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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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Assessing risk factors for heart disease using machine learning methods Maxutova, Natalya; Tussupov, Jamalbek; Kedelbayeva, Kamilya; Tynykulova, Assemgul; Balabayeva, Zulfiya; Yersultanova, Zauresh; Khamitova, Zhainagul; Zhunussova, Kamila
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6734-6742

Abstract

This paper examines various machine learning methods for assessing risk factors for cardiovascular diseases. To build predictive models, two approaches were used: the extreme gradient boosting (XGBoost) algorithm and a convolutional neural network (CNN). The focus is on analyzing the performance of each model in classification and regression tasks, as well as their ability to identify key biomarkers and risk factors such as cholesterol, ferritin, homocysteine and aspartate aminotransferase (AST) levels. XGBoost parameters have been optimized for working with tabular data, demonstrating high accuracy in risk prediction. The CNN model, despite the initial reduction in error on the training set, showed signs of overfitting when analyzing validation data. Performance evaluation using the metrics of mean squared error (MSE), coefficient of determination (R²), Akaike information criterion (AIC), and Bayesian information criterion (BIC) revealed significant differences between the models. The study results confirm the effectiveness of XGBoost in analyzing tabular data and summarizing risk factor knowledge, while the CNN model needs further optimization to handle sparse data. The work demonstrates the importance of choosing the right model architecture and training parameters to ensure reliable diagnosis of cardiovascular diseases.
Novel control strategy for the global model of wind turbine El Fadili, Yattou; Berrada, Youssef; Boumhidi, Ismail
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.pp258-267

Abstract

This paper presents a new nonlinear control for the overall model of a three-blade horizontal axis variable speed wind turbine (VSWT) including mechanical and electrical parts, with the aim of improving its performance and making it more profitable. The proposed control is an extension of the classical sliding mode control (SMC) by converting its sliding surface into a sliding sector. The classical SMC approach is widely used for nonlinear systems due to its stability against parameter variation, it is robustness against modeling uncertainties, its good results against external disturbances, and its ease of implementation in real time. Unfortunately, the SMC has a major drawback related to the chattering phenomenon. This phenomenon is due to the utility of a higher switching gain in the case of large uncertainties, it causes high-frequency oscillations once the sliding regime is reached, and it can cause a loss of accuracy by influencing the input control variables. This is the reason that aims to develop a new control law to eliminate the chattering and to guarantee stability, which is demonstrated by the Lyapunov theory. The effectiveness of the developed control is compared with the SMC and is illustrated by numerical simulations using MATLAB toolboxes.
Target loop antenna prototype with magnetic field reduction method Assamoi, Claude Daniel; Ouattara, Yelakan Berenger; Youan, Bi Tra Jean Claude; Gnamele, N'tcho Assoukpou Jean; Doumbia, Vafi
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5274-5284

Abstract

This paper proposes a new large loop antenna with a reduced magnetic field for a near field communication (NFC) target used in a metallic environment. It also defines a fairly clear method for controlling and, more specifically, reducing the magnetic field associated with loop antennas. This antenna consists of a circular winding, inside which we insert a square winding arranged in the shape of a diamond. The particular structure of this antenna shows that it is possible to dissociate the increase in the induced magnetic field, linked to its large size, from the increase in the number of windings. This is made possible by the application of the physical principle of overlapping magnetic fields, which results in partially destructive interference.
Transformations for non-destructive evaluation of brix in mango by reflectance spectroscopy and machine learning Paiva-Peredo, Ernesto; Gonzales-Rodriguez, Diego; Trujillo Herrera, William; Soria Quijaite, Juan Jesús; Quispe-Arpasi, Diana; Paulino, Christian Ovalle
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.pp532-546

