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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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Identification of monolingual and code-switch information from English-Kannada code-switch data Ramesh Chundi; Vishwanath R. Hulipalled; Jay Bharthish Simha
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5632-5640

Abstract

Code-switching is a very common occurrence in social media communication, predominantly found in multilingual countries like India. Using more than one language in communication is known as code-switching or code-mixing. Some of the important applications of code-switch are machine translation (MT), shallow parsing, dialog systems, and semantic parsing. Identifying code-switch and monolingual information is useful for better communication in online networking websites. In this paper, we performed a character level n-gram approach to identify monolingual and code-switch information from English-Kannada social media data. We paralleled various machine learning techniques such as naïve Bayes (NB), support vector classifier (SVC), logistic regression (LR) and neural network (NN) on English-Kannada code-switch (EKCS) data. From the proposed approach, it is observed that the character level n-gram approach provides 1.8% to 4.1% of improvement in terms of Accuracy and 1.6% to 3.8% of improvement in F1-score. Also observed that SVC and NN techniques are outperformed in terms of accuracy (97.9%) and F1-score (98%) with character level n-gram.
Optimal artificial neural network configurations for hourly solar irradiation estimation Mostefaoui Mohamed Dhiaeddine; Benmouiza Khalil; Oubbati Youcef
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp4878-4885

Abstract

Solar energy is widely used in order to generate clean electric energy. However, due to its intermittent nature, this resource is only inserted in a limited way within the electrical networks. To increase the share of solar energy in the energy balance and allow better management of its production, it is necessary to know precisely the available solar potential at a fine time step to take into account all these stochastic variations. In this paper, a comparison between different artificial neural network (ANN) configurations is elaborated to estimate the hourly solar irradiation. An investigation of the optimal neurons and layers is investigated. To this end, feedforward neural network, cascade forward neural network and fitting neural network have been applied for this purpose. In this context, we have used different meteorological parameters to estimate the hourly global solar irirradiation in the region of Laghouat, Algeria. The validation process shows that choosing the cascade forward neural network two inputs gives an R2 value equal to 97.24% and an normalized root mean square error (NRMSE) equals to 0.1678 compared to the results of three inputs, which gives an R2 value equaled to 95.54% and an NRMSE equals to 0.2252. The comparison between different existing methods in literature show the goodness of the proposed models.
An effective technique for increasing capacity and improving bandwidth in 5G narrow-band internet of things Abdulwahid Mohammed; Hassan Mostafa; Abdelhady Abdelazim Ammar
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5232-5242

Abstract

In recent years, the wireless spectrum has become increasingly scarce as demand for wireless services has grown, requiring imaginative approaches to increase capacity within a limited spectral resource. This article proposes a new method that combines modified symbol time compression with orthogonal frequency division multiplexing (MSTC-OFDM), to enhance capacity for the narrow-band internet of things (NB-IoT) system. The suggested method, MSTC-OFDM, is based on the modified symbol time compression (MSTC) technique. The MSTC is a compressed waveform technique that increases capacity by compressing the occupied symbol time without losing bit error rate (BER) performance or data throughput. A comparative analysis is provided between the traditional orthogonal frequency division multiplexing (OFDM) system and the MSTC-OFDM method. The simulation results show that the MSTC-OFDM scheme drastically decreases the symbol time (ST) by 75% compared to a standard OFDM system. As a result, the MSTC-OFDM system offers four times the bit rate of a typical OFDM system using the same bandwidth and modulation but with a little increase in complexity. Moreover, compared to an OFDM system with 16 quadrature amplitude modulation (16QAM-OFDM), the MSTC-OFDM system reduces the signal-to-noise ratio (SNR) by 3.9 dB to transmit the same amount of data.
Blockchain for global vaccinations efforts: State of the art, challenges, and future directions Jalal Al-Muhtadi; Abeer Hassan; Kashif Saleem; Amjad Gawanmeh; Joel Jose Puga Coelho Rodrigues
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5576-5587

