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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 650 Documents
 61   62   63   64   65 
Deploying enhanced security for blockchain MFA wireless virtual-key access management for industrial frontiers Andrew Okonji Eboka; Fidelis Obukohwo Aghware; Margaret Dumebi Okpor; Bridget Ogheneovo Malasowe; Ejaita Abugor Okpako; Arnold Adim Ojugo; Reuben Akporube Abere; Amaka Patience Binitie; Rita Erhovwo Ako; Innocent Onyemenem; Adesuwa Inaya; Victor Ochuko Geteloma
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1532-1545

Abstract

The increased adoption of automated devices and its utilization to advance data processing in many facets of societal tasks and demand operations has continued to birth accompanying security challenges. Physical infrastructures today integrates within them automated units to upscale traditional issues. The recent proliferation of automated door access with innovative embedded units employs a centralized design with single-factor authentication, which has proven inefficient for safeguarding restricted environments. Other challenges of data corruption, theft, mishandle of user credentials, all of which can be compromised due to its single point of access control. This study advances enhanced access security for virtual key door infrastructure via a blockchain-MFA strategy. It implements secure, scalable access control with a 3-layered framework of PIN + RFID + Biometric, validated using smart contracts on a blockchain network. The results demonstrated improvements in security, transparency, and auditability compared to traditional door access control, especially against adversarial attacks from single-point failures and credential vulnerabilities. Our prototype unit demonstrates 3-factor authentication for improved security that ensures forensic traceability of every authentication attempt, making it a viable solution for institutions with high-security needs.
Development of an AI-based digital startup incubation model for the empowerment of students and MSMEs towards a creative economy Umar Bakti; Maria Septijantini Alie; Iin Marliana
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1682-1695

Abstract

Micro, small, and medium enterprises (MSMEs) continue to face low digital-technology adoption, with conventional incubation programs still relying on generic training and manual assessment rather than automated, evidence-based evaluation. This study develops and validates an AI-based startup incubation model (AI-SIM), an integrated ICT platform functioning as an intelligent decision-support system for MSME digital-readiness classification, adaptive training recommendation, geospatial analysis, and student-MSME experiential learning within one operational environment. Data were collected from 4,276 MSMEs across Lampung Province, Indonesia; after strict two-level deduplication to prevent data leakage, 961 unique records were retained for modeling. A random forest classifier grouped MSMEs into three readiness categories (Traditional, Stable, Digital Ready) using two feature configurations, achieving 33.68% (10 features; 5 fold CV 36.75%) and 30.57% accuracy (7 manuscript-aligned features; 5 fold CV 37.23%), both near the 33.3% random-chance baseline. Feature importance analysis showed predictive weight spread across all seven features, led by digital infrastructure and monthly revenue, with no dominant predictor. The system was validated through field deployment, training, and focus group discussions with 84 student and MSME participants, confirming its practical value as a real-time incubation dashboard, while the classification component requires richer behavioral and transactional data to improve accuracy in future iterations.
Augmented reality application with speech recognition for malay spelling Norizan Mat Diah; Akmal Hakim Mohammad Mokhriz; Norsafinar Rahim; Suzana Ahmad; Elly Johana Johan; Zainura Idrus
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1469-1479

Abstract

This project aims to revolutionize the process of teaching Malay spelling to young learners by using new technologies, notably augmented reality (AR), and voice recognition, on the Android smartphone platform. The objective is to create speech recognition algorithms specifically adapted for mobile AR applications and extensively test their accuracy on mobile devices. The study begins with an initial phase of analysis and rapid design, followed by an iterative prototyping cycle using a systematic approach. This cycle includes developing, testing, and continuously refining the mobile spelling application, ultimately producing an improved version ready for final testing. The result is a fully working smartphone application that smoothly incorporates AR and voice recognition technology, providing children with an engaging and correct spelling learning experience. This study demonstrates the effective adoption of cutting-edge technology in education and highlights a practical and innovative tool for educators and parents seeking innovative approaches to enhance language learning.
Design and prototype model for smart autonomous self-balancing bicycle Sankirti Shiravale; Parth Patil; Rupali Dalvi; Geetha Chillarge; Swarupa M. Deshpande; Seema A. Hadke
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1989-1999

