| Title |
Deep learning applications and challenges in big data analytics
|
|---|---|
| Published in |
Journal of Big Data, February 2015
|
| DOI | 10.1186/s40537-014-0007-7 |
| Authors |
Maryam M Najafabadi, Flavio Villanustre, Taghi M Khoshgoftaar, Naeem Seliya, Randall Wald, Edin Muharemagic |
X Demographics
The data shown below were collected from the profiles of 67 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 | 11 | 16% |
| United Kingdom | 6 | 9% |
| France | 4 | 6% |
| India | 3 | 4% |
| Belgium | 3 | 4% |
| Australia | 3 | 4% |
| Brazil | 2 | 3% |
| Korea, Republic of | 1 | 1% |
| Greece | 1 | 1% |
| Other | 9 | 13% |
| Unknown | 24 | 36% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 61 | 91% |
| Scientists | 4 | 6% |
| Science communicators (journalists, bloggers, editors) | 1 | 1% |
| Unknown | 1 | 1% |
Mendeley readers
The data shown below were compiled from readership statistics for 2,947 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Mexico | 3 | <1% |
| South Africa | 3 | <1% |
| United Kingdom | 3 | <1% |
| Colombia | 2 | <1% |
| Italy | 2 | <1% |
| Russia | 2 | <1% |
| India | 2 | <1% |
| Sweden | 2 | <1% |
| Japan | 2 | <1% |
| Other | 12 | <1% |
| Unknown | 2914 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 500 | 17% |
| Student > Master | 491 | 17% |
| Student > Bachelor | 265 | 9% |
| Researcher | 183 | 6% |
| Student > Doctoral Student | 133 | 5% |
| Other | 418 | 14% |
| Unknown | 957 | 32% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 935 | 32% |
| Engineering | 390 | 13% |
| Business, Management and Accounting | 147 | 5% |
| Social Sciences | 61 | 2% |
| Agricultural and Biological Sciences | 53 | 2% |
| Other | 304 | 10% |
| Unknown | 1057 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 53. 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 May 2022.
All research outputs
#731,969
of 23,975,876 outputs
Outputs from Journal of Big Data
#15
of 361 outputs
Outputs of similar age
#9,466
of 258,160 outputs
Outputs of similar age from Journal of Big Data
#2
of 3 outputs
Altmetric has tracked 23,975,876 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 361 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.5. This one has done particularly well, scoring higher than 96% 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 258,160 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.