Journal article Open Access
Vidhyavani.A; Pooja Gopi; Sushil Ram; Sujay Sukumar
<?xml version='1.0' encoding='UTF-8'?> <record xmlns="http://www.loc.gov/MARC21/slim"> <leader>00000nam##2200000uu#4500</leader> <datafield tag="041" ind1=" " ind2=" "> <subfield code="a">eng</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">Deep learning, gated recurrent unit (GRU), Navigation prediction, user interaction, web applications.</subfield> </datafield> <controlfield tag="005">20220115134851.0</controlfield> <controlfield tag="001">5852593</controlfield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">Computer science, SRM Institute of science and Technology, Chennai, India,</subfield> <subfield code="a">Pooja Gopi</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">Computer science, SRM Institute of science and Technology, Chennai, India,</subfield> <subfield code="a">Sushil Ram</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">Computer science, SRM Institute of science and Technology, Chennai, India,</subfield> <subfield code="a">Sujay Sukumar</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">Publisher</subfield> <subfield code="4">spn</subfield> <subfield code="a">Blue Eyes Intelligence Engineering and Sciences Publication(BEIESP)</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="s">229221</subfield> <subfield code="z">md5:feb42f526be01987bfb04a543c49e611</subfield> <subfield code="u">https://zenodo.org/record/5852593/files/B3372079220.pdf</subfield> </datafield> <datafield tag="542" ind1=" " ind2=" "> <subfield code="l">open</subfield> </datafield> <datafield tag="260" ind1=" " ind2=" "> <subfield code="c">2020-07-30</subfield> </datafield> <datafield tag="909" ind1="C" ind2="O"> <subfield code="p">openaire</subfield> <subfield code="o">oai:zenodo.org:5852593</subfield> </datafield> <datafield tag="909" ind1="C" ind2="4"> <subfield code="c">190-192</subfield> <subfield code="n">2</subfield> <subfield code="p">International Journal of Recent Technology and Engineering (IJRTE)</subfield> <subfield code="v">9</subfield> </datafield> <datafield tag="100" ind1=" " ind2=" "> <subfield code="u">Computer science, SRM Institute of science and Technology, Chennai, India,</subfield> <subfield code="a">Vidhyavani.A</subfield> </datafield> <datafield tag="245" ind1=" " ind2=" "> <subfield code="a">Adaptive Prediction of User Interaction based on Deep Learning</subfield> </datafield> <datafield tag="540" ind1=" " ind2=" "> <subfield code="u">https://creativecommons.org/licenses/by/4.0/legalcode</subfield> <subfield code="a">Creative Commons Attribution 4.0 International</subfield> </datafield> <datafield tag="650" ind1="1" ind2="7"> <subfield code="a">cc-by</subfield> <subfield code="2">opendefinition.org</subfield> </datafield> <datafield tag="650" ind1="1" ind2=" "> <subfield code="a">ISSN</subfield> <subfield code="0">(issn)2277-3878</subfield> </datafield> <datafield tag="650" ind1="1" ind2=" "> <subfield code="a">Retrieval Number</subfield> <subfield code="0">(handle)B3372079220/2020©BEIESP</subfield> </datafield> <datafield tag="520" ind1=" " ind2=" "> <subfield code="a"><p>This application starter work in the region of site page expectation is introduced. The structured and actualized model offers customized association by anticipating the client&#39;s conduct from past web perusing history. Those forecasts are a short time later used to streamline the client&#39;s future connections. We propose a Profile-based Interaction Prediction Framework (PIPF), which can illuminate the occasion activated connection expectation issue productively and adequately. In PIPF, we initially change the cooperation sign into a Sliding-window Evolving Graph (SEG) to decrease the information volume and steadily update SEG as the association log develops. At that point, we construct profiles intended to introduce clients&#39; conduct by separating the static and astounding highlights from SEG. The static (separately, astonishing) stress mirrors the normality of clients&#39; conduct (individually, the transient conduct). At the point when an occasion happens, we process the closeness between the event and every competitor connects.</p></subfield> </datafield> <datafield tag="773" ind1=" " ind2=" "> <subfield code="n">issn</subfield> <subfield code="i">isCitedBy</subfield> <subfield code="a">2277-3878</subfield> </datafield> <datafield tag="024" ind1=" " ind2=" "> <subfield code="a">10.35940/ijrte.B3372.079220</subfield> <subfield code="2">doi</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">publication</subfield> <subfield code="b">article</subfield> </datafield> </record>
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