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GAN-augmented vision transformer with balanced synthetic data generation for robust rice leaf disease detection

10.11591/ijece.v16i3.pp1307-1318
Saiful Islam , Md. Nasim Akhtar , M. Mahadi Hassan , A. N. M. Rezaul Karim , Israt Binteh Habib
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.
Volume: 16
Issue: 3
Page: 1307-1318
Publish at: 2026-06-01

Retrieval-augmented generation in enterprise knowledge systems: architecture, benefits, and applications

10.11591/ijece.v16i3.pp1407-1416
Mohammad Baqar
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.
Volume: 16
Issue: 3
Page: 1407-1416
Publish at: 2026-06-01

A risk-constrained SARSA–FIS hybrid decision architecture with adaptive exploration control

10.11591/ijece.v16i3.pp1531-1542
Joni Fat , Parwadi Moengin , Pudji Astuti , Sally Cahyati
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.
Volume: 16
Issue: 3
Page: 1531-1542
Publish at: 2026-06-01

Optimization of transfer learning for facial emotion classification on the FER-2013 dataset

10.11591/ijece.v16i3.pp1213-1226
Nida Muhliya Barkah , Shofwatul ‘Uyun
Facial expressions play a key role in non-verbal communication by naturally reflecting human emotions. Facial emotion recognition (FER) using computer vision has gained attention with advances in deep learning. However, deep learning models require large datasets to perform well, posing a challenge for FER tasks with limited data. Transfer learning is a promising approach to address this issue, but a standardized method for FER is yet to be established. This study optimizes three transfer learning models ResNet-50, Inception V3, and Xception on the FER-2013 dataset. Experiments include testing input image sizes, hyperparameter tuning, data augmentation, layer addition, and training methods. Results show each model requires different input sizes for best accuracy. Hyperparameter tuning improves accuracy by 6.35%, 4.69%, and 1.04% for ResNet-50, Inception V3, and Xception, respectively. Augmenting only the disgust class yields better accuracy than augmenting all classes. The freeze fine-tuning method is less effective than fine-tuning alone on datasets with thousands of samples but outperforms the freeze layer method. The best accuracies achieved are 64.89% (ResNet-50), 65.83% (Xception), and 66.40% (Inception V3). These findings provide insights into freeze fine-tuning limitations and guidance for optimizing transfer learning in FER with limited data.
Volume: 16
Issue: 3
Page: 1213-1226
Publish at: 2026-06-01

Designing and evaluating a community-based digital dictionary system for the Balinese language: An IT innovation adoption study

10.11591/ijece.v16i3.pp1369-1381
Cokorda Pramartha , Madek Jeani Purnama , Ida Bagus Gede Sarasvananda , I Wayan Arka , Ni Luh Watiniasih
Regional and vulnerable languages increasingly depend on digital tools to remain visible and usable in everyday life, yet many dictionary initiatives are described mainly in terms of content or interface features rather than evaluated as information-system innovations. This paper presents an exploratory design science study of a community-based Balinese digital dictionary that supports bidirectional Balinese-Indonesian lookup, Latin and Balinese Unicode script, speech-level information, part-of-speech tagging, related-word search, and role-based contribution workflows. The platform is implemented as a web-based system with a three-tier architecture and relational database. To evaluate adoption readiness, 40 users completed representative tasks and then responded to an adapted Moore and Benbasat IT innovation adoption instrument covering seven constructs. The results show high ease of use, relative advantage, and compatibility, indicating strong functional value and fit with user routines. Image and visibility are moderate, while result demonstrability and visibility show lower reliability and are therefore interpreted as exploratory indicators. The study contributes both a documented digital-dictionary artefact for Balinese language support and a reusable evaluation approach for other early-stage community-facing information and communication technology (ICT) systems. The findings suggest that wider uptake depends not only on technical quality, but also on institutional visibility, outreach, and continued content enrichment.
Volume: 16
Issue: 3
Page: 1369-1381
Publish at: 2026-06-01

