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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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Real-time machine learning-based posture correction for enhanced exercise performance Khadtare, Anish; Ved, Vasistha; Kotak, Himanshu; Jain, Akhil; Vishwakarma, Pinki
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3843-3850

Abstract

Poor posture and associated physical health problems have grown more common as technology use increases, especially during workout sessions. Maintaining proper posture is essential to increasing the efficacy of your workouts and avoiding injuries. The research paper presents the development of a machine-learning model designed to provide real-time posture correction and feedback for exercises such as squats and planks. The model uses MediaPipe for precise real-time posture estimation and OpenCV for analyzing video frames. It detects poor posture and provides users with instant corrective feedback on their posture by examining the angles between important body parts, such as the arms, knees, back, and hips. This innovative method enables a thorough evaluation of form without requiring face-to-face supervision, opening it up to a wider audience. The model is trained on real-world workout datasets of people performing exercises in different positions and postures to ensure that posture detection is reliable under various user circumstances. The system utilizes cutting-edge machine-learning algorithms to demonstrate scalability and adaptability for future training types beyond squats and planks. The main goal is to provide users with a model that increases the efficacy of workouts, lowers the risk of injury, and encourages better exercise habits. The model's emphasis on usability and accessibility makes it potentially a vital tool for anyone looking to enhance their posture and general fitness levels.
Near-infrared spectroscopy and machine learning to detect olive oil type: a systematic review Ortecho, Leonardo Ledesma; José, Enrique Romero; Ovalle, Christian; Berona, Heli Alejandro Cordova
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4120-4132

Abstract

The present study evaluates the effectiveness of visible/near-infrared spectroscopy (VIS/NIR) combined with machine learning in olive oil type detection. A search strategy based on the population, intervention, comparison, and outcome (PICO) framework was employed to formulate specific equations used in Scopus, ScienceDirect, and PubMed databases. After applying exclusion criteria, 53 studies were included in the review following preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The reviewed studies demonstrate that VIS/NIR spectroscopy coupled with machine learning allows rapid and accurate identification of different types of olive oil, highlighting the detection of fatty acids, polyphenols, and other vital compounds. However, variability in samples and processing conditions present significant challenges. Although the results are promising, further research is required to fully validate the efficacy and feasibility of this technology in industrial settings. This review provides a comprehensive overview of the advances, challenges, and opportunities in this field, highlighting the need to optimize machine learning models and standardize analysis procedures for practical application in the food industry.
Techno-economic analysis of a 4 MW solar photovoltaic capacity expansion in a remote Indonesian village Akbar, Agie Maliki; Saputri, Fahmy Rinanda; Tee, David
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4133-4147

Abstract

As of the end of 2022, Indonesia’s electrification ratio reached 99.63%, reflecting significant progress. However, the province of East Nusa Tenggara lags behind with an electrification ratio below 90%, indicating a considerable gap in energy access. This challenge is particularly evident in Oelpuah village, where frequent power outages occur due to the inadequacy of the existing 5 MW solar farm. This study proposes addressing this shortfall by expanding the solar farm capacity by an additional 4 MW. Comprehensive feasibility studies were conducted, evaluating solar radiation, natural disaster risks, and land use. The analysis, supported by PVSyst simulations, identified a suitable site with high radiation levels, though it is not entirely free from disaster risks. The design requires 13,500 solar panel modules, each with a capacity of 330 Wp, and seven 500 kW inverters. Optimal system performance is achieved with a 15-degree panel tilt and a 0-degree azimuth, aligning with the site's location south of the equator. This expansion could supply electricity to up to 4,014 households, each with a typical power usage of 0.825 kW. The study highlights the need for further research to enhance electricity coverage across Indonesia.
Enhancing voltage stability of transmission network using proportional integral controlled high voltage direct current system Ohanu, Chibuike Peter; Ogbuefi, Uche C.; Ejiogu, Emenike; Sutikno, Tole
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3593-3602