Abstract

There are still a huge demand for safe and sustainable modes of transport which has led to the creation of autonomous self-balancing bicycles. The design and analysis of the self-balancing bicycle with flywheel mechanism assisted by gyroscope is presented in this study. A Lagrangian approach was used to model the tilt dynamics; it includes kinetic energy, potential energy, damping and actuator torque generated by the BLDC motors. The nonlinear system was linearized around the upright equilibrium for control implementation and simulated in MATLAB. Results indicate that an initial lean disturbance is stabilized within about 1 s, and that the motor current also reaches steady state values, thus signifying efficient use of energy. The gyroscopic response trends corroborate the realistic tilt sensing and the effectiveness of the proposed stabilization framework is confirmed for autonomous operation on straight and curved paths.
An optimised axial-mode helical antenna at 2.3 GHz for 5G signal enhancement in Suburban Areas Nasrul Nasrul; Yulindon Yulindon; Nurraudya Tuz Zahra; Fani Anjelina
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1759-1767

Abstract

This study aims to design, fabricate, and validate an axial-mode helical antenna for enhancing 5G cellular signals at 2.3 GHz, specifically to address signal attenuation in suburban areas caused by base station distance, vegetation attenuation, and propagation obstacles. Unlike previous helical antenna designs that achieved gains below 11 dBi or operated outside 5G frequency allocations, the proposed antenna was designed using computer simulation technology (CST) microwave studio and fabricated with 10 copper turns of an optimized winding diameter for 2.3 GHz operation, equipped with a triangular tapered strip as an impedance-matching element that generates circular polarization effective for mitigating suburban multipath fading. Simulation results demonstrate a return loss of −24.55 dB, a VSWR of 1.125, and a gain of 12.45 dBi, while measurements of the fabricated prototype confirm this performance with a return loss of −20.1 dB and a VSWR of 1.26, yielding a simulation–measurement deviation of 4.45 dB that remains within standard fabrication tolerances. Analysis confirms stable unidirectional radiation and sufficient gain to compensate for suburban propagation losses. The principal contributions are: (i) an optimized tapered-strip axial-mode helical antenna for 2.3 GHz suburban 5G, (ii) quantitative simulation–measurement validation, and (iii) a compact design without complex active components.
Deep learning-based classification of acanthosis nigricans as a clinical sign of high insulin resistance in type 2 diabetes mellitus Annisa Darmawahyuni; Siti Nurmaini; Sutarno Sutarno; Anggun Islami; Muhammad Raffly Riyansyah Putera; Muhammad Mukhlashyn Az Zahrawi
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1706-1715

Abstract

Acanthosis nigricans (AN) is a cutaneous manifestation strongly linked to insulin resistance and often serves as an early indicator of type 2 diabetes mellitus (T2DM). Early detection of AN is therefore essential for preventive healthcare. Conventional diagnosis typically relies on visual inspection and grading, which may be subjective and inconsistent. To address this limitation, this study proposes a computer-aided diagnostic framework based on deep learning for automatic AN detection from skin fold images. A comprehensive evaluation was conducted using nine widely adopted deep learning architectures, namely VGG16, VGG19, Dense-Net121, DenseNet201, ResNet50, ResNet101, MobileNetV2, Xception, and the vision transformer (ViT). To ensure a fair comparison, all models were trained under identical experimental settings. Model performance was assessed using standard classification metrics. The comparative analysis demonstrates that ResNet101 outperformed the other architectures, achieving an accuracy of 94.12% for AN detection, with a precision and recall of 96.55% and an F1-score of 93.33%. Overall, the results highlight the robustness and effectiveness of convolutional neural network (CNN) based models, particularly ResNet101, as non-invasive and objective tools for automated AN screening. This approach has the potential to support early detection of individuals at risk of T2DM and to enhance preventive healthcare delivery.
CPU-optimized regularized convolutional neural network architecture for multimedia-based breast cancer metastasis detection Guidoum Amina; Bouhenni Abdelhake
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1881-1892

Abstract

The histopathological diagnosis of breast cancer metastases, a subjective and lengthy process, benefits from advances in artificial intelligence for its automation. This study proposes an automatic classification method based on a convolutional neural network (CNN) architecture with progressive regularization (Batch normalization and adaptive dropout), trained on the PatchCamelyon (PCam) dataset. Our approach integrates a data augmentation strategy specific to medical images and advanced optimization for CPU environments, allowing us to balance performance, robustness, and computational accessibility. The architecture comprises only 94,000 parameters, 223 times fewer than leading models. Multi-criteria evaluation shows competitive results: area under the curve (AUC) of 92.92%, F1-score of 82.39%, accuracy of 88.74%, and specificity of 90.61%. A bootstrap robustness analysis provides 95% confidence intervals (AUC: [89.56% 92.91%]), and an ablation study quantifies the reduction in overfitting through progressive dropout (33.6%). This research validates that a well regulated and CPU-optimized CNN architecture can achieve clinically relevant performance while remaining accessible on standard environments, thus opening up concrete prospects for the development of practical AI tools in digital pathology.
Federated deep learning for early detection and progression modelling of diabetic nephropathy Aravind Kumar Kyrika; Sumit Kumar Banchhor
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1453-1458

