cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Tole Sutikno
Contact Email
ijece@iaesjournal.com
Phone
-
Journal Mail Official
ijece@iaesjournal.com
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
INDONESIA
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 6,345 Documents
 631   632   633   634   635 
Cross-lingual semantic alignment and transfer learning using multilingual language models G C, Niranjan; P, Ramakanth Kumar; H, Pavithra; Moharir, Minal
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp973-980

Abstract

Multilingual language models (MLMs) are widely used for cross-lingual tasks, yet their ability to achieve consistent semantic alignment and transfer to low-resource languages remains limited. This work examines cross-lingual semantic alignment and transfer learning through a comparative evaluation of MLMs at both the word and sentence levels. We analyze general-purpose models such as BLOOM and task-specialized models including LaBSE and XLM-R across English, French, Hindi, and Kannada. Word-level experiments show that LaBSE achieves substantially higher cosine similarity scores of above 0.80 across languages. In sentence-level natural language inference, XLM-R outperforms other models, achieving an F1 score of 68.62% on Kannada and 74.81% on French. These results indicate that model specialization and training objectives play a crucial role in cross-lingual performance, particularly for low-resource languages, and should be carefully considered when deploying multilingual natural language processing (NLP) systems.
A real-time appliance monitoring approach with anomaly detection for residential houses Madhushan, Nimantha; Rathnayake, Rasanjalee; Darshani, Dhanushika; Jeeva, Ashmini; Wijewardhana, Uditha; Dharmaweera, Nishan
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp675-686

Abstract

Monitoring electrical appliances in residential buildings is essential for minimizing energy waste and enhancing safety through the early detection of abnormal conditions. While researchers have investigated both intrusive and nonintrusive load monitoring approaches, the non-intrusive approach has emerged as preferred due to its cost-effectiveness and noninvasive implementation. Despite considerable progress in appliance monitoring and fault detection systems over the past two decades, critical challenges and limitations persist. This paper proposes a low-complexity appliance identification and monitoring solution to overcome those issues. Furthermore, the proposed solution is integrated with an abnormal condition detection mechanism for critical appliances, aiming to save energy and ensure the safety of the power system. Furthermore, the solution incorporates user feedback via a dedicated mobile application, enhancing adaptability and performance. The proposed solution has been validated in real-time environments using both custom and publicly available datasets, demonstrating improved accuracy in energy monitoring and increased consumer safety.
Assistive tool of energy metering system for power utility companies Kee, Keh-Kim; Rashidi, Ramli; Ting, Huong-Yong; Hsiung, Lo Tzu; Kee, Owen Kwong-Hong; Zheng, Yeo Hong; Ini, Michelle Anak
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp577-586

Abstract

The growing demand for electricity and the complexity of power quality management highlight the need for advanced energy monitoring systems. Existing solutions often could not provide the real-time, detailed data necessary for smart grids, smart cities, and Industrial 4.0. They also fail to monitor power quality effectively, avoid equipment damage and ensure safety. To address this, we developed an internet of things (IoT)-based tool that leverages standard energy meters. The system monitors and analyzes electrical energy consumption and its power quality in real-time. The system adopts a multi-layered IoT architecture, where fog computing handles immediate data processing and the cloud computing supports machine learning for power quality detection. In this work, measurement accuracy is validated against a commercial power multimeter, achieving mean absolute percentage error (MAPE) values below 1.0% across different appliances. A companion web portal allows for real-time data visualization, time-series analysis, remote control of appliances and power quality detection that comply with IEC and IEEE standards. The proposed system is scalable and user-friendly, offering a practical smart metering solution for modern energy management. It aligns with the needs of smart grids and smart cities, contributing to efficient and intelligent energy consumption in the context of Industry 4.0.
Influence of doping concentration on the performances of multi-junction solar cell InGaP/InGaAs/Ge Djeriouat, Khadidja; Kerai, Salim; Ghaffour, Kheireddine
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp619-628

