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Assessment of Library Users' Feedback Using Modified Multilayer Perceptron Neural Networks
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An attempt has been made to evaluate the feedbacks of library users of four different libraries by using neural network based data mining techniques. This paper presents the results of a survey of users' satisfactory level on four different libraries. The survey has been conducted among the users of four libraries of educational institutions of Kovai Medical Center Research and Educational Trust. Data were collected through questionnaires. Artificial neural network based data mining techniques are proposed and applied to assess the libraries in terms of level of satisfaction of users. In order to assess the users' satisfaction level, two neural network techniques: Modified Multilayer Perceptron Network-Supervised and Modified Multilayer Perceptron Network-Unsupervised are proposed. The proposed techniques are compared with the conventional classification algorithm Multilayer Perceptron Neural Network and found better in overall performance. It is found that the quality of service provided by the libraries is highly good and users are highly satisfied with various aspects of library service. The Arts and Science College Library secured the maximum percent in terms of user satisfaction. This shows that the users' satisfaction of ASCL is better than the other libraries. This study provides an insight into the actual quality and satisfactory level of users of libraries after proper assessment. It is strongly expected that the results will help library authorities to enhance services and quality in the near future.
Keywords
Academic Libraries, Users’ Satisfaction, Data Mining, Data Classification, Artificial Neural Networks.
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- Pijitra Jomsri, “Book Recommendation System for Digital Library Based on User Profiles by using Association Rule”, Proceedings of 4th International Conference on Innovative Computing Technology, pp. 130-134, 2014.
- Runhua Wang, Guoquan Liu, Yi Tang, and Yan Li, “Kmeans Clustering Algorithm Application in University Libraries”, Proceedings of 10th IEEE International Conference on Cognitive Informatics and Cognitive Computing, pp. 419-422, 2011.
- Xing Wu, Pawel Rozycki and Bogdan M. Wilamowski, “A Hybrid Constructive Algorithm for Single-Layer Feed Forward Networks Learning”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 26, No. 8, pp. 16591668, 2015.
- Yanhua Sun, “An Assessment Method for College Library Web Site Based on Neural Network”, Proceedings of 2nd International Conference on Intelligent Systems Design and Engineering Application, pp. 773-775, 2012.
- Hong-Liang Dai, “Imbalanced Protein Data Classification using Ensemble FTM-SVM”, IEEE Transactions on NanoBioscience, Vol. 14, No. 4, pp. 350-359, 2015.
- Veepu Uppal and Gunjan Chindwani, “An Empirical Study of Application of Data Mining Techniques in Library System”, International Journal of Computer Applications, Vol. 74, No. 11, pp. 42-46, 2013.
- Keita Tsuji, Erika Kuroo, Sho Sato, Ui Ikeuchi, Atsushi Ikeuchi, Fuyuki Yoshikane and Hiroshi Itsumura, “Use of Library Loan Records for Book Recommendation”, Proceedings of IIAI International Conference on Advanced Applied Informatics, pp. 30-35, 2012.
- Raj Kumar, Bhim Singh, D.T. Shahani, Ambrish Chandra and Kamal Al-Haddad, “Recognition of Power-Quality Disturbances using S-Transform-based ANN Classifier and Rule-based Decision Tree”, IEEE Transactions on Industry Applications, Vol. 51, No. 2, pp. 1249-1258, 2015.
- K.G. Nandha Kumar and T. Christopher, “Application of Data Mining Techniques in Academic Libraries”, International Journal of Applied Engineering Research, Vol. 10, No. 55, pp. 1500-1502, 2015.
- A.K. Pareek and Madan S. Rana, “Study of Information Seeking Behaviour and Library Use Pattern or Researchers in the Banasthali University”, Journal of Library Philosophy and Practice, pp. 1-9, 2013.
- Ping Yu, “Data Mining in Library Reader Management”, Proceedings of International Conference on Network Computing and Information Security, pp. 54-57, 2011.
- Chin-Teng Lin, Mukesh Prasath and Amit Saxena, “An Improved Polynomial Neural Network Classifier using Real-Coded Genetic Algorithm”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 45, No.11, pp. 1389-1401, 2015.
- Gregory Ditzler, Robi Polikar, and Gail Rosen, “Multi-layer and Recursive Neural Networks for Metagenomic Classification”, IEEE Transactions on NanoBioscience, Vol. 14, No. 6, pp. 608-616, 2015.
- Runhua Wang, Yi Tang and Lei Li, “Application of BP Neural Network to Prediction of Library Circulation”, Proceedings of 11th IEEE International Conference on Cognitive Informatics and Cognitive Computing, pp. 420423, 2012.
- Xing Wu, Pawel Rozycki and Bogdan M. Wilamowski, “A Hybrid Constructive Algorithm for Single-Layer Feed Forward Networks Learning”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 26, No. 8, pp. 16591668, 2015.
- Zhen Dong, Yuwei Wu, Mingtao Pei and Yunde Jia, “Vehicle Type Classification using a Semisupervised Convolutional Neural Network”, IEEE Transactions on Intelligent Transportation Systems, Vol. 16, No. 4, pp. 22472256, 2015.
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