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 Design of a Decision Making Model for Integrating Dark Data from Hybrid Sectors Subject The topic of the resource Dark data; Electronic Health Record; Entity based Recognition; Natural Language Processing Description An account of the resource The research on Dark data, from its definition to identification and utilization is a widely identified and encountered research problem since 2012 when Gartner defined Dark data as every possible information that an organization collects, process, analyze and store throughout regular business activities, but usually fails to make use of the stored information for other suitable purposes. The presence of dark data and its impact has been experienced by every sector, these data occupy large storage and remain unused. In this paper, we analyze Dark Data and proposed a design model to utilize dark data from multiple sectors and providing a solution to any critical situation a person might be in. For eg: Multiple cash transactions from an organizational bank account in a hospital successively over a period of 2-3 days may indicate a health emergency of any particular employee from that organization. Thus we are considering institutional data, medical data, and banking data in which machine learning algorithms can contribute huge changes in the current system and can help the decision-makers to make better decisions. The paper also proposes a few techniques and methods for the conversion of unstructured dark data to structured one and some extraction techniques for data using NLP and Machine Learning. Grenze Scientific Society, 2022. Creator An entity primarily responsible for making the resource Maju S.V.; Prakasi O.S.G. Source A related resource from which the described resource is derived 13th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2022, Vol-8, pp. 705-712. Publisher An entity responsible for making the resource available Grenze Scientific Society 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="" target="_blank" rel="noreferrer noopener"></a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143319396&amp;partnerID=40&amp;md5=d4b71814282f1aadf4dc913cecd0655e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143319396&amp;partnerID=40&amp;md5=d4b71814282f1aadf4dc913cecd0655e</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-171385793-8 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 Maju S.V., Computer Science and Engineering Department, Christ (Deemed to be University), Bangalore, India; Prakasi O.S.G., Computer Science and Engineering Department, Christ (Deemed to be University), Bangalore, India