Review of Information Engineering and Applications

IoT-based automated case generation system for traffic rule violations

2025-02-10T09:34:28-06:00

This project presents a smart, economical, and accessible traffic control system that utilizes RFID technology, Node MicroController Unit (NodeMCU), internet connectivity, and Light Emitting Diode (LEDs) to effectively monitor and manage vehicle flow at road intersections. It is specifically designed to address the mounting urban traffic issues in Bangladesh, a densely populated and rapidly urbanizing country. The surge in the number of vehicles has overwhelmed conventional traffic management methods, often hampered by enforcement gaps and noncompliance with regulations, resulting in persistent congestion and inefficiencies. The proposed system uses RFID to detect vehicles and assess traffic density in real time, enabling dynamic and adaptive traffic signal control. With NodeMCU and internet connectivity, it facilitates streamlined communication and remote supervision, while LEDs provide clear instructions to drivers. By automating traffic regulation, the system reduces reliance on manual operations, mitigating corruption and enhancing enforcement effectiveness. Moreover, it fosters greater adherence to traffic laws by introducing a transparent, technology-driven framework. This innovative traffic management solution not only alleviates congestion but also cultivates a culture of compliance, contributing to the overall improvement of urban transportation networks. Its emphasis on integrating modern technology into traffic control aligns with the principles of sustainable urban development, offering a scalable and transformative approach to addressing Bangladesh's escalating traffic challenges.

IoT based garbage monitoring and management system

2025-06-25T05:03:31-05:00

Solid waste management has become an increasingly critical issue in Bangladeshi cities, particularly in major urban centers like Dhaka. Overflowing garbage bins are commonly observed, leading to unhygienic environments, pollution, and the spread of various diseases. To address this problem, an IoT-based Garbage Monitoring and Management System has been designed and implemented to enhance waste collection efficiency. This system enables real-time detection of waste levels in garbage bins. Using the Blynk application on smartphones, authorities can remotely monitor the status of each bin and take prompt action to empty them before they overflow, thereby reducing environmental pollution. Each bin is equipped with a microcontroller (ATMEGA328), an ultrasonic sensor, and a Wi-Fi module installed on the lid. When the bin reaches its full capacity, it sends a signal to the control center, notifying them of its status. Upon receiving the information via the Blynk application, authorities can direct the waste collection truck to empty the full bin promptly. This system can be implemented in any city to enhance garbage monitoring and management, ensuring a cleaner and healthier environment.

A comparative analysis of multiple ML models for fake news detection

2026-01-01T05:34:21-06:00

The rapid growth of internet usage has emerged as a defining trend in recent decades, profoundly influencing how individuals communicate, access information, and engage with digital platforms. Among these, social media has become particularly prominent, with users demonstrating increasing proficiency in leveraging multiple platforms for diverse purposes. Such web resources enable any citizen to become a publisher or a distributor of news. The people and the content are not verified on these networks. People sometimes use such media to spread misleading information. The geometric growth of disseminated false information has become a critical concern in modern culture, which largely depends on information. The proliferation of fake and misleading information raises serious problems for societal well-being, the democratic process, and communal perception. Researchers have responded by increasingly using machine learning (ML) algorithms to develop automated systems to detect fake news. Based on this inspiration, we propose a resilient and effective system capable of appropriately identifying and combating misinformation propagation. This paper considered eight ML models to identify and evaluate fake news on two real-world datasets acquired on Kaggle. We use the Term Frequency-Inverted Document Frequency (TF-IDF) method in feature extraction. The best performance was recorded in the Passive Aggressive Classifier (PAC) among the other eight models, with an average accuracy of 97.26%.

Development of a smart GIS-based campus navigation and shortest path routing system for OAUSTECH, Ondo State, Nigeria

2026-01-01T23:36:32-06:00

Efficient navigation remains a persistent challenge on large campuses such as Olusegun Agagu University of Science and Technology (OAUSTECH), Nigeria, due to expansive layouts and complex pathways. Static maps are insufficient to meet users' dynamic needs, creating a demand for intelligent geospatial solutions. This study developed a GIS-based smart campus map integrated with Dijkstra’s algorithm to optimize route planning, enhance accessibility, and support campus management. Spatial data on campus infrastructure were collected through field surveys and Google Earth Pro imagery, then digitized and geo-referenced in ArcGIS. The campus network was modeled as a weighted graph, and Dijkstra’s algorithm was implemented in Python to compute the shortest paths, considering factors such as distance, pedestrian access, and restricted zones. The system was deployed as an interactive web and mobile application using HTML, CSS, JavaScript, Bootstrap, and Android Studio, enabling real-time navigation and flexible querying. The system successfully generated optimal routes across 12 major nodes, including distances of 1,129.10 meters from the main gate to the ICT Building and 1,696.93 meters to Principal Staff Housing. Usability testing with 30 participants showed 87% user satisfaction, with average route generation times under two seconds, and notable reductions in navigation time compared to paper maps. Overall, integrating GIS with Dijkstra’s algorithm improved route safety, efficiency, and user experience. While the system relies on static data, future work could incorporate AI-driven real-time updates. The approach provides a scalable model for smart campus development in resource-limited settings.