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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 4: August 2024" : 111 Documents clear
A light-weight and generalizable deep learning model for the prediction of COVID-19 from chest X-ray images Zobair, Md Jakaria; Orpa, Refat Tasfia; Ashef, Mahir; Siddiquee, Shah Md Tanvir; Chakraborty, Narayan Ranjan; Habib, Ahsan
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.pp4068-4077

Abstract

The detection of coronavirus disease (COVID-19) using standard laboratory tests, such as reverse transcription polymerase chain reaction (RT-PCR), is time-consuming. Complex medical imaging problems are currently being solved using machine learning and deep learning techniques. Our proposed solution utilizes chest radiography imaging techniques, which have shown to be a faster alternative for detecting COVID-19. We present an efficient and lightweight deep learning architecture for identifying COVID-19 using chest X-ray images which achieve 99.81% accuracy in intra-database testing and 100% accuracy in cross-validation testing on a separate data set. The results demonstrate the potential of our proposed model as a reliable tool for COVID-19 diagnosis using chest X-ray images, which can have a significant impact on improving the efficiency of COVID-19 diagnosis and treatment.
Automatic notes based on video records of online meetings using the latent Dirichlet allocation method Arianto, Rakhmat; Asmara, Rosa Andrie; Nurhasan, Usman; Rahmanto, Anugrah Nur
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.pp4147-4153

Abstract

Meeting minutes can also be used as a benchmark for whether the meeting objectives have been achieved or not. Minutes are taken during the meeting until the end of the meeting, which contain essential points from the meeting. Minutes in online meetings are currently still done manually, and generally, every meeting is recorded as documentation that requires more human resources to change the recording of the meeting file. Based on the problems above, a solution to this problem is needed by creating an automatic note-taking system that can assist the note-takers in concluding the meeting, especially in the Information Technology Department. This study uses the latent Dirichlet allocation (LDA) method to determine text summarization and topic modeling. Based on this research, the system calculation using the LDA method produces a pretty good accuracy value for text summarization of 57.91% and topic modeling with a coherence score of 64.56%. Based on this research, the implementation of the latent Dirichlet allocation method for text summarization and topic modeling provides a fairly good level of similarity accuracy when compared to the minutes that are written manually and can be implemented in the Information Technology Department.
Revolutionizing malaria diagnosis: deep learning-powered detection of parasite-infected red blood cells Hoque, Md. Jiabul; Islam, Md. Saiful; Khaliluzzaman, Md.; Muntasir, Abdullah Al; Mohsin, Mohammad Abdullah Bin
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.pp4518-4530

Abstract

Malaria is a significant global health issue, responsible for the highest rates of morbidity and mortality globally. This paper introduces a very effective and precise convolutional neural network (CNN) method that employs advanced deep learning techniques to automate the detection of malaria in images of red blood cells (RBC). Furthermore, we present an emerging and efficient deep learning method for differentiating between cells infected with malaria and those that are not infected. To thoroughly evaluate the efficiency of our approach, we do a meticulous assessment that involves comparing different deep learning models, such as ResNet-50, MobileNet-v2, and Inception-v3, within the domain of malaria detection. Additionally, we conduct a thorough comparison of our proposed approach with current automated methods for malaria identification. An examination of the most current techniques reveals differences in performance metrics, such as accuracy, specificity, sensitivity, and F1 score, for diagnosing malaria. Moreover, compared to existing models for malaria detection, our method is the most successful, achieving an accurate score of 1.00 in all statistical matrices, confirming its promise as a highly efficient tool for automating malaria detection.
Content based COVID-19 image retrieval system using local histogram equalization and deep convolutional neural network Shetty, Rani; S. Bhat, Vandana; Pujari, Jagadeesh; Shetty, Rashmi
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.pp3942-3950

