Intrusion Detection Using Text Mining in a Web-Based Telemedicine System

Adeva, Juan José García; Pikatza, Juan Manuel; Flórez, Sandra Yanet Velazco; Sobrado, Francisco Javier

doi:10.1007/11589990_131

Cited by 2 publications

(1 citation statement)

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“…In recent years, the development of information systems in the field of business, academics and medicinehasledtoanincreaseintheamountofstoreddatayearbyyear.Avastmajorityofbusiness dataarestoredindocumentsthatarevirtuallyunstructured.Thisiswherethetextminingfitsinto thepicture.Textminingtechnologyisveryhelpfulforpeopletoprocesshugeinformation (Adeva, 2005).Textmininginvolvesimposingstructureupontextsothatrelevantinformationcanbeextracted fromit (Miller,2005).Forexample,inthefieldofacademics,manyscholasticconferencestake placeeveryyear.Toextendtheknowledgeofinterestofthecurrentfocusofaconference,organizers oftendesiretoofferadditionalworkshops.Inmanycases,theseadditionaleventsareintendedto introducetheparticipantsoftheprogramtosignificantstreamsofresearchinrelatedfieldsofstudy andtrytoidentifytheemergenttechnologiesintermsofresearchinterestsandfocus.Identification ofreasonablecandidatetechnologiesforsuchworkshopsisoftensubjectiveratherthanobjectively derivingfromtheexistingandemergingresearch (Romero,2007).Anemergenttrendisatopicarea thatisgrowingininterestandutilityovertime.Thedetectionofnewphrasesandemergingtechnical termshasbecomeveryimportant (Abe,2009andAmarasirietal.,2005. Clusteringbydocumentconceptsisapowerfulwayofretrievinginformationfromalargenumber ofdocuments (Amarasiri,2005).Thistaskingeneraldoesnotmakeanyassumptiononthedata distribution.PopularautomaticpatternclusteringmethodssuchasK-meansclustering (Bekkerman, 2001)andMinimalspanningtrees (SolkaJeffery,2005)areavailableforenablingtextcategorization process.…”

Section: Introductionmentioning

confidence: 99%

Text Clustering Using PSO Based Dynamic Adaptive SOM for Detecting Emergent Trends

Chandrakala¹,

Sumathi

A³

et al. 2019

International Journal of Intelligent Information Technologies

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Detection and realization of new trends from corpus are achieved through Emergent Trend Detection (ETD) methods, which is a principal application of text mining. This article discusses the influence of the Particle Swarm Optimization (PSO) on Dynamic Adaptive Self Organizing Maps (DASOM) in the design of an efficient ETD scheme by optimizing the neural parameters of the network. This hybrid machine learning scheme is designed to accomplish maximum accuracy with minimum computational time. The efficiency and scalability of the proposed scheme is analyzed and compared with standard algorithms such as SOM, DASOM and Linear Regression analysis. The system is trained and tested on DBLP database, University of Trier, Germany. The superiority of hybrid DASOM algorithm over the well-known algorithms in handling high dimensional large-scale data to detect emergent trends from the corpus is established in this article.

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Section: Introductionmentioning

confidence: 99%