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Cloud Computing is A Remarkable technology in the IT field Various services are provided by the cloud computing over the internet. According to the usage computing resource services IT charges in the cloud Yahoo, Amazon, Google are some of the cloud used organisations which are having number of users. Cloud Computing is an internet based computing approach. So there is a sharing of applications resources and software in between the computing devices. Mentioning of loads in cloud environment is most challenging issue. Balancing of float in the main intention in making of visual machine balance which not be overload or underload. An efficient and its dynamic load balancing weighted Round Robin algorithm for virtual machine in cloud computing is presented in this paper. Task or uses the modified algorithm of inspired Honey Bee behaviour and for balancing the workload on virtual machine uses the modified Round Robin Algorithm in the cloud system. By using this energy system performance is improved minimum completion time can be achieved and better resources are utilized. The efficiency of this load balancing method is better than the previous methods. [ABSTRACT FROM AUTHOR]

Now a day's Diabetic disease(Dd) is common disease among people which causes damage to kidney, heart and may eventually lead to death. Early detection of diabetics is very much essential to avoid kidney and heart failure. Effective treatment for Dd are available through it requires early diagnosis and the continuous monitoring of diabetic patients. Also many physical tests can be used to detect Dd but are time consuming. The objective of our research paper is to give decision about the presence of diabetics by applying ensemble of machine learning classifying algorithms on features extracted from output of different datasets. It will give us accuracy of which algorithm will be suitable and more accurate for prediction of the disease. Decision making for predicting the presence of diabetic is performed using Linear Regression(LR), Logistic Regression(LoR), K Nearest Neighbors(KNN), Decision Tree(DT), Support Vector Machine(SVM), Naïve Bayes(NB), Random Forest(RF), The experimental results has been analysed by using Jupyter Notebook. Among all the mentioned Supervised machine learning algorithm RF approach show a highest classification accuracy (CA) of 89.58. From this, we can infer that for diabetic the RF approach gives the best performance compared to all other approaches. [ABSTRACT FROM AUTHOR]