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31,128 Article Results

Prognosis of vector borne dengue disease outbreak in urban areas using multivariate analysis

10.11591/ijict.v15i3.pp1066-1077
Pratik S. Machchar , Purvi N. Ramanuj , Rajan Patel , Jitendra Bhatia , Kuntesh Jani
Vector borne disease like dengue continues to pose a significant climate-sensitive public health challenge in tropical regions such as Brazil, Peru, and India. This study examines the feasibility of predicting dengue outbreaks using weekly multivariate time-series data from San Juan (SJ), Puerto Rico and Iquitos (IQ), Peru. Dengue incidence was analyzed alongside meteorological, environmental, and vegetation-based variables to capture key climatic influences. Several machine learning and deep learning approaches were evaluated, including LightGBM. Model performance was assessed using root mean square error (RMSE) and mean absolute error (MAE). The results show that LightGBM achieved the low est RMSE/MAE, indicating strong short-term predictive accuracy and excellent interpretability. Feature importance analysis and principal component analysis (PCA) identified precipitation, dew point temperature, and humidity as the most influential predictors of dengue incidence. The study demonstrates that advanced machine learning models can serve as reliable early warning systems for vector-borne diseases. While this research focuses on dengue, the methodology is adaptable to other vector-bone datasets and diseases, offering a flexible tool for public health authorities to predict and mitigate outbreaks in diverse urban contexts.
Volume: 15
Issue: 3
Page: 1066-1077
Publish at: 2026-09-01

Multi-criteria optimization of emergency unit allocation using COPRAS and SMART: a case study in Palembang

10.11591/ijict.v15i3.pp1419-1430
Evi Yuliza , Fitri Maya Puspita , Indrawati Indrawati , Sisca Octarina , Frisca Frasilia
Increasing living standards and instant eating patterns have improved people's demands for quality health services. Hospitals as health service facilities are actually real-time networks that expected to be able to provide effective and efficient services. This research uses the complex proportional assessment (COPRAS) and simple multi-attribute rating technique (SMART) methods to determine the hospital with the most optimal emergency unit (EU) services in each subdistrict based on predetermined criteria. The research results show that the COPRAS method is produces performance index values ranging from 0.0195 to 0.1317, while the SMART method yields scores between 0.054 and 0.122, both demonstrating consistent ranking outcomes. The three hospitals, with the most optimal EU performance are Dr. Mohammad Hoesin, RSU Pertamina, and RSJ Ernaldi Bahar, with Dr. Mohammad Hoesin achieving the highest utility value (0.1317). The novelty of this study lies in the integration of real-time spatial and operational data from Google Maps and RS Online into a hybrid set covering problem (SCP) framework, combining the strengths of COPRAS and SMART.
Volume: 15
Issue: 3
Page: 1419-1430
Publish at: 2026-09-01

Prototype of real-time Mexican sign language classifier

10.11591/ijict.v15i3.pp986-994
Alan Ramírez-Noriega , Yobani Martínez-Ramírez , Samantha Jiménez , Marcos Murillo-Corrales
Mexican sign language (MSL) is the language used by the deaf community in Mexico. Like Spanish, it has its own distinct grammar, syntax, and vocabulary. However, instead of relying on sounds, MSL conveys meaning through gestures, facial expressions, and body movement. This research proposes the creation of an image dataset of the MSL alphabet for real-time sign detection. A neural network model was developed to recognize these signs, achieving an accuracy of approximately 60%. Although this result is modest, the study establishes a foundation for future work that could facilitate communication for MSL users or lead to the development of educational applications for language learning.
Volume: 15
Issue: 3
Page: 986-994
Publish at: 2026-09-01

Social media interaction of halal fashion brand in Indonesia: a netnographic study of image management

