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31,377 Article Results

Predicting student academic success using entry test, language, and spiritual formation data with ensemble learning

10.11591/ijict.v15i3.pp1322-1330
Evander Banjarnahor , Budi Wibawanta , Ronald Belferik , Rijanto Purbojo
Student academic success is influenced by various factors, both academic and non-academic. This study aims to examine the correlation between key student attributes and final grade point average (GPA), as well as to develop a machine learning model to predict academic success. The correlation analysis involved academic variables such as admission scores (Mathematics, English, Indonesian, and academic aptitude test/TPA), English ability test (EAT), spiritual formation (SF), and first-year GPA (GPA_1). The results indicate that GPA_1 has the highest correlation with final GPA (0.63), followed by SF (0.44), while other variables exhibit lower correlations. To enhance prediction accuracy, a machine learning approach using three primary models was employed: Naïve Bayes, support vector machine (SVM), and an ensemble learning method based on a stacking classifier that combines SVM and Naïve Bayes. The evaluation used five train-test split ratios and performance metrics, including accuracy, precision, recall, and F1-score. Experimental results reveal that the SVM model achieves the highest accuracy at 88.40%, followed by the ensemble model combining SVM and Naïve Bayes (88.00%) and the Naïve Bayes model (87.10%). These findings confirm that the machine learning approaches, could effectively predict student academic success, providing a foundation for academic decision-making and educational intervention strategies.
Volume: 15
Issue: 3
Page: 1322-1330
Publish at: 2026-09-01

Insight invest: sentiment-aware stock prediction using LSTM and conversational interface

10.11591/ijict.v15i3.pp1115-1122
Ankit Pande , Aakhyan Jeyush , Abhishek K. Lakhote , Saket A. Rathi , Manoj B. Chandak
The volatile nature of financial markets requires sophisticated tools that integrate advanced analytics with accessible interfaces to facilitate informed investment decisions. This research introduces Insight Invest, an intelligent investment assistant that combines sentiment analysis with time-series forecasting to deliver comprehensive stock market insights. The platform introduces the emotional quotient (EQ), a novel metric derived from the sentiment analysis of financial news, to quantify market sentiment and align it with historical stock price data. Leveraging long short-term memory (LSTM) models, the system provides precise predictions of future stock trends. Automated data collection and processing are achieved through a Flask-based backend, while an OpenAI-powered chatbot delivers intuitive interpretations of predictions and EQ values. The user-centric design, implemented using Next.js, ensures a seamless and responsive experience. By integrating state-of-the-art machine learning techniques with intuitive interfaces, Insight Invest bridges the gap between complex predictive analytics and practical usability, offering a robust framework for informed investment strategies.
Volume: 15
Issue: 3
Page: 1115-1122
Publish at: 2026-09-01

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

Intelligent engineering framework for managing hospital cardiac arrest resources

10.11591/ijict.v15i3.pp1290-1302
Chams Eddine Fathoun , Mohamed Ridda Laouar , Safa Abid , Sean B. Eom
In-hospital cardiac arrest in intensive care remains frequent (often cited incidence roughly 0.5%-7.8% of admissions), while causes differ in what staff and equipment must be ready. We ask whether vital-sign trajectories from a standard EHR can classify which of three cardiac-related mechanisms is most salient arrhythmia, acute myocardial infarction (AMI), or respiratory failure or hypoxia so ICU resources can be aligned with risk. Using MIMIC-IV, we extracted diagnoses and charted vitals in the 12 hours before the index event, applied cleaning, aggregation, label encoding, sequence padding, and class balancing (3,000 cases per class), then trained and compared eXtreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and logistic regression (LR) with 5-fold cross-validation on an 80/20 split. XGBoost performed best (about 93% accuracy; sensitivity 89.15%; specificity 90.43%; AUC-ROC 0.94). Feature importance highlighted heart rate, oxygen saturation, and blood pressure patterns consistent with bedside monitoring practice. The study supports mechanism-oriented triage labels derived from widely recorded vitals, as a complement to generic early warning scores, for prioritizing telemetry, respiratory support, and cardiology pathways. External validation and prospective evaluation are needed before deployment.
Volume: 15
Issue: 3
Page: 1290-1302
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 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

