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dc.contributor.advisorRhaman, Md.Khalilur
dc.contributor.authorDas, Anindya
dc.contributor.authorNayeem, Zannatun
dc.contributor.authorFaysal, Abu Saleh
dc.contributor.authorHimu, Fardoush Hassan
dc.date.accessioned2021-05-29T08:37:03Z
dc.date.available2021-05-29T08:37:03Z
dc.date.copyright2020
dc.date.issued2020-04
dc.identifier.otherID 16101032
dc.identifier.otherID 16301021
dc.identifier.otherID 17301190
dc.identifier.otherID 17301212
dc.identifier.urihttp://dspace.bracu.ac.bd/xmlui/handle/10361/14439
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 55-57).
dc.description.abstractHealth issues often stay hidden due to not having regular health checkups. Sometimes these issues build-up to a signi cant health hazard which stays hidden until it's often too late. So we came up with a series of ideas that can deal with the abovestated problems and to some extent solve them. Our proposed device can actively check body vitals, send data through the cloud to designated doctors, and give patients noti cation of hazard from doctors. To carry out the above-stated solutions, we are designing an IoT device that interfaces multiple sensors to a microcomputer and sends the collected data to a cloud server for further manipulation which will be done by Machine Learning. After analysis, if the doctor feels there is any risk of health hazard to the patient, he/she can send in the noti cation of hazard through our proposed device.en_US
dc.description.statementofresponsibilityAnindya Das
dc.description.statementofresponsibilityZannatun Nayeem
dc.description.statementofresponsibilityAbu Saleh Faysal
dc.description.statementofresponsibilityFardoush Hassan Himu
dc.format.extent57 pages
dc.language.isoenen_US
dc.publisherBrac Universityen_US
dc.rightsBrac University theses 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.subjectHealth Issueen_US
dc.subjectIoTen_US
dc.subjectCloud serveren_US
dc.subjectMachine Learningen_US
dc.subject.lcshMachine learning
dc.subject.lcshCloud computing
dc.titleHealth monitoring IoT device with risk prediction using cloud computing and machine learningen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB. Computer Science


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