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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 automated Alzheimer’s disease detection from MRI images by exploring handcrafted and transfer learning feature extraction methods Menad, Touati; Bentoumi, Mohamed; Larbi, Arezki; Mimi, Malika; Ahmed, Abdelmalik Taleb
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1557-1571

Abstract

The rising prevalence of Alzheimer’s disease (AD) poses a significant global health challenge. Early detection of AD enables appropriate and timely treatment to slow disease progression. In this paper, we propose an enhanced procedure for automated AD detection from magnetic resonance imaging (MRI) images, focusing on two primary tasks: feature extraction and classification. For feature extraction, we have investigated two categories of methods: handcrafted techniques and those based on pre-trained convolutional neural network (CNN) models. Handcrafted methods are preceded by a preprocessing step to improve the MRI image contrast, while the pre-trained CNN models were adapted by utilizing only a part of the models as feature extractors, incorporating a global average pooling (GAP) layer to flatten the feature vector and reduce its dimensionality. For classification, we employed three different algorithms as binary classifiers to detect AD from MRI images. Our results demonstrate that the support vector machine (SVM) classifier achieves a classification accuracy of 99.92% with Gabor features and 100% with ResNet101 CNN features, competing with existing methods. This study underscores the effectiveness of feature extraction using Gabor filters, as well as those based on the adapted pre-trained CNN models, for accurate AD detection from MRI images, offering significant advancements in early diagnosis.
Architecture of multi-agent systems for generative automatic matching among heterogeneous systems Batouta, Zouhair Ibn; Dehbi, Rachid; Talea, Mohamed
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2345-2355

Abstract

This paper presents the generative automatic matching (GAM) approach, implemented through a multi-agent system (MAS), to address the challenges of heterogeneity across meta-models. GAM integrates automatic meta-model matching with model generation, offering a comprehensive solution to complex systems involving diverse architectures. The key innovation lies in its ability to automate both the detection of correspondences and the transformation of models, improving the precision and recall of matching processes. The system's scalability and adaptability are enhanced by MAS, allowing for efficient management of diverse meta-models. The approach was evaluated through relational to big data UML meta-models (RBDU) case study. The results demonstrated high accuracy, with precision and recall metrics approaching 1, underscoring the robustness of GAM in managing heterogeneous systems. Compared to traditional methods, GAM offers significant advantages, including automated matching and generation, adaptability to various domains, and superior performance metrics. The study contributes to the field of model-driven engineering (MDE) by formalizing a method that effectively bridges the gap between heterogeneous meta-models. Future research will focus on refining matching heuristics, expanding case studies.
4HAN: hypergraph-based hierarchical attention network for fake news prediction Borse, Alpana A.; Kharate, Gajanan K.; Kharate, Namrata G.
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2202-2210

Abstract

Fake News presents significant threats to both society and individuals, highlighting the urgent need for improved news authenticity verification. To deal with this challenge, we provide a novel strategy called the 4-level hierarchical attention network (4HAN), designed to enhance fake news detection through an advanced integration of hypergraph convolution and attention neural network mechanisms. The 4HAN model operates across four hierarchical levels: paragraphs, sentences, words, and contextual information (metadata). At the highest level, the model employs hypergraph-based attention and convolution neural networks to create a contextual information vector, utilizing a SoftMax activation function. This vector is then combined with a news content vector generated through word and sentence-level attention mechanisms. This architecture enables the 4HAN model to effectively prioritize the relevance of specific words and contextual information, thereby improving the overall representation and accuracy of news content. We evaluate the 4HAN model using the LIAR dataset to demonstrate its efficacy in enhancing Fake News prediction accuracy. Comparative analysis shows that the 4HAN model outperforms several of cutting-edge techniques, like recurrent neural networks (RNN), ensemble techniques, and attention mechanisms techniques. Our results indicate 4HAN model accomplishes a notable accuracy of 96%, showcasing its potential for significantly advancing fake news prediction.
Optimizing convolutional neural networks-based ensemble learning for effective herbal leaf disease detection Ginantra, Ni Luh Wiwik Sri Rahayu; Yanti, Christina Purnama; Ariantini, Made Suci
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2416-2426

