Titlebook: Explainable AI Within the Digital Transformation and Cyber Physical Systems; XAI Methods and Appl Moamar Sayed-Mouchaweh Book 2021 The Edit

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A Study on the Effect of Dimensionality Reduction on Cyber-Attack Identification in Water Storage T reduction techniques are combined with IDS. The utilized IDS also improves efficiency by reducing the memory usage and using data with better quality, which in turn increases the detection accuracy. The results have been analyzed in terms of F1-score and accuracy w.r.t. two classifiers.

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Book 2021ropriately trust the obtained results. The authors discuss the challenges involved in making machine learning-based AI explainable. Firstly, that the explanations must be adapted to different stakeholders (end-users, policy makers, industries, utilities etc.) with different levels of technical knowl

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Book 2021healthcare, finance, cybersecurity, and document summarization. It allows highlighting the benefitsand requirements of using explainable models in different application domains in order to provide guidance to readers to select the most adapted models to their specified problem and conditions..Includ

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Explainable AI Within the Digital Transformation and Cyber Physical SystemsXAI Methods and Appl

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Adolescent Sex Offender Programs,d explanations. It summarizes the gathered research contributions aiming at the development and/or the use of XAI techniques in order to address the aforementioned challenges in different applications such as healthcare, finance, cybersecurity, and document summarization.

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Alice Ordean MD, CCFP, MHSc, FCFPnerated summaries. Conducted experiments show that our approach’s scores are highly positively correlated to the state-of-the-art ROUGE ones. This research’s salient outcome is that it proposes a cognitive and explainable model of AGSE, and it pushes toward cognitive natural language processing. It

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Post-Field Data Quality Control,fference in scaled activations of the features in the last convolutional layer of the CNN for both classes. In doing so, we exploit the feature similarity between the two diseases for training purposes, thereby mitigating issues arising due to the scarcity of COVID-19 data. In models with overlappin