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With the increase in the number of cases every year, skin cancer stays as one of the most common cancers worldwide. Although dermatologists have been aided with modern research study in detection of cancer, proper treatment of cancer has been a quite challenging task due to the visual appearance of cells. In this experiment, we have studied segmentation and classification algorithms for early detection of cancerous cells, as early detection may increase survival rate of the affected person. HAM10000 dataset has been utilized in this study which has 10,015 different images into seven different classes. Three different types of segmentation algorithms have been studied in this experiment, U-Net, ResUNet and DeeplabV3+. Among all these, DeeplabV3+ appears to outperform the rest of the algorithms giving an overall accuracy of 96.21%, precision and recall of 93.26% and 93% respectively. Few pre-trained models namely ResNet50, ResNet152, SqueezeNet1.1 and DenseNet121 have been utilized for classification of skin cancer. As per the results obtained from the experiment, ResNet152 outperforms the rest of the three pre-trained models with an overall training and validation accuracy of 81.75% and 78.51% respectively. [ABSTRACT FROM AUTHOR]

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]