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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 6,393 Documents
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GAN-augmented vision transformer with balanced synthetic data generation for robust rice leaf disease detection Islam, Saiful; Akhtar, Md. Nasim; Hassan, M. Mahadi; Karim, A. N. M. Rezaul; Habib, Israt Binteh
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1307-1318

Abstract

Early and accurate identification of rice leaf diseases is essential for sustainable crop management; however, many existing convolutional neural networks (CNNs) based solutions struggle with class imbalance and limited robustness when applied to real-field data. In this work, a generative adversarial network (GAN) augmented vision transformer (ViT) framework is introduced to overcome these limitations. A deep size representative samples for underrepresented disease categories, resulting in a more balanced training dataset and achieving a Fréchet inception distance (FID) score of 18.6. The balanced dataset is then used to train a vision transformer model that leverages self-attention to capture global contextual features of rice leaf images. Experimental evaluation across ten disease classes shows that the proposed approach attains an overall classification accuracy of 96.5%, exceeding the performance of several established CNN architectures. Additionally, the model demonstrates strong generalization capability on an external field dataset, achieving 94.8% accuracy. To validate real-world applicability, the trained model is deployed on a Jetson Nano edge device, where it delivers efficient inference performance suitable for practical agricultural applications. The findings indicate that combining GAN-based data augmentation with transformer-based learning provides a reliable and scalable solution for rice leaf disease detection.
A risk-constrained SARSA–FIS hybrid decision architecture with adaptive exploration control Fat, Joni; Moengin, Parwadi; Astuti, Pudji; Cahyati, Sally
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1531-1542

Abstract

Algorithmic trading systems operate in highly dynamic and uncertain environments where learning-based decision agents must balance adaptability with strict risk control. Reinforcement learning (RL) methods provide adaptive policy optimization but often suffer from unstable exploration and limited interpretability in financial markets. This study proposes a risk-constrained SARSA–FIS hybrid decision architecture with adaptive exploration control for algorithmic trading. The framework integrates a compact SARSA-based reinforcement learning environment with a Sugeno-type fuzzy inference system (FIS) that converts reinforcement signals into interpretable trading decisions. Exploration follows a decaying ε-greedy policy with a drawdown-triggered reset mechanism to maintain bounded risk exposure during learning. The system was implemented as a MetaTrader 5 Expert Advisor and evaluated on the GBPUSD currency pair using historical market data. Experimental results show that the hybrid framework improves trading performance compared with a rule-based baseline. During a six-month out-of-sample evaluation, the system achieved a net profit of 90 USD and a profit factor of 1.35, compared with 10 USD and 1.02 for the baseline. Extended one-year testing confirmed stable profitability and controlled drawdown behavior. The results demonstrate that integrating reinforcement learning, fuzzy decision mapping, and explicit risk constraints provides a practical approach for developing adaptive trading agents.
Hybrid deep learning (ILeS-Net) for lung cancer classification in cloud-IoT healthcare systems Affrose, Affrose; Kumar, Cheruku Sandesh; Kumar, Archek Praveen
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1588-1607

Abstract

This study presents a cloud–Internet of Things (cloud-IoT) based intelligent decision support framework for lung cancer classification and treatment recommendation, centered on a hybrid deep learning model termed ILeS-Net. Computed tomography (CT) images from a benchmark dataset are first preprocessed using Gaussian filtering to enhance image quality. Cancerous regions are identified using an Improved BIRCH (I-BIRCH) segmentation approach, followed by feature extraction using shape descriptors, color features, and Improved local Gabor XOR pattern (I-LGXP) textures. The extracted features are classified using ILeS-Net, which integrates Improved LeNet-5 and SqueezeNet architectures to achieve improved classification performance with reduced overfitting. Based on the classification results, the framework provides supportive recommendations to assist clinical decision-making. Experimental results demonstrate that the proposed ILeS-Net model achieves a maximum accuracy of 0.951, outperforming several conventional and state-of-the-art methods. The cloud–IoT integration further enables scalable, real-time, and secure data processing, highlighting the framework’s potential for practical computer-aided lung cancer diagnosis.
Variance-k-means++: A deterministic centroid initialization method based on variance for enhanced clustering stability Widodo, Widodo; Ramadhan, Jiel Vayyad; Duskarnaen, Muhammad Ficky; Fauziastuti, Via Tuhamah; Pondayu, Chelsea Zaomi; Septianda, Mada Rekadarma
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1434-1448

