IT support helpdesk ticket service classification using recurrent neural network / (Record no. 26877)

MARC details
000 -LEADER
fixed length control field 02406nam a2200325 i 4500
003 - CONTROL NUMBER IDENTIFIER
control field CSPC
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240123102550.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 240123s2023 ph a b 000 0 eng d
040 ## - CATALOGING SOURCE
Transcribing agency CSPC
Original cataloging agency CSPC
Language of cataloging eng
090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN)
Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) UTH BSCS B863i 2023
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Bufete, Niño V.,
Relator term author.
245 10 - TITLE STATEMENT
Title IT support helpdesk ticket service classification using recurrent neural network /
Statement of responsibility, etc. Niño V. Bufete, [and 4 others].
300 ## - PHYSICAL DESCRIPTION
Extent 103 leaves.
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Content type term text
Source rdacontent
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Media type term unmediated
Source rdamedia
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Carrier type term volume
Source rdacarrier
502 ## - DISSERTATION NOTE
Dissertation note Undergraduate Theses (BSCS) - Computer Science
Name of granting institution Camarines Sur Polytechnic Colleges, 2023.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes bibliographical references.
520 3# - SUMMARY, ETC.
Summary, etc. Helpdesks are typically used to troubleshoot customer issues within your organization. Processing user request for another user support through a ticketing system component that businesses must offer to its customers. The classification process for this ticket classifier system's unstructured noisy data set is an important duty. Transformer model is built using an empirically developed methodology that includes the training ticket datasets, ticket data pre-processing, and machine learning algorithm tuning. RNN methods the first word and enters the results back into the layer that performs the next word. This procedure ensures customer satisfaction while also keeping customers informed. There is a great deal of confusion regarding the categories into which text sentences will be thrown with a high probability based on the user's input. Normal, medium, and high. The researchers used RNN to identify the text input of the user requesting assistance. This paper proposes a user-friendly helpdesk built with the flask framework that can accept user messages. This could only be achieved by combining the RNN and transformer models. The paper primarily addresses user messages based on their urgency and concerns.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Martinez, Daniel L.,
Relator term author
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Panambo, Erica Mae A.,
Relator term author
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Piaña, Jeric R.,
Relator term author
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Santillan, Karra Caye S.,
Relator term author
720 1# - ADDED ENTRY--UNCONTROLLED NAME
Name Fortuno, Kaela Marie N.,
Relator term adviser
720 1# - ADDED ENTRY--UNCONTROLLED NAME
Name Onesa, Rosel O.,
Relator term panelist, chair
720 1# - ADDED ENTRY--UNCONTROLLED NAME
Name Benosa, Brenda Dy-Po,
Relator term panelist
720 1# - ADDED ENTRY--UNCONTROLLED NAME
Name Ramizares, Maricris L.,
Relator term panelist
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Suppress in OPAC No
Koha item type Unpublished Materials
Classification part BSCS
Item part B863i
Call number prefix UTH
Call number suffix 2023
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Shelving location Date acquired Total Checkouts Full call number Barcode Date last seen Price effective from Koha item type
    Dewey Decimal Classification     Main Library Main Library Undergraduate Thesis Section 01/23/2024   UTH BSCS B863i 2023 006069UM 01/23/2024 01/23/2024 Unpublished Materials