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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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Enhanced TextRank using weighted word embedding for text summarization Evi Yulianti; Nicholas Pangestu; Meganingrum Arista Jiwanggi
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.pp5472-5482

Abstract

The length of a news article may influence people’s interest to read the article. In this case, text summarization can help to create a shorter representative version of an article to reduce people’s read time. This paper proposes to use weighted word embedding based on Word2Vec, FastText, and bidirectional encoder representations from transformers (BERT) models to enhance the TextRank summarization algorithm. The use of weighted word embedding is aimed to create better sentence representation, in order to produce more accurate summaries. The results show that using (unweighted) word embedding significantly improves the performance of the TextRank algorithm, with the best performance gained by the summarization system using BERT word embedding. When each word embedding is weighed using term frequency-inverse document frequency (TF-IDF), the performance for all systems using unweighted word embedding further significantly improve, with the biggest improvement achieved by the systems using Word2Vec (with 6.80% to 12.92% increase) and FastText (with 7.04% to 12.78% increase). Overall, our systems using weighted word embedding can outperform the TextRank method by up to 17.33% in ROUGE-1 and 30.01% in ROUGE-2. This demonstrates the effectiveness of weighted word embedding in the TextRank algorithm for text summarization.
A stochastic approach for evaluating production planning efficiency under uncertainty Mochamad Wahyudi; Hengki Tamando Sihotang; Syahril Efendi; Muhammad Zarlis; Herman Mawengkang; Desi Vinsensia
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.pp5542-5549

Abstract

Planning production is an essential component of the decision-making process, which has a direct bearing on the effectiveness of production systems. This study’s objective is to investigate the efficiency performance of decision-making units (DMU) in relation to production planning issues. However, the production system in a manufacturing environment is frequently subject to uncertain situations, such as demand and labor, and this can have an effect not only on production but also on profit. The robust stochastic data envelopment analysis model was proposed in this study with maximizing the number of outputs as the objective function thus means of handling uncertainty in input and output in production planning problems. This model, which is based on stochastic data envelopment analysis and a method of robust optimization, was proposed with the intention of providing an efficient plan of production for each DMU of stage production. The model is applied to small and medium-sized businesses (SMEs), with inputs consisting of the cost of labor, the number of customers, and the quantity of raw materials, and the output consisting of profit and revenue. It has been demonstrated through implementation that the proposed model is both efficient and effective.
Early detection of dysphoria using electroencephalogram affective modelling Norhaslinda Kamaruddin; Mohd Hafiz Mohd Nasir; Abdul Wahab; Frederick C. Harris Jr.
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.pp5874-5884

Abstract

Dysphoria is a trigger point for maladjusted individuals who cannot cope with disappointments and crushed expectations, resulting in negative emotions if it is not detected early. Individuals who suffer from dysphoria tend to deny their mental state. They try to hide, suppress, or ignore the symptoms, making one feel worse, unwanted, and unloved. Psychologists and psychiatrists identify dysphoria using standardized instruments like questionnaires and interviews. These methods can boast a high success rate. However, the limited number of trained psychologists and psychiatrists and the small number of health institutions focused on mental health limit access to early detection. In addition, the negative connotation and taboo about dysphoria discourage the public from openly seeking help. An alternative approach to collecting ‘pure’ data is proposed in this paper. The brain signals are captured using the electroencephalogram as the input to the machine learning approach to detect negative emotions. It was observed from the experimental results that participants who scored severe dysphoria recorded ‘fear’ emotion even before stimuli were presented during the eyes-close phase. This finding is crucial to further understanding the effect of dysphoria and can be used to study the correlation between dysphoria and negative emotions.
Virtual data integration for a clinical decision support systems Intedhar Shakir Nasir; Ayad Hameed Mousa; Sakina M. Ali Alkhafaji; Wafia Shaker Abdul Hussein; Zahraa Razzaq Jasim; Saeed Qasim Ali
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.pp5243-5252

Abstract

Clinical decision support (CDS) supplies clinicians and their patients, and relevant staff with meaningful and timely information intelligently integrated or visualized to enhance health and the health sector. Data is the backbone of decision support systems, especially (clinical) ones. Data integration (either virtual or physical manner) is a powerful technique to manipulate a vast amount of heterogeneous data and prepare it as input for the decision-making process. The difficulties in manipulating data that have a physical data integration technique motivated the decision support developers to tend to data virtualization as a data integration technique. In this paper, a clinical decision support system was developed using the virtual data integration technique. The developed system was evaluated in terms of usability and its capability of providing clinical decision support. The evaluation findings indicate that the proposed system is highly usable and has a positive impact on supporting the clinical decision-making process.
Resource-efficient workload task scheduling for cloud-assisted internet of things environment Naveen Kumar Chowdaiah; Annapurna Dammur
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.pp5898-5907

Abstract

One of the most challenging tasks in the internet of things-cloud-based environment is the resource allocation for the tasks. The cloud provides various resources such as virtual machines, computational cores, networks, and other resources for the execution of the various tasks of the internet of things (IoT). Moreover, some methods are used for executing IoT tasks using an optimal resource management system but these methods are not efficient. Hence, in this research, we present a resource-efficient workload task scheduling (RWTS) model for a cloud-assisted IoT environment to execute the IoT task which utilizes few numbers of resources to bring a good tradeoff, achieve high performance using fewer resources of the cloud, compute the number of resources required for the execution of the IoT task such as bandwidth and computational core. Furthermore, this model mainly focuses to reduce energy consumption and also provides a task scheduling model to schedule the IoT tasks in an IoT-cloud-based environment. The experimentation has been done using the Montage workflow and the results have been obtained in terms of execution time, power sum, average power, and energy consumption. When compared with the existing model, the RWTS model performs better when the size of the tasks is increased.
Quality control test for unreliable meteorological and electrical photovoltaic measurements Jose Galarza; David Condezo
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.pp4845-4855

