The phrase "automated supervised" is not standard in written English and may cause confusion.
It could be used in contexts discussing systems or processes that are both automated and supervised, but it would be clearer if rephrased.
Example: "The automated supervised system ensures that all operations are monitored in real-time for efficiency."
Alternatives: "automated oversight" or "automated monitoring".
This paper considers automated and supervised delineation in a case study of the Upper Indus Basin (UIB), Pakistan, for which published estimates of the basin area show significant disagreement, ranging from 166,000 to 266,000 km266,000
Automated classification (band calculation) methods were found to be most effective on quartzitic sandstone and siliceous crystalline bedrock, whereas, semi-automated (supervised classification) techniques were most successful on substrates comprised primarily of carbonate lithologies.
A fully automated measure of white matter lesions will be undertaken using published automated image processing algorithms [ 68] and compared with the results of the semi-automated supervised technique.
This has triggered sustained community efforts for integrative annotation, which combine automated computation with human-supervised curation, the use of quality indices, text-mining tools, biological ontologies and the semantic web [ 115- 120].
We thus propose approaches for automated decision-making based on supervised and reinforcement learning.
He also stressed the need to develop automated heuristics for both fast supervised curation of existing models and for the construction of new metabolic models.
We selected three supervised machine learning methods and designed automated data gathering pipelines for each of them.
Use of fluorescent markers reduced it to 57 percent; the automated ultraviolet radiation cut it to 35 percent; and after imposing the supervised assessment and clearance, only 7 percent of rooms had positive cultures.
Non-supervised fault-proneness prediction models, especially thresholds-based models, can easily be automated and give valuable insights to developers and testers on the classification performed.
The accuracy of the proposed methodology is verified against an automated workflow created by the output of a conventional filter bank (validated by experts) and the supervised training of a random forest classifier.
