This article presents a cloud-based multi-agent architecture for the intelligent management of aerial robots in a disaster response situation. In a disaster scenario, a team of highly maneuverable quadcopters is deployed to carry out surveillance and decision support in disaster-affected areas. In Pakistan, such events usually result from sudden unpredictable calamities such as earthquakes. The aim of this work is to develop a robust mechanism to autonomously manage and react to sensory inputs received in soft real time from an unstructured environment. The immediate goal is to locate the maximum number of trapped, injured people within a large area, and help first responders plan rescue activities accordingly. To evaluate the proposed framework, a number of simulations are carried out using GAMA platform to emulate a disaster environment. Subsequently, algorithms are developed to survey an affected geographical area through the use of small flight drones. The key challenges in this work are related to the combination of the domains of multi-agent technology, robotics, and cloud computing for effectively bridging the cyber world with the physical world. Therefore, the proposed work demonstrates the effective use of a limited number of drones to capture inputs from a disaster situation in the physical world, and such inputs are used for timely planning of rescue efforts. The results of fixed resource assignment are compared with the proposed reactive assignment strategy, and it clearly shows a significant improvement in terms of resource usage compared to traditional approach.
Birth weight is considered a major factor when monitoring any signs of abnormalities in the growth of the fetus and taking timely decisions related to labor management. Existing methods involve specialized equipment and training, which makes them less feasible for underdeveloped areas. Therefore, this study proposed a system for prediction of childbirth weight through kernel extreme reservoir machines and optimized the model parameters by the use of particle swarm optimization. Experimental results showed a significant improvement in the recommended method over existing models. The proposed approach is more economical than the traditional ultrasound making it extremely suited to underprivileged communities.
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