Corporate IT-Support Help-Desk Process Hybrid-Automation Solution with Machine Learning Approach

Abstract

Comprehensive IT support teams in large scale organizations require more man power for handling engagement and requests of employees from different channels on a 24×7 basis. Automated email technical queries help desk is proposed to have instant real-time quick solutions and email categorisation. Email topic modelling with various machine learning, deeplearning approaches are compared with different features for a scalable, generalised solution along with sure-shot static rules. Email's title, body, attachment, OCR text, and some feature engineered custom features are given as input elements. XGBoost cascaded hierarchical models, Bi-LSTM model with word embeddings perform well showing 77.3 overall accuracy For the real world corporate email data set. By introducing the thresholding techniques, the overall automation system architecture provides 85.6 percentage of accuracy for real world corporate emails. Combination of quick fixes, static rules, ML categorization as a low cost inference solution reduces 81 percentage of the human effort in the process of automation and real time implementation.

Improving Support Ticket Systems Using Machine Learning: A Literature Review

“…When analyzing the 4 papers presenting DNNbased approaches [19,21,23,29], we found that two of those papers used specific LSTM-networks, which were both the best performing approaches in that papers [19,29]. Since two papers are not a sufficiently large sample, no general statement can be derived here.…”

“…İşcen and Gürbüz [2] x Lo, Tiba [25] x x Mukunthan and Selvakumar [26] x Nayebi, Dicke [6] x x Mandal, Agarwal [11] x Misra and Podder [27] (x) x Palacios, Morillas [28] (x) x Shanmugalingam, Chandrasekara [29] x…”

“…This probably is the reason that mostly in the years 2018 and 2019, RF and SVM were the approaches most implemented and evaluated and also best performing [2,6,8,9,30,36]. Since 2019, some authors also propose artificial-neuronal-networkbased solutions, because of the good performance of neuronal network approaches in other fields of ML [19,21,23,29]. While there is some promising evidence, that Deep Neuronal Network (DNN) based solutions might be able to outperform present ML approaches like RF or SVM [29] (reported accuracies: 77% [29], 83% [19], 99% [21]), this point seems not to be fully reached yet [23].…”

“…Since 2019, some authors also propose artificial-neuronal-networkbased solutions, because of the good performance of neuronal network approaches in other fields of ML [19,21,23,29]. While there is some promising evidence, that Deep Neuronal Network (DNN) based solutions might be able to outperform present ML approaches like RF or SVM [29] (reported accuracies: 77% [29], 83% [19], 99% [21]), this point seems not to be fully reached yet [23]. Revina, Buza [23] find in their conclusion (page 11) "that simple algorithms work well if using appropriate linguistic features" and can be equally performative.…”

“…In most cases, a practical use case was on hand, such that the authors mainly developed such an artifact for or optimized an existing one in a distinct situation (for example in [1]; [8]; [6]; [29]; or [17]). This of course leads to a problem of generalizing their results as already ascertained by Misra and Podder [27].…”