Legacy electronic health record systems were not developed with the level of connectivity expected from them nowadays. Therefore, interoperability weakness inherent in the legacy systems can result in poor patient care and waste of financial resources. Simultaneously, healthcare providers are not yet ready to dispose of them. Large hospitals are also less likely to share their data with external care providers due to economic and political reasons. To overcome the barriers in the effective medical data exchange process, we present a novel hybrid cloud called MedShare, dealing with interoperability issues among disconnected but autonomously functioning healthcare providers. The proposed system architecture and its implementation is based upon: 1) custom data extractors to extract legacy medical data from the three hemodialysis centers under consideration; 2) negotiated and converted to a common data model in each of the private cloud of a provider; 3) indexed patient information using the HashMap technique into the public cloud that operates on private clouds, called a hybrid cloud; and 4) a set of services and tools installed as a coherent environment to exchange information smoothly. This paper enables healthcare professionals to appropriately access and securely share a patient's medical information. MedShare allows the healthcare providers and administrators to maintain the control of their patient data, which is always the primary concern in building a trustworthy environment for exchanging patient information. Medshare effectively addresses primary security and privacy concerns surrounding the deployment of data exchange process by including patient consent and a two-way authorization process.

Automated service classification plays a crucial role in service discovery, selection, and composition. Machine learning has been widely used for service classification in recent years. However, the performance of conventional machine learning methods highly depends on the quality of manual feature engineering. In this paper, we present a novel deep neural network to automatically abstract low-level representation of both service name and service description to high-level merged features without feature engineering and the length limitation, and then predict service classification on 50 service categories. To demonstrate the effectiveness of our approach, we conduct a comprehensive experimental study by comparing 10 machine learning methods on 10,000 real-world web services. The result shows that the proposed deep neural network can achieve higher accuracy in classification and more robust than other machine learning methods.

Bloggers possess the capability of understanding and influencing mass psychology to a wide community of fans and followers by posting their online valuable content. Their dominance over audience can be used as a helping hand in the corporate world which desires to disseminate their product or services among diversified people belonging to varying localities, and is always on the lookout for suitable and quick ways to grasp public access. Due to this reason, influential bloggers are preferred in the online market to initiate marketing campaigns which is a thought-provoking task due to loads of blogger communities. The novelty of this paper lies in the proposed Framework for Influential Blogger Prediction based on Blogger and Blog Features (IBP-BBF) using Case-Based Reasoning (CBR) which is not only capable of handling labeled data but also unstructured data (blogs) and imbalanced data in an optimized way. Detailed labelled and unstructured data are collected by online survey of 129 bloggers and text mining of their 32,200 blogs respectively. The classification results are compared and validated with state-of-the-art machine learning techniques by using standard evaluation measures respectively in the context of imbalanced data. The results show that the proposed IBP-BBF framework through CBR modeling outperforms existing techniques in classifying and adapting the influential blogger prediction. The IBP-BBF framework performed better as compared to baseline imbalanced data classification techniques. It is found that the Balanced Random Forest contributes towards the performance of CBR approach than Balanced Bagging Classifier and RUSBoost classifier. By using the CBR approach, baseline techniques can be optimized for influential blogger identification in a better way.