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31,377 Article Results

Impact of integrating solar and wind DGs on voltage profiles and power losses in a practical distribution system

10.11591/ijpeds.v17.i2.pp1279-1287
Rudresha Sogilu Jayappa , Gopinath Harsha Rajappa , Kiran Kumar Gama Ramaiah , Shekhappa G. Ankaliki , S. Shruthi
The rising inclusion of renewable energy sources into distribution networks has accelerated the adoption of distributed generation (DG) technologies such as solar photovoltaic (PV) and wind turbines. This paper explores the effect of solar and wind DG integration on voltage profiles, power losses, and economic performance in a practical 41-bus radial distribution system. Using the Power World Simulator (PWS) software, the load flow analysis is performed to evaluate different DG placement strategies and penetration levels using the loss sensitivity factor (LSF) method. The results indicate that optimal placement of solar and wind DGs notably improves voltage stability and effectively reduces both real and reactive power losses in the distribution system. Furthermore, the economic analysis demonstrates annual savings of ₹29.08 lakhs for solar DG and ₹33.40 lakhs for wind DG, with payback periods of approximately 11 years, indicating strong technical and financial feasibility. The findings highlight that strategic DG planning can simultaneously enhance system reliability, efficiency, and economic viability in modern distribution systems.
Volume: 17
Issue: 2
Page: 1279-1287
Publish at: 2026-06-01

Designing self-healing database fabrics for real-time payment rails

10.11591/ijece.v16i3.pp1360-1368
Raghu Gollapudi
Real-time payment platforms operating at scale face an unforgiving operational reality: even brief outages translate directly into failed transactions, regulatory exposure, and eroded customer trust. Database replication and failover automation have matured considerably over the past two decades, yet a troubling blind spot remains. Recovery frameworks built for general-purpose distributed systems were never designed with settlement finality in mind, and that design omission leaves payment operators exposed to split-brain scenarios that generic high-availability tooling cannot reliably prevent. This paper addresses that omission head-on through a self-healing database fabric purpose-built for payment rail environments. The proposed autonomous resilience fabric architecture (ARFA) operates across three coordinated layers: a continuous monitoring layer that harvests telemetry from compute, storage, and network subsystems; a decision layer that fuses rule-based heuristics with an ensemble of isolation forests, recurrent neural networks, and gradient boosting classifiers to separate genuine fault conditions from transient noise; and a deterministic action layer that executes recovery procedures anchored to explicit settlement finality constraints. In fault injection trials covering node crashes, network partitions, replication lag, and performance degradation, the architecture cut average recovery times by 88% against manual baselines, restoring service in roughly 8 seconds rather than the 180 seconds that human-driven remediation typically requires. False positive rates held below 2% across all failure categories, and the system achieved a 98% recovery success rate. Taken together, these results make a practical case that autonomous resilience and regulatory compliance reinforce rather than conflict with each other when the regulatory constraints are designed in from the start.
Volume: 16
Issue: 3
Page: 1360-1368
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

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 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

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

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

A critical review of information retrieval techniques: current trends and challenges

10.11591/ijict.v15i2.pp456-464
Sanket D. Patil , Zahir Aalam
The realm of information retrieval is witnessing transformative advancements, driven by the integration of deep learning techniques, specialized algorithms, and domain-specific applications. Information retrieval systems play an important role in many applications including in the Artificial Intelligence powered systems that can be seen in many applications. Information Retrieval, generally, acts an important task in the knowledge discovery phase of any query based intelligent system. This paper presents a comprehensive review by conducting a detailed analysis of the technological nuances, dataset specifications, and pivotal findings. This detailed review has been done with the special emphasis on the kind of technology used to achieve accurate information retrieval, domain of the study, and the system’s ability to retain or work with tables and figures, among other parameters. Navigating through the rich tapestry of methodologies, the paper underscores the pivotal role of deep learning frameworks in revolutionizing traditional retrieval paradigms. Furthermore, it sheds light on the innovative integration of textual information, algorithmic advancements, and specialized datasets to enhance the efficacy and granularity of information retrieval mechanisms.
Volume: 15
Issue: 2
Page: 456-464
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

