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Adaptive DBN Using Hybrid Bayesian Lichtenberg Optimization for Intelligent Task Allocation

Overview of attention for article published in Neural Processing Letters, January 2023
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1 X user

Citations

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1 Dimensions

Readers on

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6 Mendeley
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Title
Adaptive DBN Using Hybrid Bayesian Lichtenberg Optimization for Intelligent Task Allocation
Published in
Neural Processing Letters, January 2023
DOI 10.1007/s11063-022-11071-6
Authors

D. Kavitha, M. Priyadharshini, R. Anitha, S. Suma, V. Prema, A. Vidhya

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Other 2 33%
Unknown 4 67%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 1 17%
Energy 1 17%
Engineering 1 17%
Unknown 3 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 January 2023.
All research outputs
#20,887,917
of 23,510,717 outputs
Outputs from Neural Processing Letters
#287
of 797 outputs
Outputs of similar age
#342,769
of 433,516 outputs
Outputs of similar age from Neural Processing Letters
#5
of 85 outputs
Altmetric has tracked 23,510,717 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 797 research outputs from this source. They receive a mean Attention Score of 0.9. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 433,516 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 85 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.