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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 111 Documents
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Search results for , issue "Vol 14, No 2: April 2024" : 111 Documents clear
Classification of pathologies on digital chest radiographs using machine learning methods Aitimov, Murat; Shekerbek, Ainur; Pestunov, Igor; Bakanov, Galitdin; Ostayeva, Aiymkhan; Ziyatbekova, Gulzat; Mediyeva, Saule; Omarova, Gulmira
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.pp1899-1905

Abstract

This article is devoted to the research and development of methods for classifying pathologies on digital chest radiographs using two different machine learning approaches: the eXtreme gradient boosting (XGBoost) algorithm and the deep convolutional neural network residual network (ResNet50). The goal of the study is to develop effective and accurate methods for automatically classifying various pathologies detected on chest X-rays. The study collected an extensive dataset of digital chest radiographs, including a variety of clinical cases and different classes of pathology. Developed and trained machine learning models based on the XGBoost algorithm and the ResNet50 convolutional neural network using pre-processed images. The performance and accuracy of both models were assessed on test data using quality metrics and a comparative analysis of the results was carried out. The expected results of the article are high accuracy and reliability of methods for classifying pathologies on chest radiographs, as well as an understanding of their effectiveness in the context of clinical practice. These results may have significant implications for improving the diagnosis and care of patients with chest diseases, as well as promoting the development of automated decision support systems in radiology.
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.
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.
Convolutional neural network for estimation of harvest time of forage sorghum (sorghum bicolor) cultivar samurai-1 Suradiradja, Kahfi Heryandi; Sitanggang, Imas Sukaesih; Abdullah, Luki; Hermadi, Irman
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.pp1730-1738

Abstract

One of the economic alternatives to improve the quality of ruminant feed is combining grass as the main feed with high-protein forages such as sorghum. To get a quality sorghum harvest during the period, it must be right when it has good biomass content, nutrients, and digestibility. The problem is that measuring quality in the laboratory has additional costs and time, which is not short, causing delays. An approach with machine learning using a convolutional neural network can be a better solution. This research uses a convolutional neural network algorithm with the right architecture to estimate sorghum harvest time from imaging results of unmanned aerial vehicles. The stages of this research include data collection, pre-processing, modeling, and finally, the evaluation stage. This research compares the results of several convolutional neural network (CNN) algorithm architectural models: simple CNN, ResNet50 V2, visual geometry group-16 (VGG-16), MobileNet V2, and Inception V3. The result is determining the CNN algorithm architectural model that can estimate sorghum harvest time with maximum accuracy. The best result is the simple CNN architectural model with an accuracy of 0.95. This research shows that the classification model obtained from the CNN algorithm with a simple CNN architecture is the choice model for estimating sorghum harvest time.
Strengthening data integrity in academic document recording with blockchain and InterPlanetary file system Suseno, Taufiq Rizky Darmawan; Afrianto, Irawan; Atin, Sufa
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.pp1759-1769

Abstract

A diploma is a certificate or official document given by a school or college that is useful for continuing education, applying for jobs, and assessing student intelligence. The main problem with diplomas and other academic documents is that many are forged. This study aims to develop a prototype for recording student academic data using blockchain and blockchain and InterPlanetary file system (IPFS). The research stages were conducted with system conceptualization, data modeling, smart contract development, IPFS integration, data transaction development, user interface/user experience (UI/UX) development, and system testing. A blockchain is a permanent information structure formed by data blocks that are interconnected with transaction data blocks before and after it. The transaction data for each block are encrypted using asymmetric cryptography. IPFS is a peer-to-peer network protocol for storing and sharing data in a distributed file system applying the concept of decentralization to make the manipulation more difficult. The results show that student academic data and documents were successfully stored in a blockchain network using smart contracts and IPFS. Blockchain technology, smart contracts, and IPFS strengthen the value of these documents into documents that are safe, difficult to counterfeit, and easy to trace, such that authentication and integration are better preserved.
Predictive analysis of terrorist activities in Thailand's Southern provinces: a deep learning approach Ganokratanaa, Thittaporn; Ketcham, Mahasak
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.pp1797-1808

Abstract

Terrorist activities have been on the rise globally, with Thailand experiencing significant challenges, particularly in its three southern border provinces. This study offers a comprehensive analysis aiming to predict forthcoming terrorist events in these provinces. We employed historical data, categorized into nine groups based on military expert recommendations, to train our prediction model. This research tested the prediction capabilities of various methodologies, including decision trees, naïve Bayesian learning techniques, and deep learning artificial neural networks. Notably, the deep neural network emerged as the superior predictive tool, achieving an impressive accuracy of 98.21% and a root mean square error (RMSE) of 0.59%. The primary anticipated events include bombings, shootings, assaults, and acts of vandalism. Our findings also revealed that Pattani Province was the most affected, accounting for 45% of incidents. Specific districts, such as Panare and Yarang, exhibited high crime rates of 40% and 36.84%, respectively. Yala Province, particularly Bannang Sata District, was identified as the hotspot for shooting incidents, with a rate of 34%.
A taxonomy on power optimization techniques for fifth-generation heterogenous non-orthogonal multiple access networks Vishalakshi, Vishalakshi; Shivsharanappa Biradar, Gangadhar
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.pp1616-1624

