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 6,301 Documents
 602   603   604   605   606 
Investigation of microwave absorption performance of anti-radiation plastic cement brick Kasim, Linda Mohd; Idris, Hasnain Abdullah; Taib, Mohd Nasir; Noor, Norhayati Mohamad; Ahmad, Azizah; Ismail, Noor Azila; Kasim, Nazirah Mohamat; Anuar, Nur Qaisarah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2900-2910

Abstract

The increasing demand for effective anti-microwave radiation materials motivates the exploration of sustainable and eco-friendly alternatives. This research investigates the microwave absorption properties of various brick compositions, including commercial brick (CB) and solid bricks (SB1, SB2 and SB3) incorporating recycled materials, polyethylene terephthalate (PET) and palm oil fuel ash (POFA). The dimension of the developed brick is 200×100×60 mm (length×width×height). The absorption performance of the bricks was measured in 100 mm and 60 mm thickness across the frequency range of 1 to 12 GHz using the naval research laboratory (NRL) free space arch method. At 100 mm thickness, SB3 shows the highest absorption up to-32.2061 dB at 1.98 GHz. At 60 mm thickness, SB1 achieved the maximum absorption at -57.6511 dB at 2.505 GHz. SB2 shows consistent average absorption performance at 15.2064 dB at 100 mm thickness and -19.5 dB at 60 mm thickness respectively. The compressive strength of the brick was measured, and it was shown that SB2 exhibited the highest average compressive strength of 7.17 MPa. Considering the standard wall thickness and brick strength, SB2 shows the most effective performance due to its enhanced composition and consistent performance across frequencies.
Developing gallium nitride-based inverters for high-performance photovoltaic integration in alternating current grids Soomar, Arsalan Muhammad; Musznicki, Piotr
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2583-2598

Abstract

This study introduces a gallium nitride (GaN) based inverter optimized for alternating current (AC) grid integration, featuring a novel phase-locked loop (PLL) controller enhanced with sliding mode control (SMC). This hybrid PLL-SMC approach significantly improves power delivery from photovoltaic (PV) sources, achieving a total harmonic distortion (THD) of 5% and a maximum power point tracking (MPPT) efficiency of 99.1%. Extensive testing demonstrates the inverter's superior performance in grid synchronization, efficiency, and power quality compared to conventional inverters. The results underscore the critical role of advanced GaN-based inverters in enhancing solar energy utilization and advancing renewable energy integration into AC grids. This work sets a new benchmark for PV system integration, contributing to the broader adoption of renewable energy technologies.
Augmented reality learning media for electrical motor: case study in electrical engineering education Mustaqim, Bima; Sibuea, Abdul Muin; Siagian, Sahat; Junaidi, Agus; Amin, Muhammad; Sriadhi, Sriadhi; Yahaya, Wan Ahmad Jaafar Wan; Sitompul, Harun
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2545-2555

Abstract

The impact of augmented reality (AR) learning tools and students' critical thinking abilities on learning outcomes in electrical engineering education is the focus of this study. The study explores the ways in which these factors, both independently and in combination, influence student performance. Findings reveal that AR-based learning materials significantly enhance understanding and retention across self-directed and guided learning models. Critical thinking skills emerge as a key determinant of success, with students exhibiting strong critical thinking consistently outperforming peers with lower-level skills, regardless of the instructional model. The study also highlights variations in AR tools' effectiveness depending on the learning model and students’ critical thinking abilities. Guided learning with AR tools benefits all students, while self-directed AR tools prove most effective for those with advanced critical thinking skills. Students with lower critical thinking abilities face challenges in navigating less structured AR environments. These results underscore the importance of fostering critical thinking and adopting tailored strategies when integrating AR technology into engineering education. By considering both the learning model and critical thinking levels, educators can optimize AR’s potential to enhance student learning outcomes in technical fields.
Energy consumption prediction methods in a cyber-physical system Nurgaliyev, Kenzhegali; Tokhmetov, Akylbek
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3054-3063

