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A novel image encryption framework is proposed in this article. A new chaotic map and a pseudorandom bit generator are proposed. Apart from this, a novel image encryption system is designed based on the proposed map and the proposed pseudorandom bit generator. These three are the major contributions of this work that makes a complete cryptosystem. The proposed new chaotic map is proposed which will be known as the ‘RCM map’ and its chaotic property is studied based on Devaney’s theory. The proposed pseudorandom bit generator is tested using the NIST test suite. The proposed method is simple to implement and does not involve any highly complex operations. Moreover, the proposed method is completely lossless, and therefore cent percent of data can be recovered from the encrypted image. The decryption process is also simple to implement i.e. just reverse of the encryption procedure. A scrambling algorithm is also proposed to further enhance the security of the overall system. The simulation, detailed analysis, and comparative studies of the proposed overall image encryption framework will help to understand the strengths and weaknesses of it. The experimental results are very promising and show the prospects of chaos theory and its usage in the field of data security.
相似文献In the medical field, image segmentation is a paramount and challenging task. The head and vertebral column make up the central nervous system (CNS), which control all the paramount functions. These include thinking, speaking, and gestures. The uncontrolled growth in the CNS can affect a person’s thinking of communication or movement. The tumor is known as the uncontrolled growth of cells in brain. The tumor can be recognized by MRI image. Brain tumor detection is mostly affected with inaccurate classification. This proposed work designed a novel classification and segmentation algorithm for the brain tumor detection. The proposed system uses the Adaptive fuzzy deep neural network with frog leap optimization to detect normality and abnormality of the image. Accurate classification is achieved with error minimization strategy through our proposed method. Then, the abnormal image is segmented using adaptive flying squirrel algorithm and the size of the tumor is detected, which is used to find out the severity of the tumor. The proposed work is implemented in the MATLAB simulation platform. The proposed work Accuracy, sensitivity, specificity, false positive rate and false negative rate are 99.6%, 99.9%, 99.8%, 0.0043 and 0.543, respectively. The detection accuracy is better in our proposed system than the existing teaching and learning based algorithm, social group algorithm and deep neural network.
相似文献In incremental software development (ISD) functionalities are delivered incrementally and requirements keep on evolving across iterations. The requirements evolution involves the addition of new dependencies and conflicts among functional and non-functional requirements along with changes in priorities and dependency weights. This, in turn, demands refactoring the order of development of system components to minimize the impact of these changes. Neglecting the non-functional constraints in the software development process exposes it to risks that may accumulate across several iterations. In this research work, we propose a risk management framework for ISD processes that provides an estimate of risk exposure for the project when functional features are frozen while ignoring the associations with non-functional requirements. Our framework proposes suitable risk reduction strategies that work in tandem with the risk assessment module. We also provide a tool interface for our risk management framework.
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