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Lung cancer is a critical disease with growing death rate, hence, the faster identification and treatment of lung cancer is essential. In medical image processing, the traditional methods like support vector machine, relevance vector machine for classifying cancer tissues are less sensitive to false data and required optimal improvement in classification accuracy. The proposed system of accurate lung cancer classification is obtained by a hybrid fuzzy relevance vector machine (FRVM) classifier with correlation negation ant colony optimization (CNACO) algorithm. This system provides enhanced accuracy and sensitivity by implementing two stages of feature extraction, image thresholding, and tumor segmentation, with a novel feature selection and tumor classification algorithm. The best features are selected by the proposed CNACO algorithm. The selected features are labeled and classified by FRVM classifier. The proposed classification scheme is validated on lung image database consortium and image database resource initiative public database and obtained accuracy of about 98.75%.

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Model checking automatically tests whether a model meets a given specification or not. It is a technique for verifying correctness properties of finite-state systems. One of the major problems in model checking is the state-explosion. To overcome this, a probabilistic approach called Bit-state Hashing is used to reduce the memory requirements. Bit-state hashing uses a data structure called bloom filter to store the corresponding reached states in a hash table. By enlarging a bloom filter, it improves total coverage estimation using a growth curve that approximates increased in reached states. To increase the effectiveness of the existing system, coverage estimation in model checking with sequential multiple bit-state hashing has been proposed. The sequentially repeated bit state hashing technique can outperform all other hashing methods, even for very large problem size.

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