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International Journal of Informatics and Communication Technology (IJ-ICT)
Published by Institute of Advanced Engineering and Science
ISSN : 22528776     EISSN : 27222616     DOI : -
Core Subject : Science,
Computer Science & IT
International Journal of Informatics and Communication Technology (IJ-ICT) is a common platform for publishing quality research paper as well as other intellectual outputs. This Journal is published by Institute of Advanced Engineering and Science (IAES) whose aims is to promote the dissemination of scientific knowledge and technology on the Information and Communication Technology areas, in front of international audience of scientific community, to encourage the progress and innovation of the technology for human life and also to be a best platform for proliferation of ideas and thought for all scientists, regardless of their locations or nationalities. The journal covers all areas of Informatics and Communication Technology (ICT) focuses on integrating hardware and software solutions for the storage, retrieval, sharing and manipulation management, analysis, visualization, interpretation and it applications for human services programs and practices, publishing refereed original research articles and technical notes. It is designed to serve researchers, developers, managers, strategic planners, graduate students and others interested in state-of-the art research activities in ICT.
Arjuna Subject : -
Articles 601 Documents
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Design of a pyramidal slotted wide-band patch antenna for directive wireless communications over Ka-Band Safa Nasssr Nafea
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1135-1142

Abstract

A wide Band for directive wireless communications over Ka-Band was presented. The proposed antenna printed on Rogers RT/Duroid 5880 substrate and achieved a moderate gain of 6.80 dB at resonating freqenc of 28 GHz. The pyramidal antenna achieved a wide operating bandwidth of 11.55 GHz. The proposed antenna achieved high size reduction percentage with a compact overall dimension of (4.8×6.9×1.5) mm3. The antenna achieved the wide operating bandwidth based on two factors; the first is selection of low loss dielectric material to ensure etter antennas performance. The other is etching pyramidal with 2×2 squared – shapes slots array etched from patch’s surface to reduce stored reactive energy which decreases overall ualit factor of and improves the -10 dB bandwidth obviously.
Design a Gaussian mixture-based clustering model for enhancing accuracy and robustness in smart homes Kanaka Raju Rajana; Shanmuk Srinivas Amiripalli
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1047-1057

Abstract

Nowadays, smart homes have become quite complicated systems. Thus, an appropriate technique for controlling all those devices is necessary, especially considering that certain nodes are likely to be broken. In that connection, we have proposed two algorithms related to Gaussian mixture models (GMM): GMM equal and GMM unequal. They were compared with graph neural network (GNN) equal, GNN unequal, and the LucasWheel algorithms. The peculiarity of the GMM equal algorithm consists in the fact that all clusters should have similar sizes and shapes, which is quite useful for routing and balancing purposes, while the clusters in the GMM unequal algorithm can have various sizes and shapes depending on the data distribution. All five models were analyzed using 843 nodes, where failure rates ranged from zero to fifty percent. The surprising outcome of this analysis is that GMM equal performed better than the other four models in every aspect. Efficiency was steady and steadily increased in accordance with the rising failure rate. The Wiener index gradually fell from its initial value to nearly zero, suggesting a dense connection among the nodes and an evenly spread-out network. Furthermore, GMM equal attained the highest modularity among the five models at every failure level. In combination, these results indicate that GMM equal is the most balanced topology, with the best balance between reliability, efficient communication, and scalability when applied to the internet of things and smart homes.
Development of highway vehicle detection using background subtraction and Haar cascade methods Ni Gusti Ayu Dasriani; Anthony Anggrawan; Khasnur Hidjah; Christofer Satria; I Nyoman Yoga Sumadewa
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1004-1015

Abstract

Vehicle recognition is a critical component of traffic analysis and the progress of advanced transportation systems, underscoring the importance of automated, real-time methods that reduce the need for manual observation. While the field has seen notable innovations in deep learning-centric detection technologies, many of these approaches require considerable computational strength and are not well-suited for real-time application in resource-constrained environments. In response to this limitation, the present study introduces a streamlined vehicle detection framework that combines background subtraction for motion-oriented foreground extraction with a Haar cascade classifier for object identification in traffic video sequences. The system is evaluated using real-world highway traffic recordings under different illumination conditions, including both day and night scenarios. The experiment's findings show that the system achieves an overall accuracy of 82.08%, with a precision of 85.33%, a recall of 66.67%, and an F1-score of 74.86%. The system also demonstrates consistent performance across different lighting conditions. These findings indicate a trade-off between detection accuracy and computational efficiency, where the proposed approach prioritizes practical deployment feasibility. Overall, the results suggest that classical computer vision techniques remain viable alternatives for real-time traffic monitoring in environments with limited computational resources.
Integrating F-filter and K-Means SMOTE to enhance LGWUM-based turnover prediction Risyda Miftahur Rahmah; Sigit Priyanta
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1078-1086