Abstract

In recent decades, cyber-physical systems (CPS) have become an essential part of modern industry and daily life. These systems integrate physical processes with computer and network components, allowing them to interact with their environment and manage their components autonomously. One of the most significant aspects of CPS efficiency is managing energy consumption, which significantly affects their reliability, efficiency, and economic performance. CPS devices generate vast amounts of diverse data, which is crucial to accurately model. Researchers use predictive analysis to develop models that forecast trends and simulate real-world conditions, enabling them to make better-informed decisions. This article presents a comparative analysis of different predictive models for CPS data analytics, focusing on energy consumption in smart buildings. Short-term models include gradient-boosted regressor (XGBoost), random forest (RF) and long short-term memory (LSTM). The comparative results have been studied in terms of prediction errors to determine accuracy.
EvalBERT: a novel framework for assisted descriptive answers and C programming evaluation Thippeswamy, Prakruthi Sondekere; Eraiah, Manjunathswamy Byranahalli; Jabeen, Salma
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3346-3361

Abstract

Manual assessment of descriptive answers is often time-consuming, error-prone, and subject to bias. While artificial intelligence (AI) has made significant strides, current automated evaluation methods typically rely on simplistic metrics like word counts or predefined terms, which lack a deeper understanding of the content and are highly dependent on curated datasets. As demand for automated grading systems increases, there is a growing need to evaluate not only descriptive answers but also code-based responses. This study addresses these challenges by applying natural language processing (NLP) and deep learning (DL) techniques, testing three baseline models: multinomial Naïve bayes (MNB), bidirectional long short-term memory (Bi-LSTM), and bidirectional encoder representations from transformers (BERT). We propose EvalBERT, a BERT-based model fine-tuned with domain-specific academic corpora using computer processing unit (CPU) acceleration. EvalBERT automates grading for both descriptive and C programming exams, offering features like readability statistics and error detection. Experimental results show that EvalBERT achieves 94.86% accuracy, outperforming other models by 1.22 percentage points, with training time reduced by half. Additionally, EvalBERT is the first model pre-trained with academic corpora for this purpose. An interactive user interface, E-Pariksha, was also developed for administering and taking exams online. EvalBERT provides precise assessments, enabling educators to better evaluate student performance and offer more detailed feedback.
SIGAN: a generative adversarial network architecture for sketch to photo synthesis Latha, Buddannagari; Velmurugan, Athiyoor Kannan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3118-3126

Abstract

Of late, with the rise of artificial intelligence (AI) and deep learning (DL) models, image translation has become a very important phenomena which could produce realistic photographic results. Synthesizing new images is widely used in different applications including the ones used by investigation agencies. Image generation from hand-drawn sketch to realistic photos and vice versa is required in different computer vision applications. Generative adversarial network (GAN) architecture is extensively employed for generating images. However, there is need for investigating further on improvising GAN architecture and the underlying loss functions towards leveraging performance. In this paper, we put forth a GAN architecture known as sketch-image GAN (SIGAN) for synthesizing realistic photos from hand-drawn sketches. Both generator (G) and discriminator (D) components are designed based on DL models following a non-cooperative game theory towards improving image generation performance. SIGAN exploits improvised image representation and learning of data distribution. The algorithm we have proposed is known as learning-based sketch-image generation (LbSIG). This algorithm exploits SIGAN architecture for efficiently generating realistic photo from given hand-drawn sketch. SIGAN is assessed using a benchmark dataset called CUHK face sketch database (CUFS). From the empirical study, it is observed that the proposed SIGAN architecture with underlying deep learning models could outperform existing GAN models in terms of Fréchet inception distance (FID) with 38.2346%.
A novel hybrid generation technique of facial expressions using fine-tuning and auxiliary condition generative adversarial network Elbadwey, Mai; Attar, Hani; Amer, Ayman; Ibrahim, Khaled H.; Kayed, Somaya; Barakat, Tamer
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3418-3428