Abstract

One of the main causes of end-stage renal disease is diabetic nephropathy (DN). Patient privacy-sensitive data is a restriction in centralized machine-learning approaches for the healthcare domain. This research introduces a privacy-preserving federated deep-learning (Fed-DN) method for early diagnosis of DN and modelling disease progression and its impact. Fed-DN combines safe aggregation with differential privacy (DP) using Opacus and multi-modal data sources (electronic health records, fundus imaging, and histopathology). Both simulation tests with multi-modal data sets distributed across hospitals show an efficient privacy-utility trade-off, with Fed-DN achieving near-centralized performance (AUC ≈ 0.90) (Accuracy = 87%) while maintaining a privacy budget of ε ≈ 3.1. This is a good example of the trade-off achieved between model accuracy and privacy protection. The technique does not reveal patient information, which enables collaborative, scalable healthcare analytics. Furthermore, it enhances trust among patients and institutions.
An integrated deep neural learning architecture with embedded feature reduction for customer lifetime value prediction in fintech firms G. Jyothi; A. Gopinath; Meenu Baliyan; Rachel Nallathamby; K. Swanthana; Murugesan Sundaram
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1893-1901

Abstract

The financial technology (FinTech) sector has witnessed remarkable growth in recent years, significantly transforming the way individuals and businesses enable and handle financial services. Recently, deep learning (DL) models have been beneficial in attaining appropriate data concerning customer lifetime value (CLV) for the banking experts. This paper presents a deep neural network (DNN) model with embedded feature selection (FS) for CLVP (DNNEFS-CLVP) approach. An embedded FS mechanism is primarily introduced to automatically identify the most informative attributes while eliminating redundant and less relevant data features. To further enhance predictive capability, a deep hybrid architecture is developed by integrating the temporal convolutional network, a variational graph autoencoder, and a graph convolutional network. This combined framework is expected to increase the accuracy and robustness of CLV prediction while assuring efficient feature utilization. The experimental result values of the DNNEFS-CLVP method accomplishes higher proportion of very low error values across all evaluation metrics, as evidenced by mean squared error (MSE) of 0.000081, mean absolute error (MAE) of 0.007126, root mean squared error (RMSE) of 0.009011, and mean absolute percentage error (MAPE) of 0.114715, indicating strong predictive performance and minimal deviation from actual CLV values.
Advanced IoT and ML-driven personal safety technology for women P. Divya; M. Kumaresan; P. Manikandan
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1976-1988

Abstract

This article introduces VigilNet, a personal safety system powered by internet of things (IoT) and machine learning (ML) that identifies environmental irregularities instantaneously through intelligent wearable gadgets. In contrast to solutions dependent on extensive datasets and reactive alert systems VigilNet employs a few-shot learning (FSL) strategy utilizing siamese neural networks (SNNs) to achieve precise anomaly recognition, with limited training data. In the information and communication technology (ICT) realm, VigilNet is a smart IoT sensing and communication network with interconnected wearable sensor nodes continuously collecting physiological and environmental data for real-time processing. The SNNs based FSL module then fuses these multiple modal streams of sensor information and extracts the similarity-based threat assessment, which in turn drives an intelligent decision support system to alarm the operator to take timely action for any abnormal situations. VigilNet, thus, combines IoT connectivity, real-time data analytics, and machine-learning-driven decision support into a single personal safety system. The framework was tested on an IoT wearable dataset encompassing heart rate, body temperature, ambient noise and proximity data. Experimental results demonstrate that VigilNet achieves a threat detection rate (TDR) of 0.85, false alarm rate (FAR) of 0.10, and a response time (RT) of 3.5 seconds, outperforming conventional IoT–ML safety models. These findings validate the feasibility, efficiency, and robustness of the proposed architecture and confirm the potential of FSL-based techniques for next-generation women’s safety technologies.

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