10.11591/ijict.v15i3.pp1395-1407
Azhar Alam , Fatmawati Fatmawati , Muhamad Al Bagir , Raisa Aribatul Hamidah
Research on halal fashion has largely focused on consumer purchase decisions, with limited attention to how halal fashion brands interact with consumers and manage their brand image on social media platforms such as Instagram. This study addresses this gap by examining brand interaction patterns and image management strategies among leading halal fashion brands in Indonesia. Using a netnographic approach, it analyzed 1,321 Instagram posts from six halal fashion brands over six months (July–December 2022), applying content and image‑management codes to classify post types (photos and videos) and representation strategies (personalized, contextual, and celebrity use). The findings show a slightly higher proportion of photo posts (51%, 674 posts) than video posts (49%, 647 posts), with hijab fashion brands more active than Muslim and sports fashion brands in producing content. Across all brands, image management relied predominantly on personal context and non‑celebrity representation, while professional context and celebrity‑based posts were used less frequently. These results suggest that halal fashion brands strategically emphasize relatable, personalized, and non‑celebrity content to build brand image and engagement on Instagram, offering practical guidance for brand managers in designing effective social media strategies and contributing novel empirical evidence on brand interaction and image management in the halal fashion sector.
Volume: 15
Issue: 3
Page: 1395-1407
Publish at: 2026-09-01

Improving the performance of leaf disease detection and classification using beetle swarm optimization technique

10.11591/ijict.v15i3.pp967-974
Penugonda Seetha Rama Krishna , S. Nagarajan
The timely identification and diagnosis of leaf diseases is crucial for crop productivity and health. This study proposes a robust approach to this issue by combining beetle swarm optimization (BSO) with other ML models. Four different datasets were used to train our model: apple leaf, grape leaf, plant village leaf, and tomato leaf for disease detection. The process begins with preparing the leaf images, involving contrast enhancement and noise reduction. Through color-based segmentation, we can distinguish healthy regions from diseased ones, aiding in the classification process. Our research demonstrates the effectiveness of the BSO-convolutional neural networks (CNN) method in recognizing and categorizing plant diseases with high accuracy rates. Leveraging the power of BSO to adjust the model’s parameters and incorporating color-based segmentation enhances the model’s robustness and accuracy. The results of this study highlight the potential of automated disease management systems for agriculture, providing agronomists and farmers with the necessary tools to address and monitor emerging threats to their crops effectively.
Volume: 15
Issue: 3
Page: 967-974
Publish at: 2026-09-01

Design and implementation of an AI, IoT, and blockchain-based system for circular economy transition in landfill management: a case study of Quilmaná, Peru

10.11591/ijict.v15i3.pp1254-1262
Brandon Perez Flores , Juan Villantoy Peralta , Jimmy Acosta García , Jesús Zamora Mondragon , Cesar Patricio-Peralta , Luis Segura Terrones , Héctor Odín Delgado-Enríquez , Walter Patricio Peralta , Richard Aguilar Paredes
This study presents the design and implementation of an integrated system based on artificial intelligence (AI), internet of things (IoT), and blockchain to support circular economy practices in landfill management. The system addresses the lack of integrated and validated digital solutions for environmental monitoring, resource optimization, and social inclusion in resource-constrained contexts. Developed under a design science research (DSR) approach, the system combines IoT sensors for real-time monitoring, machine learning models (LSTM for methane prediction, CNN for waste classification, and reinforcement learning (RL) for biogas optimization), and a blockchain-based platform for transparent transactions and recycler formalization. The system was implemented and evaluated over 12 months using operational data. The LSTM model achieved 95% prediction accuracy, while the CNN model demonstrated high classification performance. Results indicate a 35% reduction in landfill waste, a 40% decrease in CH₄ emissions, and a 30% increase in recycler income, with 60% of informal workers formalized. These findings demonstrate that integrating AI, IoT, and blockchain enables the transformation of landfill systems into scalable circular economy platforms for sustainable waste management.
Volume: 15
Issue: 3
Page: 1254-1262
Publish at: 2026-09-01

Probabilistic inventory modeling for chlorine gas using minitab and python: a comparative study of demand distributions