Abstract

The contingencies experienced in transmission power networks often lead to unstable voltage profiles, challenging grid reliability and stability. This research aim is to enhance voltage stability using a proportional-integral (PI) controlled high voltage direct current (HVDC) system on a real life 330 kV network. The Newton-Raphson (NR) method is used for power flow analysis of the test network, and stability analysis identified Makurdi bus as the candidate bus for improvement due to its low eigenvalue and damping ratio. Application of a balanced three-phase fault at this bus resulted in a minimum voltage of 0.70 per unit (p.u.), falling outside the statutory voltage limit requirements of 0.95 to 1.05 p.u. The PI-based HVDC system was then applied along the Makurdi to Jos transmission line, which has a low loading capacity. The application of this model optimized the system response to disturbances, significantly improve voltage stability and raised the minimum voltage profile on the network to 0.80 p.u. This demonstrates 10% voltage profile improvement from the base case and reaffirms the effectiveness of the PI-based HVDC system in enhancing voltage stability during major disturbances. This research highlights the potential of integrating control systems into power networks to improve voltage stability and ensure reliable operation, even during large disturbances.
Multi-class pneumonia detection using fine-tuned vision transformer model Trivedi, Khushboo; Thacker, Chintan B.
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3996-4003

Abstract

Distinguishing between the various forms of pneumonia (bacterial, viral, fungal, and normal) using chest X-rays is a major problem in global health. Conventional approaches to pneumonia identification frequently depend on laborious and error-prone manual interpretation. Current machine learning (ML) models, like convolutional neural networks (CNNs), have demonstrated some success, but they frequently fail on jobs requiring multi-class classification or generalization. The potential of vision transformer (ViT) models, fine-tuned to address these limitations, is explored. The approach enhances the accuracy of pneumonia classification into four distinct classes by leveraging the attention mechanism in vision transformers (ViTs). Fine-tuning with a tagged chest X-ray dataset improves the algorithm's ability to detect subtle variations in pneumonia types. The findings demonstrate the model's effectiveness in multi-class pneumonia diagnosis, achieving a significant performance improvement with 98% accuracy across the four classes. This work highlights the promise of vision transformers in medical imaging, enabling the development of improved and scalable pneumonia classification methods.
Adaptive multi-radio quality of service model using neural network approach for robust wireless sensor network transmission in multipath fading environment Hakim, Galang Persada Nurani; Astuti, Dian Widi; Firdausi, Ahmad; Majid, Huda A.
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3795-3802

Abstract

Wireless sensor network loss in wireless data transmission is one of the problems that needs attention. Interference, fading, congestion, and delay are some factors that cause loss in wireless data transmission. This paper used an adaptive multi-radio model to enhance the wireless data transmission to be more robust to disturbance in a multipath fading environment. A neural network approach was used to generate the adaptive model. If we use 433 MHz as our carrier frequency with 250 kHz bandwidth and 12 spreading factors, we can get signal noise ratio (SNR) for 20 meters at about -9.8 dB. Thus, we can use the adaptive model to enhance the WSN wireless data transmission's SNR to 9 dB, automatically changing the radio configuration to 797.1 MHz frequency, with 378.1 bandwidth and 7.111 for spreading factor. Based on the result, the wireless data transmission link has been successfully enhanced using the proposed adaptive model for wireless sensor networks (WSN) in a multipath fading environment.
Machine learning approaches to cybersecurity in the industrial internet of things: a review Heier, Melanie; Prasad, Penatiyana W. Chandana; Sayeed, Md Shohel
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3851-3866

