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Implementation of an Intelligent Online Job Portal Using Machine Learning Algorithms

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dc.contributor.author Tasnim, Zarrin
dc.contributor.author Shamrat, F. M. Javed Mehedi
dc.contributor.author Allayear, Shaikh Muhammad
dc.contributor.author Ahmed, Khobayeb
dc.contributor.author Nobel, Naimul Islam
dc.date.accessioned 2022-03-28T06:47:10Z
dc.date.available 2022-03-28T06:47:10Z
dc.date.issued 2021
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/7618
dc.description.abstract Business intelligence and analytics are data management solutions implemented in companies and enterprises to collect historical and present data, while using statistics and software to analyze raw information, and deliver insights for making better future decisions. In the circumstances of today’s world, to survive and established own business need an analytical and find an easiest way or intelligence business model. The main objective is to examine the performance of various Machine Learning algorithms in order to perform with the system of an online job portal. This proposed module integrated with three phase such as, the Clusters similar kind of job search phase (CSK) is a way of knowing the demand is to create a visual graph showing clusters of similar kinds of job searched by the job seekers in the website of the job portal, the email notifications send phase (ENS) is responsible to send email notifications to the job seekers when a job circular is posted in the website, extract the job circular phase (EJC) is the way to extract the job circular post from the career section of each of the company’s website. The result shows the successful clustering of similar job search, email notification send to specific people and extracts the information from the web. en_US
dc.language.iso en_US en_US
dc.publisher Scopus en_US
dc.subject Machine learning algorithms en_US
dc.subject intelligent online job portal en_US
dc.subject K-means algorithm en_US
dc.title Implementation of an Intelligent Online Job Portal Using Machine Learning Algorithms en_US
dc.type Article en_US

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