The objective of this paper is to develop a segmentation system in order to assist the surgeons to remove the portion of lung for the treatment of certain illness such as lung cancer, and tumours. The fissures of lung lobes are not seen by naked eyes in low dose CT image, there is a proposal for automatic segmentation system. The lung lobes and nodules in CT image are segmented using two stage approaches such as modified adaptive fissure sweep and adaptive thresholding. Initially pre-processing is used to remove the noise present in CT image using filter, then the fissure regions are located using adaptive fissure sweep technique, then histogram equalization and region growing is applied to refine the oblique fissure. Lung Nodules are segmented using thresholding. The comparative analysis of manual and automatic segmentation for fissure verification has been performed statistically. The analysis is made on 20 set of images.

Cloud computing security is the most critical factor for providers, cloud users, and organizations. The various novel approaches apply host‐based or network‐based methods to increase cloud security performance and detection rate. However, due to the virtual and distributed environment of the cloud, conventional network intrusion detection systems (NIDS) have been unreliable in handling these security attacks. Therefore, we design a methodology that incorporates feature selection and classification using ensemble techniques to provide efficient and accurate intrusion detection to address these problems. This proposed model combines the three most effective feature selection techniques (gain‐ratio, chi‐squared, and information gain) to offer a qualifying result and four top classifiers (SVM, LR, NB, and DT) using enhanced weighted majority voting. Moreover, we proposed an experimental technique using a new dataset called Honeypot. All experiments utilized three datasets: Honeypots, Kyoto, and NSL: KDD. In addition, the results of this experimental study were compared with other approaches and performed the statistical significance analysis. Finally, the results reveal that the proposed intrusion detection based on the Honeypot dataset was better and more efficient than other methods because we have an accuracy of 98.29%, FAR of 0.012%, DR of 97.9%, and AUC = 0.9921.

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