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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 88 Documents
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Search results for , issue "Vol 15, No 6: December 2025" : 88 Documents clear
Hybrid artificial intelligence approach to counterfeit currency detection Tarawneh, Monther
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5804-5814

Abstract

The use of physical money continues, posing ongoing challenges in the form of counterfeit money. This problem not only poses a threat to economic stability but also undermines confidence in the financial systems in use. Traditional methods such as manual inspections and testing of security features have become ineffective in detecting advanced counterfeiting techniques on an ongoing basis. This study proposes a hybrid model that harnesses the power of artificial intelligence, combining convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and support vector machines (SVMs) for counterfeit detection. The proposed model leverages the diverse strengths of a number of artificial intelligence techniques, combining the ability to detect counterfeiting, analyse visual aspects, and sequences of banknotes. The proposed model was tested using real Jordanian currency sets of different denominations and datasets generated using generative adversarial networks (GANs). The results showed that the model was able to detect counterfeiting with high accuracy of 98.6%. and minimal errors compared to other methods. This outstanding performance demonstrates the benefits of integrating artificial intelligence (AI) technologies and that there is room for development and solutions that can keep up with advanced counterfeiting strategies. The study demonstrates the importance of integrating AI in maintaining the integrity of physical currency transactions.
A systematic review of heuristic and meta-heuristic methods for dynamic task scheduling in fog computing environments Talhouni, Hamed; Ali, Noraida Haji; Yunus, Farizah; Atiewi, Saleh; Yahya, Yazrina
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5986-6000

Abstract

The distributed fog node network and variable workloads make task distribution difficult in fog computing. Optimizing computing resources for dynamic workloads with heuristic and metaheuristic algorithms has shown potential. To address changing workloads, these algorithms enable real-time decision-making. This systematic review examines heuristic, meta-heuristic, and real-time dynamic job scheduling strategies in fog computing. Static methods like heuristic and meta-heuristic algorithms can help modify dynamic task scheduling in fog computing situations. This paper covers a current study area that stresses real-time approaches, meta-heuristics, and fog computing environments' dynamic nature. It also helps build reliable and scalable fog computing systems by spotting dynamic task scheduling trends, patterns, and issues. This study summarizes and analyzes the latest fog computing research on task-scheduling algorithms and their pros and cons to adequately address their issues. Fog computing task scheduling strategies are detailed and classified using a technical taxonomy. This work promises to improve system performance, resource utilization, and fog computing settings. The work also identifies fog computing job scheduling innovations and improvements. It reveals the strengths and weaknesses of present techniques, paving the way for fog computing research to address unresolved difficulties and anticipate future challenges.
Trends of unmanned aerial vehicles in smart farming: a bibliometric analysis Kgopa, Alfred Thaga; Manyela, Sikhosonke; Monchusi, Bessie Baakanyang
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5746-5758

Abstract

This paper presents a review of the trends of unmanned aerial vehicles (UAV) in agriculture using a bibliometric analysis. This bibliometric analysis shows that 1676 articles were accessed from the Elsevier Scopus database between 2013 and 2023. Our findings indicate research related to UAVs in agriculture has surged over the years, but the adoption and acceptance of smart farming technology in sub-Saharan Africa remains inert. This study employed VosViewer in data analysis and bibliometrics. Our findings show that China leads all countries and followed by the United States on UAV publications in smart farming research foci. Our findings indicate that UAVs are impactful in improving crop growth, crop health monitoring, and may be beneficial to small-holder farmers with increased yields. We recommend that sub-Saharan Africa nations accelerate collaboration with China and United States in advancing climate smart agriculture practices to mitigate food insecurity risks.
Bibliometric analysis to highlight the impacts of digitalization, artificial intelligence, and modern optimization on the human environment during international armed conflicts Rezk, Hegazy; Mahmoud, Montaser
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5815-5826

