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<span lang="EN-US">In elastic mobile cloud computing (EMCC), mobile devices migrate some computing tasks to the cloud for execution according to current needs and seamlessly and transparently use cloud resources to enhance their functions. First, based on the summary of existing EMCC schemes, a generic EMCC framework is abstracted; it is pointed out that the migration of sensitive modules in the EMCC program can bring security risks such as privacy leakage and information flow hijacking to EMCC; then, a generic framework of elastic mobile cloud computing that incorporates risk management is designed, which regards security risks as a cost of EMCC and ensures that the use of EMCC is. Finally, it is pointed out that the difficulty of risk management lies in risk quantification and sensitive module labeling. In this regard, risk quantification algorithms are designed, an automatic annotation tool for sensitive modules of Android programs is implemented, and the accuracy of the automatic annotation is demonstrated through experiments.</span>

The Internet of Medical Things (IoMT) faces stiff competition from the 5th Generation (5G) communication standard, which includes attributes like short and long transmission ranges, Device to Device (D2D) connectivity, low latency, and high node density. To function in the linked ecosystem, IoMT based on 5G is anticipated to have a diversity of energy and mobility. It is currently difficult to create an IoMT routing system based on 5G that maximizes energy efficiency, lowers transmission latency, and increases network lifespan. The "Quality of Services (QoS)" in 5G-based IoMT is improved by the Reliable Fuzzy-based Multi-path routing system shown in this study. The Whale Optimization Algorithm (WOA) enhances the routing protocol performance. The residual energy-based Cluster Head (CH) selection strategy rotates the CH location among nodes with greater energy levels than the others. The method chooses the following set of CHs for the network that is suitable for IoMT applications by considering initial energy, residual energy, and an ideal value of CHs. According to the simulation results, our suggested routing technique enhances QoS in comparison to current approaches.

Word sense disambiguation (WSD) refers to determining the right meaning of a vague word using its context. The WSD intermediately consolidates the performance of final tasks to achieve high accuracy. Mainly, a WSD solution improves the accuracy of text summarisation, information retrieval, and machine translation. This study addresses the WSD by assigning a set of senses to a given text, where the maximum semantic relatedness is obtained. This is achieved by proposing a swarm intelligence method, called firefly algorithm (FA) to find the best possible set of senses. Because of the FA is based on a population of solutions, it explores the problem space more than exploiting it. Hence, we hybridise the FA with a one-point search algorithm to improve its exploitation capacity. Practically, this hybridisation aims to maximise the semantic relatedness of an eligible set of senses. In this study, the semantic relatedness is measured by proposing a glosses-overlapping method enriched by the notion of information content. To evaluate the proposed method, we have conducted intensive experiments with comparisons to the related works based on benchmark datasets. The obtained results showed that our method is comparable if not superior to the related works. Thus, the proposed method can be considered as an efficient solver for the WSD task.