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31,042 Article Results

Enhanced thermal management in 3D integrated circuits coupling

10.11591/ijict.v15i3.pp1208-1216
Vempalle Rafi , Shaik Hussain Vali , Pradyumna Kumar Dhal , Sadhu Radha Krishna , Murkur Rajesh , Malagonda Siva Kumar
3D IC integration, which comprises vertically stacking several IC layers, is one of the new technologies that works well with complementary metal-oxide-semiconductor (CMOS) implementations. The layers of a three-dimensional integrated circuit (3D IC) are physically and electrically connected via copper-silicon bonding and through silicon vias (TSVs). Limitations in 3D IC designs, such as layer-to-layer thermal difficulties and TSV-to-substrate and TSV-to-TSV noise coupling, significantly impact system performance as a whole. Integrating 3D ICs relies heavily on heat spreaders and thermal through silicon vias (TTSVs). Overheating is a common cause of IC failure; however, heat spreaders and FIN to TTSV have been suggested as potential remedies for this problem in the last few years. A 3D IC might melt under the stress of an applied voltage because it becomes hotter inside. Engineers have added fins to the TTSV in a number of ways, each of which maximizes heat dissipation in a different way, in order to reduce this danger. The exceptional thermal cooling characteristics of graphene and carbon nanotubes (CNTs) have led to their widespread dissemination. This research shows that a FIN may efficiently transport thermal energy to a heat sink by using heat spreaders and optimum orientations to distribute heat in all directions. Additionally, we demonstrated the many scenarios in which the IC's potential distribution is impacted by various thermal cooling effects. We found that when it comes to transferring heat away from heat sources and TSVs, CNTs outperform Graphene. We included Al2o3, Si3N4, and SiO2 as examples to examine the consequences of modifying the model's dielectric characteristics.
Volume: 15
Issue: 3
Page: 1208-1216
Publish at: 2026-09-01

A multi-expert approach to content-based image retrieval using feature fusion and late re-ranking

10.11591/ijict.v15i3.pp1376-1384
Ali Abdulazeez Mohammed Baqer Qazzaz , Yousif Samer Mudhafar
As digital data rapidly grows, content-based image retrieval (CBIR) has become important for optimizing collections of visual data. This work proposes a retrieval framework which operates in two stages and improves accuracy by using systematic fusion of features. In the first stage, first-stage wide-scope descriptors called bag-of-visual-words (BoVW), scattering wavelet transform (SWT), discrete cosine transform (DCT), and principal component analysis (PCA) retrieve initial candidate images. The second stage undertakes detailed re-ordering of candidate images by implementing the local binary pattern (LBP), histogram of oriented gradients (HOG), and singular value decomposition (SVD) descriptors to re-evaluate similarity scores. Each individual descriptor returned results for mean average precision for the top 10 retrieved images (mAP, top-10) of between 0.63 and 0.79 and the fused framework achieved 0.88, which is evidence of the viability of complementary feature integration. These findings support the hypothesis that while multiple descriptors performed well and delivered high retrieval accuracy, hierarchical fusion of multiple handcrafted descriptors does not involve the computational costs associated with deep learning methods.
Volume: 15
Issue: 3
Page: 1376-1384
Publish at: 2026-09-01

Multi-objective optimization and multi-criteria decision analysis of passive power filters for power quality improvement in arc furnace applications

10.11591/ijape.v15.i3.pp1200-1211
Alvaro Yassif Marca Yucra , Gastón Orlando Suvire , John Armando Morales
This article presents a multi-objective optimization methodology for the optimal tuning of passive power filters in steelmaking facilities that operate with electric arc furnaces (EAFs). These industrial loads are well-known for introducing severe harmonic distortion, voltage unbalance, and flicker into the electrical network, significantly degrading power quality and equipment performance. To address these challenges, a multi-objective optimization problem is solved using the non-dominated sorting genetic algorithm II (NSGA-II), which simultaneously minimizes three key power quality indices: total harmonic distortion (THD), total demand distortion (TDD), and voltage unbalance factor (VUF). In addition, a multi-criteria decision analysis (MCDA) technique is applied to rank and select the most balanced and robust solution in different EAF operating scenarios. Unlike conventional filter design methods that prioritize a single performance criterion or rely on static harmonic assumptions, the proposed approach accounts for the nonlinear and time-varying behavior of EAFs, ensuring robust performance under diverse operating conditions. A comprehensive case study based on a Bolivian steel plant illustrates the effectiveness of the optimization strategy. Results indicate reductions of 47.6% in THD, 33.5% in TDD, and 63.6% in VUF, clearly outperforming conventional design approaches and significantly improving overall power quality. This work highlights the potential of evolutionary multi-objective algorithms for enhancing passive filter performance in complex industrial environments with highly distorted and unbalanced power conditions.
Volume: 15
Issue: 3
Page: 1200-1211
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