Abstract

This research is conducted to explore the impacts of digitalization and artificial intelligence (AI) on the human environment during international armed conflicts, aiming to identify trends, challenges, and potential solutions to improve humanitarian aid, decision-making, and conflict resolution strategies. To identify the main research issues about the effects of AI and digitalization on the human environment in conflict situations, this work employs a bibliometric analysis. A bibliometric analysis of the impacts of digitalization and artificial intelligence on the human environment during armed conflicts by examining 544 selected papers from Scopus database has been conducted. Knowledge mapping techniques involving collaboration analysis, co-citation analysis, and keywords co-occurrence analysis methods are adopted in bibliometric analysis. Based on a comprehensive analysis of literature, this work attempts to pinpoint the key areas of interest, knowledge gaps, and new problems in this domain. The findings of this bibliometric analysis contribute to a better understanding of the complex interactions between technology, armed conflicts, and the human environment, with implications for humanitarian action, international law, and conflict resolution efforts. The bibliometric analysis reveals that the United States of America (USA) is by far the leading country in research within this field, with a substantial frequency of 181 documents. It significantly surpasses that of other countries, indicating its dominant position in the research landscape. In sum, the work offers suggestions for further research and policy intervention.
Hybrid CNBLA architecture for accurate earthquake magnitude forecasting Shams, Somia A.; Mohamed, Asmaa; Desuky, Abeer S.; A. Elsharawy, Gaber; El-Sayed, Rania Salah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5879-5893

Abstract

Earthquake prediction in seismology is challenging due to sudden events and lack of warnings, requiring rapid detection and accurate parameter estimation for real-time applications. This study proposed a novel automatic earthquake detection model to enhance the processing and analysis of seismic data. The hybrid model comprises convolutional layers, normalization techniques, bidirectional long short-term memory (Bi-LSTM) networks, and attention mechanisms, collectively referred to as the hybrid convolutional–normalization–BiLSTM–attention (CNBLA) model. The attention mechanism allows the model to focus on critical segments of seismic sequences, while layer normalization stabilizes training by normalizing activations, thus reducing the effects of input scale variations. This dual approach mitigates the impact of input scale variations and enhances the model’s ability to effectively decode complex temporal patterns. The hybrid CNBLA model optimizes the extraction and processing of temporal features from raw waveforms recorded at single stations, thereby improving the accuracy and efficiency of seismic magnitude estimation. The proposed model is evaluated using two datasets: the STEAD and USGS achieving a mean square error (MSE) values 0.054 and 0.0843 and a mean absolute error (MAE) 0.15 and 0.2526 respectively. The hybrid CNBLA model outperforms two baseline models and five state-of-the-art approaches in earthquake magnitude estimation, improving seismic monitoring and early warning systems.
Augmented reality for ancient attractions Trakulmaykee, Numtip; Janpetch, Katchaphon; Ladawong, Patchanee; Khamouam, Atitaya
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5717-5727

Abstract

The study focuses on augmented reality (AR) understanding, development and evaluation. For evaluation, this paper assesses the role of multimedia types in perceived enjoyment, and investing in how perceived usefulness, ease-of-use, and enjoyment affect the adoption of AR by tourists. A quantitative approach was employed to collect data from 115 participants who experienced an AR application designed for 14 ancient attractions in Songkhla, Thailand. The multimedia content included 3D models, historical videos, drone videos, billboard navigations, and text animations. Structural equation modeling (SEM) was used to test the proposed relationships. The findings revealed that perceived ease-of-use and enjoyment significantly influence behavioral intention (BI) as significant factors at 0.01, while perceived usefulness did not affect BI in the context of ancient attractions. Moreover, the multimedia types directly impacted the perceived enjoyment at a significant level of 0.05, and indirectly impacted BI. This study contributes to the theoretical understanding of AR adoption in tourism by integrating multimedia types with tourist perceptions and BI. Practically, it provides insights for designing AR applications that enhance visitor engagement and satisfaction in heritage tourism.
Enhanced ankle physiotherapy robot with electromyography - triggered ankle velocity control Adiputra, Dimas; Nismara, Radithya Anjar; Lubis, Muhammad Rafli Ramadhan; Rizkianingtyas, Nur Aliffah; Satrio, Kensora Bintang Panji; Arif, Rangga Roospratama; Salsabila, Annisa
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5314-5326

