[HTML][HTML] Explainable AI in manufacturing and industrial cyber–physical systems: a survey
This survey explores applications of explainable artificial intelligence in manufacturing and
industrial cyber–physical systems. As technological advancements continue to integrate …
industrial cyber–physical systems. As technological advancements continue to integrate …
[HTML][HTML] Explainable AI Techniques for Comprehensive Analysis of the Relationship between Process Parameters and Material Properties in FDM-Based 3D-Printed …
This study investigates the complex relationships between process parameters and material
properties in FDM-based 3D-printed biocomposites using explainable AI techniques. We …
properties in FDM-based 3D-printed biocomposites using explainable AI techniques. We …
A systematic review on interpretability research of intelligent fault diagnosis models
A systematic review on interpretability research of intelligent fault diagnosis models Page 1
Measurement Science and Technology ACCEPTED MANUSCRIPT A systematic review on …
Measurement Science and Technology ACCEPTED MANUSCRIPT A systematic review on …
[HTML][HTML] Mutual information-based radiomic feature selection with SHAP explainability for breast cancer diagnosis
Breast cancer is a prevalent concern for women globally, with misdiagnosis potentially
leading to detrimental outcomes. Early detection is crucial, often reliant on medical imaging …
leading to detrimental outcomes. Early detection is crucial, often reliant on medical imaging …
[HTML][HTML] Improved Intelligent Condition Monitoring with Diagnostic Indicator Selection
In this study, a predictive maintenance (PdM) system focused on feature selection for the
detection and classification of simulated defects in wind turbine blades has been developed …
detection and classification of simulated defects in wind turbine blades has been developed …
Fast Estimation of Shapley Value by Stratified Sampling and its Application in Explaining Fault Diagnosis Neural Network
B He, Y Mao, Y Qin - IEEE Internet of Things Journal, 2024 - ieeexplore.ieee.org
There are two problems when the Shapley value is employed to interpret deep neural
networks. The first issue is that the computational complexity increases exponentially with …
networks. The first issue is that the computational complexity increases exponentially with …
[HTML][HTML] Explainable AI Using OBDII Data for Urban Buses Maintenance Management: A Study Case About the DPF Regeneration
B Tormos, B Pla, R Sánchez-Márquez, JL Carballo - Information, 2025 - mdpi.com
Industry 4.0, leveraging tools like AI and the massive generation of data, is driving a
paradigm shift in maintenance management. Specifically, in the realm of Artificial …
paradigm shift in maintenance management. Specifically, in the realm of Artificial …
[HTML][HTML] Time-to-Fault Prediction Framework for Automated Manufacturing in Humanoid Robotics Using Deep Learning
AR Ali, H Kamal - Technologies, 2025 - mdpi.com
Industry 4.0 is transforming predictive failure management by utilizing deep learning to
enhance maintenance strategies and automate production processes. Traditional methods …
enhance maintenance strategies and automate production processes. Traditional methods …
Hybrid Computational Intelligence Framework with Enhanced Features Analysis to Predict the Fatigue Life of Critical Components in a PCB
The combination of data-driven insights, physics-based simulations, machine learning
models, and analytical models provides an innovative solution for ensuring the reliability …
models, and analytical models provides an innovative solution for ensuring the reliability …
Through the Thicket: A Study of Number-Oriented LLMs derived from Random Forest Models
Large Language Models (LLMs) have shown exceptional performance in text processing.
Notably, LLMs can synthesize information from large datasets and explain their decisions …
Notably, LLMs can synthesize information from large datasets and explain their decisions …