10.11591/ijict.v15i3.pp1026-1037
Oki Dwipurwani , Fitri Maya Puspita , Siti Suzlin Supadi , Evi Yuliza
The availability of chlorine gas (Cl2) is a critical component in the drinking water disinfection process at the regional drinking water company (PDAM), as it plays a vital role in ensuring microbiological safety. Disruptions in the chlorine gas supply may lead to interruptions in water distribution and pose significant public health risks. This study investigates the application of a probabilistic (Q, r) inventory model for managing chlorine gas stock, incorporating several probability distributions that satisfy the underlying model assumptions. The resulting optimal inventory policies derived from each distribution are then compared. Chlorine gas demand forecasting is also performed using the seasonal autoregressive integrated moving average (SARIMA) model. The objective of this research is to generate an optimal inventory policy and accurate demand forecasts, with the entire implementation carried out in Python software. The results show that the best model obtainis the SARIMA (0,1,0)(0,1,1)12 model, with a MAPE value of 5.48%, and that the chlorine gas demand data follow normal, gamma, exponential, and erlang probability distributions. The comparison results show that the optimal policy of the gamma probabilistic model provides the best results, as well as being better than Normal and exponential policies in previous studies.
Volume: 15
Issue: 3
Page: 1026-1037
Publish at: 2026-09-01

Enhanced anomaly detection in IoT networks via feature fusion and learning-based echo state networks

10.11591/ijict.v15i3.pp1154-1166
P. Palpandi , B. Sakthivel , M. Ponnrajakumari , M. Indirani , S. Govindaraju , S. Deivasigamani
The fast development of internet of things (IoT) networks has led to an increased probability of cyberattacks. Intrusion detection systems (IDS) are needed for identifying unauthorised access and malicious activities in such dynamic environments. However, existing machine learning (ML) models failed to handle the complexity and variability of modern cyber threats. In this work, a hybrid deep learning (DL)-based anomaly detection model is presented for IoT cybersecurity. The model combines three types of features: (i) supervised feature extraction using linear discriminant analysis (LDA) to extract the most discriminative features, (ii) unsupervised feature learning through autoencoders to capture latent representations of the input data, and (iii) statistical features such as mean, variance, skewness, and kurtosis to learn input characteristics. The fused feature matrix is fed into a learning based echo state network (LBESN) for final detection. The parameters of the LBESN model are tuned using black eagle optimizer (BEO). Experimental results on standard intrusion detection datasets such as UNSW-NB15, KDD99, and InSDN show that the proposed model achieves superior performance in terms of accuracy, precision, recall, and F1-score compared to conventional DL techniques.
Volume: 15
Issue: 3
Page: 1154-1166
Publish at: 2026-09-01

Development of 15/33 level constant and variable DC source inverter for different loading conditions

10.11591/ijape.v15.i3.pp1051-1063
Vijayaraja Loganathan , Dhanasekar Ravikumar , Ganesh Kumar Srinivasan , Deepak Balachandran Kasthuri
In this paper, a design of symmetric and asymmetric multilevel inverter (MLI) with few quantities of switch is presented. The structure can be able to operate with both symmetric and asymmetric sources. The presented model is capable of producing output levels of 15 with symmetric structure and 33 with asymmetric structure. The tendered circuit is constructed with 14 switches and 7 sources. The presented MLI can be placed in moderate-voltage applications such as: electrical machine drives. The circuit's switching sequences are framed by a detailed discussion from its operation. In MATLAB/Simulink, the inverter is simulated for resistive, resistive-inductive, and induction motor loads, and the results are portrayed. Also, the working of the inverter is monitored in terms of harmonics presence in the load signals. Additionally, the presented MLI is developed in real time to evaluate its performances. The results obtained from the real time inverter are found satisfactory.
Volume: 15
Issue: 3
Page: 1051-1063
Publish at: 2026-09-01

Intelligent fault diagnosis and protection in DG-connected systems using resistive superconducting fault current limiter and ANN-based detection

