| no |
title |
author |
magazine |
year |
volume |
issue |
page(s) |
type |
| 1 |
An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors
|
Yang, Cai
|
|
|
347 |
2 |
p. 1031-1058
|
article
|
| 2 |
From rants to raves: unraveling movie critics’ reviews with explainable artificial intelligence
|
Talaei, Nolan M.
|
|
|
347 |
2 |
p. 937-957
|
article
|
| 3 |
Heterogeneous business network based interpretable competitive firm identification: a graph neural network method
|
Ye, Xiaoqing
|
|
|
347 |
2 |
p. 1133-1161
|
article
|
| 4 |
How to customize an early start preparatory course policy to improve student graduation success: an application of uplift modeling
|
Tanai, Yertai
|
|
|
347 |
2 |
p. 913-936
|
article
|
| 5 |
Interpretable machine learning and explainable artificial intelligence
|
Topuz, Kazim
|
|
|
347 |
2 |
p. 775-782
|
article
|
| 6 |
Interpretable multi-hop knowledge reasoning for gastrointestinal disease
|
Wang, Dujuan
|
|
|
347 |
2 |
p. 959-990
|
article
|
| 7 |
Leveraging interpretable machine learning in intensive care
|
Bohlen, Lasse
|
|
|
347 |
2 |
p. 1093-1132
|
article
|
| 8 |
Modified monotone policy iteration for interpretable policies in Markov decision processes and the impact of state ordering rules
|
Lee, Sun Ju
|
|
|
347 |
2 |
p. 783-841
|
article
|
| 9 |
Population-based exploration in reinforcement learning through repulsive reward shaping using eligibility traces
|
Bal, Melis Ilayda
|
|
|
347 |
2 |
p. 1059-1091
|
article
|
| 10 |
Quantifying and explaining machine learning uncertainty in predictive process monitoring: an operations research perspective
|
Mehdiyev, Nijat
|
|
|
347 |
2 |
p. 991-1030
|
article
|
| 11 |
Streamlining patients’ opioid prescription dosage: an explanatory bayesian model
|
Asilkalkan, Abdullah
|
|
|
347 |
2 |
p. 889-912
|
article
|
| 12 |
Toward interpretable machine learning: evaluating models of heterogeneous predictions
|
Zhang, Ruixun
|
|
|
347 |
2 |
p. 867-887
|
article
|
| 13 |
Understanding the challenges affecting food-sharing apps’ usage: insights using a text-mining and interpretable machine learning approach
|
Puram, Praveen
|
|
|
347 |
2 |
p. 843-865
|
article
|
Journal description