Distributed Computing for Emerging Smart Networks
Springer International Publishing
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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Dairy Cow Rumination Detection: A Deep Learning Approach
|
|---|---|
| Book title |
Distributed Computing for Emerging Smart Networks
|
| Published in |
arXiv, January 2021
|
| DOI | 10.1007/978-3-030-65810-6_7 |
| Book ISBNs |
978-3-03-065809-0, 978-3-03-065810-6
|
| Authors |
Safa Ayadi, Ahmed ben said, Rateb Jabbar, Chafik Aloulou, Achraf Chabbouh, Ahmed Ben Achballah, Ahmed Ben Said, Ayadi, Safa, Ben Said, Ahmed, Jabbar, Rateb, Aloulou, Chafik, Chabbouh, Achraf, Achballah, Ahmed Ben |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 20% |
| Netherlands | 1 | 20% |
| Unknown | 3 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 60% |
| Scientists | 1 | 20% |
| Practitioners (doctors, other healthcare professionals) | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 38 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 4 | 11% |
| Researcher | 3 | 8% |
| Other | 2 | 5% |
| Professor > Associate Professor | 2 | 5% |
| Student > Doctoral Student | 1 | 3% |
| Other | 5 | 13% |
| Unknown | 21 | 55% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 9 | 24% |
| Agricultural and Biological Sciences | 2 | 5% |
| Veterinary Science and Veterinary Medicine | 1 | 3% |
| Business, Management and Accounting | 1 | 3% |
| Psychology | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 23 | 61% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 28 January 2021.
All research outputs
#15,298,340
of 26,172,861 outputs
Outputs from arXiv
#201,865
of 974,556 outputs
Outputs of similar age
#263,297
of 533,530 outputs
Outputs of similar age from arXiv
#5,294
of 24,538 outputs
Altmetric has tracked 26,172,861 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 974,556 research outputs from this source. They receive a mean Attention Score of 4.2. This one has done well, scoring higher than 78% of its peers.
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 533,530 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 24,538 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.