Deep learning based it service desk ticket classifier using CNN (Record no. 19047)
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| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20230327102403.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230327b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 20280 |
| Author | Paramesh, S. P. |
| 245 ## - TITLE STATEMENT | |
| Title | Deep learning based it service desk ticket classifier using CNN |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.13(1), Oct |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Chennai |
| Name of publisher, distributor, etc. | ICT Academy |
| Year | 2022 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 2805-2812p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Assignment of problem tickets to a proper resolver group is an<br/>important aspect and crucial step in any IT Service management tools<br/>like IT Service desk systems. Manual categorization of tickets may lead<br/>to dispatching of problem tickets to an inappropriate expert group,<br/>reassignment of tickets, delays the response time and interrupts the<br/>normal functioning of the business. Traditional supervised machine<br/>learning approaches can be leveraged to train an automated service<br/>desk ticket classifier by using the historical ticket data. Sparsity, non-<br/>linearity, overfitting and handcrafting of features are some of the issues<br/>concerning the traditional ticket classifiers. In this research work, a<br/>deep neural network based on Convolution Neural Network (CNN) is<br/>proposed for the automated classification of service desk tickets. CNN<br/>automatically extracts the most salient features of the ticket<br/>descriptions represented using word embeddings. The extracted<br/>features are further used by the output classification layer for efficient<br/>ticket category prediction. To corroborate the efficacy of the proposed<br/>ticket classifier model, we empirically validated it using a real IT<br/>infrastructure service desk data and compared the results with the<br/>traditional classifier models like Support Vector machines, Naive<br/>Bayes, Logistic Regression and K-nearest neighbour. The proposed<br/>CNN model with proper hyperparameters tuning outperforms the<br/>traditional classifiers in terms of overall model performance.<br/>Assignment of tickets to the correct domain groups, speedy resolution,<br/>improved productivity, increased customer satisfaction and<br/>uninterrupted business are some of the benefits of the proposed<br/>automated ticket classifier model. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4622 |
| Topical term or geographic name entry element | Computer Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 20281 |
| Co-Author | Shreedhara, K. S. |
| 773 0# - HOST ITEM ENTRY | |
| Title | ICTACT Journal on Soft Computing (IJSC) |
| Place, publisher, and date of publication | Chennai ICT Academy |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://ictactjournals.in/paper/IJSC_Vol_13_Iss_1_Paper_9_2805_2812.pdf |
| Link text | Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| 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 | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 27/03/2023 | 2023-0518 | 27/03/2023 | 27/03/2023 | Articles Abstract Database |