Abstract

The facial expression generation continues to be interesting for researchers and scientists worldwide. Facial expressions are an excellent way of transmitting one's emotions or intentions to others, and they have been extensively studied in areas such as driver safety, human-computer interaction (HCI), deception detection, health care, and monitoring. The facial expression generation starts with a single neutral image and generates sequences of facial expression images, which are combined to create a video. Previous methods generated facial expression images of the same person. However, they still suffer from low accuracy and image quality. This article overcomes this problem using a novel hybrid model for facial expression video generation using fine-tuning and condition-generative model architectures to optimize the model's parameters. Results indicate that the proposed novel approach significantly improves the expression generation of the same person. The proposed method can reliably and accurately generate facial expressions, with a testing accuracy of 98.7% and a training accuracy of 99.9%.
Improved YOLOv10 model for detecting surface defects on solar photovoltaic panels Nguyen, Phat T.; Ho, Loc D.; Huynh, Duy C.
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3319-3331

Abstract

Surface defects greatly affect the performance and service life of photovoltaic (PV) modules. Detecting these defects is important to improve the management, repair and maintenance of PV panels. With the development of artificial intelligence, computer vision brings higher accuracy and lower labor costs than traditional inspection methods. This paper introduces an improved PV you only look once v10 (YOLOv10) model for detecting surface defects of PV modules. The improvement includes adding an exponential moving average (EMA) attention mechanism to the neck, using a cycle generative adversarial network (GAN) to enhance the data, and replacing the YOLOv10 head with a YOLOv9 head to retain non-maximum suppression (NMS). Experiments show that the proposed model outperforms state-of-the-art methods such as YOLOv10s, n, x, b, l, and e, achieving superior detection accuracy. Despite the increased computational cost, the proposed method improved mAP@0.5 and mAP@0.5:0.95 by 5.1% and 6.5% over the original YOLOv10s.
Deep learning for the identification of autism traits in children through facial expressions: a systematic review Palomino, Daniella Romani; Ovalle, Christian; Cordova-Berona, Heli
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3279-3290

Abstract

This study employs a bibliometric analysis to examine research on the application of artificial intelligence, specifically deep learning, in the detection of autism traits through facial expressions. Using quantitative methodologies. The analysis revealed a notable growth in scientific output from 2019, with an emphasis on techniques such as convolutional neural networks and systems based on the facial action coding system (FACS- CNN). The results highlight improvements in diagnostic accuracy thanks to the use of deep learning, although challenges related to data quality and availability remain. This study underscores the importance of international collaboration and technological innovation to advance the diagnosis and treatment of autism, offering a comprehensive perspective on current and future trends in this interdisciplinary field.
A comparison of approaches for modeling software security requirements using unified modeling language extensions Hassan, Syed Muhammad Junaid; Shahab, Aamir; Tabba, Fatima Ali; Alrammal, Muath; Abu-Amara, Fadi; Nadeem, Muhammad
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2911-2927

Abstract

The unified modeling language (UML) supports extension mechanisms called stereo-types, tagged values, and constraints to extend its modeling capabilities. These extension mechanisms are utilized to create new and customized profiles. Their applications in modeling emerging security requirements are discussed. To model authentication, availability, integrity, access control, confidentiality, data integrity, non-repudiation, authorization, encryption, hashing, and session mechanisms, a set of novel stereotypes is proposed in this paper. The proposed stereotypes inherit from baseline security requirements. Further, security concepts within the UML diagram are represented using these stereotypes. In addition, the proposed stereotypes were evaluated with the help of human subject evaluation using real-world scenarios to illustrate the usefulness of these stereotypes in modelling security requirements. The contribution of this paper is a stereotyped model security requirements and library of existing security notations with high quality symbols which can be incorporated in existing and new stereotypes and diagrams to facilitate the process of security requirement modelling. Results indicate that the proposed stereotyped model improves the modeling process of security requirements. It also provides a better representation of emerging security mechanisms in software design. Finally, during the software development process, stakeholders enjoy improved communication and understanding of security requirements.

Page 604 of 631 | Total Record : 6301

 602   603   604   605   606 

Filter by Year

2011 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