Abstract

The emergence of the coronavirus disease 2019 (COVID-19) global crisis negatively affected all aspects of human life. One of the most important methods used worldwide to survive this global crisis is the vaccination process to circumvent the proliferation of this pandemic. Many restrictions were alleviated in many countries such as access to public facilities and events. There is a huge amount of data about vaccination campaigns that are collected and maintained worldwide. Although the vaccination data can be analyzed to find out how the alleviation of restrictions can be applied if the data management process requires preserving key aspects like trust, transparency, and availability for easy and reliable access to such data. In this regard, blockchain technology is an excellent choice for meeting the requirements and providing a secure trusted framework for global verification. In this article, the related literature on blockchain technology is surveyed and summarized for all systems that embody solutions. The pros and cons of each solution are presented and provide a comparative summary. Furthermore, a detailed analysis is given to present the current problems and provide a promising mechanism to verify the vaccinated persons anywhere in the world, in a secure manner while retaining individual privacy.
Brain cone beam computed tomography image analysis using ResNet50 for collateral circulation classification Nur Hasanah Ali; Abdul Rahim Abdullah; Norhashimah Mohd Saad; Ahmad Sobri Muda
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5843-5852

Abstract

Treatment of stroke patients can be effectively carried out with the help of collateral circulation performance. Collateral circulation scoring as it is now used is dependent on visual inspection, which can lead to an inter- and intra-rater discrepancy. In this study, a collateral circulation classification using the ResNet50 was analyzed by using cone beam computed tomography (CBCT) images for the ischemic stroke patient. The remarkable performance of deep learning classification helps neuroradiologists with fast image classification. A pre-trained deep network ResNet50 was applied to extract robust features and learn the structure of CBCT images in their convolutional layers. Next, the classification layer of the ResNet50 was performed into binary classification as “good” and “poor” classes. The images were divided by 80:20 for training and testing. The empirical results support the claim that the application of ResNet50 offers consistent accuracy, sensitivity, and specificity values. The performance value of the classification accuracy was 76.79%. The deep learning approach was employed to unveil how biological image analysis could generate incredibly dependable and repeatable outcomes. The experiments performed on CBCT images evidenced that the proposed ResNet50 using convolutional neural network (CNN) architecture is indeed effective in classifying collateral circulation.
Experimental study of four-wave mixing based on a quantum dot semiconductor optical amplifier Tokhmetov Akylbek; Tussupov Akhmet; Tanchenko Liliya
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5179-5189

Abstract

The article is devoted to the study of four-wave mixing (FWM) in a semiconductor optical amplifier with quantum dots (QD-SOA). Experimental measurements of FWM signals are characterized in nonlinear media of GaAs-based QD-SOA with gain in the 1,550 nm region. Theoretical part of nonlinear effect of FWM is studied and important all-optical communication applications are listed. Experimental studies of a four-wave system are described and an analysis of FWM signals is given for various input powers of pump signals and injection currents. The devices are compared in terms of such parameters as conversion efficiency and signal-to-noise ratio. The results of the study made it possible to reveal the possibility of the effect of FWM signals on useful signals in channels with spectral division multiplexing wavelength division multiplexing (WDM) in the downstream and upstream in a semiconductor optical amplifier. Based on the experimental results, it was concluded that FWM does not affect adjacent channels of WDM signals and does not generate additional optical noise when scaling the WDM gigabit passive optical network (GPON) up to 60 km using semiconductor optical amplifiers (SOAs).
A miniature tunable quadrature shadow oscillator with orthogonal control Seangrawee Buakaew; Chariya Wongtaychatham
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp4966-4978

