[HTML][HTML] Supply chain risk management with machine learning technology: A literature review and future research directions

M Yang, MK Lim, Y Qu, D Ni, Z Xiao - Computers & Industrial Engineering, 2023 - Elsevier
Abstract Coronavirus disease 2019 (COVID-19) has placed tremendous pressure on supply
chain risk management (SCRM) worldwide. Recent technological advances, especially …

Machine learning-based anomaly detection in NFV: A comprehensive survey

S Zehra, U Faseeha, HJ Syed, F Samad, AO Ibrahim… - Sensors, 2023 - mdpi.com
Network function virtualization (NFV) is a rapidly growing technology that enables the
virtualization of traditional network hardware components, offering benefits such as cost …

A survey on explainable anomaly detection

Z Li, Y Zhu, M Van Leeuwen - ACM Transactions on Knowledge …, 2023 - dl.acm.org
In the past two decades, most research on anomaly detection has focused on improving the
accuracy of the detection, while largely ignoring the explainability of the corresponding …

Survey on explainable ai: techniques, challenges and open issues

A Abusitta, MQ Li, BCM Fung - Expert Systems with Applications, 2024 - Elsevier
Artificial Intelligence (AI) has become an important component of many software
applications. It has reached a point where it can provide complex and critical decisions in …

Prediction of next app in os

R Ma, Y Zhang, J Liu, O Petrosian… - 2022 III International …, 2022 - ieeexplore.ieee.org
With the popularity of smart devices, application responsiveness is one of the most important
indicators of user experience, and forecast the next application will be used by users is …

Collective explainable AI: Explaining cooperative strategies and agent contribution in multiagent reinforcement learning with shapley values

A Heuillet, F Couthouis… - IEEE Computational …, 2022 - ieeexplore.ieee.org
While Explainable Artificial Intelligence (XAI) is increasingly expanding more areas of
application, little has been applied to make deep Reinforcement Learning (RL) more …

[HTML][HTML] Explainable outlier detection: What, for Whom and Why?

JH Sejr, A Schneider-Kamp - Machine Learning with Applications, 2021 - Elsevier
Outlier algorithms are becoming increasingly complex. Thereby, they become much less
interpretable to the data scientists applying the algorithms in real-life settings and to end …

Understanding the effect of hydro-climatological parameters on Dam seepage using shapley additive explanation (SHAP): A case study of earth-fill tarbela Dam …

M Ishfaque, S Salman, KZ Jadoon, AAK Danish… - Water, 2022 - mdpi.com
For better stability, safety and water resource management in a dam, it is important to
evaluate the amount of seepage from the dam body. This research is focused on machine …

[HTML][HTML] Rule extraction in unsupervised anomaly detection for model explainability: Application to OneClass SVM

A Barbado, Ó Corcho, R Benjamins - Expert Systems with Applications, 2022 - Elsevier
OneClass SVM is a popular method for unsupervised anomaly detection. As many other
methods, it suffers from the black box problem: it is difficult to justify, in an intuitive and …

Shapley values for explaining the black box nature of machine learning model clustering

M Louhichi, R Nesmaoui, M Mbarek… - Procedia Computer …, 2023 - Elsevier
Abstract Machine learning (ML) models are becoming increasingly complex. In fact, a
sophisticated model (XGBoost boosting or deep learning) generally leads to more accurate …