Research Summary
The research revolves around the collection of pending hadiths on Abdullah bin Amr bin Al-Aas, may God be pleased with them, and it is required in these hadiths that they be among those in which there is no room for diligence, such as telling about unseen matters, signs of the Hour, or a statement of virtue and reward for obedience, or punishment for disobedience.
The research consists of an introduction, two topics, and a conclusion of the first topic in the translation of Abdullah bin Amr bin Al-Aas, and the second topic, the pending hadiths of Abdullah bin Amr bin Al-Aas, may God be pleased with them, in which there is no room for diligence in collecting and studying
At the end of this research, I have reached the following results: The importance of studying the reports reported on the Companions, may God be pleased with them, in order to distinguish the correct ones from the weak ones. I came across twenty pending narrations on Abdullah bin Amr bin Al-Aas, may God be pleased with them, six of which are authentic, two are good, and twelve are weak.
Explainable Artificial Intelligence (XAI) techniques enable transparency and trust in automated visual inspection systems by making black-box machine learning models understandable. While XAI has been widely applied, prior reviews have not addressed the specific demands of industrial and medical inspection tasks. This paper reviews studies applying XAI techniques to visual inspection across industrial and medical domains. A systematic search was conducted in IEEE Xplore, Scopus, PubMed, arXiv, and Web of Science for studies published between 2014 and 2025, with inclusion criteria requiring the application of XAI in inspection tasks using public or domain-specific datasets. From an initial pool of studies, 75 were included and categorized in
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