Analysis of Computer Network Security Storage System Based on Cloud Computing Environment

A fundamental component of cloud computers from a business perspective is that users are allowed to use any desire and pay with a product that desire. Its cloud services were accessible anytime and anywhere consumers needed them. As a result, consumers are free to purchase whatever IT services they want, and they don't have to worry about how easy things can be managed. The remote server is used in a new information storage computing architecture that is considered an Internet generation. Ensuring security, material at resource providers' sites is a challenge that must be addressed in cloud technology. Thus, rather than reliance on a single provider for knowledge storing, this research implies developing construction for protection of knowledge stockpiling with a variation of operations, in which knowledge is scrambled and divided into numerous cipher frames and distributed across a large number of provider places. This support was applied to provide greater security, scalability, or reliability that was suggested according to the new structure. This paper, presented an encoded model for the cloud environment to improve security. The proposed model comprises the parity metadata for the database management provision to the provider. In the developed encoder chunks parity is not stored within the single resources with the provision of the available information chunks. The constructed security architecture in the RAID layer increases the dependability of the data with the deployment of the RAID 10 deployment. The developed RAID-based encoder chunks exhibit improved efficiency for the higher uptime at a minimal cost

Predicting Influenza Antigenicity by Matrix Completion With Antigen and Antiserum Similarity

The rapid mutation of influenza viruses especially on the two surface proteins hemagglutinin (HA) and neuraminidase (NA) has made them capable to escape from population immunity, which has become a key challenge for influenza vaccine design. Thus, it is crucial to predict influenza antigenic evolution and identify new antigenic variants in a timely manner. However, traditional experimental methods like hemagglutination inhibition (HI) assay to select vaccine strains are time and labor-intensive, while popular computational methods are less sensitive, which presents the need for more accurate algorithms. In this study, we have proposed a novel low-rank matrix completion model MCAAS to infer antigenic distances between antigens and antisera based on partially revealed antigenic distances, virus similarity based on HA protein sequences, and vaccine similarity based on vaccine strains. The model exploits the correlations of viruses and vaccines in serological tests as well as the ability of HAs from viruses and vaccine strains in inferring influenza antigenicity. We also compared the effects of comprehensive 65 amino acids substitution matrices in predicting influenza antigenicity. As a result, we applied MCAAS into H3N2 seasonal influenza virus data. Our model achieved a 10-fold cross validation root-mean-squared error (RMSE) of 0.5982, significantly outperformed existing computational methods like antigenic cartography, AntigenMap and BMCSI. We also constructed the antigenic map and studied the association between genetic and antigenic evolution of H3N2 influenza viruses. Finally, our analyses showed that homologous structure derived amino acid substitution matrix (HSDM) is most powerful in predicting influenza antigenicity, which is consistent with previous studies

AI-driven synthetic biology for non-small cell lung cancer drug effectiveness-cost analysis in intelligent assisted medical systems

According to statistics, in the 185 countries' 36 types of cancer, the morbidity and mortality of lung cancer take the first place, and non-small cell lung cancer (NSCLC) accounts for 85% of lung cancer (International Agency for Research on Cancer, 2018), (Bray et al., 2018). Significantly in many developing countries, limited medical resources and excess population seriously affect the diagnosis and treatment of alung cancer patients. The 21st century is an era of life medicine, big data, and information technology. Synthetic biology is known as the driving force of natural product innovation and research in this era. Based on the research of NSCLC targeted drugs, through the cross-fusion of synthetic biology and artificial intelligence, using the idea of bioengineering, we construct an artificial intelligence assisted medical system and propose a drug selection framework for the personalized selection of NSCLC patients. Under the premise of ensuring the efficacy, considering the economic cost of targeted drugs as an auxiliary decision-making factor, the system predicts the drug effectiveness-cost then. The experiment shows that our method can rely on the provided clinical data to screen drug treatment programs suitable for the patient's conditions and assist doctors in making an efficient diagnosis