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31,377 Article Results

A multi-modal framework for improving the accuracy of phishing email detection

10.11591/ijece.v16i3.pp1608-1625
Lamees Mohamed Faraj , Sayed Abdel-Gaber , Hanan Fahmy
Phishing emails continue to pose a significant cybersecurity threat, particularly through the increasing use of malicious attachments to evade traditional text-based detection systems. Most existing approaches focus primarily on email content, creating a blind spot in attachment-aware phishing detection. This paper proposes a multi-modal phishing email classification model that integrates email header features, body text analysis, and attachment inspection within an ensemble learning framework. Independent machine learning classifiers are employed for each email component, and a majority voting mechanism is used to determine the final classification decision. The proposed model is evaluated using publicly available email and attachment datasets that are combined to simulate attachment-bearing phishing emails. Experimental results demonstrate strong detection performance across multiple evaluation metrics. Nevertheless, the study acknowledges the limitation of using synthetically paired email bodies and attachments, which may not fully capture real-world semantic relationships. The findings highlight the importance of incorporating attachment-aware analysis into phishing detection systems and provide a foundation for future research on semantic consistency modeling and transformer-based architectures.
Volume: 16
Issue: 3
Page: 1608-1625
Publish at: 2026-06-01

Semantic clustering of scientific abstracts with transformer embeddings and traditional text representations

10.11591/ijaas.v15.i2.pp532-540
Musthofa Galih Pradana , Pujo Hari Saputro , Ardhyansyah Mualo , Arbiati Faizah , Wahyuni Fithratul Zalmi
The large and diverse quantity of scientific documents in the world, and specifically in Indonesia, makes the process of processing scientific document data an interesting study. One that represents the entire scientific document is through abstracts. The approach that can be done for the process of processing and grouping documents is to apply clustering. In this case, text-based clustering is currently heavily influenced by the feature representation of the text data used. Some popular representations of features are term frequency-inverse document frequency (TF-IDF) and count vectorizer, but they still have significant weaknesses in the context of understanding the meaning of natural language. To cover the drawbacks, it can use transformers or embedding types. In this study, several test scenarios will be carried out to obtain information and an overview of how to compare the effectiveness of the traditional TF-IDF model and the bidirectional encoder representations from transformers (BERT) and sentence bidirectional encoder representations from transformers (SBERT) embedding models in Indonesian-language scientific abstract clustering with several clustering models, such as k-means and agglomerative. The results of the study showed that the most effective clustering obtained was by using an embedding model of a combination of BERT and k-means, which was the most consistent with the most optimal number of clusters being 2 clusters.
Volume: 15
Issue: 2
Page: 532-540
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

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

Multi-level redundancy with internet of things battery supply for fault mitigation in grid-tied photovoltaic systems

10.11591/ijaas.v15.i2.pp622-633
Habib Satria , Muhammad Fadlan Siregar , Indri Dayana , Dadan Ramdan , Muhammad Irwanto , Syafii Syafii
The development of grid-tied photovoltaic (PV) systems in tropical regions remains a strategic focus for achieving sustainable clean energy. However, energy conversion efficiency is often hampered by fluctuations in panel surface temperature and electrical faults. To ensure long-term system reliability, this study implements a multi-level redundancy architecture integrated with dynamic internet of things (IoT) monitoring for fault mitigation in grid-tied PV systems. The system employs a machine learning (ML) method using the k-nearest neighbors (KNN) algorithm for thermal classification, achieving an accuracy of 84% in identifying normal (25 °C to 35 °C) and overheating conditions. Furthermore, an electrical redundancy layer is designed with an automatic tripping mechanism that activates when the current exceeds a 1.30 A threshold, demonstrating a rapid response latency of 150 ms. To ensure monitoring resilience, the system is supported by a dedicated 18650 Li-ion battery backup. The implementation results confirm that this multi-level protection framework effectively monitors real time energy usage, prevents critical component damage, and enhances the overall safety of household-scale PV installations. This research provides a scalable and intelligent solution for fault mitigation, supporting the broader adoption of renewable energy in tropical environments.
Volume: 15
Issue: 2
Page: 622-633
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

Optimized ResNet-50 framework for mammogram-based breast cancer classification: a comparative evaluation with EfficientNet-B0

10.11591/ijece.v16i3.pp1199-1212
Muhammad Subali , Lulu Mawaddah Wisudawati , Teresa Teresa
Breast cancer remains one of the most prevalent malignancies worldwide, underscoring the need for accurate and reliable mammographic interpretation. Computer-aided diagnosis (CAD) based on deep learning has emerged as a promising approach to improve both screening performance and diagnostic consistency, yet fairness-driven comparisons between popular convolutional backbones on public mammogram benchmarks remain limited. This study provides a statistically validated, fairness-driven comparison of two widely used convolutional neural network architectures, ResNet-50 and EfficientNet-B0, for mammogram-based breast cancer classification under a rigorously controlled, clinically motivated protocol. The proposed “optimized ResNet-50” framework is defined by patient-level stratified undersampling, paired 5-fold cross-validation with identical partitions, harmonized augmentation and training configurations, and dual statistical testing (paired t-tests and Wilcoxon signed-rank tests), emphasizing methodological rigor rather than architectural novelty. Across MIAS and CBIS-DDSM benchmarks, the models demonstrated complementary strengths, with EfficientNet-B0 excelling in screening-oriented tasks (normal vs. abnormal) and ResNet-50 offering more robust performance for diagnostic-oriented tasks (benign vs. malignant). These findings highlight the value of fairness-driven evaluation protocols in CAD research and support the feasibility of integrating lightweight convolutional neural networks (CNNs) into tiered clinical workflows, where different backbones are strategically deployed for initial screening and confirmatory assessment.
Volume: 16
Issue: 3
Page: 1199-1212
Publish at: 2026-06-01

