Academic support agent using SVM and logistic regression

The main resource of different universities is their students. Universities and students both contribute significantly to the production of highly qualified graduates through their achievements in the classroom. Academic performance refers to a student’s attainment of their educational objective and...

Full description

Saved in:
Bibliographic Details
Main Authors: Ashokkumar, K., Diviyash, R., Dineshwaran, B., Reddy, Venna Sai Teja
Format: Conference Proceeding
Language:English
Subjects:
Colleges & universities
Datasets
Machine learning
Performance evaluation
Scientific visualization
Students
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page
container_issue 1
container_start_page
container_title
container_volume 3075
creator Ashokkumar, K.
Diviyash, R.
Dineshwaran, B.
Reddy, Venna Sai Teja
description The main resource of different universities is their students. Universities and students both contribute significantly to the production of highly qualified graduates through their achievements in the classroom. Academic performance refers to a student’s attainment of their educational objective and can be evaluated and tested through exams, assessments, and other measurement tools. However, because pupils may have varying levels of performance achievement, academic performance attainment varies. In this project, we create a student database using machine learning models and analyse their performances. We use the datasets of other groups and perform data visualization for the whole dataset. The input is the students’ dataset, and the output is the graphs and accuracy level analysing the academic performances.
doi_str_mv 10.1063/5.0217592
format conference_proceeding
fullrecord <record><control><sourceid>proquest_scita</sourceid><recordid>TN_cdi_scitation_primary_10_1063_5_0217592</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3085724288</sourcerecordid><originalsourceid>FETCH-LOGICAL-p632-a778dc551f57db99daf4196abe3d2d64b0ed010b2b48860a8baa8b1a908543393</originalsourceid><addsrcrecordid>eNotkM9LwzAcxYMoWKcH_4OCN6Hzm59NjmPoFCYeHOItJE1aMra2Ju3B_97Idni8y4f3Hg-hewxLDII-8SUQXHNFLlCBOcdVLbC4RAWAYhVh9Psa3aS0ByCqrmWB5Koxzh9DU6Z5HIc4labz_VTOKfRd-fn1XprelYehC2nKUPRd9CmFob9FV605JH939gXavTzv1q_V9mPztl5tq1FQUplc4pq8o-W1s0o50zKshLGeOuIEs-AdYLDEMikFGGlNFjYKJGeUKrpAD6fYMQ4_s0-T3g9z7HOjppmpCSNSZurxRKUmTGbK8_QYw9HEX41B_x-juT4fQ_8AFnFT6Q</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype><pqid>3085724288</pqid></control><display><type>conference_proceeding</type><title>Academic support agent using SVM and logistic regression</title><source>American Institute of Physics:Jisc Collections:Transitional Journals Agreement 2021-23 (Reading list)</source><creator>Ashokkumar, K. ; Diviyash, R. ; Dineshwaran, B. ; Reddy, Venna Sai Teja</creator><contributor>Godfrey Winster, S ; Pushpalatha, M ; Baskar, M ; Kishore Anthuvan Sahayaraj, K</contributor><creatorcontrib>Ashokkumar, K. ; Diviyash, R. ; Dineshwaran, B. ; Reddy, Venna Sai Teja ; Godfrey Winster, S ; Pushpalatha, M ; Baskar, M ; Kishore Anthuvan Sahayaraj, K</creatorcontrib><description>The main resource of different universities is their students. Universities and students both contribute significantly to the production of highly qualified graduates through their achievements in the classroom. Academic performance refers to a student’s attainment of their educational objective and can be evaluated and tested through exams, assessments, and other measurement tools. However, because pupils may have varying levels of performance achievement, academic performance attainment varies. In this project, we create a student database using machine learning models and analyse their performances. We use the datasets of other groups and perform data visualization for the whole dataset. The input is the students’ dataset, and the output is the graphs and accuracy level analysing the academic performances.</description><identifier>ISSN: 0094-243X</identifier><identifier>EISSN: 1551-7616</identifier><identifier>DOI: 10.1063/5.0217592</identifier><identifier>CODEN: APCPCS</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Colleges &amp; universities ; Datasets ; Machine learning ; Performance evaluation ; Scientific visualization ; Students</subject><ispartof>AIP Conference Proceedings, 2024, Vol.3075 (1)</ispartof><rights>Author(s)</rights><rights>2024 Author(s). Published under an exclusive license by AIP Publishing.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>309,310,314,780,784,789,790,23930,23931,25140,27924,27925</link.rule.ids></links><search><contributor>Godfrey Winster, S</contributor><contributor>Pushpalatha, M</contributor><contributor>Baskar, M</contributor><contributor>Kishore Anthuvan Sahayaraj, K</contributor><creatorcontrib>Ashokkumar, K.</creatorcontrib><creatorcontrib>Diviyash, R.</creatorcontrib><creatorcontrib>Dineshwaran, B.