This paper proposes a novel model for automatic construction of business processes called IPCASCI (Intelligent business Processes Composition based on multi-Agent systems, Semantics and Cloud Integration). The software development industry requires agile construction of new products able to adapt to the emerging needs of a changing market. In this context, we present a method of software component reuse as a model (or methodology), which facilitates the semi-automatic reuse of web services on a cloud computing environment, leading to business process composition. The proposal is based on web service technology, including: (i) Automatic discovery of web services; (ii) Semantics description of web services; (iii) Automatic composition of existing web services to generate new ones; (iv) Automatic invocation of web services. As a result of this proposal, we have presented its implementation (as a tool) on a real case study. The evaluation of the case study and its results are proof of the reliability of IPCASCI.

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.

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.