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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 1: February 2024" : 111 Documents clear
Generate fuzzy string-matching to build self attention on Indonesian medical-chatbot Suwarningsih, Wiwin; Nuryani, Nuryani
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.pp819-829

Abstract

Chatbot is a form of interactive conversation that requires quick and precise answers. The process of identifying answers to users’ questions involves string matching and handling incorrect spelling. Therefore, a system that can independently predict and correct letters is highly necessary. The approach used to address this issue is to enhance the fuzzy string-matching method by incorporating several features for self-attention. The combination of fuzzy string-matching methods employed includes Jaro Winkler distance + Levenshtein Damerau distance and Damerau Levenshtein + Rabin Carp. The reason for using this combination is their ability not only to match strings but also to correct word typing errors. This research contributes by developing a self-attention mechanism through a modified fuzzy string-matching model with enhanced word feature structures. The goal is to utilize this self-attention mechanism in constructing the Indonesian medical bidirectional encoder representations from transformers (IM-BERT). This will serve as a foundation for additional features to provide accurate answers in the Indonesian medical question and answer system, achieving an exact match of 85.7% and an F1-score of 87.6%.
Wide-band spectrum sensing with convolution neural network using spectral correlation function Rajanna, Anupama; Kulkarni, Srimannarayana; Narasimha Prasad, Sarappadi
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.pp409-417

Abstract

Recognition of signals is a spectrum sensing challenge requiring simultaneous detection, temporal and spectral localization, and classification. In this approach, we present the convolution neural network (CNN) architecture, a powerful portrayal of the cyclo-stationarity trademark, for remote range detection and sign acknowledgment. Spectral correlation function is used along with CNN. In two scenarios, method-1 and method-2, the suggested approach is used to categorize wireless signals without any previous knowledge. Signals are detected and classified simultaneously in method-1. In method-2, the sensing and classification procedures take place sequentially. In contrast to conventional spectrum sensing techniques, the proposed CNN technique need not bother with a factual judgment process or past information on the signs’ separating qualities. The method beats both conventional sensing methods and signal-classifying deep learning networks when used to analyze real-world, over-the-air data in cellular bands. Despite the implementation’s emphasis on cellular signals, any signal having cyclo-stationary properties may be detected and classified using the provided approach. The proposed model has achieved more than 90% of testing accuracy at 15 dB.
Impacts of COVID-19 lockdown period on the Algerian power grid demand Draidi, Abdellah; Assabaa, Mohamed; Bouchahed, Adel; Mehimmedetsi, Boudjemaa
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.pp31-43

Abstract

The coronavirus disease-2019 (COVID-19) spread out at the end of 2019 has sadly caused millions of human losses and hundreds of millions of cases and stressful health situations. As a result, governments forced the worldwide population to stay confined and change their social activities and working behaviors. Under such conditions all economic sectors have been impacted, therefore global electricity consumption pattern has changed consequently. The object of this study is to calculate energy drop for such circumstances to make strategies to face such events in the future. The study we conducted during the period of confinement aims to identify the effects of the Corona epidemic on electricity consumption in Algeria by emphasizing four months (March, April, May, and June) for four years (2018, 2019, 2020, and 2021) by comparing monthly load curves and calculating load deviation for each month.
A 5G beam-steering microstrip array antenna using both-sided microwave integrated circuit technology Hossain, Md. Farhad; Das, Debprosad; Hossain, Md. Azad
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.pp457-468

Abstract

In this paper a beam steering 2×2 microstrip array antenna is proposed and simulated for the 5G sub-6 GHz frequency band. The array antenna is designed at the resonant frequency of 3.5 GHz. The antenna has four patches excited by two microstrip lines. Microstrip lines on top of teflon substrate of 0.8 mm height and slot line in the ground plane makes a hybrid junction. The design uses both sided microwave integrated circuit (MIC) to feed signal to the patch elements. This designed array antenna has the beam steering capability of maximum -17º to +17º while keeping the side lobe gain below 10 dB. The simulation results show that the array antenna is designed through good input impedance matching. The antenna has a return loss of -43 dB at center frequency 3.5 GHz. The results also show that the array antenna has a high gain of 12.57 dBi and directivity of 25.11 dB. The maximum gain of this antenna is 24.1 dB at -17º and +17º. The proposed work is simulated on keysight technologies advanced designed system (ADS).
Energy use and CO2 emissions of the Moroccan transport sector Oubnaki, Hasnaa; Haouraji, Charifa; Mounir, Badia; Mounir, Ilham; Farchi, Abdelmajid
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.pp86-98

