DeepER: A Deep Learning based Emergency Resolution Time Prediction System

Bejarano, Gissella; Kulkarni, Adita; Luo, Xianzhi; Seetharam, A.; Ramesh, A.

doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics50389.2020.00090

Cited by 1 publication

(1 citation statement)

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“…Bejarano et al [25] proposed a new method for forecasting emergency resolution called a Deep Learning-based Emergency Resolution Time Prediction System (DeepER). Deeper is a sequence-to-sequence model that employs recurrent neural networks (RNNs) as the neural network architecture.…”

Section: ) Comparison Of Dt Rf and Xgboostmentioning

confidence: 99%

Performance Evaluation on Resolution Time Prediction Using Machine Learning Techniques

Tong-Ern,

Su-Cheng,

Kok-Why

et al. 2024

JOIV : Int. J. Inform. Visualization

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The quality of customer service emphasizes support tickets. An excellent support ticket system qualifies businesses to provide clients with the finest level of customer support. This enables enterprises to guarantee the consistency of quality customer service delivered successfully, ensuring all clients have a good experience regardless of the nature of their inquiry or issue. To further achieve a higher efficiency of resource allocation, this is when the prediction of ticket resolution time comes into place. The advancing technologies, including artificial intelligence (AI) and machine learning (ML), can perform predictions on the duration required to tackle specific problems based on past similar data. ML enables the possibility of automatically classifying tickets, making it possible to anticipate the time resolution for cases. This paper explores various ML techniques widely applied in the Resolution Time Prediction system and investigates the performance of three selected ML techniques via the benchmarking dataset obtained from the UCI Machine Learning Repository. Implementing selected techniques will involve creating a graphical user interface and data visualization to provide insight for data analysis. The best technique will be concluded after performing the ML technique evaluation. The evaluation metrics involved in this step include Root Mean Square Error (RMSE) and Root Mean Absolute Error (MAE). The experimental evaluation shows that the best performance among the selected ML techniques is Random Forest (RF).

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Section: ) Comparison Of Dt Rf and Xgboostmentioning

confidence: 99%