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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 70 Documents
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Search results for , issue "Vol 15, No 4: August 2025" : 70 Documents clear
Numerical modelling of photocurrent for CuInxGa1-xSe2-based bifacial photovoltaic cell Bouchekouf, Seloua; Guentri, Hocine; Hassinet, Liamena; Merzougui, Amina; Kebaili, Farida
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.pp3649-3659

Abstract

Research on thin-film solar cells based on CuInSe2 has demonstrated the potential of this compound for photovoltaic conversion. The introduction of gallium as a substitute for indium has led to the creation of the CuInxGa1-xSe2 (CIGS) structure, which could serve as one of the foundational materials for high-performance solar cells. This paper focuses on modelling the bifacial back surface field (BSF) solar cell. We took the CdS/CIGS thin-film structure as an application example to optimize, through simulation, the physical-electronic and geometric parameters of the various layers of the cell. Our study has led us to interesting results that clearly show that the performance of the cell is precisely controlled by the space charge region associated with the CIGS absorber layer, which is promising for research in photovoltaics due to its high absorption coefficient and the ability to vary its bandgap, allowing for increased conversion efficiency. The high-doped P+ layer (Wbsf) enhances the total photocurrent of the bifacial.
Detecting sensor faults in wireless sensor networks for precision agriculture using long short-term memory Aitamar, Yassine; Abbadi, Jamal El
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.pp3803-3812

Abstract

The reliable acquisition of soil data from wireless sensor networks (WSNs) deployed in farmlands is critical for optimizing precision agriculture (PA) practices. However, sensor faults can significantly degrade data quality, hindering PA techniques. Our work proposes a novel long short-term memory (LSTM) network-based method for fault detection in WSNs for PA applications. Unlike traditional methods, our approach utilizes a lightweight, transfer learning-based LSTM architecture specifically designed to address the challenge of limited labeled training data availability in agricultural settings. The model effectively captures temporal dependencies within sensor data sequences, enabling accurate predictions of normal sensor behavior and identification of anomalies indicative of faults. Experimental validation confirms the effectiveness of our method in diverse real-world WSN deployments, ensuring data integrity and enhancing network reliability. This study paves the way for improved decision-making and optimized PA practices.
Chaotic red-tailed hawk algorithm to optimize parameter power system stabilizer Aribowo, Widi; Abualigah, Laith; Oliva, Diego; Aljohani, Abeer; Sabo, Aliyu
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.pp3536-3545

Abstract

This article introduces a recently created adaptation of the red-tailed hawk (RTH) algorithm. The proposed approach is a modified version of the original RTH algorithm, incorporating chaotic elements to enhance its integrity and performance. The RTH algorithm emulates the hunting behavior of the red-tailed hawk. This article demonstrates the adjustment of the power system stabilizer using the suggested technique in a case study involving a single-machine system. The suggested method was validated by benchmarking against known functions and evaluating its performance on a single-machine system in terms of transient responsiveness. The essay employs the original RTH algorithm as a means of comparison. The simulation results demonstrate that the proposed technique exhibits promising performance.
Cyber-fraud detection methodology by using machine learning algorithms Abu-Khadrah, Ahmed; Al-Washmi, Sahar; Mohd Ali, Ali; Jarrah, Muath
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.pp3949-3956

Abstract

Cybercrime covers a wide array of illegal online activities such as hacking and identity theft, while cyber fraud specifically involves deceptive practices like phishing and fraudulent financial transactions. The rise in technology and digital communication has exacerbated cyber fraud. Although prevention technologies are advancing, fraudsters continually adapt, making effective detection methods essential for identifying and addressing fraud when prevention fails. The proposed model aims to reduce online fraud through new detection algorithms. It utilizes statistical and machine learning techniques, including logistic regression, random forest, and naïve Bayes, to identify non-transactional fraud behaviors. By analyzing a meticulously collected and fine-tuned dataset, the study enhances detection capabilities beyond traditional transaction-focused approaches. The algorithms monitor user interactions and device characteristics to create profiles of normal behaviors and detect deviations indicative of fraud. The evaluation of proposed model showed 100% accuracy. A unified model incorporating all decision-making processes was used, leading to a voting phase and accuracy assessment. This approach consolidates multiple algorithms into a single framework, proving highly effective for comprehensive fraud detection. The research demonstrates the value of integrating machine learning techniques with real-world data to advance fraud detection and emphasizes the importance of continual adaptation to address evolving cyber threats.
Modernizing quality management with formal languages and neural networks Utepbergenov, Irbulat; Toibayeva, Shara
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.pp4031-4042

Abstract

This paper explores the integration of formal languages and neural networks into quality management systems to enhance efficiency and sustainability. Formal languages standardize regulatory documents, reducing misinterpretation and simplifying modification, contributing to innovative infrastructure (SDG 9). Recurrent neural networks (RNNs) automate document analysis, non-conformance detection, and decision-making, improving production efficiency and promoting responsible consumption (SDG 12). Automation in quality management reduces costs, enhances competitiveness, and aligns with decent work and economic growth (SDG 8). Standardizing documentation and automating quality control enhance workforce competencies and support quality education (SDG 4). These technologies strengthen regulatory transparency, reduce legal risks, and improve governance, supporting strong institutions (SDG 16). The proposed approach fosters sustainable development through digitalization and automation, ensuring efficiency, innovation, and compliance with environmental and social standards.
Impact of integrating the concentrated solar power on the reliability of the Moroccan electricity system Fahssi, Mohammed EL; Ouchbel, Taoufik; Zouggar, Smail; Elhafyani, Mohamed Larbi
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.pp3546-3555