Abstract

Recently, because of the high costs of experimentation, researchers have turned to simulation. This type of simulation makes it possible to determine, at any point in the volume of a component, the densities of carriers, electrons and holes, the energies, the recombination rates, the electric fields and other parameters that can be deduced from it, such as currents and voltages. Our paper presents the simulation results of the heterojunction solar cell made of GaInP/GaInAs/Ge materials using Silvaco's Atlas software to optimize its electrical efficiency by acting on the doping of photoactive layers. We have chosen a tandem structure when the top cell is constructed by Ga0.4In0.6P, in the middle cell, we used Ga0.1In0.9As and the bottom cell is formed by germanium (Ge). The simulation is performed under the following conditions: 1-sun (0.1 w/cm2), AM1.5G illumination and at temperature 300 K. We obtained an efficiency of 24.65%.
Data analytics and prediction of cardiovascular disease with machine learning models: a systematic literature review Sonthana, Ravipa; Tangprasert, Sakchai; Nilsiam, Yuenyong; Bhumpenpein, Nalinpat; Nuchitprasitchai, Siranee
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp914-923

Abstract

Cardiovascular disease (CVD) remains one of the leading causes of death globally, underscoring the need for effective early risk prediction. This systematic literature review analyzes research published between 2013 and 2023 on the application of machine learning (ML) in CVD risk prediction. Key areas examined include feature selection, data preprocessing, algorithm choice, and model evaluation. Studies were selected from ACM Digital Library, IEEE Xplore, ScienceDirect, and Scopus based on predefined research questions. Common challenges include limited or low-quality datasets, inconsistent preprocessing methods, and the need for clinically interpretable models. Widely used algorithms include random forest (RF), support vector machine (SVM), decision tree (DT), logistic regression (LR), naïve Bayes (NB), k-nearest neighbor (K-NN), and extreme gradient boosting (XGBoost). The review highlights that robust preprocessing, optimal feature selection, and thorough model validation significantly improve predictive accuracy. It also emphasizes the importance of balancing performance with interpretability for clinical adoption. Finally, the study proposes a structured framework to guide future research and practical implementation, including the integration of genetic and behavioral data to support more personalized and effective cardiovascular care.
Analyzing normalized beta wave power in EEG signals: a comparative study between C4-A1 and EMG1-EMG2 channels for RBD sleep disorder detection Siddiqui, Mohd. Maroof; Valsalan, Prajoona
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp818-826

Abstract

Sleep disorders are medical conditions affecting the sleep patterns of individuals or living beings, with some being severe enough to disrupt normal physical, mental, and emotional functioning. This research article discusses the analysis of the attributes and waveforms of electroencephalogram (EEG) signals in humans. The major objective is to present the findings through signal spectrum analysis, highlighting changes through various sleep stages. The objective of this research is to assess the potential effectiveness of EEG patterns in diagnosing sleep disorders, particularly those associated with rapid eye movement behavior disorder. These conditions frequently lead to detectable alterations in the electrical and chemical processes within the brain, which can be analyzed by examining brain signals and images. This research paper utilizes the short time-frequency analysis of power spectrum density (STFAPSD) method on EEG signals to diagnose various types of sleep disorders. Calculated values are normalized and the average power of the spectral signal spectra, relating to EEG wave components (delta: 1-4 Hz; theta: 4-8 Hz; alpha: 8-13 Hz; beta 13--25~30 Hz). These indices are used as diagnoses to discriminate among different types of sleep disturbances. The results comparison performs accurate power spectral density (PSD) estimations for several sleep disorders, which makes this technique highly efficient to analyze a large database in a short time. Importantly, we achieve significantly results when analyzing the normalized beta power of both C4-A1 and EMG1-EMG2 channels during the rapid eye movement (REM) stage in the EEG signal. This observation demonstrates a strong difference in PSD values (beta normalized) between normals and REM sleep behavior disorders (RBDs).
Internet of things and YOLOv11 for orangutan intestinal nematode parasite detection Teguh, Rony; Nugrahaningsih, Nahumi; Panda, Adventus
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp981-990