Abstract

K-means++ is developed to improve the performance of k-means when choosing a starting centroid. However, both algorithms in clustering still select an initial centroid randomly. Randomly selecting initial centroids has the potential to produce unstable clusters. This paper proposes a deterministic centroid initialization method called variance-k-means++, which utilizes statistical properties—mean and variance—to generate pseudo-centroids and derive initial centroids. The method aims to improve clustering stability and reduce the number of iterations. For the initial study, we used low-dimensional data to conduct the experiment series. Then, we employed two baseline methods for benchmarking, k-means and k-means++. The results show that variance-k-means++ outperformed the baseline method on average. Evaluating in Davies-Bouldin Index (DBI) and convergence analysis, we obtained DBI values at 0.756 and 0,771 for vertical and horizontal variance k-means++ with Iris dataset. At the same time, baseline methods have 0.802 and 0.830 for k-means++ and k-means, respectively. In convergence analysis, the results are 5.158 for vertical and 5.474 for horizontal, while baseline methods are 9.000 and 8.842. The primary contribution of this study lies in its achievement of minimizing the number of iterations while enhancing cluster stability.
A survey of retrieval algorithms in ad and content recommendation systems Zhao, Yu; Liu, Fang; Yuan, Yuan; Dang, Yifan
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1518-1530

Abstract

This paper presents a survey of retrieval algorithms used in advertising recommendation and organic content recommendation systems. Modern digital platforms rely on retrieval-based models to efficiently match users with relevant advertisements or personalized content. This survey reviews key techniques including inverted index methods, collaborative filtering, content-based filtering, hybrid recommendation models, and the two-tower neural network architecture widely used in large-scale recommendation systems. The paper compares the objectives, data utilization strategies, and evaluation metrics of ad targeting and organic retrieval systems. Practical challenges such as cold-start problems, data quality, scalability, and privacy considerations are also discussed. This survey further highlights the growing connection between industrial recommendation pipelines and emerging retrieval mechanisms used in large language model (LLM) systems. This survey provides insights into the design principles of modern retrieval systems and outlines future research directions at the intersection of recommendation systems and LLM.
Analytic algebraic Riccati solution for a robust control system: application to 2-DOF arm robot Meriem, Menad; Zoubir, Ahmed Foitih; Abdellah, Mokhtari
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1159-1174

Abstract

An analytic solution to the Riccati algebraic equation has been investigated by employing eigenvalue–eigenvector techniques combined with the Gram–Schmidt orthogonality process. An analytic solution to the Riccati algebraic equation has been investigated by employing eigenvalue–eigenvector techniques combined with the Gram–Schmidt orthogonalization process. The applied method is used to improve robust control of second and third-order state-dependent systems by handling nonlinearities. An H∞ controller is designed in this context via backstepping technique to enhance robustness and reduce computational effort. The effectiveness of this method has been demonstrated on a two-degree-of-freedom (2-DOF) robotic manipulator arm. Simulation results validate the performance of the controller, showing improved tracking accuracy, disturbance rejection, and overall system stability, thereby confirming the efficiency and applicability of the combined analytic Riccati algebraic equation and H∞ backstepping approach for nonlinear robotic systems.
Sepsis detection using biomarkers and machine learning Vu, Tuan Anh; Bac, Dang Hoai; Nguyen, Minh Tuan
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1286-1297

Abstract

Life-threatening dysfunction of organs, known as sepsis, is caused by an imbalanced response of host to infection. In this work, an efficient algorithm is proposed to address vital biomarkers for identification of sepsis using immune-related differential expression genes. A total of 16 gene datasets are processed for the extraction of a gene intersection between different gene datasets and the immune-related gene group, which improve the generalization of the final detection algorithm due to diversity of the input data. A novel gene selection method using sequential forward gene selection, machine learning, and ranked genes based on their importance calculated by a random forest model. A subset of 36 potential immune-related genes, which are identified as the biomarkers from 560 input genes, show an efficiency of the proposed gene selection algorithm. The biomarkers are validated the performance using various machine learning and deep learning related to sepsis diagnosis. The highest statistical performance is shown for the random forest model using the biomarkers as the input with an accuracy of 96.83%, sensitivity of 98.86%, specificity of 86.70%, and AUC of 98.67%. The proposed detection algorithm includes a random forest model and 36 biomarkers, which is simple, effective, and reliable for the applications in clinic environments.
Integrating BERT fine-tuning and genetic algorithm for superior depression detection in social media Aouragh, Abd Allah; Bahaj, Mohamed; Toufik, Fouad
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1474-1484

