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There is the new promise of Cloud Integration (CI) for outsourcing Electronic Health (eHealth) data, computation and processes that provide real-time data exchange of various application systems and repositories. CI technology combines eHealth data in cloud environments, which are operated by multiple devices over the network and Internet. In addition, many Healthcare Organisations (HCOs) are migrating their systems to a cloud platform to reduce their business cost. Although CI provides attractive and cost-effective opportunities, it is essential to remember that Cloud Computing (CC) paradigms are still under development and not mature. One of the main challenges in CI is that all data management, security, availability, maintenance and domain control is done via a third-party service provider. Therefore, HCOs have no control over these matters. Trust of a third-party service provider is one of the essential factors for CI and this adds a new dimension to the opportunity. As a result of the scalability of applications and the lower costs, CI is viewed as the preferred infrastructure for most eHealth systems, but without comprising the privacy of data. In this paper, we present a novel CI approach using the Hybrid Integration Method (HIM) combined with the promising features of Fuzzy-Ontology. The aim is to strengthen the management of eHealth business and IT environments. The key outcome of this paper is to present a new technique to merge data and to identify conflicting data entries. A series of experiments were performed to prove that the proposed HIM is effective and accurate for eHealth data integration.

Deep learning (DL) is one of the core subsets of the semantic machine learning representations (SMLR) that impact on discovering multiple processing layers of non-linear big data (BD) transformations with high levels of abstraction concepts. The SMLR can unravel the concealed explanation characteristics and modifications of the heterogeneous data sources that are intertwined for further artificial intelligence (AI) implementations. Deep learning impacts high-level abstractions in data by deploying hierarchical architectures. It is practically challenging to model big data representations, which impacts on data and knowledge-based representations. Encouraged by deep learning, the formal knowledge representation has the potential to influence the SMLR process. Deep learning architecture is capable of modelling efficient big data representations for further artificial intelligence and SMLR tasks. This chapter focuses on how deep learning impacts on defining deep transfer learning, category, and works based on the techniques used on semantic machine learning representations.

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