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During recent years, maintaining scientific and medical databases has turned out to be a major challenge in the medical sector. The medical data regarding the patients comprise diverse diagnostic medical reports and features for such disease need to be recorded carefully for providing high-quality treatments. Therefore, this paper proposes a hybrid deep convolutional neural network (HDCNN)-based COOT approach to predict the risk of diseases. Initially, an improved crossover-based levy flight optimization algorithm (ICLFDO) algorithm is employed in selecting and processing the unstructured textual data. Then later, the HDCNN-COOT approach predicts the diseases more accurately. In addition to this, the classifier predicts whether the patient is at risk of disease in the future or not. The efficiency of the proposed model is evaluated by using a dataset obtained from University Hospital of Ludwig Maximilian University of Munich, Germany. The data collected in the data center include 29,477,035 data items from 36,082 patients. The proposed model is capable of using structured data and uses textual data of the patients. Finally, the higher classification accuracy and improved performance of the classifiers can be viewed using the experimental results conducted on five different datasets. [ABSTRACT FROM AUTHOR]

To support the grid and integrate renewables, demand response schemes reward the power flexibility of energy consumers. Data centers can profit from this by using power management techniques on all levels of data center architecture: infrastructure, hardware, workload, applications. Even though lately, demand response with data centers has been well researched, most works focus on just one or two techniques and one or two valorization options. This leaves data centers stranded that are not represented by the specific combinations of assumptions and techniques presented in research, and thus a huge potential remains barely touched. To address this challenge, the goal of the presented work is to provide data centers with a framework that can be flexibly instantiated by each data center to assess its individual demand response potential. To achieve this goal, this work presents Sim2Win, a data center simulation framework that can replay any set of different power management strategies in the face of any set of markets for power flexibility. A part of the framework is then instantiated and applied to the workload of a real high-performance data center. It uses workload shifting and frequency scaling in order to market their flexibility on the EPEX spot market and the secondary reserve market in Germany. The results show that by using the inherent flexibility of their power profile on the EPEX spot market the considered data center in 2014 could have earned savings of 7.3% of their power bill. [ABSTRACT FROM AUTHOR]