Abstract

Early detection of depression is crucial for minimizing its adverse effects on mental and physical health. Recent advancements in natural language processing facilitate the large-scale analysis of social media texts to identify depressive tendencies. Our study introduces a novel approach by integrating a genetic algorithm for hyperparameter tuning, optimizing the classification performance beyond conventional methods. We provide a comprehensive comparison of vectorization techniques, including term frequency-inverse document frequency (TF-IDF), Word2Vec, and a fine-tuned bidirectional encoder representation from transformers (BERT) model specifically adapted to our dataset. Using a dataset of 7,731 entries, we implemented standard pre-processing steps such as stop word removal and lemmatization before vectorizing the text. Five machine learning algorithms—decision tree, logistic regression, random forest, gradient boosting, and support vector machine—were evaluated, with hyperparameter tuning performed using a genetic algorithm. The highest accuracy (95.99%) and F1-score (95.91%) were achieved with the combination of fine-tuned BERT, support vector machine, and genetic algorithm optimization. This study demonstrates the advantages of integrating BERT fine-tuning with genetic optimization, outperforming traditional TF-IDF and Word2Vec approaches in depression detection.
Retrieval-augmented generation in enterprise knowledge systems: architecture, benefits, and applications Baqar, Mohammad
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1407-1416

Abstract

This paper presents an adaptive retrieval-augmented generation (RAG) framework for enterprise knowledge systems that combines multi-source ingestion, semantic indexing with Hugging Face embeddings and Facebook artificial intelligence similarity search (FAISS), metadata-aware retrieval, and grounded large language model generation. The research addresses a persistent enterprise gap: critical knowledge is distributed across documentation, tickets, code repositories, and collaboration tools, while static keyword search and periodically retrained language models cannot keep pace with rapidly changing operational data. The proposed approach contributes a privacy-preserving architecture, a retrieval-and-feedback loop that improves ranking quality over time, and a unified workflow that links evidence retrieval to solution recommendation. In an evaluation over a 1.2 million-document corpus and a six-week pilot, the framework improved Precision@10 from 0.58 to 0.81, reduced documentation retrieval latency from 45.6 s to 12.3 s, and shortened average bug-resolution time from 18.4 h to 7.2 h. These findings indicate that enterprise RAG can materially improve troubleshooting speed, knowledge reuse, and decision support while maintaining stronger control over sensitive organizational data. The broader implication is that adaptive, governed RAG systems can serve as a practical foundation for future enterprise artificial intelligence (AI) assistants, analytics platforms, and compliance-aware decision workflows.
Analyzing learners' perceptions of engagement and learning interaction in gamified massive open online courses for TVET using SEM-PLS Yusoff, Azizul Mohd; Salam, Sazilah; Mohamad, Siti Nurul Mahfuzah; Saputro, Rujianto Eko
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1319-1328

Abstract

The introduction of gamified massive open online courses (G-MOOCs) represents a novel advancement in technical and vocational education and training (TVET). The use of gamification in education has been shown to increase engagement and motivation, which are crucial for effective learning. However, there is limited research on the specific impacts of G-MOOCs on learner outcomes in TVET. A key feature of G-MOOCs is the integration of gamification elements to enhance learner engagement and interest. This research employs structural equation modelling with partial least squares (SEM-PLS) to examine learners' perceptions of their participation and learning experiences in G-MOOCs for TVET. Specifically, the study aims to identify how gamification approaches such as fun, engagement, and learner interaction influence knowledge acquisition, skills development, satisfaction, and overall learning outcomes. The analysis reveals that G-MOOCs have a strong positive correlation (0.505) with learning engagement. Additionally, learning engagement significantly moderates learning outcomes (p=0.002). Interaction also has a significant impact (p=0.381) on learning outcomes. Overall, the findings indicate a significant positive relationship between learners' activities and their performance in G-MOOCs.

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