Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Classification Framework for Fraud Detection Using Hidden Markov Model Subject The topic of the resource Credit card; Emission probability; Fraud detection; Hidden Markov method; Initial probability; Machine learning; Supervised learning Description An account of the resource Machine learning is described as a computer program that learns from experience E with regard to some task T and some performance measure P, if its performance on T improves with E as measured by P. Suppose we have a credit card fraud detection which watches which transactions we mark as fraud or not, and on the basis, it knows how to filter better fraudulent transactions then, E is watching your transactions is fraud or not, T is classifying your transactions as fraud or not, P is number of transactions correctly differentiated as spam or not spam. Machine learning has two types: supervised learning and unsupervised learning. Supervised learning is the type of machine learning where machine is provided with input mapped with its output, and these inputs and outputs are used to make a machine learn a particular function from the trained dataset. There are two branches of supervised learning, i.e., classification and regression. In unsupervised learning, we do not supervise model instead we allow machine to work on its own to discover information. Clustering is type of unsupervised learning. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Hegde D.S.; Samanta D.; Dutta S. Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems, Vol-291, pp. 29-36. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-16-4284-5_3" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-16-4284-5_3</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116791878&amp;doi=10.1007%2F978-981-16-4284-5_3&amp;partnerID=40&amp;md5=1db508346aa18c79dcafa8a2203d2adc" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116791878&amp;doi=10.1007%2f978-981-16-4284-5_3&amp;partnerID=40&amp;md5=1db508346aa18c79dcafa8a2203d2adc</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23673370; ISBN: 978-981164283-8 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Hegde D.S., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Samanta D., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Dutta S., Institute of Engineering and Management, Kolkata, India