Abstract

High employee turnover poses a significant challenge for organizations. While uplift modeling offers a prescriptive analytics approach by estimating differential treatment effects to optimize retention programs, its performance is often hindered by irrelevant features and imbalanced class distributions. To address these issues, this study proposes an employee turnover model utilizing lai’s generalized weighted uplift method (LGWUM), enhanced with F-Filter feature selection and K-Means SMOTE for a refined feature space and balanced treatment representation. Evaluated across three HR datasets with four engineered uplift classes (CN, CR, TN, TR), the integrated framework significantly improves uplift performance, yielding increased Qini coefficients of 0.0755 and 0.0870 on Datasets 2 and 3, respectively. Furthermore, top-decile probability distribution analysis confirms a clearer separation between positive and negative responders, with XGBoost demonstrating the most robust and reliable uplift discrimination across the models.
QSUMeMarket: a decision support system framework for smes to process customer orders Winston G. Domingo; Jennifer A. Gamay; Virdi C. Gonzales; Selino S. Malunao
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp975-985

Abstract

With the progression of big data analytics (BDA), point of sale (PoS) could be amalgamated with an inventory management (IM) system. The problem of SMEs is to generate a financial report only using simple calculations based on income and expenses. To develop a web-based computerized system to solve the problems encountered and ease the marketing office’s operations. The used of a descriptive research design and software development life cycle (SDLC) methodology. The ISO/EIC 25010:2011 software quality standard was chosen as one of the most comprehensive software-quality evaluation models. The software performs exceptionally well in several key areas, including performance efficiency (4.00), usability (3.94), portability (3.89), compatibility (3.83), security (3.80), maintainability (3.70), and reliability (3.67). These ratings highlight its strengths in speed, security, and overall user experience. However, with the lowest score being 3.44, there is still potential for improvement to better align the software with user needs and business objectives. While it meets industry standards and is suitable for practical use, ongoing enhancements are essential to sustain and elevate its quality.
Cross-modal attention fusion using vision transformers for robust student attentiveness estimation Rajasekaran Mariswamy; Praveen Sundar
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp935-943

Abstract

Automated student attentiveness estimation is a fundamental component of intelligent e-learning systems and adaptive classroom analytics. Traditional convolutional and recurrent architectures often struggle to model long-range temporal dependencies and complex inter-modal relationships inherent in engagement behavior. To address these limitations, this paper proposes a cross-modal attention fusion framework built upon a vision transformer (ViT) backbone for robust student attentiveness estimation. The proposed architecture leverages patch-based visual encoding through a ViT to capture global spatial dependencies, while behavioral cues such as gaze direction, head pose, and blink dynamics are embedded into a shared latent representation space. A cross-modal multi-head attention mechanism is introduced to dynamically learn interactions between visual and behavioral modalities, replacing static weighted fusion strategies. Temporal dynamics are modeled using a Transformer encoder, enabling effective long-range sequence modeling without recurrent dependencies. Experimental evaluation on a benchmark attentiveness dataset demonstrates superior performance compared to CNN–LSTM-based models, achieving improved accuracy, F1 score, and robustness under challenging lighting and occlusion conditions. Ablation studies validate the contribution of cross-modal attention and transformer-based temporal modeling. The proposed framework maintains real-time feasibility while significantly enhancing discriminative capability.
Classroom behavior mining in adolescents: a cognitive and data-driven approach using BEHAVE_Apriori Suresh Govindarajalu; Muthukumaran Subramaniyan; Kamatchy Balakrishnan; Kalaichelvi Nagarajan; Nandhini Krishnamoorthy
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1058-1065

Abstract

Adolescence is a critical developmental stage that leads to essential changes in social, emotional, and cognitive domains that affect conduct in the classroom. Students' perceptions, processing, and reactions to their learning environment are better-understood thanks to cognitive psychology. However, contemporary data mining techniques frequently ignore the environmental, emotional, and cognitive elements influencing teenage behavior in learning environments. This research presents a comprehensive approach to analyzing teenage college students' classroom behavior by integrating cognitive psychology with data-driven methods to identify key behavioral traits shaped by both external and internal factors. A brand-new algorithm called the behavioral evaluation via hybrid attributes and valuable extraction using the Apriori (BEHAVE_Apriori) approach is presented. Also, a variety of feature selection (FS) strategies, including information gain (IG), chi-squared (CS), and tree-based approaches, are used for FS. Then, using the Apriori algorithm, association rules are found that relate behavior patterns to elements like family history, academic involvement, and peer influence. The IG-based FS combined with the Apriori algorithm delivered the best performance, generating 95 rules in 0.0241 seconds, outperforming CS (154 rules, 0.0629s) and tree-based FS (251 rules, 0.1394s), while the unfiltered dataset produced 514 rules in 0.2853 seconds.
An effective blockchain-based system for tracking viral content origins in social media Nageswararao Sirisala; Srinivasulu Sirisala; Nalavala Ramanjaneya Reddy; Syed Anisha
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1313-1321

