional neural network (CNN) learning model architecture has been proposed that is trained on a large set of annotated images. Various techniques have been used to process noisy images, including the use of data augmentation, the application of filters, and the use of image reconstruction techniques. As a result of the experiments, it was shown that the proposed model using deep learning methods significantly improves the accuracy of object recognition in noisy images. Compared to single-tasking models, the multi-tasking model showed the superiority of this approach in performing multiple tasks simultaneously and saving training time. This study confirms the effectiveness of using multitasking models using deep learning for object recognition in noisy images. The results obtained can be applied in various fields, including computer vision, robotics, automatic driving, and others, where accurate object recognition in noisy images is a critical component.
A semantic-based approach for domain specific language development Negm, Eman; Salah, Akram; Makady, Soha
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.pp5366-5380

Abstract

A domain specific language (DSL) ties the business and technical models, by letting technical developers write programs with the business domain properties. Yet, DSLs are not used due to the cost of developing them. Such cost stems from the needed expertise within both the domain knowledge and language development technicalities for any DSL engineer who would design such a language. This paper proposes a semantic-based DSL development approach that utilizes an ontology as a formal way for domain representation. The domain ontology is semi-automatically transformed into a DSL. Then, an ontology reasoning algorithm provides reasoning services on the DSL structure and the programs developed using such DSL by application developers. Such reasoning services can automatically detect flaws in the DSL design like possible inconsistency or the presence of unsatisfiable or redundant classes thus serving the DSL engineer. The reasoning services can also discover inconsistency or redundant classes in programs built using the designed DSL, thus serving the application developer. The proposed approach was implemented within a language workbench using projectional-editing and was evaluated on two different ontologies from varied domains. The results show correct transformation of the input ontology, valid instantiation of designed application, and efficient reasoning services.
Comparison of convolutional neural network models for user’s facial recognition Pinzón-Arenas, Javier Orlando; Jimenez-Moreno, Robinson; Martinez Baquero, Javier Eduardo
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.pp192-198

Abstract

This paper compares well-known convolutional neural networks (CNN) models for facial recognition. For this, it uses its database created from two registered users and an additional category of unknown persons. Eight different base models of convolutional architectures were compared by transfer of learning, and two additional proposed models called shallow CNN and shallow directed acyclic graph with CNN (DAG-CNN), which are architectures with little depth (six convolution layers). Within the tests with the database, the best results were obtained by the GoogLeNet and ResNet-101 models, managing to classify 100% of the images, even without confusing people outside the two users. However, in an additional real-time test, in which one of the users had his style changed, the models that showed the greatest robustness in this situation were the Inception and the ResNet-101, being able to maintain constant recognition. This demonstrated that the networks of greater depth manage to learn more detailed features of the users' faces, unlike those of shallower ones; their learning of features is more generalized. Declare the full term of an abbreviation/acronym when it is mentioned for the first time.
A comprehensive review of early detection of COVID-19 based on machine learning and deep learning models Al-Khafaji, Ali J. Askar; Sjarif, Nilam Nur Amir
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.pp4167-4174

Abstract

This paper reviews the use of machine learning (ML) and deep learning (DL) for early coronavirus disease (COVID-19) detection, highlighting their potential to overcome the limitations of traditional diagnostic methods such as long processing times and high costs. We analyze studies applying ML and DL to imaging, clinical, and genomic data, assessing their performance in terms of accuracy, sensitivity, specificity, and efficiency. The review discusses the advantages, limitations, and challenges of these models, including data quality, generalizability, and ethical considerations. It also suggests future research directions for improving model efficacy, such as integrating multi-modal data and developing more interpretable models. This concise review serves as a guide for researchers, healthcare practitioners, and policymakers on the advancements and prospects of ML and DL in early COVID-19 detection, promoting further innovation and collaboration in this vital public health domain.

Page 563 of 631 | Total Record : 6301

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