River flooding is a natural phenomenon that can have a devastating effect on human life and economic losses. There have been various approaches in studying river flooding; however, insufficient understanding and limited knowledge about flooding conditions hinder the development of prevention and control measures for this natural phenomenon. This paper entails a new approach for the prediction of water level in association with flood severity using the ensemble model. Our approach leverages the latest developments in the Internet of Things (IoT) and machine learning for the automated analysis of flood data that might be useful to prevent natural disasters. Research outcomes indicate that ensemble learning provides a more reliable tool to predict flood severity levels. The experimental results indicate that the ensemble learning using the Long-Short Term memory model and random forest outperformed individual models with a sensitivity, specificity and accuracy of 71.4%, 85.9%, 81.13%, respectively.

With the widespread adoption of social (aka Web 2.0) technologies like social networks, blogs, and wikis, there is a growing interest in looking into how enterprises should capitalize on these technologies. The social enterprise is the one that on top of having an online presence on the Internet, strives to open up new communication channels with stakeholders using social technologies. Building upon previous works on social business processes and on practical experiences on adopting such technologies in different Brazilian companies, this paper suggests an architecture and guiding framework for the social enterprise in terms of how to bridge the gap between the business world and social world and how to integrate social technologies into the enterprise's day-to-day operations.

Cloud services are often distributed across several data centers requiring new scalable approaches to efficiently perform searching to reduce the energy and price cost of fulfilling requests. Multiagent-based systems have arisen as a powerful technique for improving distributed processing on a wide scale, which can operate in environments where partial observability is the norm and the cost of prolonged search can be exponential. In this paper, we present a multiagent-based service composition approach, using agent-matchmakers and agent-representatives, for the efficient retrieval of distributed services and propagation of information within the agent network to reduce the amount of brute-force search. Our extensive simulation results indicate that by introducing localised agent-based memory searches, the amount of actions (with their associated energy costs) can be reduced by over 50% which results in a lower energy cost per composition request.