The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
Using particle swarm optimization in fuzzy association rules-based feature selection and fuzzy ARTMAP-based attack recognition
Feature selection is a classic research topic in data mining, and it has attracted much interest in many fields such as network security. In addition, data mining approaches such as fuzzy association rule mining (FARM) can improve the performance of intrusion detection systems. In this study, a FARM-based feature selector is proposed in order to reduce the dimension of input features to the misuse detector. Furthermore, a fuzzy ARTMAP neural network is used as the classifier. The accuracy of the proposed approach depends strongly on the precision of the parameters of FARM-based feature selector module and fuzzy ARTMAP neural classifier. Particle swarm optimization (PSO) algorithm is incorporated into the proposed method to determine optimum values of parameters. In this way, the performance of PSO algorithm is compared with genetic algorithm (GA), as well. Experimental results indicate that PSO outperforms GA both in population size and number of evolutions and can converge faster. This is very important for enhancing the mining performance in large datasets such as intrusion detection datasets. When compared with some other machine learning methods, the proposed system indicates better performance in terms of detection rate, false alarm rate, and cost per example. Copyright (c) 2012 John Wiley & Sons, Ltd.
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