Abstract

In Morocco's electrical grid, the percentage of renewable energy used is rising. This growth can have significant impacts on the electrical system's ability to meet load because of unpredictable solar energy production. To evaluate the effects of concentrated solar power (CSP) generation and load evolution on the hierarchical level I (HLI: the capacity to cover the load on the premise of an endless node), this study is evaluating, by employing a Monte Carlo non-sequential simulation, decreasing the impacts on the ability and increasing the reliability of the Moroccan electrical grid. For that, we determine the CSP based on the hourly direct normal irradiation (DNI) for each site, the hourly conventional generation and the hourly load. Then we use these data as input elements in the Monte Carlo simulation to calculate the reliability indices like loss of load probability (LOLP), loss of load expectation (LOLE) and loss of energy expectation (LOEE).
Maximum power point tracking technique based on the grey wolf optimization-perturb and observe hybrid algorithm for photovoltaic systems under partial shading conditions Bilal, Leghrib; Nadia, Bensiali; Mohamed, Adjabi
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.pp3566-3582

Abstract

Photovoltaic panels represent the most abundant source of renewable energy and the cleanest form of electrical energy derived from the sun. However, partial shading can lead to the appearance of multiple local maximum power points (LMPP) in the power-voltage (P-V) characteristics of solar panels. This situation traps classical power maximization algorithms, such as perturb and observe (P&O) or incremental conductance, as these algorithms tend to deviate from the global maximum power point (GMPP), resulting in reduced electrical energy production. To overcome this major challenge in the electrical industry, we propose in this study a hybrid grey wolf optimization-perturb and observe hybrid (GWO-P&O) algorithm, designed to converge towards the global maximum power without being trapped in local peaks. To demonstrate its effectiveness, the proposed algorithm was simulated in MATLAB/Simulink under various complex and uniform partial shading conditions. Furthermore, a comparative study was conducted with the P&O and GWO algorithms to evaluate precision, tracking, response time, and efficiency. The simulation results revealed superior performance for the proposed technique, particularly in terms of constant tracking of the global peak, with efficiencies of 99.95% and 99.98% in the best cases, faster response times (ranging from 0.07 to 0.04 s), and minimal, almost negligible oscillations around the GMPP.
Gradient boosting algorithm for predicting student success Jabir, Brahim; Merzouk, Soukaina; Hamzaoui, Radoine; Falih, Noureddine
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.pp4181-4191

Abstract

The idea of using machine learning resolution techniques to predict student performance on an online learning platform such as Moodle has attracted considerable interest. Machine learning algorithms are capable of correctly interpreting the content and thus predicting the performance of our students. Algorithms namely gradient boosting machines (GBM) and eXtreme gradient boosting (XGBoost) are highly recommended by most researchers due to their high accuracy and smooth boosting time. This research was conducted to analyze the effectiveness of the XGBoost algorithm on Moodle platform to predict student performance by analyzing their online activities, practicing various types of online activities. The proposed algorithm was applied for the prediction of academic performance based on this data received from Moodle. The results demonstrate a strong correlation between many activities like the number of hours spent online and the achievement of academic goals, with a remarkable prediction rate of 0.949.
Synchronized transform-aggregate model for big data analytics towards in distributed cloud ecosystem Dembala, Rajeshwari; Ananthapadmanabha, Kavya; Dhananjaya, Shashank
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.pp4259-4267

Abstract

The massively generated data from various technologically advanced applications hosted in the cloud and internet of things (IoT) in present times calls for effective management towards balancing the demands of both service providers and users. The conventional usage of distributed frameworks for such big data management is witnessed with various ongoing challenges. Hence, this manuscript presents a novel analytical framework for big data that can offer reduced cost and reduced time demanded to evaluate the distributed big data from multiple data points in the cloud in an optimal way. The core ideology of this framework is to gain a synchronized optimality for cost and time for executing a task demanded for big data analytics complying with the constraints associated with task deadline. The proposed framework is capable of fine-tuning the positioning of task operation using transform and aggregate strategy to exhibit 37% reduced delay, 41% efficient task completion performance, and 28% reduced execution time in contrast to existing frameworks.
Internet of things-based water quality monitoring design to improve freshwater lobster farming management Muthmainnah, Muthmainnah; Khasanah, Iva Khuzaini; Hananto, Farid Samsu; Romadani, Arista; Tazi, Imam; Mulyono, Agus; Tirono, Mokhamad
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.pp3717-3726

Abstract

The development of lobster farming requires careful water quality monitoring to ensure optimal growth and health. This study introduces a novel internet of things (IoT)-based water quality monitoring system designed specifically for lobster farming applications, operating on the Antares IoT platform. The system incorporates pH, temperature, and turbidity sensors to measure critical water quality parameters. The sensors were calibrated and validated using standard methods, yielding high accuracy, with average values of 98.74% for pH, 98.78% for temperature, and 98.56% for turbidity. The study also involved direct monitoring over five days, with pH values ranging between 8-10, temperatures between 23-27°C, and stable turbidity at 90-99 NTU. The novelty of this system lies in its ability to provide real-time, reliable data and predictive analysis to support effective water quality management in lobster farming. Unlike traditional water quality monitoring systems that lack real-time data analysis or predictive capabilities, this system integrates both monitoring and forecasting features, allowing for more proactive management. Additionally, it offers higher accuracy and lower sensor drift compared to older, manual water quality monitoring methods. Experimental results indicate that the proposed monitoring system can deliver accurate and reliable data, supporting optimal farming conditions. These findings align with and expand upon existing literature, offering a more integrated and efficient solution for real-time and accurate monitoring in lobster farming.

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