The complexity of breast cancer classification is a drastic concern for a long time. Therefore, different algorithms for data mining have been developed and used to effectively classify the dataset of breast cancer. But, due to inefficient subset choice of this operation, these algorithms have problems in accuracy of classification. In this article, levy flight–based cuckoo search optimization (LFCSO) with parallel support vector machine (PSVM) is proposed for enhancing the classification accuracy of breast cancer. The proposed LFCSO‐PSVM technique includes three key steps: (i) preprocessing, (ii) feature selecting subset, and (iii) classification. Preprocessing is carried out using k‐means clustering that is required to eliminate the values. To acquire maximum detailed feature, these attributes are transmitted to the process the feature selecting subset, which is executed by LFCSO algorithm, and the aspiration function is used to determine the essential feature on the basis of proper fitness values. Then, the PSVM algorithm is implemented to classify the model for training and testing. This is used to increase the accuracy of the classification for the breast cancer dataset. The experimental outcomes demonstrate that the proposed LFCSO‐PSVM algorithm provides higher efficiency of classification than the existing algorithms in terms of precision, recall, f‐measurement, and reliability.

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In this paper we put forward a unique access to disclose variation in synthetic aperture radar (SAR) images. In this approach we classify the changed and unchanged region by the help of the fuzzy c-means (FCM) clustering along with the use of a Higher-order Markov random field (MRF). It is important to cope with speckle noise so we use a form of the Higher-order MRF along with an additional term that is the Bayesian denoising technique to reduce the speckle noise found in the SAR images. In this we use two approaches for the detection of change in synthetic aperture radar images. First, we use the log-ratio operator for getting the difference image. Secondly, with the help of FCM and the higher-order MRF we detect the change in the SAR images. We also apply the Wavelet denoising technique to reduce the speckle noise. The main advantage of the proposed method is its superiority in reducing speckle noises and its computational simplicity.

Comparing behaviours of program models has become an important task in software maintenance and regression testing. Combinatorial testing focuses on recognizing faults that happen due to interaction of values of a small number of input parameters.In this paper presents the Black-Box Regression Maximization (BBM) Algorithm with Density-based Spatial Clustering Algorithm (DSC) using Greedy Search optimization algorithm focuses on combinatorial testing and proactively exposes behavioural deviations by checking inside block transitions. In this method presents new approach of BBM with Internal block transitions to measure the dissimilarity statements in large program data. To identify specific faults, an adaptive testing rule repeatedly constructs and tests configurations in order to determine, for each interaction of interest, whether it is faulty or not.

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