<p class="0abstract">Various mobile applications such as Mobile Health (mHealth) have been developed and spread across the world which has played an important role in mitigating the Coronavirus pandemic (COVID-19). As the COVID-19 pandemic spreads, several people have drawn parallels to influenza. While both viruses cause respiratory infections, they propagate in very different ways. This has a major impact on the public health measures that can be used to fight each virus. These viruses are pandemic-causing in the same way. That is, they both cause respiratory disease, and can present themselves in several ways, ranging from asymptomatic to severe and deadly. A proposal is presented in this paper that uses two algorithms to define and classify these pandemics, they are: The Back Propagation (BP) classification algorithm and the Fuzzy C-Mean (FCM) clustering algorithm. Two stages are implemented in the proposed system: in the first step, the FCM algorithm is used to find out the type of virus, and this algorithm is capable of handling ambiguous features of viruses. In the second step, a BP neural network is used as a classifier to detect the pandemic class. The proposed system was trained and tested using a well-known dataset (covid-19 vs influenza). Information Gain (IG) is used to optimize the related features that affect the classification process to improve speed and accuracy. The proposed mobile application is developed to support users easily detecting the COVID-19 infection by inputting the medical tests as significant features to the proposed system. The proposed system's accuracy is up to (89%), the framework was created using the Matlab programming environment and an Android Studio for Mobil application designing.</p>
<p class="0abstract"><strong>—</strong>In the communication networks, guidance has become an important factor, with a significant impact on network performance, where the network orientation area has been and continues to be an ongoing development, intensive research for many years aimed at optimizing the network. This paper performs three modifications for a multipath routing protocol to solve the problem of routing in a DCell network simulation and apply online solutions on the network, the goal is to improve the transition efficiency of data. The modifications used to avoid data transmission failures which are delay problem<strong>, </strong>link failure problem, and power off (rack problem). The implementation of multipath routing protocol on the DCell network in actual simulation using the NS-3 program, which represents the rule that the DCell network was built and simulated. Finally, the modifications succeeded and return good results decreasing the delay time and solving the data transaction problems.</p>
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