Artificial Intelligence and Image Processing
Vol: 33, Issue: 2, Page: 142-145
1985
- 410Usage
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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.
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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
- Usage410
- Downloads271
- Abstract Views139
Article Description
The evolution of artificial intelligence since the 1950s is discussed, especially as it is being applied in radiology to image processing. Developments in artificial intelligence are now being used to provide a new approach to image processing. Initially, the computer dealt with numeric representations using languages such as FORTRAN and BASIC. Now symbolic languages such as LISP and PROLOG have expanded the use of the computer into nonnumeric symbolic reasoning that is just being applied to image understanding. This paper explains the new languages and their application to image understanding.
Bibliographic Details
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