| Title |
A Novel Framework for Medical Web Information Foraging Using Hybrid ACO and Tabu Search
|
|---|---|
| Published in |
Journal of Medical Systems, October 2015
|
| DOI | 10.1007/s10916-015-0350-z |
| Pubmed ID | |
| Authors |
Yassine Drias, Samir Kechid, Gabriella Pasi |
| Abstract |
We present in this paper a novel approach based on multi-agent technology for Web information foraging. We proposed for this purpose an architecture in which we distinguish two important phases. The first one is a learning process for localizing the most relevant pages that might interest the user. This is performed on a fixed instance of the Web. The second takes into account the openness and dynamicity of the Web. It consists on an incremental learning starting from the result of the first phase and reshaping the outcomes taking into account the changes that undergoes the Web. The system was implemented using a colony of artificial ants hybridized with tabu search in order to achieve more effectiveness and efficiency. To validate our proposal, experiments were conducted on MedlinePlus, a real website dedicated for research in the domain of Health in contrast to other previous works where experiments were performed on web logs datasets. The main results are promising either for those related to strong Web regularities and for the response time, which is very short and hence complies the real time constraint. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Argentina | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 23 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 22% |
| Other | 3 | 13% |
| Student > Doctoral Student | 2 | 9% |
| Student > Master | 2 | 9% |
| Professor > Associate Professor | 2 | 9% |
| Other | 5 | 22% |
| Unknown | 4 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 3 | 13% |
| Computer Science | 3 | 13% |
| Psychology | 2 | 9% |
| Engineering | 2 | 9% |
| Neuroscience | 2 | 9% |
| Other | 4 | 17% |
| Unknown | 7 | 30% |
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 19 November 2015.
All research outputs
#18,430,915
of 22,833,393 outputs
Outputs from Journal of Medical Systems
#808
of 1,149 outputs
Outputs of similar age
#204,945
of 284,665 outputs
Outputs of similar age from Journal of Medical Systems
#28
of 42 outputs
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