Abstract:The challenge for -omics research is to tackle the problem of fragmentation of knowledge by integrating several sources of heterogeneous information into a coherent entity. It is widely recognized that successful data integration is one of the keys to improve productivity for stored data. Through proper data integration tools and algorithms, researchers may correlate relationships that enable them to make better and faster decisions. The need for data integration is essential for present -omics community, because -omics data is currently spread world wide in wide variety of formats. These formats can be integrated and migrated across platforms through different techniques and one of the important techniques often used is XML. XML is used to provide a document markup language that is easier to learn, retrieve, store and transmit. It is semantically richer than HTML. Here, we describe bio warehousing, database federation, controlled vocabularies and highlighting the XML application to store, migrate and validate -omics data.
Artificial Intelligence (AI) is a domain that works on various complex applications, such as E-government services. In order to provide government services to the people, an online AI-based Deep Learning (DL) model has been developed to check the availability of government schemes. However, several E-government services are not available to the citizens based on their usage. Many challenges have been identified while using E-Government services. This paper introduces the DL model, Convolutional Neural Networks (CNN), to solve the issues in E-Government services. The system focuses on maintaining E-government data resources, and CNN is primarily used to automate E-Government services. Finally, CNN has developed an innovative E-Government environment to support the design, development, and implementation of applications.
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