Abstract

Non-orthogonal multiple access (NOMA) is an anticipated technology for fifth-generation networks for increasing mass connectivity, spectrum efficiency, user-fairness, and higher capacity. NOMA allows end-clients to share indistinguishable radio resources such as spreading code, subcarrier, and time slots simultaneously. Thus, the main challenge involved in conceptualizing effective NOMA design is selection of resource allocation (i.e., user clustering, power allocation, and quality-of-service (QoS) assurance) algorithms. NOMA can be easily integrated with current fifth-generation multi-access methodologies. In this survey paper, the NOMA methodologies are discussed, and provide an overview of the methodologies and algorithms designed for optimizing power allocation, interference management, and network selection management in the heterogenous multiple carrier NOMA. The survey highlights the current limitation of the existing resource provisioning framework and presents a solution to overcome the current limitation.
Application of deep learning methods for automated analysis of retinal structures in ophthalmology Kassymova, Akmaral; Konyrkhanova, Assem; Issembayeva, Aida; Saimanova, Zagira; Saltayev, Alisher; Ongarbayeva, Maral; Issakova, Gulnur
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.pp1987-1995

Abstract

This article examines a current area of research in the field of ophthalmology the use of deep learning methods for automated analysis of retinal structures. This work explores the use of deep learning methods such as EfficientNet and DenseNet for the automated analysis of retinal structures in ophthalmology. EfficientNet, originally proposed to balance between accuracy and computational efficiency, and DenseNet, based on dense connections between layers, are considered as tools for identifying and classifying retina features. Automated analysis includes identifying pathologies, assessing the degree of their development and, possibly, diagnosing various eye diseases. Experiments are performed on a dataset containing a variety of images of retinal structures. Results are evaluated using metrics of accuracy, sensitivity, and specificity. It is expected that the proposed deep learning methods can significantly improve the automated analysis of retinal images, which is important for the diagnosis and monitoring of eye diseases. As a result, the article highlights the significance and promise of using deep learning methods in ophthalmology for automated analysis of retinal structures. These methods help improve the early diagnosis, treatment and monitoring of eye diseases, which can ultimately lead to improved healthcare quality and improved patient lives.
Effective driver distraction warning system incorporating fast image recognition methods Nguyen, Van Binh; Trinh, Phu Duy
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.pp1572-1582

Abstract

Modern cars are equipped with advanced automatic technology featuring various safety measures for car occupants. However, the growing density of vehicles, especially in areas where infrastructure development lags, poses potential dangers, particularly accidents caused by driver subjectivity. These incidents may occur due to driver distraction or the presence of high-risk obstacles on the road. This article presents a comprehensive solution to assist drivers in mitigating these risks. Firstly, the study introduces a novel method to enhance the recognition of a driver's facial features by analyzing benchmarks and the whites of the eyes to assess the distraction level. Secondly, a domain division method is proposed to identify obstacles and lanes in front of the vehicle, enabling the assessment of the danger level. This information is promptly relayed to the driver and relevant individuals, such as the driver's manager or supervisor. An experimental device has also been developed to evaluate the effectiveness of the algorithms, solutions, and processing capabilities of the system.
An efficient convolutional neural network-extreme gradient boosting hybrid deep learning model for disease detection applications Bhaskar, Navaneeth; Ajithkumar, Aswathy Maruthompilli; Tupe-Waghmare, Priyanka
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.pp2035-2042

Abstract

In this paper, we present an efficient deep-learning hybrid model comprising an extreme gradient boosting (XGBoost) supervised learning algorithm and convolutional neural networks (CNN) for the automated detection of diseases. The proposed model is implemented and tested to detect type-2 diabetes by measuring the acetone concentration in the exhaled breath. Acetone will be present in much higher concentrations in type-2 diabetic patients compared to non-diabetic people. A novel sensing module is designed and implemented in our study to measure the acetone concentration in exhaled breath. The proposed approach delivered good results, with a classification accuracy of 97.14%. The findings of this study show how effectively the proposed detection module functions in disease diagnosis applications. As the detection process is simple and non-invasive, people can undergo routine checks for diabetes with the proposed detection module.

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