English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

As the use of artificial intelligent (AI) models becomes more prevalent inindustries such as engineering and manufacturing, it is essential that thesemodels provide transparent reasoning behind their predictions. This paperproposes the AI-Reasoner, which extracts the morphological characteristics ofdefects (DefChars) from images and utilises decision trees to reason with theDefChar values. Thereafter, the AI-Reasoner exports visualisations (i.e.charts) and textual explanations to provide insights into outputs made bymasked-based defect detection and classification models. It also provideseffective mitigation strategies to enhance data pre-processing and overallmodel performance. The AI-Reasoner was tested on explaining the outputs of anIE Mask R-CNN model using a set of 366 images containing defects. The resultsdemonstrated its effectiveness in explaining the IE Mask R-CNN model'spredictions. Overall, the proposed AI-Reasoner provides a solution forimproving the performance of AI models in industrial applications that requiredefect analysis.

Morphological Image Analysis and Feature Extraction for Reasoning with AI-based Defect Detection and Classification Models