Abstract

The industrial internet of things (IIoT) is increasingly used within various sectors to provide innovative business solutions. These technological innovations come with additional cybersecurity risks, and machine learning (ML) is an emerging technology that has been studied as a solution to these complex security challenges. At time of writing, to the author’s knowledge, a review of recent studies on this topic had not been undertaken. This review therefore aims to provide a comprehensive picture of the current state of ML solutions for IIoT cybersecurity with insights into what works to inform future research or real-world solutions. A literary search found twelve papers to review published in 2021 or later that proposed ML solutions to IIoT cybersecurity concerns. This review found that federated learning and semi-supervised learning in particular are promising ML techniques being proposed to combat the concerns around IIoT cybersecurity. Artificial neural network approaches are also commonly proposed in various combinations with other techniques to ensure fast and accurate cybersecurity solutions. While there is not currently a consensus on the best ML techniques to apply to IIoT cybersecurity, these findings offer insight into those approaches currently being utilized along with gaps where further examination is required.
Renewable energy impact integration in Moroccan grid-load flow analysis Essaid, Safaa; Lazrak, Loubna; Ghazaoui, Mouhsine
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3632-3648

Abstract

This paper analyzes the behavior of a Moroccan electric transportation system in the presence of an integration of renewable energy sources, which represents a significant challenge due to their intermittent nature. The aim is to evaluate the performance of the transportation system in various situations and possible configurations. The current study enables the calculation of power flow in the network using the Newton-Raphson method under the MATLAB/Simulink software. To achieve this, a series of power flow simulations were conducted on a 5-bus Moroccan electrical network, examining four distinct scenarios. In addition, this article offers an evaluation of the power flow performance of the same electric transportation system with varying percentages of renewable energy penetration. In order to provide a complete critical analysis, many simulations were conducted to obtain the voltage and active power profile generated at different bus locations, as well as an evaluation of the losses in the studied network.
An in-depth analysis of a tutoring solution by digital technology Nai, Soukaina; Rifai, Amal; Sadiq, Abdelalim; Elbaghazaoui, Bahaa Eddine
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4058-4073

Abstract

In Morocco, the dropout rate in primary and secondary education remains high due to environmental, social, familial, and educational factors. To address this issue, students rely on private tutoring or online platforms. However, socio-economic disparities make private tutoring inaccessible to many, while technical and pedagogical challenges limit the effectiveness of online platforms, deepening educational inequalities. This article proposes a nationwide participatory tutoring approach involving educational administration and teachers to ensure equitable and quality learning. We analyze existing models to identify their limitations and propose a structured tutoring system tailored to different student profiles. This system is based on a specific algorithm that defines skill assessment, remediation, and progress tracking. Unified modeling language UML is used to structure and present our approach in detail. Then, we compare current Moroccan platforms, particularly Massar, with our system, evaluating student engagement, pedagogical monitoring, curriculum alignment, and remediation effectiveness. Finally, we discuss our results, highlighting our system’s potential to reduce learning gaps, improve education, and significantly decrease the dropout rate in Morocco.
Explainable artificial intelligence and feature based technique for the classification of kidney ultrasound images Kausar, Fizhan; Bojan, Ramamurthy
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4148-4159

Abstract

Millions of people worldwide are affected by chronic kidney disease (CKD), which is one of the main causes of death. Using machine learning (ML) models, this study attempts to create a computer-aided diagnostic (CAD) system that can autonomously detect chronic kidney disease (CKD) with improved interpretability. An online medical database provided 340 ultrasound images used in this study, which included both normal and abnormal instances. 94 texture and intensity attributes were obtained from these images using Pyrandiomics. Six machine learning methods were used for classification: According to the evaluation results, support vector machine (SVM), decision tree (DT), random forest (RF), k-nearest neighbors (k-NN), XG-Boost, and naïve Bayes (NB) models were considered. Among these models, the random forest model demonstrated the highest accuracy. Explainable artificial intelligence (XAI) methods, namely Shapley additive explanation (SHAP), were utilized to improve model transparency. Clinicians could be assisted in comprehending the reasoning behind the predictions using SHAP analysis, which identifies the most important features impacting the ML model and visualizes the ranking of each individual feature.

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