Ticket automation: An insight into current research with applications to multi-level classification scenarios

Zangari, Alessandro; Marcuzzo, Matteo; Schiavinato, Michele; Gasparetto, Andrea; Albarelli, Andrea

doi:10.1016/j.eswa.2023.119984

Cited by 7 publications

(3 citation statements)

References 35 publications

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“…The body attribute contains textual data, whereas all other fields contain numerical data. Hence, a separate analysis was conducted to identify the performance of classification algorithms using the body attribute [22]. The range for logistic regression is between 0 and 1, but the range for linear regression is unbounded.…”

Section: Methodology 41 Attributes In the Data Setmentioning

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: Methodology 41 Attributes In the Data Setmentioning

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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“…Project #8-Ticket automation [33] This project deals with the problem of handling large amounts of customer requests: machine-learning algorithms are fundamental in streamlining support ticket processing workflows. Two commonly used datasets, both characterized by a two-level hierarchy of labels, are employed and they are descriptive of the ticket's topic at different levels of granularity.…”

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confidence: 99%

The Dilemma of Rapid AI Advancements: Striking a Balance between Innovation and Regulation by Pursuing Risk-Aware Value Creation

Ricciardi Celsi

2023

Information

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This paper proposes the concept of risk-aware actual value as a pivotal metric for evaluating the viability and desirability of AI projects and services in accordance with the AI Act. The framework establishes a direct correlation between the level of risk associated with a product or service and the resulting actual value generated. The AI Act reflects a concerted effort to harness the potential of AI while mitigating risks. The risk-based approach aligns regulatory measures with the specific attributes and potential hazards of distinct AI applications. As trilogue negotiations continue, the regulatory approach of the EU is evolving, highlighting its commitment to responsible and forward-thinking AI governance. Through a dedicated analysis of the AI Act, it becomes evident that products or services categorized as high-risk carry substantial compliance obligations, consequently diminishing their potential value. This underscores the imperative of exercising caution when engaging in projects with elevated risk profiles. Conversely, products or services characterized by lower risk levels are poised to accrue more substantial benefits from their AI and data potential, highlighting the incentive for a discerning approach to risk assessment. Methodologically, we propose an extension of an integrated AI risk management framework that is already existing in the literature, combining it with existing frameworks for measuring value creation from harnessing AI potential. Additionally, we contribute to the applied field of AI by implementing the proposed risk framework across nine industry-relevant use cases. In summation, this paper furnishes a comprehensive approach to achieving equilibrium between innovation and regulation in the realm of AI projects and services. By employing the risk-aware actual value metric, stakeholders are empowered to make informed decisions that prioritize safety and maximize the potential benefits of AI initiatives. This framework may stand as a reference point in this time when fostering responsible and sustainable AI development within the industry becomes of paramount importance.

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“…As state by Gumilar et al (2021), F1score is used to measure how much the model able to predict the class correctly. Additionally, F1-score is the harmonic mean of precision and recall, where a higher F1-score indicates a better balance between precision and recall(Zangari et al, 2023; Kasihmuddin et al, 2023). In this study, the ACC will portray on how well estimation the performance of model on a given dataset and F1-score will portray on evaluation the ability of the model to accurately classify instances in a binary classification problem.…”

mentioning

confidence: 99%

Solving Complexity Dataset in e-Ticketing using Machine Learning to Determine Optimum Feature

Mohd Jamaludin,

Majahar Ali,

Vun

et al. 2023

Mal. J. Fund. Appl. Sci.

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e-ticketing is one of the common applications used in technical support in Information Technology (IT) and has been used worldwide in any field of company. The benefits of e-ticketing can reduce the human efforts, increase the sufficiency of system and provides the benefits and efficiency to the customers. Also, e-ticketing ticketing system can enhance worker safety, improving productivity, increasing project efficiency and cause the good impact on the performance of the business in terms of profitability. The main objective of this study is defining the model performance in each important feature by analysing the complex dataset by using logistic regression as a Machine Learning (ML) algorithm. In evaluation the performance of classifier, the dataset is injected to Python programming and split into 90% as training set and 10% for the testing set. From the analysis, the study found that only 3 out of 11 independent features in dataset that are relevant chosen to proceed the ML analysis. From the result, the accuracy for sct_short_description, sct_cmdb_ci, and sct_assignment_group is 41.65%, 48.77% and 96.49%, respectively. It showed that the accuracy’s result for the sct_assignment_group resulted that the model is very good accuracy and indicate that the model is well performing. Meanwhile, the value of F1-score is 96.11% in each feature. This result indicates that the model has a good balance of precision and recall in its binary classification predictions. Hence, the study considers the sct_assignment_group as a best features to proceed the analysis. The future study will consider dealing the combination of complexity features by implementing more analysis on ML such as Support Vector Machines and Naïve Bayes.

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Ticket automation: An insight into current research with applications to multi-level classification scenarios

Cited by 7 publications

References 35 publications

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

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

The Dilemma of Rapid AI Advancements: Striking a Balance between Innovation and Regulation by Pursuing Risk-Aware Value Creation

Solving Complexity Dataset in e-Ticketing using Machine Learning to Determine Optimum Feature

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