10.11591/ijape.v15.i3.pp1009-1022
Lekshmi R. Chandran , Ilango Karuppasamy , Manjula G. Nair
Ensuring reliable fault diagnosis and rapid recovery in distributed generator (DG)-connected distribution systems is critical, as the integration of DG sources significantly elevates fault current levels. This study proposes an integrated approach that combines a resistive superconducting fault current limiter (RSFCL) with an artificial neural network (ANN)-based intelligent fault diagnosis framework. The objective is to limit excessive fault currents while improving detection accuracy under varying network configurations. The RSFCL is strategically placed by analyzing fault current magnitude, voltage quality, and resistance value to achieve effective current limitation without compromising system stability. Meanwhile, the ANN employs symmetrical components of current and voltage as diagnostic features. To enhance robustness, correlated variables are identified and eliminated during feature selection, strengthening the model’s fault discrimination capability. Simulation results demonstrate that the optimal RSFCL placement reduces fault current contribution ratios by up to 82.16% under symmetrical fault conditions. The ANN-based fault detection model achieves a validation accuracy of 99.7%, outperforming conventional threshold-based methods by minimizing nuisance tripping and improving circuit breaker coordination. Overall, the combined RSFCL–ANN framework provides an effective and intelligent solution for fault diagnosis and protection in DG-integrated power systems.
Volume: 15
Issue: 3
Page: 1009-1022
Publish at: 2026-09-01

A multi-cancer detection framework using deep learning and hybrid machine learning approaches

10.11591/ijict.v15i3.pp1443-1452
Karan Singh , Amruta Pawar , Drishya Tomar , Amrita Yadav , Aditi Chhabria , Vaibhav Narawade
The diagnostic solutions offered by the present artificial intelligence (AI) solutions suffer from non-generalizability and heavy reliance on complex models. In an attempt to solve these issues, we propose a lightweight yet versatile method consisting of a combination of ResNet50 transfer learning and hybrid machine learning. Image features are extracted using dermoscopy, magnetic resonance imaging (MRI), and histopathological images. These are subjected to principal component analysis (PCA) dimensionality reduction followed by classification using support vector machine (SVM), random forest (RF), logistic regression (LR), and XGBoost algorithms. This segregation of the two processes improves efficiency. The hybrid approach using ResNet50 + LR yielded an accuracy of 91.01% in the case of breast cancer detection compared to 86.26% of a baseline convolutional neural network (CNN). Also, ResNet50 gave an accuracy of 96.61% in diagnosing skin cancer. Custom CNN provided an accuracy of 99.42% for lung cancer and 96.33% for brain tumor detection.
Volume: 15
Issue: 3
Page: 1443-1452
Publish at: 2026-09-01

Advanced encryption standard with asymmetric key exchange for text encryption

10.11591/ijict.v15i3.pp1263-1271
Ravindra K Reddy , Vijayalakshmi P
When creating communication systems, securing data is crucial and improved randomization in creating secret keys contributes to more secure systems. Unfortunately, the symmetric ciphers used for data encryption, such as the advanced encryption standard (AES), may be subject to attacks that exploit timing measurements to deduce the secret key used for encryption, resulting in a significant lack of research on the security of hybrid implementations (usually defined as incorporating AES and asymmetric ciphers) for AES encryption. We introduce a new hybrid encryption method that combines the AES encryption standard with elliptic curve cryptography (ECC). In this hybrid form of encryption, ECC is utilized to facilitate secure encryption and transmission of the AES key and cycle through 16 rounds of AES to encrypt the majority of the data. We provide a performance comparison between the new algorithm and the AES-128 encryption standard. Our findings indicate that the new hybrid encryption method produced an average encryption time of 0.0002 seconds for 10MB files, significantly faster than the AES-128 encryption standard, which takes an average of 0.0016 seconds. The new hybrid method exhibits significant resistance to cryptanalytical attempts, experiencing an average avalanche effect of 49.84%, while maintaining the AES nonlinearity value at 112. As such, we conclusively state that the new hybrid encryption method utilizing ECC and AES will provide effective security for user data against timing side-channel attacks while remaining an efficient method for performing encryption.
Volume: 15
Issue: 3
Page: 1263-1271
Publish at: 2026-09-01

Lightweight parallel feedback network based on CRL with policy transfer and enhancement for image super-resolution

