Polynomial Regression and Faster R-CNN Models for University Library Decision Implementation Discovery based on Deep Learning

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Hajrulla S., ÖZER Ö., Demir T.

WSEAS Transactions on Information Science and Applications, cilt.22, ss.83-92, 2025 (Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 22
  • Basım Tarihi: 2025
  • Doi Numarası: 10.37394/23209.2025.22.9
  • Dergi Adı: WSEAS Transactions on Information Science and Applications
  • Derginin Tarandığı İndeksler: Scopus
  • Sayfa Sayıları: ss.83-92
  • Anahtar Kelimeler: Annotated Datasets, Computer Vision, Convolutional, Efficiency in Seat Management, Futuristic Library Systems, Math Regression, Model Training and Evaluation, Numerical Models, Object Detection, R-CNN Model
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Kırklareli Üniversitesi Adresli: Evet

Özet

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.