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

Arobust outlier detection based filtering for noise removal in grayscale images

10.11591/ijict.v15i2.pp508-522
Ali Salem Al Rawash , Farah Aini Abdullah , Ahmad Kadri Junoh , Abdallah Alshbeel , Mohammed Banikhalid
Salt-and-pepper noise severely degrades the visual quality of digital images, par ticularly at high noise densities where conventional denoising techniques often fail. Median- and mean-based filters tend to oversmooth images and blur fine structures when the majority of pixels within a local window are corrupted. This paper proposes a robust dual-layer denoising framework for grayscale images that integrates rank-based prescreening, interquartile range (IQR)-based statis tical outlier detection using Tukey fences, and a lightweight post-processing sharpening stage. In the first layer, a rank-4 trimmed estimator suppresses ex treme impulse values and stabilizes local statistics. In the second layer, adap tive IQR thresholds are employed to detect and replace residual outliers, even in heavily corrupted neighborhoods. A final step involving selective sharpen ing combined with mild smoothing enhances edge details without amplifying residual noise. Extensive experiments on standard grayscale images (Lenna, Barbara, lake, boat, and living room) across salt-and-pepper noise levels from 10% to 90% demonstrate that the proposed approach consistently outperforms conventional methods, including mean, median, Gaussian, modified decision based unsymmetrical trimmed median filter (MDBUTMF), and pixel density based filter (BPDF). Quantitative evaluation indicates peak signal-to-noise ratio (PSNR) values reaching 38.23dB, structural similarity index (SSIM) values up to 0.99, and significant reductions in mean squared error (MSE), particularly at higher noise densities. These results confirm that the proposed framework ef fectively suppresses noise while preserving edges and textures, making it well suited for practical applications such as medical imaging, remote sensing, and surveillance.
Volume: 15
Issue: 2
Page: 508-522
Publish at: 2026-06-01

An enhancement of stock price forecasting based on hybrid BiLSTM-Transformer model

10.11591/ijece.v16i3.pp1298-1306
Pham Hoang Vuong , Lam Hung Phu , Le Nhat Duy , Pham The Bao , Tan Dat Trinh
Stock price forecasting presents a challenging problem due to factors like nonlinearity, seasonality, and economic volatility in financial data. Deep learning approaches can handle nonlinearity and complexity of financial data, but they often face limitations in capturing both local and global dependencies. This study introduces a hybrid Transformer–bidirectional long short-term memory (BiLSTM) model to improve stock price forecasting. Our method combines the strength of BiLSTM with the global context understanding of the Transformer by embedding a 1D convolutional layer. The model can efficiently capture short-term and long-term dependencies in stock data. Experimental results on various datasets show that our hybrid model outperforms other well-known models.
Volume: 16
Issue: 3
Page: 1298-1306
Publish at: 2026-06-01

Harnessing NLP and AI to decode political discourse: speech patterns, sentiment analysis, and public perception

10.11591/ijict.v15i2.pp674-682
Malayaj Kumar , Anuj Kumar Singh , Soumitra Das
Using natural language processing (NLP) and artificial intelligence (AI), this study analyzes the frequencies of words and phrases in political leaders’ speeches to track patterns in political discourse. The objective is to identify language patterns, sentiments, and topics of political addresses using state of-the-art methods like automatic transcription (Whisper), Bidirectional gated recurrent unit (GRU) for sentiment analysis, and BERTopic. Through the use of Whisper’s state-of-the-art transcription service, we were able to transcribe the political speeches into machine-readable text, which in turn provides for other types of analysis. Bidirectional GRU classifies sentiment as positive, negative, or neutral with the aim to study how politicians use sentiment to manipulate their listeners. Furthermore, we use BERTopic for tracking the evolution of rhetoric, key trend summarisation, and topic mining and analysis. It illustrates how politicians employ discursive strategies and epilinguistic elements to manage the public mind and reality. Achievements and objectives are framed with positive and defensive emotions aimed at threats or criticisms. The emotional grab of it all is still important. It locates in these the thematic coherence and shifting sentiment that lie at the heart of political storytelling. It shows how political communication is evolving to stay relevant in the digital media age and delivers language – even real-time language pattern tracking – via the use of AI and big data. Further study is needed of multimodal and flexible techniques for analysing political discourse across languages and time periods.
Volume: 15
Issue: 2
Page: 674-682
Publish at: 2026-06-01

IoT-enabled smart hydroponic system using nutrient film technique for precision agriculture

10.11591/ijict.v15i2.pp900-908
Varuna Kumara , Akshatha Naik , Fatima Tahsir , Sinchana Bommayya Devadiga , Vinitha Ramesh Naik
The study aims to develop an internet of things (IoT)-enabled automated hydroponic system using the nutrient film technique (NFT) to optimize plant growth with minimal human intervention. The system integrates sensors, microcontrollers, and cloud-based monitoring to maintain optimal conditions for crops. The system utilizes Arduino Uno, ESP8266 Wi-Fi module, and sensors including pH, TDS, DHT11 and water level sensors. Data collected from these sensors is processed in real time, allowing automated adjustments through relay-controlled water and nutrient pumps. The system transmits data to the ThingSpeak IoT platform, enabling remote monitoring and predictive analytics. The proposed hydroponic system ensures stable environmental conditions, improving plant growth efficiency. Key parameters such as pH, TDS levels and humidity are maintained within optimal ranges. The automated system reduces manual intervention, enhances water and nutrient efficiency, and increases yield consistency compared to traditional farming methods. The IoT-based NFT hydroponic system demonstrates significant potential in urban agriculture and controlled environment farming. By leveraging automation, AI-driven analytics, and cloud-based monitoring, it provides a scalable and sustainable solution for precision farming. Future advancements may include AI-based predictive analytics, solar-powered energy solutions, and robotic automation for further optimization.
Volume: 15
Issue: 2
Page: 900-908
Publish at: 2026-06-01
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