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dc.contributor.advisorChakrabarty, Dr. Amitabha
dc.contributor.advisorMostakim, Moin
dc.contributor.authorKhan, Behroz Newaz
dc.contributor.authorSaroar, Sk Golam
dc.contributor.authorAlam, Md. Mosfaiul
dc.contributor.authorGomes, Sebastian Romy
dc.date.accessioned2017-05-11T06:08:08Z
dc.date.available2017-05-11T06:08:08Z
dc.date.copyright2017
dc.date.issued2017-04
dc.identifier.otherID 12101023
dc.identifier.otherID 13101251
dc.identifier.otherID 13101047
dc.identifier.otherID 13101058
dc.identifier.urihttp://hdl.handle.net/10361/8116
dc.descriptionThis thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.en_US
dc.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (page 71-74).
dc.description.abstractThis research investigates a comparison between two different approaches for classifying emails based on their categories. Naive Bayes and Hidden Markov Model (HMM), two different machine learning algorithms, both have been used for detecting whether an email is important or spam. Naive Bayes Classifier is based on conditional probabilities. It is fast and works great with small dataset. It considers independent words as a feature. HMM is a generative, probabilistic model that provides us with distribution over the sequences of observations. HMMs can handle inputs of variable length and help programs come to the most likely decision, based on both previous decisions and current data. Various combinations of NLP techniques- stopwords removing, stemming, lemmatizing have been tried on both the algorithms to inspect the differences in accuracy as well as to find the best method among them. Along with classifying emails, this paper also describes the methodologies used for automatic meeting scheduling by an intelligent email assistant. Users who regularly send or receive messages for setting up meetings will be greatly benefitted by this system as it will classify their emails and schedule their meetings automatically.en_US
dc.description.statementofresponsibilityKhan, Behroz Newaz
dc.description.statementofresponsibilitySaroar, Sk Golam
dc.description.statementofresponsibilityAlam, Md. Mosfaiul
dc.description.statementofresponsibilityGomes, Sebastian Romy
dc.format.extent74 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University thesis are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectEmail classificationen_US
dc.subjectMeeting schedulingen_US
dc.subjectClassifier algorithmen_US
dc.titleEmail classification and meeting scheduling using classifier algorithmen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering


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