Abstract

Meteorological and electrical measurements using predictive computational techniques have been used in the analysis of photovoltaic system operation and maintenance. International standards establish general and no standardized criteria on the quality control and validation of these measurements. In the present work, a methodology has been developed to correct erroneous photovoltaic experimental measurements: radiation, temperature, current, and voltage. We validated the proposed approach with 12 case studies with more than 5,000 meteorological and electric measurements from an experimental 3 kWp photovoltaic system. The approach is based on a set of non-intrusive criteria developed from the one diode model, the approach allowed to correct about 80% of the erroneous data, 30% more using polynomial regression. As for the regression methodology, we have shown that the proposed methodology includes 4 meteorologicalelectrical variables allowing a more rigorous analysis. For 75% of the cases evaluated, the proposed methodology achieves a better data correction.
Optimal power flow with distributed energy sources using whale optimization algorithm TentuPapi Naidu; Ganapathy Balasubramanian; Bathina Venkateshwar Rao
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.pp4835-4844

Abstract

Renewable energy generation is increasingly attractive since it is non-polluting and viable. Recently, the technical and economic performance of power system networks has been enhanced by integrating renewable energy sources (RES). This work focuses on the size of solar and wind production by replacing the thermal generation to decrease cost and losses on a big electrical power system. The Weibull and Lognormal probability density functions are used to calculate the deliverable power of wind and solar energy, to be integrated into the power system. Due to the uncertain and intermittent conditions of these sources, their integration complicates the optimal power flow problem. This paper proposes an optimal power flow (OPF) using the whale optimization algorithm (WOA), to solve for the stochastic wind and solar power integrated power system. In this paper, the ideal capacity of RES along with thermal generators has been determined by considering total generation cost as an objective function. The proposed methodology is tested on the IEEE-30 system to ensure its usefulness. Obtained results show the effectiveness of WOA when compared with other algorithms like non-dominated sorting genetic algorithm (NSGA-II), grey wolf optimization (GWO) and particle swarm optimization-GWO (PSO-GWO).
Predicting user behavior using data profiling and hidden Markov model Bahaa Eddine Elbaghazaoui; Mohamed Amnai; Youssef Fakhri
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.pp5444-5453

Abstract

Mental health disorders affect many aspects of patient’s lives, including emotions, cognition, and especially behaviors. E-health technology helps to collect information wealth in a non-invasive manner, which represents a promising opportunity to construct health behavior markers. Combining such user behavior data can provide a more comprehensive and contextual view than questionnaire data. Due to behavioral data, we can train machine learning models to understand the data pattern and also use prediction algorithms to know the next state of a person’s behavior. The remaining challenges for this issue are how to apply mathematical formulations to textual datasets and find metadata that aids to identify the person’s life pattern and also predict the next state of his comportment. The main idea of this work is to use a hidden Markov model (HMM) to predict user behavior from social media applications by analyzing and detecting states and symbols from the user behavior dataset. To achieve this goal, we need to analyze and detect the states and symbols from the user behavior dataset, then convert the textual data to mathematical and numerical matrices. Finally, apply the HMM model to predict the hidden user behavior states. We tested our program and identified that the log-likelihood was higher and better when the model fits the data. In any case, the results of the study indicated that the program was suitable for the purpose and yielded valuable data.
Adaptive traffic lights based on traffic flow prediction using machine learning models Idriss Moumen; Jaafar Abouchabaka; Najat Rafalia
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.pp5813-5823

Abstract

Traffic congestion prediction is one of the essential components of intelligent transport systems (ITS). This is due to the rapid growth of population and, consequently, the high number of vehicles in cities. Nowadays, the problem of traffic congestion attracts more and more attention from researchers in the field of ITS. Traffic congestion can be predicted in advance by analyzing traffic flow data. In this article, we used machine learning algorithms such as linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor to predict traffic flow and reduce traffic congestion at intersections. We used the public roads dataset from the UK national road traffic to test our models. All machine learning algorithms obtained good performance metrics, indicating that they are valid for implementation in smart traffic light systems. Next, we implemented an adaptive traffic light system based on a random forest regressor model, which adjusts the timing of green and red lights depending on the road width, traffic density, types of vehicles, and expected traffic. Simulations of the proposed system show a 30.8% reduction in traffic congestion, thus justifying its effectiveness and the interest of deploying it to regulate the signaling problem in intersections.
Applying textural Law’s masks to images using machine learning Gulzira Abdikerimova; Moldir Yessenova; Akbota Yerzhanova; Zhanat Manbetova; Gulden Murzabekova; Dinara Kaibassova; Roza Bekbayeva; Madina Aldashova
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.pp5569-5575

Abstract

Currently, artificial neural networks are experiencing a rebirth, which is primarily due to the increase in the computing power of modern computers and the emergence of very large training data sets available in global networks. The article considers Laws texture masks as weights for a machine-learning algorithm for clustering aerospace images. The use of Laws texture masks in machine learning can help in the analysis of the textural characteristics of objects in the image, which are further identified as pockets of weeds. When solving problems in applied areas, in particular in the field of agriculture, there are often problems associated with small sample sizes of images obtained from aerospace and unmanned aerial vehicles and insufficient quality of the source material for training. This determines the relevance of research and development of new methods and algorithms for classifying crop damage. The purpose of the work is to use the method of texture masks of Laws in machine learning for automated processing of high-resolution images in the case of small samples using the example of problems of segmentation and classification of the nature of damage to crops.

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