Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/29631
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dc.contributor.authorBabaee, Mohammadrezaen_UK
dc.contributor.authorSarabadani, Hamidrezaen_UK
dc.date.accessioned2019-05-30T14:32:29Z-
dc.date.available2019-05-30T14:32:29Z-
dc.date.issued2019en_UK
dc.identifier.urihttp://hdl.handle.net/1893/29631-
dc.description.abstractThis paper explores the application of different data mining and machine learning algorithms to propose an effective technique to filter out spam SMSs. Due to high competitive nature of MNO business; filtering spam SMSs will have a great impact on the protection of business and profit making. This is mostly because subscribers refuse to use the services of MNOs that are not vigilant about spam SMSs. Based on the CRISP-DM method which is an open standard process model for data analytics projects, machine learning algorithms and data preparation methods have been conducted on a MNO unstructured dataset to transform characters, delete stop words, extract word stems, roots, N-Grams, and classification. Next, numerical Vector Space Models were created utilizing all four types of word vector creation methods. After producing test and train models with machine learning algorithms; accuracy and error rate, recall, precision and the area under curve for each classification algorithm has been measured. Finally, the Bagging algorithm by implementing Binary Term Occurrence vector space creation method showed the highest efficiency rate which can have the highest application in the big data ecosystem of the industry for spam filtering.en_UK
dc.language.isoenen_UK
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_UK
dc.relationBabaee M & Sarabadani H (2019) Data Analytics in Text Messages: A Mobile Network Operator Case Study. In: 9th International Symposium on Telecommunication, IST 2018. 2018 9th International Symposium on Telecommunications (IST), Tehran, Iran, 17.12.2018-19.12.2018. Piscataway, NJ: Institute of Electrical and Electronics Engineers Inc. pp. 330-336. https://doi.org/10.1109/ISTEL.2018.8660969en_UK
dc.rightsThe publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.en_UK
dc.subjectMachine learning algorithmsen_UK
dc.subjectBig Dataen_UK
dc.subjectData modelsen_UK
dc.subjectText miningen_UK
dc.subjectNumerical modelsen_UK
dc.subjectBusinessen_UK
dc.titleData Analytics in Text Messages: A Mobile Network Operator Case Studyen_UK
dc.typeConference Paperen_UK
dc.rights.embargodate2999-12-31en_UK
dc.rights.embargoreason[IEEE-08660969.pdf] The publisher does not allow this work to be made publicly available in this Repository therefore there is an embargo on the full text of the work.en_UK
dc.identifier.doi10.1109/ISTEL.2018.8660969en_UK
dc.citation.spage330en_UK
dc.citation.epage336en_UK
dc.citation.publicationstatusPublisheden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.author.emailBabaee.it110@gmail.comen_UK
dc.citation.btitle9th International Symposium on Telecommunication, IST 2018en_UK
dc.citation.conferencedates2018-12-17 - 2018-12-19en_UK
dc.citation.conferencelocationTehran, Iranen_UK
dc.citation.conferencename2018 9th International Symposium on Telecommunications (IST)en_UK
dc.citation.date07/03/2019en_UK
dc.citation.isbn9781538682746en_UK
dc.publisher.addressPiscataway, NJen_UK
dc.contributor.affiliationUniversity of Nottinghamen_UK
dc.identifier.scopusid2-s2.0-85063880982en_UK
dc.identifier.wtid1279861en_UK
dc.date.accepted2018-10-24en_UK
dc.date.filedepositdate2019-05-30en_UK
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings

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