Abstract

In this paper, optimized models based on two different machine learning (ML) methods were developed to forecast the transport energy consumption (TEC) and carbon dioxide (CO2) emissions in Morocco by 2030. More precisely, artificial neural networks (ANN) and support vector regression (SVR) were used for modelling non-linear TEC and CO2 emissions data. This study uses data from 1990 to 2020 and employs various independent parameters, including population, gross domestic product, urbanization rate, evolution of the number of vehicles, and the number of electric vehicle introductions. Four statistical metrics are derived to assess the effectiveness of the ML algorithms used. The forecasts for 2030 were based on six scenarios, including three scenarios for the growth of gross domestic product (GDP) and two scenarios for the evolution of electric cars’ introduction into Moroccan vehicle fleet. The ANN model outputs showed that a decrease in TEC and CO2 emissions is expected until 2030. However, the SVR model predicts outputs values close to those in 2020. The study's results also indicate that: i) TEC and transport CO2 emissions are positively impacted by economic growth in Morocco and ii) electric vehicles will be essential components enabling substantial reductions in overall CO2 emissions in future transport systems.
Impact of initialization of a modified particle swarm optimization on cooperative source searching Ab. Majid, Mad Helmi; Arshad, Mohd Rizal; Yahya, Mohd Faid; Ibrahim, Abu Bakar
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.pp218-229

Abstract

Swarm robotic is well known for its flexibility, scalability and robustness that make it suitable for solving many real-world problems. Source searching which is characterized by complex operation due to the spatial characteristic of the source intensity distribution, uncertain searching environments and rigid searching constraints is an example of application where swarm robotics can be applied. Particle swarm optimization (PSO) is one of the famous algorithms have been used for source searching where its effectiveness depends on several factors. Improper parameter selection may lead to a premature convergence and thus robots will fail (i.e., low success rate) to locate the source within the given searching constraints. Additionally, target overshooting and improper initialization strategies may lead to a nonoptimal (i.e., take longer time to converge) target searching. In this study, a modified PSO and three different initializations strategies (i.e., random, equidistant and centralized) were proposed. The findings shown that the proposed PSO model successfully reduce the target overshooting by choosing optimal PSO parameters and has better convergence rate and success rate compared to the benchmark algorithms. Additionally, the findings also indicate that the random initialization give better searching success compared to equidistant and centralize initialization.
A checkpointing mechanism for virtual clusters using memory-bound time-multiplexed data transfers Yaothanee, Jumpol; Chanchio, Kasidit
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.pp1147-1165

Abstract

Transparent hypervisor-level checkpoint-restart mechanisms for virtual clusters (VCs) or clusters of virtual machines (VMs) offer an attractive fault tolerance capability for cloud data centers. However, existing mechanisms have suffered from high checkpoint downtimes and overheads. This paper introduces Mekha, a novel hypervisor-level, in-memory coordinated checkpoint-restart mechanism for VCs that leverages precopy live migration. During a VC checkpoint event, Mekha creates a shadow VM for each VM and employs a novel memory-bound timed-multiplex data (MTD) transfer mechanism to replicate the state of each VM to its corresponding shadow VM. We also propose a global ending condition that enables the checkpoint coordinator to control the termination of the MTD algorithm for every VM in a VC, thereby reducing overall checkpoint latency. Furthermore, the checkpoint protocols of Mekha are designed based on barrier synchronizations and virtual time, ensuring the global consistency of checkpoints and utilizing existing data retransmission capabilities to handle message loss. We conducted several experiments to evaluate Mekha using a message passing interface (MPI) application from the NASA advanced supercomputing (NAS) parallel benchmark. The results demonstrate that Mekha significantly reduces checkpoint downtime compared to traditional checkpoint mechanisms. Consequently, Mekha effectively decreases checkpoint overheads while offering efficiency and practicality, making it a viable solution for cloud computing environments.
Data quality processing for photovoltaic system measurements Galarza, Jose; Condezo Hurtado, David; Saenz, Bartolome
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.pp12-21