Abstract

The health of Bornean orangutans is increasingly threatened by intestinal nematode parasites, which cause significant morbidity and mortality. Traditional microscopic diagnosis is accurate but slow, labor-intensive, and impractical in remote conservation areas. This paper presents a proof-of-concept smart diagnostic automated system that integrates internet of things (IoT) enabled mobile microscopy with a deep learning model based on you only look once version 11 (YOLOv11). A publicly available dataset of 4,000 annotated parasite egg images, derived from human fecal samples and used as a proxy for orangutan infections, was employed for model training and evaluation. The proposed system achieved a mean average precision (mAP) of 0.9957 and a mean intersection over union (IoU) of 0.9098 across four target classes. Compared with prior works using YOLOv4, YOLOv5, and lightweight models, our approach provides higher segmentation fidelity and is embedded in an IoT-based framework suitable for field deployment. Importantly, a pilot test conducted in the field using real orangutan fecal samples confirmed the system feasibility, with near real-time inference (~300 ms per image) and usability by non-specialist users under low-resource conditions. While broader validation with larger orangutan specific datasets remains necessary, this study demonstrates how IoT and computer vision can be combined into a scalable diagnostic tool for wildlife health monitoring and conservation applications.
An extensive review of islanding detection approaches in microgrids for distribution generations S. R., Resna; B., Devi Vighneshwari
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp608-618

Abstract

Microgrids integrated with distributed systems provide several benefits to the power grid, including faster detection times, superior power quality, and energy savings. Microgrids are managed using various methodologies in both grid-connected and island states. Microgrids must detect inadvertent islanding to protect individuals and prevent device damage. Monitoring and identifying magnitude anomalies are the foundation of the majority of islanding detection approaches (IDAs). This study summarizes the IDAs used in microgrids. An islanding fault is a microgrid that inadvertently disconnects from itself owing to a problem in the utility grid. A through categorization of IDAs is provided, with a focus on both local and remote approaches. Local IDAs can be further classified using passive, active, and hybrid methods. Furthermore, the power-quality effect, nondetection zone (NDZ), detection time (DT), and error detection rate (EDR) statistical comparison of the IDAs is examined. The benefits, drawbacks, and research gaps in the current work are evaluated. Lastly, challenges and recommendations for future research are highlighted.
Fractional-order chaos modelization and sliding mode control in a biological enzyme system Benrabah, Sakina; Bourouba, Bachir; Ladaci, Samir
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp729-738

Abstract

This paper proposes two main contributions to fractional-order modeling and control of biological systems that may exhibit chaotic behavior. First, a fractional-order chaotic model is designed to represent a biological enzyme using bifurcation diagrams and fractional orders tuning inspired by the available integer order model. This new approach improves the biological model by introducing physical properties specific to fractional order systems such as the memory effect, fractal properties, tissue heterogeneity and non-local behavior. Furthermore, this makes the use of a more effective, robust and powerful fractional-order control easier and more natural. The second main contribution is to propose a fractional-order sliding mode surface in order to derive a sliding mode control (SMC) controller that is able to stabilize this fractional-order biological system asymptotically. We successfully performed the stability analysis using the Lyapunov theory. Numerical simulations using MATLAB are given to demonstrate the efficiency of the proposed fractional-order controller with a drastic improvement in convergence time comparatively to the integer-order counterpart.
Design and prototyping of the planar inverted-F antenna for V2X communications Berrich, Loubna; Addaim, Adnane
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp842-849

Abstract

In the context of intelligent transportation systems (ITS) development, vehicle-to-everything (V2X) communication plays a central role by enabling information exchange between vehicles (V2V), infrastructure (V2I), pedestrians (V2P), and the network (V2N). The effectiveness of these systems relies heavily on the performance of the antennas employed, which must meet strict requirements in terms of compactness, bandwidth, gain, and electromagnetic compatibility. One of the main challenges lies in designing antennas suitable for the embedded vehicular environment, where space is limited and the propagation conditions are complex. In this context, the present study aims to design, simulate, fabricate, and experimentally evaluate a planar inverted-F antenna (PIFA) dedicated to V2X communication in the 5.8 GHz band. The primary objective is to develop an antenna that is both compact and high-performing, tailored to the specific constraints of V2X applications. The adopted methodology involves a comprehensive parametric study, focusing on several key design parameters that influence the antenna’s performance, such as substrate selection, feeding point location, and the addition of a slot in the structure. These factors are analyzed to optimize the radiation characteristics, resonant frequency, and impedance matching of the antenna. The results demonstrate the feasibility of a PIFA antenna that offers an excellent trade-off between miniaturization and performance, making it well suited for V2X communication applications at 5.8 GHz.

