A brief review of explainable artificial intelligence reviews and methods
Explainable Machine Learning for Multimedia Based Healthcare Applications, Page: 151-167
2023
- 2Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures2
- Readers2
Book Chapter Description
Thanks to the recent advances in technology and ever-growing data, Artificial Intelligence (AI) has remarkably developed and has become an integral part of people's daily lives. Due to the growing interest in AI, the number of research on the topic has increased significantly in recent years. AI based methods are used to reveal information, make decisions, and detect data behaviors. Albeit AI-based models outperform traditional techniques, they are inherently "black box" and lack the ability to explain how a decision is made. In order to fill this gap, the concept of explainable artificial intelligence (XAI) is proposed. This chapter aims at providing a brief review of XAI terminologies, review studies, and methods.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85194305007&origin=inward; http://dx.doi.org/10.1007/978-3-031-38036-5_8; https://link.springer.com/10.1007/978-3-031-38036-5_8; https://dx.doi.org/10.1007/978-3-031-38036-5_8; https://link.springer.com/chapter/10.1007/978-3-031-38036-5_8
Springer Science and Business Media LLC
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