Establishing an Interactive Virtual Library for Medical Manuscript Preservation Using KNN/SVM and an Amazon Elastic MapReduce Model

Abstract

The objective of this study was to assess the viability of establishing an interactive Virtual National Library of Medicine (VNL-M). This research proposes a solution that leverages machine learning algorithms and cloud computing technologies, including advanced techniques such as Hadoop, MapReduce, and Cascading, to virtualize a significant number of medical manuscripts from the National Library of Tunisia. The proposed solution has two phases: •The first presents a hybrid (KNN/SVM) approach for an optical character recognition (OCR) system. Because this technology necessitates high bandwidth and computational capacity, spreading it via distributed architecture or platforms may be a viable alternative for improving performance. •The second entails considering cloud computing as an infrastructure (IaaS) to deploy virtualization techniques for the National Library of Tunisia's “medicine and health” subject area, which belongs to the “natural sciences and mathematics” class in the Dewey Decimal Classification (DDC) system. Furthermore, Cloud storing as a Service (SaaS) is employed for the storing and retrieval of enormous amounts of medical manuscript information. The proposed solution was evaluated by conducting experiments using S3 and Amazon EC2 Elstic Map Reduce with an interesting-scale dataset from a database of manuscript heritage. Lastly, the VNL-M is published as a Web service (VNL-Mweb Service).