Resolving the message riddle: A multi-pronged approach to infer trouble tickets

Shimpi, Vikrant; Patel, Aakash; Kshirsagar, Siddartha; Natu, Maitreya; Bhave, Monika

doi:10.1109/dsaa53316.2021.9564152

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𝐼 𝑡 (𝐶𝑁𝑁 𝑤𝑖𝑡ℎ 𝑆𝐴𝑀𝑠)−𝐼 𝑡 (𝑂𝑟𝑖𝑔𝑖𝑛𝑎𝑙 𝐶𝑁𝑁)1

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2023

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“…Thus, the size of the hidden layer is C//r. The information obtained from all the channels is fused using the rectified linear unit (ReLU) activation function [30]. The block wise information of the long short-term memory (LSTM) is integrated through the environmental impact assessment (EIA) module.…”

Section: 𝐼 𝑡 (𝐶𝑁𝑁 𝑤𝑖𝑡ℎ 𝑆𝐴𝑀𝑠)−𝐼 𝑡 (𝑂𝑟𝑖𝑔𝑖𝑛𝑎𝑙 𝐶𝑁𝑁)mentioning

confidence: 99%

Utilizing deep learning, feature ranking, and selection strategies to classify diverse information technology ticketing data effectively

Deshai

Rao

Chittineni

et al. 2023

IJ-AI

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In today's internet world, information technology (IT) ticketing services are potentially increasing across many corporations. Therefore, the automatic classification of IT tickets becomes a significant challenge. Feature selection becomes most important, particularly in data sets with several variables and features. However, enhance classification's precision and performance by stopping insignificant variables. Through our earlier research, we have categorized the unsupervised ticket dataset. As a result, we have converted the dataset into a supervised dataset. In this article, the classification of different IT tickets on Machine learning algorithms, Feature ranking, and feature selection techniques are used to improve the efficiency of machine learning algorithms. However, compared to the machine learning (ML) algorithms, the convolutional neural network (CNN) algorithm provides a better classification of the token IDs and provide better accuracy.

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Section: 𝐼 𝑡 (𝐶𝑁𝑁 𝑤𝑖𝑡ℎ 𝑆𝐴𝑀𝑠)−𝐼 𝑡 (𝑂𝑟𝑖𝑔𝑖𝑛𝑎𝑙 𝐶𝑁𝑁)mentioning

confidence: 99%