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            <name>Title</name>
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                <text>Conference Papers</text>
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    <name>Conference Paper</name>
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          <name>Title</name>
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              <text>Rating of Online Courses: A Machine Learning Based Prediction Model</text>
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          <name>Subject</name>
          <description>The topic of the resource</description>
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              <text>ANN-PSO; Online courses; Prediction; Ratings</text>
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          <name>Description</name>
          <description>An account of the resource</description>
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              <text>Online courses market has provided an economical and easy access to knowledge. When it comes to make a decision related to purchase of online course, little is known about what attributes can be depended upon to guess the quality of an online course. Ratings for online courses act as a reliable signal for assessing the quality of a course. The study discusses the prediction of ratings for online courses using Artificial Neural Network based on Particle Swarm Optimization (ANN-PSO). The experimental results suggests that ANN-PSO model has the capacity to predict the ratings for online courses on the basis of its attributes with accuracy.  2021 IEEE.</text>
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          <name>Creator</name>
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              <text>Inder S.; Dua G.K.; Verma R.; Sinha S.</text>
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          <name>Source</name>
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              <text>2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2021</text>
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          <name>Publisher</name>
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            <elementText elementTextId="182759">
              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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          <name>Date</name>
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              <text>2021-01-01</text>
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          <name>Identifier</name>
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              <text>&lt;a href="https://doi.org/10.1109/ICRITO51393.2021.9596449" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICRITO51393.2021.9596449&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123363463&amp;amp;doi=10.1109%2FICRITO51393.2021.9596449&amp;amp;partnerID=40&amp;amp;md5=30cc891e37f901b39c93becd5abc7b9b" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123363463&amp;amp;doi=10.1109%2fICRITO51393.2021.9596449&amp;amp;partnerID=40&amp;amp;md5=30cc891e37f901b39c93becd5abc7b9b&lt;/a&gt;</text>
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          <name>Rights</name>
          <description>Information about rights held in and over the resource</description>
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            <elementText elementTextId="182762">
              <text>Restricted Access</text>
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          <name>Relation</name>
          <description>A related resource</description>
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            <elementText elementTextId="182763">
              <text>ISBN: 978-166541703-7</text>
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          <name>Format</name>
          <description>The file format, physical medium, or dimensions of the resource</description>
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              <text>Online</text>
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          <name>Language</name>
          <description>A language of the resource</description>
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              <text>English</text>
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          <name>Type</name>
          <description>The nature or genre of the resource</description>
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              <text>Conference paper</text>
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          <name>Coverage</name>
          <description>The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant</description>
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              <text>Inder S., Chitkara University, Chitkara Business School, Punjab, India; Dua G.K., Chitkara University, Chitkara Business School, Punjab, India; Verma R., Chitkara University, Chitkara Business School, Punjab, India; Sinha S., CHRIST (Deemed to Be University), India</text>
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