Page 633 of 635 | Total Record : 6345

 631   632   633   634   635 

Filter by Year

2011 2026


Reset
Filter By Issues
All Issue Vol 16, No 2: April 2026 Vol 16, No 1: February 2026 Vol 15, No 6: December 2025 Vol 15, No 5: October 2025 Vol 15, No 4: August 2025 Vol 15, No 3: June 2025 Vol 15, No 2: April 2025 Vol 15, No 1: February 2025 Vol 14, No 6: December 2024 Vol 14, No 5: October 2024 Vol 14, No 4: August 2024 Vol 14, No 3: June 2024 Vol 14, No 2: April 2024 Vol 14, No 1: February 2024 Vol 13, No 6: December 2023 Vol 13, No 5: October 2023 Vol 13, No 4: August 2023 Vol 13, No 3: June 2023 Vol 13, No 2: April 2023 Vol 13, No 1: February 2023 Vol 12, No 6: December 2022 Vol 12, No 5: October 2022 Vol 12, No 4: August 2022 Vol 12, No 3: June 2022 Vol 12, No 2: April 2022 Vol 12, No 1: February 2022 Vol 11, No 6: December 2021 Vol 11, No 5: October 2021 Vol 11, No 4: August 2021 Vol 11, No 3: June 2021 Vol 11, No 2: April 2021 Vol 11, No 1: February 2021 Vol 10, No 6: December 2020 Vol 10, No 5: October 2020 Vol 10, No 4: August 2020 Vol 10, No 3: June 2020 Vol 10, No 2: April 2020 Vol 10, No 1: February 2020 Vol 9, No 6: December 2019 Vol 9, No 5: October 2019 Vol 9, No 4: August 2019 Vol 9, No 3: June 2019 Vol 9, No 2: April 2019 Vol 9, No 1: February 2019 Vol 8, No 6: December 2018 Vol 8, No 5: October 2018 Vol 8, No 4: August 2018 Vol 8, No 3: June 2018 Vol 8, No 2: April 2018 Vol 8, No 1: February 2018 Vol 7, No 6: December 2017 Vol 7, No 5: October 2017 Vol 7, No 4: August 2017 Vol 7, No 3: June 2017 Vol 7, No 2: April 2017 Vol 7, No 1: February 2017 Vol 6, No 6: December 2016 Vol 6, No 5: October 2016 Vol 6, No 4: August 2016 Vol 6, No 3: June 2016 Vol 6, No 2: April 2016 Vol 6, No 1: February 2016 Vol 5, No 6: December 2015 Vol 5, No 5: October 2015 Vol 5, No 4: August 2015 Vol 5, No 3: June 2015 Vol 5, No 2: April 2015 Vol 5, No 1: February 2015 Vol 4, No 6: December 2014 Vol 4, No 5: October 2014 Vol 4, No 4: August 2014 Vol 4, No 3: June 2014 Vol 4, No 2: April 2014 Vol 4, No 1: February 2014 Vol 3, No 6: December 2013 Vol 3, No 5: October 2013 Vol 3, No 4: August 2013 Vol 3, No 3: June 2013 Vol 3, No 2: April 2013 Vol 3, No 1: February 2013 Vol 2, No 6: December 2012 Vol 2, No 5: October 2012 Vol 2, No 4: August 2012 Vol 2, No 3: June 2012 Vol 2, No 2: April 2012 Vol 2, No 1: February 2012 Vol 1, No 2: December 2011 Vol 1, No 1: September 2011 More Issue