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 45 Documents
 1   2   3   4   5 
Search results for , issue "Vol 16, No 1: February 2026" : 45 Documents clear
AI SWLM: artificial intelligence-based system for wildlife monitoring Krishnan, Arun Govindan; Bhuvana, Jayaraman; Thai, Mirnalinee Thanga Nadar Thanga; Ramanujam, Bharathkumar Azhagiya Manavala
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp216-229

Abstract

Detection and recognition of wild animals are essential for animal surveillance, behavior monitoring and species counting. Intrusion of animals and the disaster to be caused can be averted by the timely recognition of intruding animals. An artificial intelligence-based system for wildlife monitoring (AI SWLM) is designed and implemented on the camera trap images. The challenges such as detecting and recognizing animals of different sizes, shape, angles and scale, recognizing the animals of same and different species, detecting them under various illumination conditions, with pose variants and occlusion are addressed by identifying the optimal weights of the deep learning architecture, AI SWLM. Models were trained using Gold Standard Snapshot Serengeti dataset with random weights and the best weights of model were used as initial weights for training the augmented data. This has doubled the performance in terms of mean average precision, which can be interpreted.
Machine learning-based predictive maintenance framework for seismometers: is it possible? Putra, Arifrahman Yustika; Lestari, Titik; Saputro, Adhi Harmoko
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp187-205

Abstract

Seismometers are crucial in earthquake and tsunami early warning systems, since they record ground vibrations due to significant seismic events. The health condition of a seismometer is strongly related to the measurement of seismic data quality, making seismometer health condition maintenance critical. Predictive maintenance is the most advanced control or measurement system maintenance method, since it informs about the faults that have occurred in the system and the remaining lifetime of the system. However, no research has proposed a seismometer predictive maintenance framework. Thus, this article reviews general predictive maintenance methods and seismic data quality analysis methods to find the feasibility of developing a predictive maintenance framework for seismometers in seismic stations. Based on the review, it is found that such a framework can be built under particular challenges and requirements. Finally, machine learning is the best approach to build the classification and regression models in the predictive maintenance framework due to its robustness and high prediction accuracy.
An integrated FSM-BABER-SROA framework for secure and energy-efficient internet of things networks using blockchain consensus Yaragal, Achyut; Bendigeri, Kirankumar
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp518-534

Abstract

The rapid expansion of the internet of things (IoT) and wireless sensor networks (WSNs) has intensified the demand for energy-efficient, reliable, and secure data transmission. Traditional clustering and static sleep scheduling approaches often fail to ensure long-term sustainability and tamper-resistant communication. This paper presents BABER-SROAChain, a hybrid optimization and security framework that integrates four core modules: i) Fuzzy similarity matrix (FSM)-based clustering for spatial-energy-aware node grouping, ii) Binary Al-Biruni earth radius (BABER) optimization for intelligent cluster head (CH) selection, iii) ship rescue optimization algorithm (SROA) for adaptive sleep scheduling, and iv) a lightweight blockchain protocol with modified practical byzantine fault tolerance (PBFT) consensus for secure inter-cluster communication. The unified objective function incorporates cluster efficiency, redundancy minimization, latency reduction, and packet delivery ratio maximization. Simulation experiments on large-scale WSNs (100–300 nodes) demonstrate that BABER-SROAChain achieves up to 20% improvement in network lifetime, 18% lower energy consumption, and 15% higher packet delivery ratio compared to state-of-the-art models. Additionally, it minimizes blockchain consensus latency while ensuring high data integrity. The proposed framework offers a scalable, secure, and energy-aware solution suitable for real-time IoT applications, including smart cities, healthcare monitoring, and industrial automation, while addressing the dual challenges of performance optimization and blockchain-based security.
Design of a thermionic electron gun of 6 MeV linac by using neural network based surrogate model Nuraini, Elin; Sihana, Sihana; Taufik, Taufik; Darsono, Darsono; Saefurrochman, Saefurrochman; Utama, Rajendra Satriya
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp477-487