Increasing the efficiency of deep learning performance using adaptive filters

10.11591/ijaas.v15.i2.pp775-789
Suad Khairi Mohammed , Sabah A. Gitaffa , Reem I. Dawai
Deep learning algorithms have become one of the most important innovative technologies that have entered almost all areas of life. These technologies perform complex operations and deal with huge data sets. One of the benefits of deep learning is the inherent flexibility in developing approximate estimates for vast and diverse data sets. Data scientists can develop approximate estimates of almost anything using deep learning and neural networks. The main challenges in deep learning include the problem of data quality and quantity while ensuring large, diverse, and high-quality datasets. It also suffers from the problem of providing computational resources due to the high demand for powerful devices, such as processing and memory units. Additionally, it suffers from the problem of interpretability of the case due to difficulty in understanding and explaining typical decisions. This study proposes an innovative method to reduce these problems in the working mechanisms of deep learning algorithms by merging their layers and hybridizing them using adaptive digital filters. These filters help provide devices for efficient resources and memory units, in addition to the capabilities of analyzing and interpreting various states of processing unit availability. In this study, models of hybrid deep learning techniques with adaptive digital filters were designed and implemented, obtaining good results in reducing training error rates, improving the efficiency of outputs, and reducing the computational effort to high levels.
Volume: 15
Issue: 2
Page: 775-789
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

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

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

A path generation and control framework for 6-DOF robot in precision writing and drawing

10.11591/ijra.v15i2.pp281-294
Khoi Hoang Dinh , Khanh Tran Duy , Thien Bui Thanh , Quy Vo Quoc
Robots are becoming increasingly integrated into everyday life, not only in industrial applications but also in creative, educational, and entertainment contexts. With recent advancements, collaborative robots are now lighter, safer, and easier to deploy alongside humans, making them well-suited for tasks that require precision and adaptability. This paper presents an integrated control framework for the ABB GoFa 6-DOF collaborative robot, enabling it to autonomously perform precise writing and drawing tasks. The system leverages CAD-based path design in SolidWorks and ABB RobotStudio’s AutoPath tool to generate motion trajectories from a library of modeled characters, symbols, and figures. A socket-based communication interface connects the robot controller with a user-friendly human-machine interface (HMI), allowing users to input custom text or select predefined figures in real time. The framework has been implemented and validated on the physical ABB GoFa robot, demonstrating high accuracy, repeatability, and usability for applications such as public exhibitions and educational settings.
Volume: 15
Issue: 2
Page: 281-294
Publish at: 2026-06-01

Enhancing road damage detection performance using the YOLOv9 model

10.11591/ijict.v15i2.pp616-624
Muhammad Farkhan Adhitama , Sutikno Sutikno , Rismiyati Rismiyati
Roads are essential infrastructure that support community mobility, and their condition significantly impacts road user safety. However, manual road damage detection remains inefficient, time-consuming, costly, and prone to human error. To address this issue, this study proposed the YOLOv9 model for automated road damage detection and explored parameter combinations to optimize its performance. The proposed solution leverages the YOLOv9 model, which offers enhanced detection speed and accuracy compared to previous YOLO versions, due to its improved backbone and dynamic label assignment techniques. The method uses pre-trained weights and performs parameter tuning to adapt the model for identifying common road defects, including potholes, longitudinal, lateral, and alligator cracks. A publicly available dataset of road condition images was used for training and evaluation. Experimental results demonstrated that the optimized YOLOv9 model achieved a mean average precision (mAP) of 62.8%, indicating a promising ability to detect multiple types of road damage accurately. This study highlights the potential of YOLOv9 as an effective tool for road monitoring systems, contributing to proactive maintenance strategies and more efficient infrastructure management.
Volume: 15
Issue: 2
Page: 616-624
Publish at: 2026-06-01

Numerical Analysis of the Comparison of Mathematical Models of Materials on the Performance of Link Coupling Beams in EBF Structures

10.11591/ijaas.v15.i2.pp861-871
Musbar Ibrahim
This study explores the failure phenomena of Eccentrically Braced Frame (EBF) structures under cyclic loading, focusing on the performance of Link Coupling Beams (LCB). The aim is to compare different modeling approaches to determine the most effective numerical analysis concept for EBF structures. The research divides test specimens into two models: a mathematical material model and a plasticity model, with three test specimens. The mathematical models employed are the Ramberg-Osgood (R-O) model and the Kaufmann cyclic model, while the plasticity models include the multi-linear kinematic hardening model and the multi-linear isotropic hardening model. LCB link specimens are designed according to AISC 341-22 standards for long links. Numerical analysis results indicate that different mathematical material models produce similar behaviors in terms of hysteretic curves, strength, and plastic rotation angle. However, notable differences arise between the plasticity models. The choice of plasticity model has a more significant impact than the choice of material mathematical model.
Volume: 15
Issue: 2
Page: 861-871
Publish at: 2026-06-01
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