10.11591/ijict.v15i3.pp944-954
S V R Manimala , T Kavitha
Image super-resolution (SR) is essential in applications such as surveillance, medical imaging, and remote sensing, but existing deep learning (DL) models often require high computational resources and struggle to recover fine details in lightweight architectures. Although feedback and attention based methods have shown improvements, they still lack an effective combination of efficient feature refinement, edge enhancement, and low parameter complexity. To address this gap, we propose a lightweight parallel feedback network (LPFN) that combines three key components: a feedback block for repeated feature refinement, a dispersion-aware attention residual block (DARB) for highlighting important spatial and channel details, and EdgeNet for edge sharpening for sharper boundaries. These components are supported by curriculum reinforcement learning (CRL), an adaptive training strategy that gradually improves the model’s learning behavior. Instead of relying on a fixed loss function, LPFN uses a dynamically learned global feedback loss to refine reconstruction quality at each stage. Experiments on DIV2K and Flickr2K show that LPFN achieves higher PSNR and SSIMscores while keeping the model lightweight and efficient. This study emphasizes an effective lightweight feedback framework, an enhanced attention and edge-refinement mechanism, and an adaptive learning strategy that improves both accuracy and stability under different degradation conditions.
Volume: 15
Issue: 3
Page: 944-954
Publish at: 2026-09-01

Navigating digital parenting: a bibliometric exploration of trends on children’s digital soothing practices

10.11591/ijict.v15i3.pp1431-1442
Rita Wong Mee Mee , Noor Hanim Harun , Lim Seong Pek , Suzulaikha Mohamed , Tengku Shahrom Tengku Shahdan , Nurul Asyiqin Jalil , Anisa Ahmad , Tirzah Zubeidah Zachariah
The digital age has transformed parenting practices, with an increasing reliance on digital devices for managing children’s behavior, particularly as calming tools. This study addresses the growing phenomenon of digital parenting, highlighting its implications on child development and family dynamics. Despite the benefits of digital media, concerns persist regarding its overuse for emotional regulation, which may impede children’s self-regulation skills and parent-child interactions. This study aims to explore the evolution of research on digital parenting using bibliometric analysis. A comprehensive dataset was extracted from the Scopus database, focusing on publications from 2020 to 2024 within the Social Sciences domain. The inclusion criteria included peer-reviewed, open-access articles written in English. A systematic methodology ensured the analysis of performance metrics, trends, and co-authorship patterns. Results indicate a significant increase in scholarly attention to digital parenting, with 837 articles meeting the inclusion criteria. Leading contributions emerged from journals such as Sustainability Switzerland and Education Sciences, with prolific authors and institutions from the United Kingdom and the United States dominating the field. The analysis underscores the interdisciplinary nature of the topic, reflecting contributions from education, media studies, and child development. This study offers valuable theoretical insights and practical recommendations, emphasizing balanced digital media use and informed parenting strategies to foster healthier family dynamics.
Volume: 15
Issue: 3
Page: 1431-1442
Publish at: 2026-09-01

Advanced materials for crosstalk and power optimization in TSV-enabled 3D ICs

10.11591/ijict.v15i3.pp1143-1153
Tappeta Chinna Sanjeeva Rayudu , Merrin Prasanna Nagadasari
The continued scaling of semiconductor devices has exposed the limitations of traditional two-dimensional (2D) integrated circuit architectures. To address performance bottlenecks and interconnect constraints, the industry is increasingly adopting three-dimensional (3D) integration technologies. through-silicon vias (TSVs) are a fundamental enabler of this advancement, facilitating vertical signal transmission between stacked silicon layers. Despite their benefits, TSVs face critical challenges related to crosstalk, power dissipation, and signal delay issues that are especially pronounced in dense via arrays. This research explores the use of multi-walled carbon nanotube (MWCNT) based TSVs insulated with different dielectric liners, including silicon dioxide (SiO₂), PPC, polyimide, and benzocyclobutene (BCB). HSPICE simulations are used to evaluate crosstalk noise, power dissipation, power delay product (PDP), and energy delay product (EDP) across varying TSV pitches. Among the materials studied, BCB demonstrates the most promising results. Specifically, MWCNT TSVs with BCB at a 10,000 μm pitch achieve up to 58% reduction in functional crosstalk, 75% in dynamic crosstalk, 78% in power dissipation, and a 52% improvement in PDP compared to single-walled CNT (SWCNT) based TSVs. These findings confirm the suitability of combining MWCNT cores with low-k BCB liners for enhancing performance, energy efficiency, and signal reliability in advanced 3D integrated circuits.
Volume: 15
Issue: 3
Page: 1143-1153
Publish at: 2026-09-01
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