In this paper we propose Decision support system implementation for health care system with the use of decision support system. The system integrates the input data from relevant sources into an autonomous data warehouse, while the client front-end ensures adequate output presentation to the user so as reveal significant details and dependencies. Applying the system as an "on -the -fly" decision-support facility for the policy-makers has resulted in significant acceleration of planning procedure, raised the overall awareness with respect to the underlying methodology and, ultimately, enabled more efficient academic administration.
Migration of the healthcare applications to cloud computing needs certain features to be fulfilled for complete user satisfaction. The users quench for satisfaction can be fulfilled by attaining the cloud computing characteristics such as data storage, accessibility, information sharing, interoperability, security, service availability, scalability, resiliency, resource management, and monitoring. The work is a survey on the cloud computing characteristics and its implementation in healthcare applications.
In recent times, massive quantity of data and its exponential rise have modified the significance of data security and analysis systems for Big Data. An intrusion detection system (IDS) is a process which helps to monitor and analyze the data for detecting the intrusions in the network. The huge quantity, variation, and high speed of data produced in the system pose a difficulty to the conventional techniques for the detection of attacks. Big Data methods are utilized in IDS for dealing with Big Data for precise and effective data analytic processes. This paper introduces a novel feature subset selection (FSS) with deep belief network (DBN) based intrusion detection in big data environment, called FSS-DBN model. The presented model involves data preprocessing stage to improve the data quality to a certain extent. In addition, the FSS can be considered as an optimization problem and is effectively solved by the use of teaching and learning based optimization (TLBO) algorithm. Moreover, DBN model is applied for identifying the class labels (i.e. intrusions) in the network. For validating the proficient results analysis of the FSS-DBN model, an extensive set of simulations were performed and the superior performance is also highlighted with the maximum sensitivity of 0.9898, specificity of 0.9865, accuracy of 0.9854, F-score of 0.9872, and kappa of 0.9867.
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