Abstract

High performance electron guns are fundamental components in linear accelerators (linacs), directly influencing beam quality and downstream system efficiency. However, designing electron guns for applications such as a 6 MeV linac presents complex trade-offs between current, perveance, and beam emittance. Traditional simulation-driven optimization methods are computationally expensive and limit rapid prototyping. In this study, we develop a neural network-based surrogate model trained on CST Studio Suite simulation data to predict the electron gun's performance metrics. Our approach significantly accelerates the optimization process by providing real-time predictions of beam current and perveance across a wide design parameter space. The surrogate model achieves high prediction accuracy, with training and validation losses on the order of 10⁻⁷. Results demonstrate that neural network models can serve as reliable and efficient tools for electron gun design, offering considerable computational savings while maintaining accuracy. Future extensions include expanding the surrogate model to multi-objective optimization and incorporating thermal and mechanical effects into the design process.
From YOLO V1 to YOLO V11: comparative analysis of YOLO algorithm (review) Beqqali Hassani, Imane; Benhida, Soufia; Lamii, Nabil; Oqaidi, Khalid; Ouiddad, Ahmed; Ghiadi, Soukaina
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp450-462

Abstract

Object detection in images or videos faces several challenges because the detection must be accurate, efficient and fast. The you only look once (YOLO) algorithm was invented to meet these criteria. But with the creation of several versions of this algorithm (from V1 to V11), it becomes difficult for researchers to choose the best one. The main objective of this review is to present and compare the eleven versions of the yolo algorithm in order to know when using the appropriate one for the study. The methodology used for this work is aligned with preferred reporting items for systematic reviews and meta-analyses (PRISMA) principles and the results demonstrate that the choice of the best version mainly depends on the priorities of the study. If the study prioritizes accuracy and detection of small objects, it should use YOLO V4, YOLO V5, YOLO V6, YOLO V7, YOLO V8, YOLO V9, YOLO V10 or YOLO V11. While studies that prioritize detection speed should use YOLO V5, YOLO V6, YOLO V7, YOLO V8, YOLO V10 or YOLO V11. In complex environment, researchers should avoid using YOLO V1, YOLO V2, YOLO V3, YOLO V5, YOLO V7 and YOLO V9. And researchers who are looking for a good accuracy and speed and a reduced number of parameters should use YOLO V10 or YOLO V11.
Study on the acceleration process of three-phase induction motors driving elevator loads Can, Do Van; Tri, Phan Gia
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp135-148

Abstract

Three-phase induction motor drive systems, especially in elevator applications and other precision motion systems, require optimized acceleration profiles to minimize vibrations and extend mechanical lifespan. Previous studies have primarily focused on fast speed response control but often overlooked the impact of jerk, which affects smoothness and operational safety. This paper proposes a combination of field-oriented control (FOC) and S-curve acceleration profiles to reduce jerk and improve motion quality. A dynamic model of the drive system is developed to simulate the acceleration process, demonstrating that the S-curve significantly reduces torque and current oscillations, thus enhancing stability. The S-curve trajectory generation algorithm is implemented and deployed on a field programmable gate array (FPGA) hardware platform. Experimental hardware results confirm that the generated speed control signals possess high resolution and fast response, making the method suitable for embedded control systems in elevator drives and other sensitive motion-control applications. This integrated approach not only addresses the limitations of previous methods but also provides a practical solution to improve comfort, safety, and durability in various electromechanical drive systems.
Cumulative aging effects of five-year intermittent exposure on flexible amorphous solar cells Salima, Djerroud; Amine, Boudghene Stambouli; Noureddine, Benabadji; Abdelghani, Lakhdari
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp65-75

Abstract

Amorphous silicon (a-Si) is rarely used for large scale photovoltaic energy production, it remains relevant in flexible electronic applications, where mechanical flexibility and lightweight design are prioritized, where exposure to sunlight is typically limited or irregular. This study conducts an experimental analysis of the long-term aging effects on the proprieties of an amorphous solar cells, under five years of intermittent outdoor climate conditions. Unlike conventional aging studies that focus on degradation over time, this research highlights the cumulative effects of environmental exposure, considering the discontinuous nature of exposure cycles and the non-linearity of degradation phenomena because of the abrupt transitions between outdoor exposure phases and indoor laboratory rest periods. The results show that nearly 50% of the panel’s performances is reduced, with the losses observed as follows: a substantial decline in the fill factor from 55.3% to 30%, a decrease in energy conversion efficiency from 11.36% to 5.5%. This accelerated deterioration mainly attributed to harsh environmental transitions caused by intermittent exposure, which amplify aging mechanism compared to continuous exposure. Beyond the experimental findings, the approach presented here, constitutes a meaningful scientific contribution. By introducing a realistic and underexplored aging scenario, it lays the groundwork for a new line of research.
A systematic review of software fault prediction techniques: models, classifiers, and data processing approaches Ramasamy, R. Kanesaraj; Rajendran, Venushini; Subramanian, Parameswaran
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp545-554

