Untangling the Concept of Artificial Intelligence, Machine Learning, and Deep Learning
Artificial Intelligence in Medicine: Applications, Limitations and Future Directions, Page: 3-21
2022
- 1Citations
- 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.
Book Chapter Description
The rise of Artificial Intelligence (AI), Machine Learning and Deep Learning in the twenty-first century has witnessed widespread advances in several disciplines where technology has not been used for such purpose prior. Relying on AI and Machine learning to unravel novel domain knowledge, deliver increased performance in the work or new value for the organization, and a degree of automation that allows for fast and achievable results, are just some of the main benefits organizations expect when introducing these new technologies. This can provide quite a momentum shift in the progress of an organization, but with such wide variety of AI, Machine Learning and Deep Learning methods available, it can be overwhelming to know where to start exploring the area or to check if the methods we already use are the right choice. In this chapter we will provide a summary of these concepts with clear examples that aim to help anyone wishing to introduce AI, Machine Learning and Deep Learning concepts in their work and wants to do that efficiently, effectivly and with confidence.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85153593088&origin=inward; http://dx.doi.org/10.1007/978-981-19-1223-8_1; https://link.springer.com/10.1007/978-981-19-1223-8_1; https://dx.doi.org/10.1007/978-981-19-1223-8_1; https://link.springer.com/chapter/10.1007/978-981-19-1223-8_1
Springer Science and Business Media LLC
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