Analyzing learners' perceptions of engagement and learning interaction in gamified massive open online courses for TVET using SEM-PLS

10.11591/ijece.v16i3.pp1319-1328
Azizul Mohd Yusoff , Sazilah Salam , Siti Nurul Mahfuzah Mohamad , Rujianto Eko Saputro
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.
Volume: 16
Issue: 3
Page: 1319-1328
Publish at: 2026-06-01

Hybrid deep learning (ILeS-Net) for lung cancer classification in cloud-IoT healthcare systems

10.11591/ijece.v16i3.pp1588-1607
Affrose Affrose , Cheruku Sandesh Kumar , Archek Praveen Kumar
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.
Volume: 16
Issue: 3
Page: 1588-1607
Publish at: 2026-06-01

An internet of things-telemedicine platform empowered by 5G mobile networks for Tunisian Rural places

10.11591/ijece.v16i3.pp1261-1271
Ibrahim Monia , Dadi Mohamed Bechir , Rhaimi Belgacem Chibani
With the advent of Internet of Things (IoT) technologies, offering new possibilities for remote healthcare delivery, the medicine sector has undergone significant advancements in recent years. New tools are used, and diagnostics have become more accurate. We suggest creating a platform that can be extended for several applications. This platform has been realized to attest and demonstrate how IoT technology offers devices that could be integrated to provide novel services like remote consultations. Our proposed platform contains novel functionalities such as real-time video calls, instantaneous messaging, live notifications, vital signs monitoring, and electronic health record access. This is accomplished with enhanced qualities of remote healthcare services. Added to this, healthcare access equity will be guaranteed. The paper emphasizes the potential of Laravel 11 as a framework offering powerful features for creating modern and high-performance applications. We have integrated Laravel Reverb, a powerful real-time communication package, to provide seamless real-time communication with users. With our application, notifications and interactions are dynamically created. This allows instant updates to delivery and engages the user experience. The database was designed based on the latest version of MySQL 8, coupled with the advanced capabilities of PHP 8.2. This combination provides unparalleled performance, scalability and reliability. Added to that, IoT’s technology usage helps to improve healthcare access and delivery, especially in underserved areas. Human and machine cooperation is a main factor of the 5th industry level. This is widely respected by our platform. This offers great help, especially for those isolated and underserved areas, as we hope.
Volume: 16
Issue: 3
Page: 1261-1271
Publish at: 2026-06-01

Bearing fault classification using decision trees and neural networks

10.11591/ijece.v16i3.pp1466-1473
Raid Houssem Eddine Sellaoui , Brahim Boulebtateche , Salah Bensaoula
In this study, we test three machine learning methodologies − binary tree, k-nearest neighbors (k-NN), and neural networks (NN) − using a range of hyperparameters. These methods are applied to a dataset consisting of extracted time series characteristics (root mean square (RMS), skewness, and kurtosis from vibration signals of various bearings subjected to different fault conditions from the intelligent maintenance systems (IMS) dataset. We evaluate how effectively these methods classify the condition of the bearings using the provided dataset. We observe the top two methods, artificial neural network (ANN) 99.29% and binary tree 98.84%. With a difference of 0.45%, the binary tree is preferred over the complex ANN due to its ease of interpretation, transparency, and minimal computation requirements. Its integration as code in embedded controllers or electronic control units (ECUs) is more efficient, which makes them faster for real-time processing and safety-critical electric vehicle (EV) systems.
Volume: 16
Issue: 3
Page: 1466-1473
Publish at: 2026-06-01

