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[HTML][HTML] Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks
Artificial Intelligence (AI) techniques of deep learning have revolutionized the disease
diagnosis with their outstanding image classification performance. In spite of the outstanding …
diagnosis with their outstanding image classification performance. In spite of the outstanding …
[HTML][HTML] Application of explainable artificial intelligence in medical health: A systematic review of interpretability methods
This paper investigates the applications of explainable AI (XAI) in healthcare, which aims to
provide transparency, fairness, accuracy, generality, and comprehensibility to the results …
provide transparency, fairness, accuracy, generality, and comprehensibility to the results …
Interpretable deep learning framework for land use and land cover classification in remote sensing using SHAP
An interpretable deep learning framework for land use and land cover (LULC) classification
in remote sensing using Shapley additive explanations (SHAPs) is introduced. It utilizes a …
in remote sensing using Shapley additive explanations (SHAPs) is introduced. It utilizes a …
Advancing computational toxicology by interpretable machine learning
Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals
have a critical impact on human health. Traditional animal models to evaluate chemical …
have a critical impact on human health. Traditional animal models to evaluate chemical …
An explainable predictive model for suicide attempt risk using an ensemble learning and Shapley Additive Explanations (SHAP) approach
Abstract Machine learning approaches have been used to develop suicide attempt
predictive models recently and have been shown to have a good performance. However …
predictive models recently and have been shown to have a good performance. However …
Global and local interpretability techniques of supervised machine learning black box models for numerical medical data
The most effective machine learning classification techniques, such as artificial neural
networks, are not easily interpretable, which limits their usefulness in critical areas, such as …
networks, are not easily interpretable, which limits their usefulness in critical areas, such as …
A survey on explainable artificial intelligence for cybersecurity
The “black-box” nature of artificial intelligence (AI) models has been the source of many
concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a …
concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a …
An optimized model for network intrusion detection systems in industry 4.0 using XAI based Bi-LSTM framework
Industry 4.0 enable novel business cases, such as client-specific production, real-time
monitoring of process condition and progress, independent decision making and remote …
monitoring of process condition and progress, independent decision making and remote …
Integrating prior knowledge to build transformer models
Abstract The big Artificial General Intelligence models inspire hot topics currently. The black
box problems of Artificial Intelligence (AI) models still exist and need to be solved urgently …
box problems of Artificial Intelligence (AI) models still exist and need to be solved urgently …
Artificial intelligence technologies in cardiology
As the world produces exabytes of data, there is a growing need to find new methods that
are more suitable for dealing with complex datasets. Artificial intelligence (AI) has significant …
are more suitable for dealing with complex datasets. Artificial intelligence (AI) has significant …