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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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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.
To ensure public safety internet of things and convolutional neural network algorithm for a surveillance system enabled with 5G priya, Chandrasekar; Kumuthapriya, Kesavan; Sagayamary, Savarimuthu; Livingston, L. M. Merlin; Venkatesan, Marimuthu
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.pp4268-4278

Abstract

Public safety and security are top priorities in the constantly urbanizing society and research develops and implements a smart surveillance system using fifth generation (5G) of wireless communication technology and internet of things (IoT) technologies to improve public safety. It developed a comprehensive and responsive monitoring solution using machine learning methods, especially convolutional neural networks (CNNs). IoT devices, including high-definition cameras, environmental sensors, and drones, are carefully deployed in urban centers, transit hubs, and essential infrastructure. These devices provide data to a central processing unit through the 5G network and CNNs analyze incoming data in real-time. The CNNs are taught to recognize objects, anomalies, faces, and license plates. These tasks help the system identify risks, odd activities, and intriguing people and warn authorities of real-time irregularities and security issues, simplifying emergency responses. Predictive analytics analyzes previous data to forecast security issues, enabling preventative steps and data are protected by strict privacy protections. According to this analysis, 5G-enabled IoT surveillance systems and machine learning may improve public safety, situational awareness, and emergency response times and approach ensures that security advancements respect privacy and integrity.
Enhancing anomaly detection performance using ResNet50 and BiLSTM networks on benchmark datasets Ramoliya, Dipak; Ganatra, Amit
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.pp3727-3736

Abstract

Detection of abnormal activity from large video sequences is one of the biggest challenges because of ambiguity in different activities. Over the last many years, several cameras have been placed to cover the public and private sectors to monitor abnormal human activity and surveillance. In recent years, deep learning and computer vision have significantly impacted this kind of surveillance. Intelligent systems that can automatically identify unusual events in video streams are currently in high demand. A deep learning-based combinational model has been proposed to detect abnormal activity from input video streams. The proposed study uses a combination of convolution and sequential models. A ResNet50 network with a residual connection was used for initial feature extraction. The proposed bidirectional long short-term memory (BiLSTM) network has improved the extracted ResNet50 features. Simulation of the proposed model was experimented on two benchmark datasets for anomaly detection UCF Crime and ShanghaiTech. Simulation of proposed architecture has achieved 97.55% and 91.94% remarkable accuracy for UCF Crime and ShanghaiTech datasets respectively.
DriveGuard: enhancing vehicle breakdown assistance through mobile geolocation technology Ariff, Mohamed Imran Mohamed; Halim, Abdul Hadi Abdul; Ahmad, Samsiah; Abdullah, Mohammad Nasir; Zulkifli, Zalikha; Salleh, Khairulliza Ahmad
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.pp3957-3964

Abstract

The DriveGuard mobile application addresses the growing demand for efficient vehicle breakdown assistance by connecting users to nearby workshops using advanced geolocation technologies. With the rise in private vehicle ownership, sudden breakdowns are increasingly common, necessitating quick access to assistance. DriveGuard utilizes GPS, GSM/CDMA Cell IDs, and Wi-Fi positioning for precise location tracking, enabling users to locate assistance rapidly and accurately. Developed through the waterfall model, the application offers a user-friendly interface built with the Flutter framework. Test results indicate high functionality and user satisfaction, achieving usability ratings between 88% and 90%. DriveGuard’s design improves road safety by reducing waiting times for emergency services, alleviating the stress often associated with breakdown situations. Future work will focus on expanding service options, enhancing security, and refining user interactions to provide a more comprehensive roadside assistance tool. DriveGuard demonstrates the potential of mobile technology in promoting safe and efficient transportation.
A hybrid model to mitigate data gaps and fluctuations in tax revenue forecasting Taufik, Rahman; Aristoteles, Aristoteles; Ilman, Igit Sabda
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.pp4099-4108

Abstract

This study addresses the critical challenge of advancing tax revenue forecasting models to effectively handle distinctive data gaps and inherent fluctuations in tax revenue data. These challenges are evident in Lampung Province, Indonesia, where limited temporal granularity and non-linear variability hinder accurate fiscal planning. Despite advancements in statistical, machine learning, and hybrid approaches, existing models often fall short in simultaneously managing these challenges. A hybrid model integrating random forest regressors for data interpolation and Long Short-Term Memory for capturing complex temporal patterns was proposed. The model was evaluated, achieving an R² of 0.86, root mean squared error (RMSE) of 9.65 billion, and mean absolute percentage error (MAPE) of 3.49%. Although the model has limitations in generalizing to unseen data, the results demonstrate that it outperforms existing forecasting models regarding accuracy and reliability. Integrating random forest regressors and long short-term memory delivers a tailored solution to the complexities of tax revenue forecasting, contributing to fiscal forecasting and setting a foundation for further exploration into hybrid approaches.

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