Lung cancer is the overgrowth of cells in digestive organs. Identifying different types of lung cancer (squamous cell cancer, large cell carcinoma and adenocarcinoma) from lung histopathological images is outrageous works that shorten the chance of infected with lung cancer in the future. This research propounds an accurate diagnosis scheme using various neural network features and fusion of contourlet transform from lung histopathological image. This lesson has used several pre-train models (Alexnet, ResNet50, and VGG-16) in addition to divers scratch models while the pre-train Resnet50 model works better. The two reduction techniques (Principle Component Analysis (PCA) and Minimum Redundancy Maximum Relevance (MRMR)) have used to classify the type of lung cancer with the extraction of the most significant properties. In Convolution Neural Network (CNN) based lung cancer detection, the reduction approach PCA performs better. This proposed methodology is performed on ordinary datasets and establishes comparative better performance. The accuracy of this paper is 98.5%, sensitivity 96.50, specificity 97.00%, which is more effective than other approaches.

Big data typically coins with large volume of data and various enterprises are involving day to day in cloud environment. Nowadays, Cloud facility adoption has enlarged. With the acceptance of cloud facility, numerous of the enterprises are expending to store and process Large Data in cloud. Safety methods provided by the facility providers might not be sufficient to safe the data in the cloud. Enterprise as well as users are suffering with proper security aspect to store, retrieve and process big data in cloud environment. An access control model with honeypot is presented in this paper. Access control model deals with various parameters of authentication, log etc. Various link and areas are included as honeypot to catch hackers or unauthorized users.

No abstract

No abstract