</creatorcontrib><creatorcontrib>Reddy, Venna Sai Teja</creatorcontrib><title>Academic support agent using SVM and logistic regression</title><title>AIP Conference Proceedings</title><description>The main resource of different universities is their students. Universities and students both contribute significantly to the production of highly qualified graduates through their achievements in the classroom. Academic performance refers to a student’s attainment of their educational objective and can be evaluated and tested through exams, assessments, and other measurement tools. However, because pupils may have varying levels of performance achievement, academic performance attainment varies. In this project, we create a student database using machine learning models and analyse their performances. We use the datasets of other groups and perform data visualization for the whole dataset. The input is the students’ dataset, and the output is the graphs and accuracy level analysing the academic performances.</description><subject>Colleges &amp; universities</subject><subject>Datasets</subject><subject>Machine learning</subject><subject>Performance evaluation</subject><subject>Scientific visualization</subject><subject>Students</subject><issn>0094-243X</issn><issn>1551-7616</issn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2024</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNotkM9LwzAcxYMoWKcH_4OCN6Hzm59NjmPoFCYeHOItJE1aMra2Ju3B_97Idni8y4f3Hg-hewxLDII-8SUQXHNFLlCBOcdVLbC4RAWAYhVh9Psa3aS0ByCqrmWB5Koxzh9DU6Z5HIc4labz_VTOKfRd-fn1XprelYehC2nKUPRd9CmFob9FV605JH939gXavTzv1q_V9mPztl5tq1FQUplc4pq8o-W1s0o50zKshLGeOuIEs-AdYLDEMikFGGlNFjYKJGeUKrpAD6fYMQ4_s0-T3g9z7HOjppmpCSNSZurxRKUmTGbK8_QYw9HEX41B_x-juT4fQ_8AFnFT6Q</recordid><startdate>20240729</startdate><enddate>20240729</enddate><creator>Ashokkumar, K.</creator><creator>Diviyash, R.</creator><creator>Dineshwaran, B.</creator><creator>Reddy, Venna Sai Teja</creator><general>American Institute of Physics</general><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>20240729</creationdate><title>Academic support agent using SVM and logistic regression</title><author>Ashokkumar, K. ; Diviyash, R. ; Dineshwaran, B. ; Reddy, Venna Sai Teja</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p632-a778dc551f57db99daf4196abe3d2d64b0ed010b2b48860a8baa8b1a908543393</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Colleges &amp; universities</topic><topic>Datasets</topic><topic>Machine learning</topic><topic>Performance evaluation</topic><topic>Scientific visualization</topic><topic>Students</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ashokkumar, K.</creatorcontrib><creatorcontrib>Diviyash, R.</creatorcontrib><creatorcontrib>Dineshwaran, B.</creatorcontrib><creatorcontrib>Reddy, Venna Sai Teja</creatorcontrib><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ashokkumar, K.</au><au>Diviyash, R.</au><au>Dineshwaran, B.</au><au>Reddy, Venna Sai Teja</au><au>Godfrey Winster, S</au><au>Pushpalatha, M</au><au>Baskar, M</au><au>Kishore Anthuvan Sahayaraj, K</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Academic support agent using SVM and logistic regression</atitle><btitle>AIP Conference Proceedings</btitle><date>2024-07-29</date><risdate>2024</risdate><volume>3075</volume><issue>1</issue><issn>0094-243X</issn><eissn>1551-7616</eissn><coden>APCPCS</coden><abstract>The main resource of different universities is their students. Universities and students both contribute significantly to the production of highly qualified graduates through their achievements in the classroom. Academic performance refers to a student’s attainment of their educational objective and can be evaluated and tested through exams, assessments, and other measurement tools. However, because pupils may have varying levels of performance achievement, academic performance attainment varies. In this project, we create a student database using machine learning models and analyse their performances. We use the datasets of other groups and perform data visualization for the whole dataset. The input is the students’ dataset, and the output is the graphs and accuracy level analysing the academic performances.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/5.0217592</doi><tpages>7</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0094-243X
ispartof AIP Conference Proceedings, 2024, Vol.3075 (1)
issn 0094-243X
1551-7616
language eng
recordid cdi_scitation_primary_10_1063_5_0217592
source American Institute of Physics:Jisc Collections:Transitional Journals Agreement 2021-23 (Reading list)
subjects Colleges & universities
Datasets
Machine learning
Performance evaluation
Scientific visualization
Students
title Academic support agent using SVM and logistic regression
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-03T22%3A08%3A22IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_scita&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Academic%20support%20agent%20using%20SVM%20and%20logistic%20regression&rft.btitle=AIP%20Conference%20Proceedings&rft.au=Ashokkumar,%20K.&rft.date=2024-07-29&rft.volume=3075&rft.issue=1&rft.issn=0094-243X&rft.eissn=1551-7616&rft.coden=APCPCS&rft_id=info:doi/10.1063/5.0217592&rft_dat=%3Cproquest_scita%3E3085724288%3C/proquest_scita%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-p632-a778dc551f57db99daf4196abe3d2d64b0ed010b2b48860a8baa8b1a908543393%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=3085724288&rft_id=info:pmid/&rfr_iscdi=true