Explainable artificial intelligence: an analytical review
This paper provides a brief analytical review of the current state‐of‐the‐art in relation to the
explainability of artificial intelligence in the context of recent advances in machine learning …
explainability of artificial intelligence in the context of recent advances in machine learning …
Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues
Abstract Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial
Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare …
Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare …
Digital twins for well-being: an overview
Digital twin (DT) has gained success in various industries, and it is now getting attention in
the healthcare industry in the form of well-being digital twin (WDT). In this paper, we present …
the healthcare industry in the form of well-being digital twin (WDT). In this paper, we present …
Deterministic local interpretable model-agnostic explanations for stable explainability
Local Interpretable Model-Agnostic Explanations (LIME) is a popular technique used to
increase the interpretability and explainability of black box Machine Learning (ML) …
increase the interpretability and explainability of black box Machine Learning (ML) …
A decision support system for diagnosis of COVID-19 from non-COVID-19 influenza-like illness using explainable artificial intelligence
The coronavirus pandemic emerged in early 2020 and turned out to be deadly, killing a vast
number of people all around the world. Fortunately, vaccines have been discovered, and …
number of people all around the world. Fortunately, vaccines have been discovered, and …
Investigating explainability methods in recurrent neural network architectures for financial time series data
W Freeborough, T van Zyl - Applied Sciences, 2022 - mdpi.com
Statistical methods were traditionally primarily used for time series forecasting. However,
new hybrid methods demonstrate competitive accuracy, leading to increased machine …
new hybrid methods demonstrate competitive accuracy, leading to increased machine …
Explaining intrusion detection-based convolutional neural networks using shapley additive explanations (shap)
Artificial intelligence (AI) and machine learning (ML) models have become essential tools
used in many critical systems to make significant decisions; the decisions taken by these …
used in many critical systems to make significant decisions; the decisions taken by these …
Exemplary natural images explain CNN activations better than state-of-the-art feature visualization
J Borowski, RS Zimmermann, J Schepers… - arXiv preprint arXiv …, 2020 - arxiv.org
Feature visualizations such as synthetic maximally activating images are a widely used
explanation method to better understand the information processing of convolutional neural …
explanation method to better understand the information processing of convolutional neural …
[HTML][HTML] Explainable AI models for predicting drop coalescence in microfluidics device
In the field of chemical engineering, understanding the dynamics and probability of drop
coalescence is not just an academic pursuit, but a critical requirement for advancing process …
coalescence is not just an academic pursuit, but a critical requirement for advancing process …
Detecting cyberthreats in Metaverse learning platforms using an explainable DNN
The rapid integration of the Internet of Artificial Intelligence and Internet of Things (AI-IoT)
technologies has given rise to a pivotal element of the upcoming digital era, the Metaverse …
technologies has given rise to a pivotal element of the upcoming digital era, the Metaverse …