Abstract

Tracking the origins of viral content in social media is crucial for identifying misinformation, ensuring accountability, and protecting intellectual property. By tracing content back to its source, platforms can curb the spread of false narratives, hold malicious actors responsible, and safeguard creators’ rights. In this work, “Effective blockchain based system for tracking viral content origins in social media-(BCTVCO)” is proposed. The BCTVCO is a blockchain-based content authentication platform that leverages interplanetary file system (IPFS), smart contracts, and cryptographic hashing to verify digital assets and detect unauthorized reuse. This decentralized application addresses the challenges of content authenticity, ownership verification, and intellectual property protection in the digital space. The system integrates Ethereum smart contracts (Solidity) to store immutable content records and uses SHA-256 hashing for secure file integrity verification. Content is uploaded to IPFS via Web3. Storage, ensuring distributed and tamper-resistant storage. The React-based frontend with MetaMask authentication allows users to seamlessly register, upload, and track their content. In implementation, BCTVCO outperformed existing methods and proved to be a scalable, transparent, and secure blockchain-based content verification system. Future enhancements of BCTVCO involve multi-chain support and AI-powered content analysis to strengthen security and usability.
Explainable AI-based hybrid GNN-MLP model for strawberry fruit disease detection using hyperspectral imaging Varsha Kiran Bhosale; Chin-Shiuh Shieh
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1364-1375

Abstract

Strawberries are severely affected by the main fungal diseases such as anthracnose fruit rot, grey mould, and powdery mildew, directly reducing commercial yield and post-harvest quality. In this paper, we propose an enhanced hybrid deep-learning method by combining graph neural networks (GNNs) and multi-layer perceptrons (MLPs) for effective strawberry disease detection in real environments of fields. This dataset contains images acquired from both public domain repositories as well as farm operational settings, encompassing three classes of diseases with varying lighting conditions, background noise, and occlusion. A structured pre-processing workflow comprising contrast enhancement, denoising, and synthetic hyperspectral simulation is used to enhance subtle lesion features and stabilize the subsequent feature extraction. The hybrid GNN–MLP framework possesses the merits of spatially local lesion topology and two-point contextual information, which can improve disease classification compared with common CNN-based structures. 5-fold cross-validation shows that the model holds 93.59% accuracy and a macro F1-score of 90.39%, showing good generalization even with heterogeneous input regimes. Transparent models use local interpretable model-agnostic explanations (LIME) and gradient-weighted class activation mapping (Grad-CAM) in combination, highlighting disease-relevant areas that give an interpretable rationale for each prediction. To conclude, the developed system offers an accurate and explainable solution that has a computationally efficient commitment for real-time monitoring of disease in smart agriculture settings, particularly on low-cost hardware assets.
Digital forensics for cultural preservation: multi-device image classification of the historic Surabaya City Hall Ulfa Meilinda Putri; Imam Yuadi
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp995-1003

Abstract

Cultural heritage preservation increasingly relies on digital forensics to ensure authenticity and consistency in heritage documentation. This study presents a digital forensic framework based on machine learning for classifying multi-device images of the historic Surabaya City Hall. The dataset was collected from nine smartphone devices and preprocessed through standardization, 360° rotational augmentation, and three filtering methods: gaussian, median, and laplacian. Three supervised algorithms (support vector machine (SVM), K-nearest neighbor (KNN), and logistic regression (LR)) were evaluated using accuracy, macro average, and weighted average of precision, recall, and F1-score. The results indicate that image preprocessing substantially affects model performance, with the gaussian-filtered KNN achieving the best result, reaching 92% accuracy, and balanced macro and weighted F1-scores of 0.92-0.93. Confusion-matrix analysis revealed minor misclassifications among iPhone models with similar sensor characteristics, while other devices were accurately identified. The findings confirm that gaussian filtering improves feature consistency and that KNN’s distance-based classification exhibits robustness across heterogeneous image sources. However, the study is limited to a single heritage object and a restricted number of devices, which may affect generalizability. The proposed framework provides a reproducible and interpretable method that supports digital authenticity verification and aligns with UNESCO’s vision for open, transparent cultural heritage preservation.

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