Interpretability of deep neural networks: A review of methods, classification and hardware
Artificial intelligence, and especially deep neural networks, have evolved substantially in the
recent years, infiltrating numerous domains of applications, often greatly impactful to …
recent years, infiltrating numerous domains of applications, often greatly impactful to …
[HTML][HTML] Fuzzy decision-making framework for explainable golden multi-machine learning models for real-time adversarial attack detection in Vehicular Ad-hoc …
This paper addresses various issues in the literature concerning adversarial attack detection
in Vehicular Ad-hoc Networks (VANETs). These issues include the failure to consider both …
in Vehicular Ad-hoc Networks (VANETs). These issues include the failure to consider both …
B-LIME: An improvement of LIME for interpretable deep learning classification of cardiac arrhythmia from ECG signals
Deep Learning (DL) has gained enormous popularity recently; however, it is an opaque
technique that is regarded as a black box. To ensure the validity of the model's prediction, it …
technique that is regarded as a black box. To ensure the validity of the model's prediction, it …
Assessing XAI: unveiling evaluation metrics for local explanation, taxonomies, key concepts, and practical applications
Within the past few years, the accuracy of deep learning and machine learning models has
been improving significantly while less attention has been paid to their responsibility …
been improving significantly while less attention has been paid to their responsibility …
[HTML][HTML] Interpreting convolutional neural network by joint evaluation of multiple feature maps and an improved NSGA-II algorithm
Z Wang, Y Zhou, M Han, Y Guo - Expert Systems with Applications, 2024 - Elsevier
The'black box'characteristics of Convolutional Neural Networks (CNNs) present significant
risks to their application scenarios, such as reliability, security, and division of …
risks to their application scenarios, such as reliability, security, and division of …
A posture-based measurement adjustment method for improving the accuracy of beef cattle body size measurement based on point cloud data
Highlights•Body size automatic measurement based on beef cattle point clouds was
achieved.•Twelve micro-pose features were defined to describe beef cattle postures.•The …
achieved.•Twelve micro-pose features were defined to describe beef cattle postures.•The …
: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities and Models
Abstract While Explainable Artificial Intelligence (XAI) techniques have been widely studied
to explain predictions made by deep neural networks, the way to evaluate the faithfulness of …
to explain predictions made by deep neural networks, the way to evaluate the faithfulness of …
Knowledge features enhanced intelligent fault detection with progressive adaptive sparse attention learning for high-power diesel engine
H Li, F Liu, X Kong, J Zhang, Z Jiang… - … Science and Technology, 2023 - iopscience.iop.org
High-power diesel engines are core power equipment in some key fields, and fault
diagnosis is of great significance for improving their long-term operational reliability and …
diagnosis is of great significance for improving their long-term operational reliability and …
[PDF][PDF] M4: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities and Models.
M4: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across
Metrics, Modalities, and Models Page 1 M4: A Unified XAI Benchmark for Faithfulness …
Metrics, Modalities, and Models Page 1 M4: A Unified XAI Benchmark for Faithfulness …
Improving semantic segmentation under hazy weather for autonomous vehicles using explainable artificial intelligence and adaptive dehazing approach
Haze-level discriminators are crucial for autonomous vehicles to handle segmentation tasks
successfully in hazy and foggy outdoor environments. Deep learning (DL) networks trained …
successfully in hazy and foggy outdoor environments. Deep learning (DL) networks trained …