Abstract

This article presents a new design of a quadrature shadow oscillator. The oscillator is realized using one input and two outputs of a second-order filter cell together with external amplifiers in a feedback configuration. The oscillation characteristics are controlled via the external gain without disturbing the internal filter cell, following the concept of the shadow oscillator. The proposed circuit configuration is simple with a small component-count. It consists of, two voltage-different transconductance amplifiers (VDTAs) along with a couple of passive elements. The frequency of oscillation (FO) and the condition of oscillation (CO) are controlled orthogonally via the dc bias current and external gain. Moreover, with the addition of the external gain, the frequency range of oscillation can be further extended. The proposed work is verified by computer simulation with the use of 180 nm complementary metal–oxide–semiconductor (CMOS) model parameters. The simulation gives satisfactory results of two sinusoidal output signals in quadrature with some small total harmonic distortions (THD). In addition, a circuit experiment is performed using the commercial operational transconductance amplifiers LM13700 as the active components. The circuit experiment also demonstrates satisfactory outcome which confirms the validity of the proposed circuit.
Improved vision-based diagnosis of multi-plant disease using an ensemble of deep learning methods Rashidul Hasan Hridoy; Arindra Dey Arni; Aminul Haque
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5109-5117

Abstract

Farming and plants are crucial parts of the inward economy of a nation, which significantly boosts the economic growth of a country. Preserving plants from several disease infections at their early stage becomes cumbersome due to the absence of efficient diagnosis tools. Diverse difficulties lie in existing methods of plant disease recognition. As a result, developing a rapid and efficient multi-plant disease diagnosis system is a challenging task. At present, deep learning-based methods are frequently utilized for diagnosing plant diseases, which outperformed existing methods with higher efficiency. In order to investigate plant diseases more accurately, this article addresses an efficient hybrid approach using deep learning-based methods. Xception and ResNet50 models were applied for the classification of plant diseases, and these models were merged using the stacking ensemble learning technique to generate a hybrid model. A multi-plant dataset was created using leaf images of four plants: black gram, betel, Malabar spinach, and litchi, which contains nine classes and 44,972 images. Compared to existing individual convolutional neural networks (CNN) models, the proposed hybrid model is more feasible and effective, which acquired 99.20% accuracy. The outcomes and comparison with existing methods represent that the designed method can acquire competitive performance on the multi-plant disease diagnosis tasks.
Soft computing for hazardous waste routing in Malaysia: a review Muhamad Hafiz Masran; Syariza Abdul-Rahman; Wan Nor Munirah Ariffin
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5717-5726

Abstract

Nowadays, a significant number of researchers are focusing on utilizing soft computing approaches to address the issue of scheduling in applications concerned with hazardous waste management. In Malaysia, there is thoughtless awareness of the management of hazardous waste, even though the production of wastes in hazardous domains at the industrial and domestic levels has been rising lately. According to previous research findings, the location routing problem (LRP) can be designated as one of the models closer to the actual situation, evaluating the most suitable and optimal location for establishing facilities and utilizing transportation for pick-up and distribution. Recent studies have focused on enhancing the LRP model, and its methodologies approach to solve the waste management problem in hazardous domains. In this paper, a comprehensive review of the better promising and practicable mathematical model of LRP and its methodology approach is discussed, as well as an analysis of the publishing pattern and the trend of research over the preceding five years and more, as retrieved from the web of science (WoS) database. In conclusion, this research is significant in ensuring the effectiveness of reliable mathematical model development and suitable methodologies in the future for solving hazardous waste management problems.
System of gender identification and age estimation from radiography: a review Nur Nafi’iyah; Chastine Fatichah; Darlis Herumurti; Eha Renwi Astuti; Ramadhan Hardani Putra; Esa Prakasa; Yosi Kristian
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5491-5500

Abstract

Under extreme conditions postmortem, dental radiography examinations can play an essential role in individual identification. In forensic odontology, individual identification traditionally compares antemortem dental records radiographs with those obtained on postmortem examination. As such, these traditional methods are vulnerable to oversights or mistakes in the individual identification of unidentified bodies. Digital technology can develop forensic odontology well. An automatic individual identification system is needed to support the forensic odontology process more easily and quickly because there are still opportunities to be created. We aimed to review the complete range of recent developments in identifying individuals from panoramic radiographs. We study methods in gender identification, age estimation, radiographic segmentation, performance analysis, and promising future directions.

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