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All Journal Journal of Information Systems and Informatics Journal of Technology Informatics and Engineering
Ansa, Godwin
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Computer Science & IT

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Enhancing Network Security in Mobile Applications with Role-Based Access Control Mpamugo, Ezichi; Ansa, Godwin
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.863

Abstract

In today's dynamic networking environment, securing access to resources has become increasingly challenging due to the growth and progress of connected devices. This study explores the integration of Role-Based Access Control (RBAC) and OAuth 2.0 protocols to enhance network access management and security enforcement in an Android mobile application. The study adopts a waterfall methodology to implement access control mechanisms that govern authentication and authorization. OAuth 2.0, a widely adopted open-standard authorization framework, was implemented to secure user authentication by allowing third-party access without exposing user credentials. Meanwhile, RBAC was leveraged to streamline access permissions based on predefined user roles, ensuring that access privileges are granted according to hierarchical role structures. The main outcomes of this study show significance towards the improvements in security enforcement and user access management. Specifically, the implementation of multi-factor authentication, session timeout mechanisms, and user role-based authorization ensured robust protection of sensitive data while maintaining system usability. RBAC proved effective in controlling access to various system resources, such as database operations which was presented in scenario of physical access to doors, while OAuth 2.0 provided a secure communication channel for authentication events. These protocols, working in tandem, addressed key issues like unauthorized access, data integrity, and scalability in network security policy enforcement. This research deduces that combining RBAC and OAuth 2.0 protocols in mobile applications enhances security posture, simplifies access management, and mitigates evolving threats.
Enhancing Hazard Detection and Risk Severity Assessment in Construction through Multinomial Naive Bayes and Regression Akwaisua, Akaninyene Michael; Ekong, Anietie; Ansa, Godwin
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.979

Abstract

This research delves into the crucial area of hazard detection and risk severity assessment within the construction industry, using machine learning techniques. The dataset utilized is from the Chinese Construction Company (CCECC), Uyo, Nigeria. Comprising over 100,000 instances, it captures various hazard categories prevalent in construction sites, providing a comprehensive foundation for predictive analysis. In the first phase of the study, the system is designed to detect hazards present in construction sites. Leveraging these data, the machine learning models are trained to predict potential hazards based on the information provided. Through TF-IDF vectorization, a feature extraction technique, the textual data is transformed into numerical representations. Multinomial Naive Bayes is employed for hazard classification due to its efficacy in handling text data, and with it, an accuracy of 0.99 was obtained. Subsequently, the trained model was evaluated to assess its performance and the severity of identified hazards are evaluated. The system quantifies the potential risk posed by each hazard using the risk severity attribute. Using the Linear Regression algorithm, the model predicts the severity of risks based on textual descriptions of a hazard. In practical application, the research stresses the significance of risk management strategies in the construction industry to mitigate potential harm to personnel and infrastructure. This research contributes to advancing safety protocols within the construction sector, advocating for a culture of vigilance and precaution to address risks effectively.
Contextual Framework for Remote Intelligent Monitoring and Detection System for Prediction of Pregnancy Complications Udonna, Uduakobong; Umoren, Imeh; Ansa, Godwin
Journal of Technology Informatics and Engineering Vol. 4 No. 2 (2025): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i2.244

Abstract

Maternal health disorders can cause complications and harmful incidents in women during pregnancy. To minimize risks, this research developed a platform powered by a supervised machine learning model (SMLM) to support remote intelligent monitoring and prediction of pregnancy complications caused by hypertensive disorder, gestational diabetes, and related indicators. The study used real-world datasets with six UCI Machine Learning Repository features to identify and predict Maternal Health Risk (MHR) factors. A Support Vector Machine (SVM) algorithm was applied to construct the classifier model, which was trained and evaluated using StratifiedKFold cross-validation (k=10). The model achieved 80% accuracy with a precision, recall, and F1-score of 77%. The outcome of this work is the P-Health mobile application, designed to record and track blood pressure, blood sugar, heart rate, body temperature, and weight, while predicting the risk level of pregnancy complications through inference from the integrated machine learning model. Developed with Kotlin and Android Studio, the application enables healthcare practitioners to remotely monitor patients’ vitals in real time. This innovation addresses the challenge of early detection of pregnancy complications and provides continuous monitoring and assessment. The findings suggest that P-Health can improve early detection and timely intervention, helping medical specialists minimize maternal health risks. The system also has the potential to raise public awareness of maternal health issues, thereby contributing to the prevention of complications during pregnancy.
Co-Authors Akwaisua, Akaninyene Michael Ekong, Anietie Mpamugo, Ezichi Udonna, Uduakobong Umoren, Imeh