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Multivariate EMD can overcome the limitations of standard EMD when dealing with data from multiple sources.
When dealing with data from multiple sensors and multiple health conditions, standard EMD faces two problems.
Read our issue brief "Data Archaeology – Uncovering the Past Through Column Binary Conversion". It's a dive into the tricks of the trade when dealing with data sets encoded on paper punchcards.
This approach is especially relevant when dealing with data from a set of heterogeneous sources.
So when they interact with AI, it presents new challenges that don't arise when dealing with data sets in a lab.
Also we need approaches to merge heterogeneous data access control policies when dealing with data originating from multiple sources a common situation in many big data applications.
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Therefore statistical methods applicable to data from scale assessments differ from the traditional methods for quantitative data, especially when dealing with paired data [ 38- 43].
The results showed that an optimisation in the execution time allowed for a near-immediate response in data processing even when dealing with large data sets.
These formally bind the applicant to the NHS duty of confidentiality when dealing with patient data (including de-identified patient data) [ 27].
As such, we follow two general guidelines when dealing with missing data: 1. Use only available data when computing dissimilarities (delta_{ij}).
Data imputation is a common practice encountered when dealing with incomplete data.
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