Machine learning
Anomaly Detection and Complex Event Processing over IoT Data Streams, Page: 149-191
2022
- 32Captures
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
- Captures32
- Readers32
- 32
Book Chapter Description
Healthcare is at the dawn of a new era of intelligent systems and improved human relationships. The potential of artificial intelligence (AI) and machine learning (ML) technologies to support decision-making, optimize workflows, and free up quality human time is revolutionizing how people deliver and receive care. The success and performance of AI-based expert-level diagnostic systems have inspired unprecedented optimism. However, there are growing concerns about ethics, safety, and equity in the delivery of care. The lack of clarity about how it works and the resulting mistrust has negatively affected the relationship between AI and caregivers and recipients, preventing adoption. This chapter provides a general overview of the various AI learning areas and a detailed introduction to the area of federated learning, a key to the application of machine learning in the future vision of healthcare.
Bibliographic Details
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