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SeyedehRoksana Mirzaei, Hua Mao, Raid Rafi Omar Al-Nima and Wai Lok Woo
Explainable Artificial Intelligence (XAI) evaluation has grown significantly due to its extensive adoption, and the catastrophic consequence of misinterpreting sensitive data, especially in the medical field. However, the multidisciplinary nature of XAI ...
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David Solís-Martín, Juan Galán-Páez and Joaquín Borrego-Díaz
The aim of predictive maintenance, within the field of prognostics and health management (PHM), is to identify and anticipate potential issues in the equipment before these become serious. The main challenge to be addressed is to assess the amount of tim...
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Alexandr Oblizanov, Natalya Shevskaya, Anatoliy Kazak, Marina Rudenko and Anna Dorofeeva
In recent years, artificial intelligence technologies have been developing more and more rapidly, and a lot of research is aimed at solving the problem of explainable artificial intelligence. Various XAI methods are being developed to allow the user to u...
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Vidhya Kamakshi and Narayanan C. Krishnan
Explainable Artificial Intelligence (XAI) has emerged as a crucial research area to address the interpretability challenges posed by complex machine learning models. In this survey paper, we provide a comprehensive analysis of existing approaches in the ...
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Diogo Ribeiro, Luís Miguel Matos, Guilherme Moreira, André Pilastri and Paulo Cortez
Within the context of Industry 4.0, quality assessment procedures using data-driven techniques are becoming more critical due to the generation of massive amounts of production data. In this paper, we address the detection of abnormal screw tightening pr...
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Kwang Hyeon Kim, Woo-Jin Choi and Moon-Jun Sohn
This study aimed to analyze feature importance by applying explainable artificial intelligence (XAI) to postural deformity parameters extracted from a computer vision-based posture analysis system (CVPAS). Overall, 140 participants were screened for CVPA...
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Anastasiia Kolevatova, Michael A. Riegler, Francesco Cherubini, Xiangping Hu and Hugo L. Hammer
A general issue in climate science is the handling of big data and running complex and computationally heavy simulations. In this paper, we explore the potential of using machine learning (ML) to spare computational time and optimize data usage. The pape...
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