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All Journal Journal of Information Systems and Informatics Journal of Computing Theories and Applications
Evwiekpaefe, Abraham Eseoghene
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Computer Science & IT Decision Sciences, Operations Research & Management

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An Artificial Neural Network Model for Predicting Children at Risk of Defaulting from Routine Immunization in Nigeria Evwiekpaefe, Abraham Eseoghene; Lawi, Valerie Plangnan
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.689

Abstract

It has been widely recognized that immunization remains one of the most successful for decreasing child mortality rates and preventing several serious childhood diseases globally. This study proposed a prediction model for accurate identification of routine immunization defaulters in Nigeria. The proposed framework classified defaulters at five different risk stages: insignificant risk, minor risk, moderate risk, major risk and severe risk to reinforce targeted interventions by accurately predicting children at risk of defaulting from the immunization schedule. Data from Nigerian Demographic and Health Survey 2018 was obtained for this study and thirty-four (34) demographic and socio-economic factors were used to predict children at risk of defaulting from routine immunization in Nigeria by using Artificial Neural Network (ANN) to train the dataset. The results indicated that ANN model produced an accuracy of 99.16% for correctly identifying children who are likely to default from immunization series at different risk stages. Other performance measures include Precision of 99%, Recall of 99% and F1 Score of 99%. The model was further validated using one thousand (1000) dataset, out of which nine hundred and seventy four (974) were correctly predicted.
A Blockchain-Based Digital Library System Integrated with CryptoJS for Enhanced Security and Transparency Evwiekpaefe, Abraham Eseoghene; Chinyio, Darius Tienhua; Ajakaiye, Fiyinfoluwa; Aleke, Paschal Obioma
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1176

Abstract

In the context of digital library systems, blockchain presents a promising framework for enhancing the security, integrity, and transparency of operations such as book transactions, cataloging, and user authentication. Library systems face several challenges, including lack of transparency and security vulnerabilities. Previous research efforts have explored various centralized digital library management systems, but they often suffer from single points of failure and insufficient security measures. The methodology involves integrating blockchain technology using CryptoJS for advanced encryption and hashing, the backend was designed using PHP (Laravel), while the technologies used in the front end includes HTML, CSS and Javascript. The blockchain technology was implemented using Cryptojs which provides security by implementing AES encryption to safeguard user credentials and book transaction records, preventing unauthorized usage. The system was tested in a digital library environment and diverse user set, where results demonstrated enhanced data security and improved operational efficiency. The system is scalable and adaptable to academic, research, and public libraries, providing real-time verification of transactions and enhanced protection against unauthorized access. By combining blockchain’s immutability with strong encryption and modern web technologies, the platform delivers a secure, transparent, and future-ready solution for digital library management with 88% effectiveness. Findings indicate that the proposed blockchain-integrated system not only resolves existing issues in digital library management, but also introduces new opportunities for innovation, including real-time transaction verification and improved trust among users.
The Llama–ARCS Adaptive Learning framework: AI–VR Integration System for Real-Time Motivational Feedback in Higher Education Evwiekpaefe, Abraham Eseoghene; Chinyio, Darius Tienhus; Tohomdet, Loreta Katok
Journal of Computing Theories and Applications Vol. 3 No. 3 (2026): JCTA 3(3) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.15031

Abstract

This study developed and evaluated an AI-integrated Virtual Reality (VR) system designed to enhance personalized learning in higher education. While VR improves engagement, existing systems often lack adaptivity or experience high latency during AI interactions. To address these limitations, this research introduces a novel integration of a cache-optimized Llama 2 Large Language Model (LLM) that delivers real-time, motivationally grounded feedback. The system was implemented using Unity 3D and validated with 50 undergraduate students. Technical validation showed that the cache layer reduced interaction latency from 17.7 ms to 14.2 ms and maintained zero system crashes throughout the pilot. Learner motivation was assessed using Keller’s ARCS model, yielding mean scores ranging from 4.08 to 4.69 across all dimensions. Independent t-tests (p > 0.05) and negligible effect sizes (Cohen’s d < 0.2) revealed no significant difference between technical (ICT) and non-technical (Physics) students. These findings confirm that the proposed system effectively bridges technological and motivational gaps, providing a robust model for adaptive, immersive education.
Co-Authors Ajakaiye, Fiyinfoluwa Aleke, Paschal Obioma Chinyio, Darius Tienhua Chinyio, Darius Tienhus Lawi, Valerie Plangnan Tohomdet, Loreta Katok