Abstract

Previous ankle physiotherapy robots, called picobot rely on predefined trajectories continuous passive movement without considering patient intent, limiting the encouragement of user-intent motion. This study then integrates electromyography (EMG) signals as triggers into picobot with an ankle velocity-based control system. The upgraded robot activates movement in specific gait phases based on muscle activity, synchronizing therapy with the patient’s intent. Functionality test on 7 young male healthy subjects investigates leg muscles, such as Tibialis Anterior, Soleus, and Gastrocnemius muscles for the most significantly contribute to ankle movements. Then, the muscle is tested to trigger picobot movements. Functionality tests revealed the Tibialis muscle significantly contributes to gait phases 2, the Soleus is prominent in phases 3 and 4, and gastrocnemius is active on phase 1. The robot successfully performs plantarflexion when EMG signals exceed a 1.58 V threshold, reaching a target position of -0.11 rad at a constant velocity of -0.62 rad/s. These findings establish a foundation for future trials since patient testing has not yet been conducted. By promoting active participation, this innovation has the potential to enhance rehabilitation outcomes. Incorporating user-intent triggers may accelerate recovery and improve healthcare accessibility in Indonesia, offering a significant advancement in physiotherapy technologies.
Exploring feature engineering and explainable AI for phishing website detection: a systematic literature review Alsuqayh, Norah; Mirza, Abdulrahman; Alhogail, Areej
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5863-5878

Abstract

Detecting phishing websites is a rapidly evolving field aimed at identifying and mitigating cyberattacks targeting individuals, organizations, and governments. Ongoing progress in artificial intelligence (AI) has the potential to revolutionize phishing detection by enhancing model accuracy and improving transparency through eXplainable AI (XAI). However, significant challenges remain, particularly in integrating feature engineering with XAI to address sophisticated phishing strategies including zero-day attacks, that evade traditional detection mechanisms. To overcome these challenges, this examines the impact of feature engineering and XAI in phishing detection, emphasizing their ability to enhance accuracy while providing interpretability. By integrating feature extraction with interpretable models, these techniques improve decision-making transparency and system robustness. This paper presents the first systematic literature review (SLR) focusing on the impact of feature engineering and XAI on state-of-the-art phishing detection approaches. Additionally, it identifies critical research gaps and challenges, including scalability issues, the evolution of phishing techniques, and balancing complexity with interpretability. The findings provide valuable academic insights while offering practical recommendations for developing accurate and interpretable phishing detection systems, aiding organizations in strengthening cybersecurity measures.
Machine learning model for accurate prediction of coronary artery disease by incorporating error reduction methodologies Dogiparthi, Santhosh Gupta; K., Jayanthi; Pillai, Ajith Ananthakrishna; Nakkeeran, K.
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5655-5666

Abstract

Coronary artery disease (CAD) remains a leading cause of mortality worldwide, with an especially high burden in developing countries such as India. In light of increasing patient loads and limited medical resources, there is an urgent need for accurate and reliable diagnostic support systems. This study introduces a machine learning (ML) framework that aims to enhance CAD prediction accuracy by specifically addressing the reduction of false negatives (FN), which are critical in medical diagnostics. Utilizing a stacked ensemble model comprising five base classifiers and a meta-classifier, the framework integrates cost-sensitive learning, classification threshold tuning, engineered features, and manual weighting strategies. The model was developed using a clinically acquired dataset from the Jawaharlal Institute of postgraduate medical education and research (JIPMER), consisting of 428 patient records with 36 original features. Evaluation metrics show that the proposed model achieved an accuracy of 92.19%, sensitivity of 98%, and an F1-score of 95.15%. These improvements are significant in a clinical context, potentially reducing missed diagnoses and improving patient outcomes. The model is intended for deployment in cardiology outpatient settings and demonstrates a scalable, adaptable approach to medical diagnostics.
Platforma: a modular and agile framework for simplified platformer game development Roedavan, Rickman; Leman, Abdullah Pirus; Pudjoatmodjo, Bambang
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5535-5542

Abstract

Research on game development frameworks has been extensively conducted; however, most frameworks are still too general. Conventional game frameworks are challenging for students who are new to game development, especially with their limited information and skills. Beginner game developers should ideally be guided by a practical and specific framework to help them better understand the structure of game development in a more directed manner. This paper proposes platformer modular and agile framework (Platforma) that specifically designed for platformer game development. The framework is built based on the atomic design model, breaking down each minor feature of a platformer game element and grouping these features into more specific modules. The framework was tested on three teams of students. Each team was tasked with developing a platformer game with a minimum of 15 levels of the reach game goals typology. Testing results involving 100 respondents using the game experience questionnaire (GEQ) indicated that the games developed had a positive aspect score of 3.48 and a negative aspect score of 2.65. Overall, these results suggest that the Platforma can serve as an effective guide for beginners in developing platformer games.

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