Explaining the Unseen: Leveraging XAI to Enhance the Trustworthiness of Black-Box Models in Performance Testing-Reference-Cited by-同舟云学术

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Explaining the Unseen: Leveraging XAI to Enhance the Trustworthiness of Black-Box Models in Performance Testing

Published:2023 Issue: Volume:224 Page:83-90
ISSN:1877-0509
Container-title:Procedia Computer Science
language:en
Short-container-title:Procedia Computer Science

Author:

Shoemaker Eric,Malik Haroon,Narman Husnu,Chaudri Jamil

Publisher

Elsevier BV

Subject

General Engineering

Reference10 articles.

1. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. Available: https://christophm.github.io/interpretable-ml-book/.

2. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI);Adadi;IEEE Access,2018

3. Causes of Failures in Web Applications;Pretet;Parallel Data Laboratory,2005

4. Pinpointing the subsystems responsible for the performance deviations in a load test;Malik,2010

5. Automatic Comparison of Load Tests to Support the Performance Analysis of Large Enterprise Systems;Malik,2010

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1. Improving Tuberculosis Diagnosis using Explainable Artificial Intelligence in Medical Imaging;Journal of Mathematical Sciences and Modelling;2024-05-08

2. The Development of a Medical Equipment Inventory Information System;2024 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS);2024-02-21

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