AI-driven log reduction and storage optimization for security operations

10.11591/ijece.v16i3.pp1417-1424
Nutthakorn Chalaemwongwan
In this study, we present an AI-driven framework that integrates semantic log reduction with compliance-aware storage optimization, specifically designed for security operations center (SOC) and managed security service provider (MSSP) environments. Traditional approaches such as uniform compression, keyword filtering, and static tiering often either miss critical anomalies or preserve redundant noise, leading to excessive storage use, slower search performance, and analyst fatigue. The proposed framework addresses these challenges by combining three components: semantic reduction of repetitive entries, anomaly-focused retention supported by self-supervised models, and adaptive tiering aligned with regulatory requirements. Evaluations on HDFS, BGL, CICIDS2017, and Suricata datasets achieved 70%–80% log reduction, 55%–65% storage savings, recall rates above 95%, and a one-third reduction in query latency. These results demonstrate that pre-index reduction, together with anomaly- and compliance-aware retention, offers a scalable and regulator-ready solution for operational security environments.
Volume: 16
Issue: 3
Page: 1417-1424
Publish at: 2026-06-01

Hybrid systems modelling and control using multiple mixed logical dynamical predictive model control: Application to a three-tank spherical system

10.11591/ijece.v16i3.pp1148-1158
Tahar Benaissa , Mohamed Fouzi Belazreg , Khaled Halbaoui , Belaid Djaroum , Djamel Boukhetala
This study employs the mixed logical dynamical (MLD) framework for modelling, simulating, and controlling hybrid dynamical systems. Hybrid systems, which combine continuous-time dynamics and discrete logical events, pose significant challenges for conventional control strategies, such as proportional-integral-derivative (PID) controllers, particularly under complex operational constraints. To address these challenges, the MLD formalism provides a unified representation that integrates differential equations, logical rules, and inequality constraints. Based on the MLD model, a multivariable hybrid model predictive control (HMPC) approach is designed to optimize control system performance and operational efficiency over a prediction time horizon. At each sampling time step, a mixed quadratic programming (MIQP) optimization problem is solved online to determine the control law. The proposed control approach is applied to a three-spherical tank system, where simulation and experimental results demonstrate its effectiveness in ensuring stability, minimizing tracking errors, and satisfying physical constraints. These results underscore the relevance of MLD-based predictive control approaches for the optimization and advanced control of complex multivariable hybrid dynamical systems in industrial fields.
Volume: 16
Issue: 3
Page: 1148-1158
Publish at: 2026-06-01

Performance analysis of single and multi-stage metaheuristic optimization on DFFNN for electrocardiogram-based emotion classification

10.11591/ijece.v16i3.pp1562-1575
Giovanni Dimas Prenata , Ahmad Ridho’i
Emotion classification based on electrocardiogram (ECG) signals has attracted increasing attention in affective computing and biomedical signal processing. However, training deep feedforward neural networks (DFFNN) using conventional gradient-based learning often suffers from local minima and slow convergence, particularly when dealing with nonlinear and limited datasets. This study presents a comprehensive performance analysis of single-stage and multi-stage metaheuristic optimization strategies applied to DFFNN for ECG-based emotion lassification in elderly participants. Five models were evaluated: Pure DFFNN, DFFNN optimized using genetic algorithm (GA), particle swarm optimization (PSO), grey wolf optimizer (GWO), and a hybrid multi-stage DFFNN+GA+GWO model. Experimental results from six independent trials demonstrate a substantial reduction in mean squared error (MSE) when metaheuristic optimization is applied. Pure DFFNN produced final MSE values in the range of 0.07462–0.08977, whereas DFFNN+GWO reduced MSE to 0.01894–0.02411. The proposed multi-stage DFFNN+GA+GWO achieved the lowest MSE of 0.014286 in the best run and an average MSE of approximately 0.0212 across trials. Training accuracy improved from 57.14%–66.67% (Pure DFFNN) to 80.95%–85.71% using metaheuristic pproaches. Although testing accuracy remained relatively stable at 33.33%–50.00% due to dataset size constraints, convergence behavior analysis shows that multi-stage optimization enhances stability and reduces oscillatory updates. These findings confirm that multi-stage metaheuristic optimization significantly improves training stability and error minimization in DFFNN models, offering a promising strategy for robust ECG-based emotion classification under small-sample conditions.
Volume: 16
Issue: 3
Page: 1562-1575
Publish at: 2026-06-01