Abstract

This study aims to optimize convolutional neural networks (CNN)-based ensemble learning models to enhance accuracy and stability in detecting herbal leaf diseases. The dataset used in this study is sourced from the “Lontar Taru Pramana” collection, which includes various images of herbal leaves affected by different diseases such as Ancak Bacterial Spot, Dapdap Mosaic Virus, and Kelor Powdery Mildew. Several CNN models, including VGG16, AlexNet, ResNet50, DenseNet121, MobileNetV2, and InceptionV2, were evaluated. Among these, the ensemble models combining VGG16, DenseNet121, and MobileNetV2 were selected due to their superior performance. The ensemble model achieved precision scores of 0.81 for class 1, 0.76 for class 2, and 0.78 for class 3, with corresponding recall scores of 0.8167, 0.74, and 0.7633, and F1-scores of 0.8133, 0.75, and 0.7717 respectively. These results indicate significant improvements in model performance and robustness.
Modelling and control of LCL voltage source converter-based hybrid high voltage alternating current/high voltage direct current system Saadeh, Mahmood; Hamdan, Mohammad; Saadeh, Osama
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1297-1321

Abstract

Voltage source converters (VSCs) have revolutionized high voltage direct current (HVDC) transmission, offering numerous advantages such as black start capability, absence of commutation failure, and efficient control of bidirectional power flow. This study introduces a comparative analysis of advanced VSC technologies, focusing on a novel series hybrid converter incorporating an inductor-capacitor-inductor (LCL) passive circuit. This configuration is explored for its potential to enhance both high voltage alternating current (HVAC) and high voltage direct current (HVDC) side fault suppression capabilities and improve DC output voltage quality, addressing critical drawbacks of traditional VSCs. Through comprehensive simulations in MATLAB/Simulink, this research evaluates and compares three different converter topologies: the three-level neutral point clamped converter, the hybrid converter with AC side cascaded H-bridge cells, and the LCL hybrid converter. The comparison is based on key performance metrics such as DC output voltage quality, fault suppression capabilities, and system efficiency during normal and fault conditions. The study finds that the LCL hybrid converter outperforms traditional converters by significantly improving DC output voltage quality and enhancing fault suppression capabilities in HVDC systems. It effectively reduces ripple and maintains stability during faults, making it a superior choice for future HVDC converter designs and applications, offering valuable insights for advancing HVDC technology.
Broken rotor bar detection of three phase induction motor using frequency response analysis Khan, Rizwanullah; Mohd Yousof, Mohd Fairouz; Rahman, Rahisham Abd; Azis, Norhafiz; Al-Ameri, Salem; Ali, Asjad
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1289-1296

Abstract

Three phase induction motors (TPIMs) are broadly utilized for various applications in the industry, but they are prone to different faults that can affect their performance and reliability. One common fault is a broken rotor bar, which leads to vibration, noise, and reduced efficiency. Therefore, detecting and identifying this fault early is important to avoid further damage and reduce maintenance costs. This paper proposes a novel method using frequency response analysis (FRA) to diagnose broken rotor bars in a TPIM. The response of normal motor is measured to obtain the baseline. Subsequently, the rotor was inflected with physical damage to represent a broken rotor bar. By comparing normal and faulty rotors, the measurement shows that frequency response analysis is sensitive toward various fault severity based on the number of broken rotor bars.
Towards an automated weather forecasting and classification using deep learning, fully convolutional network, and long short-term memory Shelke, Nilesh; Maurya, Sudhanshu; Ithape, Rupali; Shaikh, Zarina; Somkunwar, Rachna; Pimpalkar, Amit
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1868-1879

