This paper presents the design and development of an innovative multiagent system based on virtual organizations. The multiagent system manages information from wireless sensor networks for knowledge discovery and decision making in rural environments. The multiagent system has been built over the cloud computing paradigm to provide better flexibility and higher scalability for handling both small-and large-scale projects. The development of wireless sensor network technology has allowed for its extension and application to the rural environment, where the lives of the people interacting with the environment can be improved. The use of "smart" technologies can also improve the efficiency and effectiveness of rural systems. The proposed multiagent system allows us to analyse data collected by sensors for decision making in activities carried out in a rural setting, thus, guaranteeing the best performance in the ecosystem. Since water is a scarce natural resource that should not be wasted, a case study was conducted in an agricultural environment to test the proposed system's performance in optimizing the irrigation system in corn crops. The architecture collects information about the terrain and the climatic conditions through a wireless sensor network deployed in the crops. This way, the architecture can learn about the needs of the crop and make efficient irrigation decisions. The obtained results are very promising when compared to a traditional automatic irrigation system.

Cluster analysis of DNA microarray data is an important but difficult task in knowledge discovery processes. Many clustering methods are applied to analysis of data for gene expression, but none of them is able to deal with an absolute way with the challenges that this technology raises. Due to this, many applications have been developed for visually representing clustering algorithm results on DNA microarray data, usually providing dendrogram and heat map visualizations. Most of these applications focus only on the above visualizations, and do not offer further visualization components to the validate the clustering methods or to validate one another. This paper proposes using a visual analytics framework in cluster analysis of gene expression data. Additionally, it presents a new method for finding cluster boundaries based on properties of metric spaces. Our approach presents a set of visualization components able to interact with each other; namely, parallel coordinates, cluster boundary genes, 3D cluster surfaces and DNA microarray visualizations as heat maps. Experimental results have shown that our framework can be very useful in the process of more fully understanding DNA microarray data. The software has been implemented in Java,

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Gene selection is a major research area in microarray analysis, which seeks to discover differentially expressed genes for a particular target annotation. Such genes also often called informative genes are able to differentiate tissue samples belonging to different classes of the studied disease. Despite the fact that there is a wide number of proposals, the complexity imposed by this problem remains a challenge today. This research proposes a gene selection approach by means of a clustering-based multi-agent system. This proposal manages different filter methods and gene clustering through coordinated agents to discover informative gene subsets. To assess the reliability of our approach, we have used four important and public gene expression datasets, two Lung cancer datasets, Colon and Leukemia cancer dataset. The achieved results have been validated through cluster validity measures, visual analytics, a classifier and compared with other gene selection methods, proving the reliability of our proposal.