Transformer-based hybrid classification for plant leaf disease detection using vision transformer, principal component analysis, and support vector machine

10.11591/ijece.v16i3.pp1399-1406
Vijayalakshmi S. Abbigeri , Geetha D. Devanagavi
Plant diseases remain a critical challenge in agriculture, causing substantial yield losses and threatening food security. In this work, we propose a hybrid deep feature engineering framework that integrates deep learning-based feature extraction with classical machine learning for accurate plant disease detection. A pretrained vision transformer (ViT) model is employed to extract discriminative features from leaf images, effectively capturing complex spatial relationships. To address the curse of dimensionality, principal component analysis (PCA) is applied, retaining 98% of the variance while reducing feature space complexity. The refined features are then classified using a support vector machine (SVM) optimized through hyperparameter tuning. Experimental results on the bean leaf lesions dataset demonstrate strong performance, achieving 92% accuracy and a weighted F1-score of 0.92. The proposed ViT–PCA–SVM pipeline effectively balances accuracy, computational efficiency, and generalization, making it a promising solution for real-time smart farming applications.
Volume: 16
Issue: 3
Page: 1399-1406
Publish at: 2026-06-01

A new multiplier less memcapacitor emulator with non-linear applications

10.11591/ijece.v16i3.pp1132-1147
Suresha Basavanna , Chandra Shankar , Rudraswamy S. B.
This study describes a memcapacitor emulator without a multiplier that make use of second-generation current conveyor (CCII), operational trans-conductance amplifier (OTA) and the fewest possible passive components. The proposed memcapacitor is proved mathematically and verified using several simulation approaches, such as process corner, non-volatile and hysteresis analysis. Also, provided the layout of CCII and OTA as well. The standard CMOS 90 nm technology is used in the Cadence Virtuoso tool to simulate the proposed memcapacitor emulator. This article also includes the use of memcapacitor emulator in the applications of R-C frequency selective network as well as adaptable neuromorphic structure. To investigate the experimental outcomes, an experimental setup was constructed with commercially available integrated circuits (ICs) CCII’s AD844AN and OTA’s CA3080EZ.
Volume: 16
Issue: 3
Page: 1132-1147
Publish at: 2026-06-01

Ensemble windows intrusion detection system using XGBoost and deep learning

10.11591/ijict.v15i2.pp565-577
Pranitha Kedambady Shiva , Pushparaj D. Shetty
Intrusion detection systems (IDS) are critical for preserving the Windows environment from an ever-changing collection of cyber threats. Current IDS uses deep learning (DL), which are heavy models if used for detection, while others use machine learning (ML) techniques, which require external feature extraction. To resolve this challenge, this paper introduces XGBNN, a new ensemble model that combines the benefits of ML and DL to identify and mitigate attacks against Windows machines effectively. The various ML methods are trained on the publicly available dataset to classify eight types of attacks in a Windows environment. Additionally, deep neural networks (DNNs) are proposed by optimizing the layers and hyperparameters to achieve the best accuracy. Then, the DNN model and XGBoost model are integrated to detect intrusions by utilizing the feature extraction ability of DNN and providing the intermediate features extracted from the last second layer of the DNN to the XGB for classification. The Ensemble model XGBNN optimizes features and offers better decisions. The proposed model achieves an exceptional accuracy of 100%, as demonstrated by the empirical results, and outperforms the benchmark models with an improvement of 0.004%. The purpose of this study is to highlight the effectiveness of hybrid architectures in intrusion detection. These architectures offer a more robust, scalable, and effective method to improve the security of the Windows system against more sophisticated attacks.
Volume: 15
Issue: 2
Page: 565-577
Publish at: 2026-06-01
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