Abstract

Doctors play a critical role in interpreting medical images as part of their core responsibilities. They need to find comparable examples that can assist in making informed decisions, especially when encountering ambiguous visuals. Traditionally, Systems such as content-based medical image retrieval (CBMIR) have been used for this. The proposed method employs a novel technique, local histogram equalization (LHE) for preprocessing, transfer learning-based convolutional neural network to extract the representative features with Manhattan and Euclidean distance metrics to assess how similar the query image and database image are to one another. This model is trained on a standard dataset namely Chest X-Ray images. Top-k, Precision and Recall measure is employed to assess system performance. From the results, the suggested enhanced convolutional neural network (CNN) model demonstrates significantly superior performance in the top 10 retrieval rates of 97.13% for coronavirus disease 2019 (COVID-19), 96.84% for normal, 82.63% for pneumonia-bacterial, and 81.72% for pneumonia-viral and precision@recall10 of 93.14% for COVID-19, 91.88% for normal, 77.84% for pneumonia-bacterial and 74.71% for pneumonia-viral.
Contactless logging and disinfection solution with automated face mask detection Bolima, Dominic; Huerto, Christian Albert
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.pp3913-3921

Abstract

Coronavirus disease-19 (COVID-19) mitigation includes health screening procedures at entrances of public and private establishments. Conventional methods include manual operations on temperature and face mask checking, personal identification, and hand disinfection. In this paper, a gateway kiosk is designed by integrating and automating the primary screening procedures. It uses radio-frequency identification (RFID) for contactless access control and attendance, with an infrared thermometer for body temperature monitoring. Face mask detection is automated through artificial intelligence (AI), while proximity sensors activate the disinfection system. Internet-of-things (IoT) interfaces these subsystems, and local access is available via an Internet browser. RFID overcomes the slower response rate of the quick response (QR) code-based solution. The repeated-measure analyses showed that the system’s thermometer has only +0.28 °C error while its residual neural network-10 (ResNet-10) and MobileNetV2 models for detecting masked faces achieved a 98.2% accuracy. The system reduces the primary key performance indicators–service and queuing times by 56.86% and 79.95%, respectively. Its audio and visual notifications ensure the proper screening implementation, thus reducing unnecessary and risky interactions with entrance personnel. It improves the screening procedures by significantly reducing human interactions and enhancing queuing.
Deep learning approaches for recognizing facial emotions on autistic patients El Rhatassi, Fatima Ezzahrae; Ghali, Btihal El; Daoudi, Najima
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.pp4034-4045

Abstract

Autistic people need continuous assistance in order to improve their quality of life, and chatbots are one of the technologies that can provide this today. Chatbots can help with this task by providing assistance while accompanying the autist. The chatbot we plan to develop gives to autistic people an immediate personalized recommendation by determining the autist’s state, intervene with him and build a profil of the individual that will assist medical professionals in getting to know their patients better so they can provide an individualized care. We attempted to identify the emotion from the image's face in order to gain an understanding of emotions. Deep learning methods like Convolutional neural networks and Vision Transformers could be compared using the FER2013. After optimization, CNN achieved 74% accuracy, whereas the VIT achieved 69%. Given that there is not a massive dataset of autistic individuals accessible, we combined a dataset of photos of autistic people from two distinct sources and used the CNN model to identify the relevant emotion. Our accuracy rate for identifying emotions on the face is 65%. The model still has some identification limitations, such as misinterpreting some emotions, particularly "neutral," "surprised," and "angry," because these emotions and facial traits are poorly expressed by autistic people, and because the model is trained with imbalanced emotion categories.
Transient response of a megawatt-scale solar photovoltaic in an electric distribution utility Judith, Paolo Justine; Dellosa, Jeffrey T.
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.pp3739-3754