Abstract

Mango is a very popular climacteric fruit in America and Europe. Among the internal properties of the mango, total soluble solids (TSS) are an adequate indicator to estimate the quality of mango, however, the measurement of this indicator requires destructive tests. Several research have addressed similar issues; they have made use of pre-processing transformations without making it clear which of them is statistically better. Here, we created a new spectral database to build machine learning (ML) models. We analyzed a total of 18 principal component regression (PCR) models and 18 partial least squared regression (PLSR) models, where 4 types of transformations, 3 different feature extractors, and 3 different pre-processing techniques are combined. The research proposes a double cross validation (CV) both to determine the optimal number of components and to obtain the final metrics. The best model had a root mean square error (RMSE) of 1.1382 °Brix and a RMSE on the transformed scale of 0.5140. The best model used 4 components, used y2 transformation, reflectance R as the independent variable and MSC as a pre-processing technique.
Voltage and frequency control of microgrid in presence of micro-turbine interfaced to matrix converter Toupchi Khosroshahi, Mahdi; Ajami, Ali; Sutikno, Tole
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2466-2479

Abstract

The active and reactive load changes have a significant impact on voltage and frequency. In this paper, in order to stabilize the microgrid (MG) against load variations in islanding mode, the active and reactive power of all distributed generators (DGs), including energy storage (battery), diesel generator, and micro-turbine, are controlled. The micro-turbine generator is connected to MG through a three-phase to three-phase matrix converter, and the droop control method is applied for controlling the voltage and frequency of MG. In addition, a method is introduced for voltage and frequency control of micro-turbines in the transition state from grid-connected mode to islanding mode. A novel switching strategy of the matrix converter is used for converting the high-frequency output voltage of the micro-turbine to the grid-side frequency of the utility system. Moreover, using the switching strategy, the low-order harmonics in the output current and voltage are not produced, and consequently, the size of the output filter would be reduced. In fact, the suggested control strategy is load-independent and has no frequency conversion restrictions. The proposed approach for voltage and frequency regulation demonstrates exceptional performance and favorable response across various load alteration scenarios. The suggested strategy is examined in several scenarios in the MG test systems, and the simulation results are addressed.
Automated feature selection using improved migrating birds optimization for enhanced medical diagnosis El Aboudi, Naoual; Riouali, Youness; Maminou, Ahmed Reda; Jabri, Hassane; Benhlima, Laila
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3159-3167

Abstract

The feature selection task is a crucial phase in data analysis, aiming to identify a minimized set of relevant features for the target class, thereby eliminating irrelevant and redundant attributes used for model training. While population-based feature selection approaches offer prominent solutions for classification performance, their computational time can be prohibitive. To mitigate delays and optimize resource utilization, this study adopts machine learning operations (MLOps). MLOps involves the seamless transition of experimental machine learning models into production, serving them to end users and automating the feature selection phase. This paper introduces a novel feature selection method based on improved migrating bird optimization and its automated variant integrated into MLOps. Experiments conducted on six medical datasets validate the effectiveness of our proposed feature selection method in improving the outcomes of medical diagnosis systems. The results showcase satisfactory performance in terms of classification compared to concurrent feature selection algorithms.
Optical coherence tomography angiography image classification and analysis of diabetic retinopathy, using Wasserstein generative adversarial network augmentation Hatode, Pranali Pradeep; Edinburgh, Maniroja
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7046-7056

Abstract

Deep learning algorithms effectively work with, a significant amount of data. trained on small datasets tend to have poor generalization. Data augmentation techniques can be used to make better use of existing training data, improving the applicability of deep learning methods. However, traditional data augmentation methods often produce limited additional credible data. The deep learning approach's performance can be enhanced by generating new data by employing generative adversarial networks (GANs). Although GANs have been extensively used to improve the performance of convolutional neural networks (CNNs), there has been relatively less research on data augmentation methods specifically for GAN training. This study focuses on using a Wasserstein GAN (WGAN) architecture for generating synthetic optical coherence tomography angiography (OCTA) images of diabetic retinopathy to aid in the detection of different types of diabetic retinopathy diseases, including proliferative diabetic retinopathy (PDR), Severe non-PDR (NPDR), Moderate NPDR, and Mild NPDR. WGAN, provides the generator with a more informative learning signal, making training more stable, particularly in high-dimensional spaces. The trained WGAN model is saved in .h5 file format (HDF), converted to portable network graphics (PNG) image format, and then classified into different categories of diabetic retinopathy using a ResNet50 model with various fine-tuning methods. The proposed model has demonstrated better results than the previous study. 99.95% accuracy is exhibited.
Developing a restaurant recommended system via the Vietnamese food image classification Pham, Viet Hoang; Nguyen, Anh Thai; Phung, Bao The; Phan, Truong Ho-Viet
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1711-1719