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

Physical-maturation with health education on the incidence of anemia among adolescents who married at a young age

10.11591/ijphs.v15i3.26734
Desta Ayu Cahya Rosyida , Nyna Puspita Ningrum , Nina Hidayatunnikmah , Solichatin Solichatin
Physical maturity and the incidence of anemia in adolescents who marry at a young age have an impact on the biological and physiological unpreparedness of the adolescent body that is not yet fully mature against the increased risk of anemia, especially in early pregnancy. The solution in this paper provides health education and increased awareness of the dangers of early marriage to physical health, especially the risk of anemia. Nutrition intervention programs and iron supplements for adolescent girls. The purpose of this study was to evaluate the effect of physical maturity on the incidence of anemia in adolescents who marry at a young age. Quantitative research method with quasi-experimental pre-post test design with control group. The population of the study was 54 young women who were married at a young age. The sample size of 44 respondents. Sampling was done using the accidental technique. Data were analyzed using the Chi-square test. The difference in mean hemoglobin levels in adolescents before and after the intervention was analyzed using the paired t-test. Based on the results of statistical test, a p-value of 0.000 was obtained (p-value ≤ 0.05), which indicated that the difference was statistically significant.
Volume: 15
Issue: 3
Page: 777-786
Publish at: 2026-09-01

Biophysiological responses of premature infants in neonatal nursing care: a descriptive study of infant and maternal characteristics

10.11591/ijphs.v15i3.27051
Dwi Hastuti , Anggorowati Anggorowati , Zubaidah Zubaidah , Tri Nur Kristina , Siti Yuyun Rahayu Fitri
Preterm infants are physiologically vulnerable and require continuous cardiorespiratory and thermal monitoring during neonatal nursing care. Evidence describing physiological indicators in relation to infant maturity and maternal sociodemographic characteristics remains limited. This study aimed to describe physiological indicators (heart rate, respiratory rate, oxygen saturation, and temperature) among preterm infants and examine their associations with infant and maternal characteristics. An observational descriptive study was conducted from August to December 2025 among preterm infants (
Volume: 15
Issue: 3
Page: 726-736
Publish at: 2026-09-01

FEA-based optimization of switches for reluctance motors

10.11591/ijpeds.v17.i3.pp1675-1687
Hiba Esam Aziz , Abdullah K. Shanshal , Imad Idan Abed Al-Khalaf , Tamer Kamel
Switched reluctance motors (SRMs) are considered one of the important machines used in industry sectors due to their simple structure, robustness, and high-speed capability. However, the performance is often limited by high torque ripple and acoustic noise due to the uneven magnetic flux distribution. This paper presents an optimization study for improving flux distribution by shape design modifications of the stator and rotor geometry using finite element analysis (FEA) combined with the whale optimization algorithm (WOA). More specifically, FEA is utilized to calculate the magnetic field behavior, torque characteristics, and core losses for different structural geometries of the proposed design, while WOA systematically searches for the optimum values of shape parameters. Thus, the simulation results show that the proposed approach significantly improves the uniformity of flux distribution, hence reducing torque ripple and improving the efficiency. The integration of FEA with the WOA provides a practical and effective way to achieve better SRM performance without further complication of control algorithms.
Volume: 17
Issue: 3
Page: 1675-1687
Publish at: 2026-09-01

Design of an integrated forecasting and scheduling model for power plants to balance solar and wind energy variability using real-time weather data

10.11591/ijpeds.v17.i3.pp2112-2126
Syafii Syafii , Novizon Novizon , Imra Nur Izrillah
The integration of variable renewable energy sources such as solar and wind creates challenges for power system stability and operational scheduling due to their intermittent characteristics. This study proposes an integrated forecasting and scheduling framework using real-time weather data for a hybrid renewable power system consisting of photovoltaic, wind, geothermal, and hydropower plants. Solar irradiance and wind speed data were collected using pyranometer and anemometer sensors and modeled using ARIMA for 24-hour-ahead forecasting. Based on AIC and BIC evaluation, ARIMA (2, 1, 2) and ARIMA (1, 1, 1) were selected for solar irradiance and wind speed forecasting, respectively. The forecasting results achieved MAPE values of 18.43% for solar irradiance and 14.12% for wind speed. The forecasted renewable outputs were integrated into a generation scheduling model, where geothermal power operated as a base-load unit and hydropower acted as a balancing source. The proposed scheduling strategy was evaluated through a 24-hour Newton-Raphson load flow simulation. Results showed that system power losses remained below 2% and bus voltage levels were maintained within acceptable limits, demonstrating reliable operation under fluctuating weather conditions.
Volume: 17
Issue: 3
Page: 2112-2126
Publish at: 2026-09-01

