Unlocking the black box: an in-depth review on interpretability, explainability, and reliability in deep learning
Deep learning models have revolutionized numerous fields, yet their decision-making
processes often remain opaque, earning them the characterization of “black-box” models …
processes often remain opaque, earning them the characterization of “black-box” models …
Potential Applications of Explainable Artificial Intelligence to Actuarial Problems
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 …
users to understand artificial intelligence knowledge and increase the reliability of the results …
Hybrid black-box classification for customer churn prediction with segmented interpretability analysis
Customer retention management relies on advanced analytics for decision making. Decision
makers in this area require methods that are capable of accurately predicting which …
makers in this area require methods that are capable of accurately predicting which …
Enhancing Auto Insurance Risk Evaluation with Transformer and SHAP
The evaluation of auto insurance risks is a fundamental task for financial institutions, crucial
for setting equitable premiums and managing risks effectively. Traditional machine learning …
for setting equitable premiums and managing risks effectively. Traditional machine learning …
The implementation of machine learning in the insurance industry with big data analytics
This study demonstrates how Machine Learning techniques and Big Data Analytics can be
used in the insurance sector. Due to various web technologies, mobile devices, and sensor …
used in the insurance sector. Due to various web technologies, mobile devices, and sensor …
[HTML][HTML] Towards specific cutting energy analysis in the machining of Inconel 601 alloy under sustainable cooling conditions
Currently, the research efforts on machining indices such as tool wear, surface roughness,
power consumption etc. is well reported in literature, but energy analysis based on material …
power consumption etc. is well reported in literature, but energy analysis based on material …
A high-precision and transparent step-wise diagnostic framework for hot-rolled strip crown
C Ding, J Sun, X Li, W Peng, D Zhang - Journal of Manufacturing Systems, 2023 - Elsevier
The strip crown plays a crucial role in determining the quality of products in strip hot rolling.
Machine learning (ML) methods have shown promise in crown prediction by effectively …
Machine learning (ML) methods have shown promise in crown prediction by effectively …
[HTML][HTML] A novel Bayesian Pay-As-You-Drive insurance model with risk prediction and causal map**
The modern vehicle insurance industry is increasingly adopting Pay-As-You-Drive (PAYD)
insurance models, aligning premium costs with driving behavior. Our study introduces a …
insurance models, aligning premium costs with driving behavior. Our study introduces a …
Sampling-based machine learning models for intrusion detection in imbalanced dataset
Cybersecurity is one of the important considerations when adopting IoT devices in smart
applications. Even though a huge volume of data is available, data related to attacks are …
applications. Even though a huge volume of data is available, data related to attacks are …
Spatial map** of land susceptibility to dust emissions using optimization of attentive Interpretable Tabular Learning (TabNet) model
Dust pollution poses significant risks to human health, air quality, and food safety,
necessitating the identification of dust occurrence and the development of dust susceptibility …
necessitating the identification of dust occurrence and the development of dust susceptibility …