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Software fault prediction (SFP) is a complex problem that meets developers in the software development life cycle. Collecting data from real software projects, either while the development life cycle or after lunch the product, is not a simple task, and the collected data may suffer from imbalance data distribution problem. In this research, we proposed an Enhanced Binary Moth Flame Optimization (EBMFO) with Adaptive synthetic sampling (ADASYN) to predict software faults. BMFO is employed as a wrapper feature selection, while ADASYN enhances the input dataset and address the imbalanced dataset. Converting MFO algorithm from a continues version to the binary version using transfer functions (TFs) from two different groups (S-shape and V-shape) is investigated in this work and proposed an EBFMFO version. Fifteen real projects data obtained from PROMISE repository are employed in this work. Three different classifiers are used: the k-nearest neighbors (k-NN), Decision Trees (DT), and Linear discriminant analysis (LDA). The reported results demonstrate that the proposed EBMFO enhances the overall performance of classifiers and outperforms the results in the literature and show the importance of TF for feature selection algorithms.

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This paper focuses on the study of multilevel COVID-19 X-ray T image segmentation based on swarm intelligence optimization to improve the diagnostic level of COVID-19. We present the first ant colony optimization with the Cauchy mutation and the greedy Levy mutation, termed CLACO, for continuous domains. Specifically, the Cauchy mutation is applied to the end phase of ant foraging in CLACO to enhance its searchability and to boost its convergence rate. The greedy Levy mutation is applied to the optimal ant individuals to confer an improved ability to jump out of the local optimum. Furthermore, this paper develops a novel CLACO-based multilevel image segmentation method, termed CLACO-MIS. Using 2D Kapur's entropy as the CLACO fitness function based on 2D histograms consisting of non-local mean filtered images and grayscale images, CLACO-MIS was succefully applied to the segmentation of COVID-19 X-ray images. A comparison of CLACO with some relevant variants and other excellent peers on 30 benchmark functions from IEEE CEC2014 demonstrates the superior performance of CLACO in terms of search capability, and convergence speed as well as ability to jump out of the local optimum. Moreover, CLACO-MIS was shown to have a better segmentation effect and a stronger adaptability at different threshold levels than other methods in performing segmentation experiments of COVID-19 X-ray images. Therefore, CLACO-MIS has great potential to be used for improving the diagnostic level of COVID-19. This research will host a webservice for any question at https://aliasgharheidari.com .

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