Results 21 to 30 of about 23,034 (235)

Explainable human‐in‐the‐loop healthcare image information quality assessment and selection

CAAI Transactions on Intelligence Technology, EarlyView., 2023
Abstract Smart healthcare applications cannot be separated from healthcare data analysis and the interactive interpretability between data and model. A human‐in‐the‐loop active learning approach is introduced to reduce the cost of healthcare data labelling by evaluating the information quality of unlabelled medical data and then screening the high ...
Yang Li, Sezai Ercisli
wiley   +1 more source

Multiple Explainable Approaches to Predict the Risk of Stroke Using Artificial Intelligence

Information, 2023
Stroke occurs when a brain’s blood artery ruptures or the brain’s blood supply is interrupted. Due to rupture or obstruction, the brain’s tissues cannot receive enough blood and oxygen. Stroke is a common cause of mortality among older people.
Susmita S, Krishnaraj Chadaga, Niranjana Sampathila, Srikanth Prabhu, Rajagopala Chadaga, Swathi Katta S   +5 more
doaj   +1 more source

A Systematic Review of Human–Computer Interaction and Explainable Artificial Intelligence in Healthcare With Artificial Intelligence Techniques

IEEE Access, 2021
Artificial intelligence (AI) is one of the emerging technologies. In recent decades, artificial intelligence (AI) has gained widespread acceptance in a variety of fields, including virtual support, healthcare, and security.
Mobeen Nazar, Muhammad Mansoor Alam, Eiad Yafi, Mazliham Mohd Su'ud   +3 more
doaj   +1 more source

Face Aging by Explainable Conditional Adversarial Autoencoders

Journal of Imaging, 2023
This paper deals with Generative Adversarial Networks (GANs) applied to face aging. An explainable face aging framework is proposed that builds on a well-known face aging approach, namely the Conditional Adversarial Autoencoder (CAAE).
Christos Korgialas, Evangelia Pantraki, Angeliki Bolari, Martha Sotiroudi, Constantine Kotropoulos   +4 more
doaj   +1 more source

Exact and Approximate Rule Extraction from Neural Networks with Boolean Features [PDF]

, 2019
Rule extraction from classifiers treated as black boxes is an important topic in explainable artificial intelligence (XAI). It is concerned with finding rules that describe classifiers and that are understandable to humans, having the form of (I f...T ...
Howe, J. M., Mereani, F.
core   +1 more source

Explainable Artificial Intelligence (XAI)

, 2021
Complex machine learning models perform better. However, we consider these models as black boxes. That s where Explainable AI (XAI) comes into play. Understanding why a model makes a specific prediction can be as crucial as its accuracy for many applications, researchers, and decision-makers.
openaire   +1 more source

Explainable Artificial Intelligence in Echocardiography [PDF]

Advanced Ultrasound in Diagnosis and Therapy
Recent advancements in artificial intelligence (AI) have generated novel opportunities and challenges in ultrasound imaging. Deep learning algorithms exhibit significant potential in analyzing echocardiographic images, encompassing tasks such as view ...
Hu Xuelin, Zhu Ye, Zhang Zisang, Quan Yuanting, Chen Wenwen, Chen Leichong, Xu Guangyu, Qin Luning, Xie Mingxing, Zhang Li
doaj   +1 more source

Robust Network Intrusion Detection through Explainable Artificial Intelligence (XAI)

IEEE Networking Letters, 2022
<p>In this letter, we present a two-stage pipeline for robust network intrusion detection. First, we implement an extreme gradient boosting (XGBoost) model to perform supervised intrusion detection, and leverage the SHapley Additive exPlanation (SHAP) framework to devise explanations of our model. In the second stage, we use these explanations to
Pieter Barnard, Nicola Marchetti, Luiz A. DaSilva   +2 more
openaire   +2 more sources

Potential Applications of Explainable Artificial Intelligence to Actuarial Problems

Mathematics
Explainable artificial intelligence (XAI) is a group of techniques and evaluations that allows users to understand artificial intelligence knowledge and increase the reliability of the results produced using artificial intelligence.
Catalina Lozano-Murcia, Francisco P. Romero, Jesus Serrano-Guerrero, Arturo Peralta, Jose A. Olivas   +4 more
doaj   +1 more source

The Grammar of Interactive Explanatory Model Analysis

, 2021
The growing need for in-depth analysis of predictive models leads to a series of new methods for explaining their local and global properties. Which of these methods is the best? It turns out that this is an ill-posed question.
Baniecki, Hubert, Biecek, Przemyslaw
core