WSEAS Transactions on Information Science and Applications
Print ISSN: 1790-0832, E-ISSN: 2224-3402
Volume 22, 2025
Polynomial Regression and Faster R-CNN Models for University
Library Decision Implementation Discovery based on Deep Learning
Authors: , ,
Abstract: We deal with the development of a seat occupancy detection algorithm for the University’s library utilizing the Faster R-CNN algorithm. The university’s library is widely used by students, particularly during exam season when it can become difficult to find a seat. Going from one building to another is often time-consuming and useless when there are no available seats. This system uses the features of Faster R-CNN in a way to facilitate an automatic seat occupancy monitoring system. Unlike conventional methods of using manual monitoring or weights and occupancy switches as inanimate indicators, it provides real-time seat availability data which allows no human intervention to be a part of the process. Data collection and model training evaluation are considered using annotated datasets with images of library seating layouts. The Faster R-CNN model is trained such that it can accurately detect vacancy or occupancy at library seats. This work takes a futuristic approach towards smart library management systems, in which user needs are changing, and considers the use of high-end computer vision technologies to be integrated into all such libraries. The proposed system aims to leverage the effectiveness of Faster R-CNN and go a long way in redefining seat occupancy management for university libraries by enabling better efficiency, resource utilization, as well as user satisfaction in prospect.
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Keywords: Numerical Models, Object Detection, Math Regression, Convolutional, R-CNN Model, Model Training and Evaluation, Computer Vision, Annotated Datasets, Efficiency in Seat Management, Futuristic Library Systems
Pages: 83-92
DOI: 10.37394/23209.2025.22.9