Journal of Information and Technology

Hospital Emergency Queue Detection Using Inter-Vlan Routing

2025-08-20T13:29:35+00:00

Ultimately, this project aims to contribute to smart healthcare infrastructure in Kigali by providing a scalable, secure, and intelligent system that improves emergency department efficiency and saves lives through faster, more organized care delivery. This study focuses on developing a Hospital Emergency Queue Detection System to streamline patient flow using vlan routing, minimize delays, and enhance healthcare outcomes. Utilizing cutting-edge technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and data analytics, the system monitors real-time patient queues, predicts wait times, and prioritizes treatment based on urgency. In modern healthcare systems, reducing patient waiting times in emergency departments is critical for enhancing service quality and saving lives. This project proposes a novel approach to Hospital Emergency Queue Detection using VLAN (Virtual Local Area Network) routing to optimize network traffic and accurately monitor patient flow in real time. By segmenting hospital network infrastructure into VLANs dedicated to specific zones—such as triage, diagnostics, treatment, and discharge—data traffic from medical devices, RFID patient tags, and real-time location systems can be efficiently routed and analyzed. This system integrates queue detection algorithms with VLAN-based data segregation to identify bottlenecks and abnormal delays in patient movement. The architecture improves data throughput, reduces latency in communications, and ensures critical patient data is prioritized. The use of VLAN routing allows for scalable and secure monitoring of multiple departments while maintaining performance and privacy. The implementation results in improved emergency department efficiency, quicker decision-making, and enhanced patient experience.

Designing and Developing a Mobile Application to Enhance Access to University Services in Rwanda

2025-08-20T13:42:36+00:00

Higher education institutions in Rwanda face significant challenges in service delivery, including inefficient communication channels, limited access to academic resources, and administrative bottlenecks. This study proposes the design and development of a Mobile Application for University Services Access (MAUSA) to address these challenges and enhance the quality-of-service delivery at the University. The application integrates multiple university services into a single platform, leveraging mobile technology to improve student registration, academic resource access, and administrative processes. Using a user-centered design approach and modern development frameworks, the system provides features including digital student ID verification, real-time course information, library access, and payment integration. A pilot implementation at EAUR demonstrates that the application significantly improves service delivery efficiency, with students experiencing a 45% reduction in service access time, 78% reporting improved communication with faculty, and 92% expressing satisfaction with the application's usability. The findings suggest that implementing mobile technology solutions for university service delivery has the potential to substantially enhance the educational experience in Rwandan higher education institutions and similar resource-constrained settings.

Machine Learning and IoT Integration in Kigali Traffic

2025-08-20T14:02:13+00:00

This paper presents the development and implementation of Machine Learning and IoT Integration in Kigali designed to mitigate urban congestion in Kigali, Rwanda. The system integrates real-time traffic monitoring, adaptive signal control, and predictive analytics to optimize traffic flow across the city's major corridors. The research follows the Structured System Analysis and Design Method (SSADM) and employs machine learning algorithms for traffic pattern prediction. The paper details system architecture, sensor integration, real-time processing capabilities, and implementation results from pilot deployment across five major intersections in Kigali. The study concludes that the proposed Machine Learning and IoT Integration in Kigali reduces average travel time by 32% and decreases fuel consumption by 28%. Additionally, it discusses the potential impact of AI-driven solutions in optimizing urban mobility and reducing environmental impact.