Abstract

The operation and maintenance activities in photovoltaic systems use meteorological and electrical measurements that must be reliable to check system performance. The International Electrotechnical Commission (IEC) standards have established general criteria to filter erroneous information; however, there is no standardized process for the evaluation of measurements. In the present work we developed 3 procedures to detect and correct measurements of a photovoltaic system based on the single diode model. The performance evaluation of each criterion was tested with 6 groups of experimental measurements from a 3 kWp installation. Based on the error of the 3 procedures performed, the most unfavorable case has been prioritized. Then, the reduction of errors between the estimated and measured value has been achieved, reducing the number of measurements to be corrected. For the clear sky categories, the coefficient of determination is 0.9975 and 0.9961 for the high irradiance profile. Although an increase of 2.5% for coefficient of determination has been achieved, the overcast sky categories should be analyzed in more detail. Finally, the different causes of measurement error should be analyzed, associated with calibration errors and sensor quality.
Deep sequential pattern mining for readability enhancement of Indonesian summarization Maylawati, Dian Sa'adillah; Kumar, Yogan Jaya; Kasmin, Fauziah; Ramdhani, Muhammad Ali
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.pp782-795

Abstract

In text summarization research, readability is a great issue that must be addressed. Our hypothesis is readability can be accomplished by using text representations that keep the meaning of text documents intact. Therefore, this study aims to combine sequential pattern mining (SPM) in producing a sequence of a word as text representation with unsupervised deep learning to produce an Indonesian text summary called DeepSPM. This research uses PrefixSpan as an SPM algorithm and deep belief network (DBN) as an unsupervised deep learning method. This research uses 18,774 Indonesian news text from IndoSum. The readability aspect is evaluated by recall-oriented understudy for gisting evaluation (ROUGE) as a co-selection-based analysis; Dwiyanto Djoko Pranowo metrics, Gunning fog index (GFI), and Flesch-Kincaid grade level (FKGL) as content-based analysis; and human readability evaluation with two experts. The experiment result shows that DeepSPM yields better than DBN, with the F-measure value of ROUGE-1 enhanced to 0.462, ROUGE-2 is 0.37, and ROUGE-L is 0.41. The significance of ROUGE results also be tested using T-Test. The content-based analysis and human readability evaluation findings are conformable with the findings of co-selection-based analysis that generated summaries are only partially readable or have a medium level of readability aspect.
Modified fuzzy rough set technique with stacked autoencoder model for magnetic resonance imaging based breast cancer detection Kumar Mamdy, Sachin; Petli, Vishwanath
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.pp294-304

Abstract

Breast cancer is the common cancer in women, where early detection reduces the mortality rate. The magnetic resonance imaging (MRI) images are efficient in analyzing breast cancer, but it is hard to identify the abnormalities. The manual breast cancer detection in MRI images is inefficient; therefore, a deep learning-based system is implemented in this manuscript. Initially, the visual quality improvement is done using region growing and adaptive histogram equalization (AHE), and then, the breast lesion is segmented by Otsu thresholding with morphological transform. Next, the features are extracted from the segmented lesion, and a modified fuzzy rough set technique is proposed to reduce the dimensions of the extracted features that decreases the system complexity and computational time. The active features are fed to the stacked autoencoder for classifying the benign and malignant classes. The results demonstrated that the proposed model attained 99% and 99.22% of classification accuracy on the benchmark datasets, which are higher related to the comparative classifiers: decision tree, naïve Bayes, random forest and k-nearest neighbor (KNN). The obtained results state that the proposed model superiorly screens and detects the breast lesions that assists clinicians in effective therapeutic intervention and timely treatment.

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