Neural network-based diagnosis of type 2 diabetes using an iridology approach

10.11591/ijict.v15i3.pp1226-1237
Alaa Abdulkareem Ahmed , Mohammad Tariq Yaseen
The growing global occurrence of type 2 diabetes requires the development of non-invasive and effective diagnostic methods. This work proposes a novel approach to detecting type 2 diabetes using iridology and machine learning (ML) techniques. By analyzing the iris of the right eye, a single region of interest (ROI) corresponding to the head of the pancreas is recognized for feature extraction. A total of 112 statistical and texture features are extracted using gray-level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT) algorithms. Five neural network (NN) models, narrow, medium, wide, bi-layered, and tri-layered are deployed to classify healthy and diabetic people. The models are trained and assessed using a range of k-fold values (2 to 20) to optimize performance. The highest classification accuracy of 83.2% was reached using the narrow neural network (NNN) model at 7-fold cross-validation. This work exhibts the potential of iridology-based ML approaches for non-invasive diabetes diagnosis, providing a promising substitute to traditional blood tests.
Volume: 15
Issue: 3
Page: 1226-1237
Publish at: 2026-09-01

Early detection of vascular streak dieback (VSD) disease in cocoa plants using deep learning

10.11591/ijict.v15i3.pp1087-1096
Dewi Marini Umi Atmaja , Arif Rahman Hakim , Fadhil Rozi Hendrawan , Komang Diah Devi Pramesty , Naufal Fadhilah Fitrah , Faiz Rochmatullah Widhaputra
Vascular streak dieback (VSD), caused by Ceratobasidium theobromae, poses a significant threat to cocoa (Theobroma cacao) production, leading to substantial yield losses and plant mortality. Early detection is critical to mitigate disease spread and reduce economic impact. While convolutional neural network (CNN) architectures like VGG-16 and ResNet-50 excel in leaf disease detection, no prior studies address stem-based VSD symptomatology where the disease originates in vascular tissues. This study presents the first CNN specifically developed for cocoa stem VSD detection, achieving 99.16% test accuracy with a lightweight architecture that outperforms VGG16/ResNet50 in both accuracy and mobile inference speed. A novel dataset of 215 cocoa stem images was curated and augmented for robustness. Additional evaluation metrics, including precision, recall, specificity, and F1-score, further confirm the reliability of the model. The model was successfully converted into TensorFlow Lite (TFLite) format, enabling deployment on mobile devices for real-time disease detection. This study highlights the potential of integrating deep learning into mobile and drone-based agricultural systems to support precision farming and early intervention strategies.
Volume: 15
Issue: 3
Page: 1087-1096
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

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

Smart drones for human detection in disaster response

10.11591/ijict.v15i3.pp1179-1187
Menakadevi Nanjundan , Y. L. Ajay Kumar , V. R. Seshagiri Rao , Nagarjuna Telagam , Seetha Chaithanya , Manikadan S.
Natural disasters require swift, coordinated responses to minimise human casualties and infrastructure damage. This paper presents a novel AI-assisted drone system designed to enhance disaster relief efforts through advanced human detection and a distributed emergency Wi-Fi network. This drone system, equipped with state-of-the-art machine learning algorithms and thermal imaging, excels at locating and identifying individuals even in challenging conditions, such as smoke, debris, or low visibility. The drone fleet operates autonomously, dynamically forming an ad-hoc network that adapts to the evolving needs of the disaster zone. By integrating real-time data processing with efficient network management, our system provides a critical lifeline for communication and a powerful tool for rescuers to navigate and respond effectively. The detection and communication times are observed to be within the range of 5 to 8 seconds for this proposed system, which is widely used in disaster response.
Volume: 15
Issue: 3
Page: 1179-1187
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

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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