Effectiveness of nursing-led microlearning with spaced reinforcement on sexual violence awareness among rural adolescents in Indonesia

10.11591/ijphs.v15i3.27019
Risnah Risnah , Muthahharah Muthahharah , Eka Hadrayani , Aisyah Bahar , Harmawati Harmawati , Muhammad Irwan , Sudarman Sudarman
Sexual violence among adolescents remains a major public health concern, particularly in rural areas where access to prevention education is limited. This study evaluated the effectiveness of a nursing-led microlearning intervention with spaced reinforcement in improving adolescents’ awareness of sexual violence. A repeated-measures experimental study was conducted among 120 adolescents aged 13-16 years from rural junior high schools. Participants received either microlearning with spaced reinforcement (n = 60) or leaflet-based education (n = 60). Awareness was assessed at baseline, post-intervention, and four-week follow-up. Data were analyzed using linear mixed-effects models and generalized estimating equations. Baseline awareness scores were comparable between groups. Compared with leaflet education, the microlearning group demonstrated significantly greater improvements immediately after the intervention (β = 9.1, 95% CI: 7.2-11.0, p < 0.001) and maintained higher scores at follow-up (β = 8.6, 95% CI: 6.5-10.7, p
Volume: 15
Issue: 3
Page: 706-716
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

Machine learning models for predicting daily profitability in Mauritanian digital banking operations: a case study

10.11591/ijict.v15i3.pp925-934
Mohamed Lemine Sidibba , Mohamedou Cheikh Tourad , Ahmad Outfarouin , Nema Sidi Mohamed Mawloud , Mohamedade Farouk Nanne
This article presents a case study on predicting daily profitability in Mauritanian digital banking operations using machine learning models. We utilized a dataset containing detailed information on daily operations to assess predictive models and predict profitability. The study assesses various machine learning models like logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), and multi-layer perceptron classifier (MLPClassifier), in order to identify the most precise model for predicting daily profitability. A systematic approach guides the analysis of banking transactions by performing detailed preprocessing operations which include type conversions, feature selection and missing value management. The dataset receives systematic partitioning into three parts for training, validation and testing to establish model reliability. LR had a recall rate of 99% and an F1-score of 99%, SVM had a recall rate of 53% and an F1-score of 54%, KNN had a recall rate of 96% and an F1-score of 95%, and MLPClassifier had a recall rate of 94% and an F1-score of 97%. The findings show that the LR model performed better than the other models in terms of both recall and F1-score. Future research will investigate both deep learning approaches and hybrid models to enhance prediction accuracy.
Volume: 15
Issue: 3
Page: 925-934
Publish at: 2026-09-01

Design and analysis of low-k dielectric TSV liners for noise mitigation in high-frequency 3D ICs

10.11591/ijict.v15i3.pp1188-1196
Pathakunta Guru Prathap Reddy , Sravan Abhilash Kothapalli
Moore’s Law has driven the development of very large-scale integration (VLSI) technology, allowing continuous transistor scaling to increase speed, density, and performance. However, as two-dimensional (2D) integrated circuits (ICs) near their physical and performance boundaries, and 2.5D ICs still face interconnect delay and power issues, three-dimensional (3D) integration has become a practical solution. In 3D ICs, multiple active layers are vertically stacked and connected via through-silicon vias (TSVs), providing short, high-bandwidth interconnects between layers. Electrical TSVs are essential for signal transmission, but also cause noise coupling between adjacent TSVs, where an aggressive TSV can induce interference in a nearby TSV. This coupling can impair signal integrity, increasing delay and power consumption. To mitigate this, low-dielectric-constant (low-k) materials are used to reduce capacitive coupling. In this study, materials such as benzocyclobutene (BCB), Perylene-N, and Teflon AF 1600 are compared with conventional SiO₂. Generally, TSVs are two structures — single-liner and stacked-liner — which are analysed at 10 GHz and 1 THz frequencies. At 10 GHz, the single-liner structure incorporating SiO₂ exhibits a noise reduction of about 6.56 dB, whereas the stacked-liner configuration using Teflon AF 1600 provides a noticeably greater reduction of 8.40 dB. As the operating frequency increases to 1 THz, the advantage of the low-k dielectric becomes more evident, yielding 9.63 dB noise reduction for the single-liner and 12.04 dB for the stacked-liner structure. These results indicate that low-k materials effectively suppress capacitive coupling and mitigate high-frequency interference in 3D ICs. The stacked-liner design contributes additional isolation by creating a secondary dielectric barrier, which further minimizes electric field interaction between neighboring interconnects. Thus, the integration of low-k dielectrics with optimized liner architectures significantly enhances signal integrity and overall electromagnetic performance in advanced high-frequency 3D IC systems.
Volume: 15
Issue: 3
Page: 1188-1196
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

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
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