Resource management is addressed using infrastructure as a service. On demand, the resource management module effectively manages available resources. Resource management in cloud resource provisioning is aided by the prediction of central processing unit (CPU) and memory utilization. Using a hybrid ARIMA-ANN model, this study forecasts future CPU and memory utilization. The range of values discovered is utilized to make predictions, which is useful for resource management. In the cloud traces, the ARIMA model detects linear components in the CPU and memory utilization patterns. For recognizing and magnifying nonlinear components in the traces, the artificial neural network (ANN) leverages the residuals derived from the ARIMA model. The resource utilization patterns are predicted using a combination of linear and nonlinear components. From the predicted and previous history values, the Savitzky-Golay filter finds a range of forecast values. Point value forecasting may not be the best method for predicting multi-step resource utilization in a cloud setting. The forecasting error can be decreased by introducing a range of values, and we employ as reported by Engelbrecht HA and van Greunen M (in: Network and Service Management (CNSM), 2015 11th International Conference, 2015) OER (over estimation rate) and UER (under estimation rate) to cope with the error produced by over or under estimation of CPU and memory utilization. The prediction accuracy is tested using statistical-based analysis using Google's 29-day trail and BitBrain (BB).

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Diabetes predictions have gained major attention due to its consequences on the healthy well-being of an individual. When glucose levels go high due to non- availability of the hormone called insulin which digest glucose, together with other side effects like frequent urination, excessive thirst, and hunger with sudden weight reduction, one can be confirmed of suffering from diabetes. This requires a consistent treatment and monitoring of its complications which are considered fatal in some cases. There are various ways to keep a tract of the glucose level in blood to adjust the diet and dosage of insulin. However, predicting it as early as possible is a challenging task due to its inter-dependency factor that causes trouble to human organs like viscera, peripherals, nervous system, cardiovascular, eyes and excretory system. This research paper aims to provide five different machine learning methods for the prediction of diabetes such as SVM, Logistics regression, KNN Classifier, Random Forest and Logistic algorithm. These proposed methods are effective techniques for earlier detection of the diabetes.