Abstract

There is an increasing trend among customers of an electrical distribution utility to adopt grid-tied solar photovoltaic systems. This shift offers multiple benefits to consumers, including lower monthly electricity bills and a contribution to the development of green energy. For the electrical distribution utility, various impacts may arise due to varying levels of solar energy penetration. This study investigates the effects of integrating varying levels of solar photovoltaic penetration into the commercial consumer network of Cagayan de Oro Electric Power and Light Company (CEPALCO) in the Philippines. Utilizing PowerWorld simulator, the research evaluates 11 different scenarios with solar penetration levels adjusted according to the percentage of load demand. Key findings include alterations in solar megavolt ampere of reactive power output, bus voltage levels, transformer power loading, and transmission line ampacity, with frequency levels remaining stable across scenarios. The optimal solar penetration level was identified at 70%, balancing the benefits of solar energy integration with the need to maintain grid stability and operational limits. This optimal level ensures the effective utilization of renewable energy sources without compromising the performance of CEPALCO’s electrical infrastructure. The research concludes with recommendations for maintaining grid stability and operational limits at the optimal solar penetration limits.
Analysis and classification of power quality disturbances using variational mode decomposition and hybrid particle swarm optimization Idan Hussein, Husham; Majeed Ghadban, Ahmed; Rodríguez Gómez, Alejandro; Jesus Muñoz Gutierrez, Francisco
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.pp3768-3782

Abstract

Power quality disturbances (PQD) threaten electrical power systems, especially in distributed generation with renewable energy sources and in smart grids where PQD takes a complex form. Providing accurate information on the status and characteristics of the electrical signal facilitates the identification of practical solutions to this threat. In this paper, a variational mode decomposition (VMD) signal processing tool is proposed to analyze complex PQD. In VMD, the input signal is decomposed into different band-limited intrinsic mode functions (IMF) or non-recursively reconstructed modes. The input signal analysis by VMD, which considers the frequency values and spectral decomposition for each mode, describes the changes in the input waveform, and the IMFs help extract the behavioral patterns of these disturbances. A new hybrid particle swarm optimization-technique for order of preference by similarity to ideal solution (PSO-TOPSIS) algorithm is also proposed to classify the disturbances based on the features extracted from the signals decomposed using VMD. The performance of this method is then extensively validated by using different PQDs (including complex, stationary, and non-stationary (PQDs) and through a comparison with deep learning methods, such as convolutional and recurrent neural networks. Results show that VMD has several advantages over Fourier, wavelet, and Stockwell transforms, such as its lack of any modal aliasing effect, its capability to diagnose disturbances across four noise levels, and its ability to separate harmonics from other events. The proposed VMD in combination with PSO-TOPSIS performs more accurately than the other methods across all noise levels.
Optimizing credit card fraud detection: a deep learning approach to imbalanced datasets Ndama, Oussama; Bensassi, Ismail; En-Naimi, El Mokhtar
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.pp4802-4814

Abstract

Imbalanced datasets pose a significant challenge in credit card fraud detection, hindering the training effectiveness of models due to the scarcity of fraudulent cases. This study addresses the critical problem of data imbalance through an in-depth exploration of techniques, including cross-entropy loss minimization, weighted optimization, and synthetic minority oversampling technique-based resampling, coupled with deep neural networks (DNNs). The urgent need to combat class imbalances in credit card fraud datasets is underscored, emphasizing the creation of reliable detection models. The research method delves into the application of DNNs, strategically optimizing and resampling the dataset to enhance model performance. The study employs a dataset from October 2018, containing 284,807 transactions, with a mere 492 classified as fraudulent. Various resampling techniques, such as undersampling and SMOTE oversampling, are evaluated alongside weighted optimization. The results showcase the effectiveness of SMOTE oversampling, achieving an accuracy of 99.83% without any false negatives. The study concludes by advocating for flexible strategies, integrating cutting-edge machine learning methods, and developing adaptive defenses to safeguard against emerging financial risks in credit card fraud detection.
A machine learning model for predicting phishing websites Odette Boussi, Grace; Gupta, Himanshu; Hossain, Syed Akhter
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.pp4228-4238

Abstract

There are various types of cybercrime, and hackers often target specific ones for different reasons, such as financial gain, recognition, or even revenge. Cybercrimes are not restricted by geographical boundaries and can occur globally. The prevalence of specific types of cybercrime can vary from country to country, influenced by factors such as economic conditions, internet usage levels, and overall development. Phishing is a common cybercrime in the financial sector across different countries, with variations in techniques between developed and developing nations. However, the impact, often leading to financial losses, remains consistent. In our analysis, we utilized a dataset featuring 48 attributes from 5,000 phishing webpages and 5,000 legitimate webpages to predict the phishing status of websites. This approach achieved an impressive 98% accuracy.

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