## 8 Conclusions  
This paper leveraged state-of-art machine-learning (NB, SVM, GB, XGBoost, CatBoost), deep-learning (GRU, CNN, Bi-LSTM), and transformer architectures (BERT, Roberta, and ERNIE) to classify the provided incidents as major (MIR) and non-major (NMIR) to address the challenge of IT incident prediction. This paper was the first attempt to employ deep learning and transformers for solving IT service management problems, to the best of our knowledge. We experimented with three different sources of incidents: agency, customer, and employee. The transformer architectures (BERT, RoBERTa, and ERNIE) and XGBoost outperformed all other methods, with a 93% ROC for agency records, and with CNN, a 95% and 74% for employee and customers, respectively. To address the problem of data imbalance in our work, we resampled and curated synthetic data (SMOTE). With resampling, we achieved 95% with ERNIE for agency, 95% with BERT and ERNIE for employee, 99% with transformer and deep-learning models for customers, and 93% with BERT and ERNIE for all combined (employee, agency, and customer). For synthetic data, XGBoost had 94% for agency, 86% for employees, and with GB, 85% for customers. With the resampling and synthetic data curation, we noticed significant relative improvements, at 2.10% for agency and employees and 33% for the customer. We also identified the limitations of deep learning models for SMOTE, which we plan to address in the future. Additionally, our proposed framework computed the average prediction horizon, which was 2 h 30 min and leveraged this to minimize the meantime to resolve incidents. In the future, we will implement our framework for managing and reducing change failure rates associated with the incident mean time to resolution (MTTR). For future work, we are planning to perform a complete analysis of ITSM with state-of-the-art cross-validation techniques because the literature lacks this domain.  
S.A.: Writing--review and editing, Writing--original draft, Proposed Framework. M.S.: Methodology, Writing--the original draft, Writing review and editing, Supervision. B.D.: review and Project administration. E.R.: Proofreading, Writing--review and editing, Supervision. K.H.: review and Project administration. M.B.: Proofreading, Writing--review and editing, Supervision and Funding acquisition. D.C.: Methodology, Writing--the original draft, Writing--review, editing, Supervision, Project administration, and Funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This work was supported by U.K. Research and Innovation Turing AI Fellowship 2021-2025 funded by the Engineering and Physical Sciences Research Council (grant number EP/V025724/1).  
Not applicable.  
Data Availability Statement: Data will be made available on request.  
We are grateful for access to the tier 2 high-performance computing resources provided by the Northern Ireland High-Performance Computing (NI-HPC) facility, funded by the U.K. Engineering and Physical Sciences Research Council (EPSRC), Grant Nos. EP/T022175/ and EP/W03204X/1. [PERSON] is supported by the UKRI Turing AI Fellowship 2021-2025 funded by the EPSRC (grant number EP/V025724/1). [PERSON] is supported by a [PERSON] Ph.D. scholarship.  
The authors declare no conflict of interest.