Prediction and Estimation of Book Borrowing in the Library: Machine learning

Sun, Jia

doi:10.31449/inf.v45i1.3431

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…To become compliant with changing and stricter regulatory demands in the world, banks are focused on undertaking preventive measures for effective credit risk management. One of its drivers is to strengthen existing learning models [1,2] to increase their predictive power and help detect future defaults in advance.…”

Section: Introductionmentioning

confidence: 99%

Impact of Data Balancing During Training for Best Predictions

Alsaif

Hidri

2021

IJCAI

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To protect the middle class from over-indebtedness, banking institutions need to implement a flexible analytic-based evaluation method to improve the banking process by detecting customers who are likely to have difficulty in managing their debt. In this paper, we test and evaluate a large variety of data balancing methods on selected machine learning algorithms (MLAs) to overcome the effects of imbalanced data and show their impact on the training step to predict credit risk. Our objective is to deal with data unbalance to achieve the best predictions. We investigated the performance of these methods by different learners when classification models are trained using MLAs.Povzetek: Predstavljena je metoda strojnega učenja z neuravnoteženimi podatki za oceno tveganja prezadolžitve srednjega razreda.

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Section: Introductionmentioning

confidence: 99%

Impact of Data Balancing During Training for Best Predictions

Alsaif

Hidri

2021

IJCAI

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Machine Learning Algorithms for Predicting and Estimating Book Borrowing in University Libraries

Zhang

2024

JACIII

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Accurate prediction of borrowing volume of library books is conducive to the decision-making of the managers. This study briefly introduces the backpropagation neural network (BPNN) algorithm used to predict the borrowing volume of university libraries. The factor analysis method and genetic algorithm were employed to optimize the BPNN algorithm to improve its prediction performance. The book borrowing records of 2022 from Handan College Library were considered the subject of simulation experiments. The designed algorithm was compared with the extreme gradient boosting and traditional BPNN algorithms in the experiments. The results showed that average borrowing time, book lending ratio, book return ratio, and average grade of borrowers could be used as the input features of BPNN. The improved BPNN algorithm demonstrated faster convergence and a smaller error during training. The borrowing volume predicted by the improved BPNN algorithm closely matched the actual volume, and the increase in prediction time did not lead to a significant change in the prediction error.

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QR Code-Integrated ML Analytics Library System

Pandey,

Sahai,

Singh

et al. 2024

Advances in Computational Intelligence and Robotics

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Following the global COVID-19 outbreak, libraries are forced to adopt touchless technologies to protect their clients and employees. This chapter examines an innovative approach that, by seamlessly fusing QR codes and Machine Learning (ML) approaches, exceeds conventional library administration. Beginning with a thorough analysis of traditional library management systems, the chapter reveals their shortcomings and draws attention to the flaws in Radio Frequency Identification (RFID) technology and insufficient automation techniques, highlighting the demand for a complete touchless solution. The idea of a unique QR code for every student, closely connected to their identification information, lies at the heart of the proposed system. The unique ID mapping system, which gives each book a unique identity, is a crucial invention. This chapter describes a ground-breaking touchless library concept with low extra expenditures, less on-site personnel requirements, simplified inventory management, and simple book issuing and return procedures characterized by the need for health and safety.

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Prediction and Estimation of Book Borrowing in the Library: Machine learning

Cited by 3 publications

References 22 publications

Impact of Data Balancing During Training for Best Predictions

Impact of Data Balancing During Training for Best Predictions

Machine Learning Algorithms for Predicting and Estimating Book Borrowing in University Libraries

QR Code-Integrated ML Analytics Library System

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