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Applying supervised learning in robot navigation encounters serious challenges such as inconsistent and noisy data, difficulty for gathering training data, and high error in training data.
Clustering analysis has following common problems: difficulty in handling mass data and distribution data, difficulty in determining parameters, low efficiency and poor clustering quality.
However, due to the use of different BIM-based software among collaborators during the design, a variety of problems have occurred, including loss of data, difficulty in communication, and poor work efficiency.
The drawbacks of the PCA are well known and include the lack of sparseness, i.e., activations are linear combinations of the input data, difficulty to interpret the results in terms of high level data shapes, and the upper limit on the number of achievable basis vectors.
Limitations of such studies include imprecision in capture of diabetes onset date, inaccuracies in electronic data, difficulty in distinguishing between type 1 and type 2 diabetes, and biases inherent to retrospective and observational studies.
This analysis, however is cumbersome both from the patients point of view (long test, usually with minimal wearing) and from the examiner point of view (long test, numerous data, difficulty in understanding the report etc).
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When considering smart city applications utilizing big data difficulties arise in various areas.
In agreement with our own data, difficulties with SYBR Green based analysis of 1lox/2lox allele ratios have also been reported independently [ 8].
We outline some confusion in the literature about this type of data, difficulties in comparing such data between publications, and its application to studies of disease prevalence in different populations.
The study, published last week in The European Heart Journal, used questionnaires to gather data on difficulty falling asleep, difficulty staying asleep, and waking unrefreshed among more than 54,000 Norwegian adults in a population-wide health survey.
First, within our data set, Difficulty and Time Required and Difficulty and Student Experience were sometimes found in the same conversations.
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