Abstract

A recommendation system is a system that recommends products and services to users based on daily online searching habits. The recommender system is applied in many fields such as job searching, health care, education, music, and tourism. However, few studies have combined computer vision and collaborative filtering to build a restaurant recommendation system in the tourism sector. In this study, we presented a solution to build a restaurant recommendation system through Vietnamese food image classification. First, we used ResNet-34 which is a variant of the convolutional neural network to classify Vietnamese food images. Then, the system applied the alternative least square technique in matrix factorization and Apache Spark in distributed computing to train the restaurant location dataset. The output was the most relevant restaurant places list to show many choices to users. The experimental datasets included the Vietnamese image and the restaurant location datasets that were collected from kaggle.com and foody.vn websites. For image classification task evaluation, we compared ResNet-34 to variants of ResNet. For the restaurant recommendation task evaluation, we compared alternative least squares with k-nearest neighbor. The comparison results show that the proposed solution is better than traditional popular models.
A novel received signal strength indicator method for modeling Massive MIMO beamforming via multi-task deep learning Ramadan, Ibrahim El-Metwally; AbdElHalim, Eman; Saleh, Ahmed Ibrahim; Mostafa, Hossam El-Din Salah
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5285-5296

Abstract

To achieve the best performance in terms of accuracy and complexity of massive multiple-input multiple-output (Massive MIMO) in wireless communication systems, hybrid beamforming (HBF) is a promising technique that provides high data rate multiplexing gains and enhances the spectral efficiency (SE) of the system. In this paper, a novel received signal strength indicator (RSSI) method is proposed to design an HBF for Massive MIMO BF via multitasking deep learning (DL) that minimizes the reliance on the channel state information (CSI) feedback. The trade-off between the enhancement SE of the system and the deep neural networks (DNNs) performance is optimized, and the results reveal that the proposed novel DL techniques achieve predicted spectral efficiencies with accuracy of 99.23% and 95.64% for Deep-HBF and Deep-AFP, respectively. The processing times for Deep-HBF and Deep-AFP are 709.2914 sec and 1425.864 sec, respectively. Notably, Deep-AFP exhibits a higher range of computational complexity compared to Deep-HBF. It is worth mentioning that the proposed techniques utilize the same DNN architecture.
EksPy: a new Python framework for developing graphical user interface based PyQt5 Kirsan, Aidil Saputra; Takano, Kosuke; Zebada Mansurina, Sallie Trixie
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.pp520-531

Abstract

This study introduces EksPy Python framework, a novel framework designed for developing graphical user interface (GUI) applications in Python. EksPy framework is built on PyQt5, which is a collection of Python bindings for the Qt libraries, and it provides a user-friendly and intuitive interface. The comparative analysis of EksPy framework with existing frameworks such as Tkinter and PyQt highlights its notable features, including ease of use, rapid development, enhanced performance, effective database management, and the model-view-controller (MVC) concept. The experimental results illustrate that EksPy framework requires less code and enhances code readability, thereby facilitating better understanding and efficient development. Additionally, EksPy framework offers a modern and customizable appearance, surpassing Tkinter’s capabilities. Furthermore, it incorporates a built-in object-relational mapping (ORM) feature to simplify database interactions and adheres to the MVC architectural pattern. In conclusion, EksPy Python framework emerges as a powerful, user-friendly, and efficient framework for GUI application development in Python.

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