Abstract

Historically, weather forecasting was unreliable and imprecise, relying on intuition and local knowledge. Inaccurate weather forecasts can cause severe impacts on agriculture, construction, and daily life. Existing methods struggle with rural and urban weather prediction, requiring faster and more accurate solutions. This research proposes a deep learning system using real- time images to address this challenge. This research employs a deep learning model fully convolutional network-long short-term memory (FCN-LSTM) to analyze images and predict weather conditions. In this case, the model forecasts a sunny and cloudy environment, which facilitates defining the ideal conditions for every given climatic zone in the weather classification model. The model is trained on a dataset of weather images obtained from Kaggle. The performance of the proposed model FCN-LSTM achieves an accuracy of 96.88% and a validation accuracy of 91.22%. Also, the mean squared error (MSE) is 7.11, which is significantly lower and supports efficient enhancement in weather forecasting. This significant improvement demonstrates the potential of deep learning for real-time weather forecasting. The model provides efficient weather classification, enabling informed decision-making across various sectors. This research lays the foundation for automated weather analysis using deep learning, eliminating human bias and improving accuracy.
Electrical power submeter for quality and energy monitoring using Wemos microcontroller Supriyono, Heru; Hogantara, Azra Reza Satria; Budiman, Aris
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2436-2444

Abstract

Voltage, current, and frequency are three electrical energy variables that need to be monitored because if they do not comply with established standards, they can cause damage to electronic devices that use electrical energy. The objective of this article is to develop a submeter that can be used for monitoring both energy consumption and three power quality variables. The system was developed by using commercially available instruments involving the PZEM 004t sensor, the Wemos D1 mini microcontroller, and the Blynk platform on the smartphone. The use of the Blynk platform enables the system to log the monitored variables continuously in the form of a spreadsheet file and send them via email in order to be downloaded and used for further analysis. The results of calibration tests carried out using varying loads showed that the developed system has voltage measurement results with a difference of 1.35% when compared to measurement results using a commercial multimeter, while the difference for current measurements is 0.85%.
User behavior analysis for insider attack detection using a combination of memory prediction model and recursive feature elimination algorithm Triana, Yaya Sudarya; Osman, Mohd Azam; Stiawan, Deris; Budiarto, Rahmat
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1793-1804

Abstract

Existing defense tools against the insider attacks are rare, not in real time fashion and suffer from low detection accuracy as the attacks become more sophisticated. Thus, a detection tool with online learning ability and better accuracy is required urgently. This study proposes an insider attack detection model by leveraging entity behavior analysis technique based on a memory prediction model combined with the recursive feature elimination (RFE) feature selection algorithm. The memory-prediction model provides ability to perform online learning, while the RFE algorithm is deployed to reduce data dimensionality. Dataset for the experiment was created from a real network with 150 active users, and mixed with attacks data from publicly available dataset. The dataset is simulated on a testbed network environment consisting of a server configured to run 4 virtual servers and other two computers as traffic generator and detection tool. The experimental results show 94.01% of detection accuracy, 95.64% of precision, 99.28% of sensitivity, and 96.08% of F1-score. The proposed model is able to perform on-the-fly learning to address evolving nature of the attacks. Combining memory prediction models with the RFE for user behavior analysis is a promising approach, and achieving high accuracy is definitely a positive outcome.
Enhancing accuracy in greenhouse microclimate forecasting through a hybrid long short-term memory light gradient boosting machine ensemble approach Abdelmadjid, Mokeddem Kamal; Noureddine, Seddiki; Amina, Bourouis; Khelifa, Benahmed
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2392-2403

Abstract

Greenhouse cultivation is one of the main methods for improving agricultural yield and quality. With the world needing more and more production, improving greenhouses using innovative technology becomes a must. These high-tech, aka, smart greenhouses depend much on the accuracy and availability of sensor data to perform at their best. In challenging situations such as sensor malfunctions or data gaps, utilizing historical data to predict microclimate parameters within the greenhouse is essential for maintaining optimal growing conditions and effective sustainable resource management control. In this work, and by employing a synthesis technique across various time series models, we forecast internal temperature and humidity, the two main parameters for a greenhouse, by incorporating diverse characteristics as input into a customized forecasting model. The selected architecture integrates deep learning and nonlinear learning models, specifically long short-term memory (LSTM) and light gradient boosting machine (LightGBM) as an ensemble approach, providing a comprehensive framework for time-series prediction, evaluated through mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²) metrics. With a focus on improving accuracy in anticipating environmental changes, we have achieved high precision in predicting temperature (98.45%) and humidity (99.61%).

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