No abstract

No abstract

Due to exponential growth and rapid modernization in urban areas, there is a disparity in the socio-economic urban and rural populace. The wellbeing of these population is peril change in lifestyle affecting the health of an individual or their families. Moreover, the many countries has health issues in Communicable and Non-Communicable Diseases and malnutrition. As per World Health Organization (WHO), the Doctor to Patient Ratio (DPR) is 1:1000. It is an apprehensive task for a Doctor to monitor health concerns of the patients. The time to spent by a doctor to a patient is an average of 7-10 min, where most of the time, the doctorsare busy in taking notes symptoms or feeding the data to the Health Care System. The smart devices and Computational Intelligence (CI)and Soft Computing Techniques (SCT) may help the doctors to monitor the data of their patients suffering with various health care issues and also to diagnose and to provide state-of-the-art treatment. The collected data may be used by theconglomeration of doctors for their superfluous analysis and predictions, local government authorities may use the data to improve sanitation and controlling the outbreak of epidemics and also for other health care predictions. Applying SCT, identification of correlated features, feature ranking or importance and feature selection are performed on UCI Machine learning Datasets and also classification and prediction are performed on the Datasets to examine the accuracy of the predictions for the classification algorithms - rpart, knn and svm.

No abstract

Elasticity and scalability are prominent issues in cloud computing which are resolved effectively using federated clouds. The agent-based model is simulated in our work in which all the elements of cloud computing are categorized into specific agents like cloud consumer agent, cloud provider agent and cloud broker agent. The collaborated cloud providers who are contributing resources are treated as collated cloud provider agents. The residue-based resource provisioning is carried at cloud broker by performing multi-criteria decision for finding dominant collated provider agent in providing resources within the limit of service level agreement and horizontal scaling of the virtual machine is done based on greedy cloud ranker algorithm to rank the cloud which contributes the virtual VM which satisfy the consumer agent request within specific turnaround time without violating service level agreement. The features of the interoperable cloud are simulated using python classes and realization of horizontal scaling is tested for computing percentage of request satisfaction with full or partial and transaction rate completion of allocating virtual machine to cloud consumer agent.