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            <name>Title</name>
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                <text>Articles</text>
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    <name>Article</name>
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          <name>Title</name>
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              <text>Analyzing students academic performance using multilayer perceptron model</text>
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          <name>Subject</name>
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              <text>K-means; K-Nearest Neighbor (KNN); Machine Learning; Multilayer Perceptron; Students Performance</text>
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              <text>Identification of the students behavior in the class room environment is very important. It helps the lecturer to identify the needs of the students. It also aids in identifying the strength and weakness of the individual and guide them to improve on their performance. Observing and supervising the students regularly can improve their performance. The data has been collected from 120 students who took the common the course taught by two different lectures. The students were observed based on the internal assignments and quizzes and the model exam given by the respective lecturers. In this paper the students are categorized into different groups based on their performance using Multilayer Perceptron (MLP) and also different factors which are influencing the performance of the students are identified.  BEIESP.</text>
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          <name>Creator</name>
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              <text>Sinthia G.; Balamurugan M.</text>
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              <text>International Journal of Recent Technology and Engineering, Vol-7, No. 5, pp. 156-160.</text>
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          <name>Publisher</name>
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              <text>Blue Eyes Intelligence Engineering and Sciences Publication</text>
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          <name>Date</name>
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              <text>2019-01-01</text>
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              <text>&lt;a href="" target="_blank" rel="noreferrer noopener"&gt;&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068001944&amp;amp;partnerID=40&amp;amp;md5=86e0dc54ef165ac1c593cc4522645ec0" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068001944&amp;amp;partnerID=40&amp;amp;md5=86e0dc54ef165ac1c593cc4522645ec0&lt;/a&gt;</text>
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          <name>Rights</name>
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              <text>Restricted Access</text>
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          <name>Relation</name>
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              <text>ISSN: 22773878</text>
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              <text>Online</text>
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              <text>English</text>
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              <text>Sinthia G., Dept. of Computer Science and Engineering CHRIST (Deemed to be University), Faculty of Engineering, Bangalore, Karnataka, India; Balamurugan M., Dept. of Computer Science and Engineering CHRIST (Deemed to be University), Faculty of Engineering, Bangalore, Karnataka, India</text>
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