Abstract

Software fault prediction (SFP) plays a critical role in improving software reliability by enabling early detection and correction of defects. This paper presents a comprehensive review of 25 recent and significant studies on SFP techniques, focusing on data preprocessing strategies, classification algorithms, and their effectiveness across various datasets. The review categorizes the approaches into traditional statistical models, machine learning methods, deep learning architectures, and hybrid techniques. Notably, wrapper-based feature selection, neural network classifiers, and support vector machines (SVM) are identified as the most effective in achieving high accuracy, particularly when dealing with imbalanced or noisy datasets. The paper also highlights advanced approaches such as variational autoencoders (VAE), Bayesian classifiers, and fuzzy clustering for fault prediction. Comparative analysis is provided to assess performance metrics such as accuracy, F-measure, and area under the curve (AUC). The findings suggest that no single method fits all scenarios, but a combination of appropriate preprocessing and robust classification yields optimal results. This review provides valuable insights for researchers and practitioners aiming to enhance software quality through predictive analytics. Future work should explore ensemble learning and real-time SFP systems for broader applicability.
New control strategy for maximizing power extraction in the grid-connected CHP-PV-Wind hybrid system Maakoul, Othmane; Boulal, Abdellah
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp36-48

Abstract

This work represents a significant contribution to the advancement of modern electrical systems by combining advanced control strategies with robust protection solutions to address the challenges of the energy transition. It focuses on the integration of renewable energy within electrical grids, with particular attention to wind energy, cogeneration (CHP), and photovoltaic energy. The main contributions include the development of innovative methods to enhance system stability and improve energy quality. This is achieved notably through the use of advanced control algorithms, such as the synchronously rotating frame (SRF) transformation, applied to converters and voltage source converter-based high voltage direct current (VSC-HVDC) systems. These approaches enable precise voltage regulation, optimized power flow management, and significant reduction of harmonic distortion. The paper also explores novel techniques, such as control based on the ANFIS algorithm, to improve voltage regulation, current stability, and converter efficiency. Finally, an effective protection solution against voltage faults is proposed, ensuring the stable and reliable transfer of energy produced by offshore wind farms to onshore grids.
A novel stretched-compressed exponential low-pass filter and its application to electrocardiogram signal denoising de Fazio, Roberto; Al-Naami, Bassam; Rawash, Yahia; Al-Hinnawi, Abdel-Razzak; Al-Zaben, Awad; Visconti, Paolo
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp230-245

Abstract

The study investigates a novel stretched-compressed exponential low-pass (SCELP) filter to denoise electrocardiogram (ECG) signals. As an extension of Gaussian filter and unlike other denoising filters, the SCELP filter utilizes the stretched-compressed exponential function (SCEF) in the convolution kernel, being the Gaussian function its particular case. A MATLAB implementation is provided with a single parameter (β), which allows to modify the filter strength, to increase the signal-to-noise ratio (SNR) and reduce the mean squared error (MSE). The SCELP filter’s advantages over traditional denoising filters (i.e., Gaussian, Mittag–Leffler, and Savitzky-Golay filters) were assessed on 100 ECG signals, 50 normal and 50 abnormal (affected by sleep apnea), provided by the PhysioNet dataset. The SCELP filter’s efficacy in rejecting noise was evaluated as the β parameter varies, quantifying the filters' performance in terms of mean SNR and MSE to determine the optimal β value. The obtained results showed that the SCELP filter's best performances are achieved for β equal to = 1.6 (i.e., 16.9508 dB and 13.7574 dB SNR values, and 0.01025 and 0.01178 MSE values for normal and abnormal ECGs, respectively). Furthermore, the SCELP filter was tested on ECG signals with added white noise; compared to Gaussian, Mittag–Leffler, and Savitzky-Golay filters, the SCELP filter yields better performance regarding SNR (16.495 and 14.940 dB) and MSE (0.0106 and 0.0114) values, for normal and abnormal ECGs, respectively, suggesting its applicability for ECG signals' denoising.

Page 1 of 5 | Total Record : 45

 1   2   3   4   5 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue 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