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Skin cancer is the irregular growth of skin cells, which is most often termed as cancer, developed by exposure of ultraviolet rays from sun. In this research paper, deep learning enabled hybrid optimization is followed for skin cancer detection and lesion segmentation. Two optimization algorithms are followed for skin lesion segmentation and cancer detection. Here, pre-processing is done by anisotropic diffusion followed by skin lesion segmentation. Here, Multi-Scale Residual Fusion Network (MSRFNet) is utilized for skin lesion segmentation, which is trained by proposed Average Subtraction Student Psychology Based Optimization (ASSPBO). After skin lesion segmentation, necessary features are extracted, followed by skin cancer detection. Skin cancer is detected by Deep Residual Network (DRN) trained by proposed Fractional ASSPBO (FrASSPBO). Moreover, performance of proposed FrASSPBO-DRN is analysed by three performance metrics like testing accuracy, True Positive Rate (TPR), and False Positive Rate (FPR) with values of 93.4%, 94%, and 8.2%. [ABSTRACT FROM AUTHOR]

The number of questions asking in online Q/A forms are rapidly increasing day by day: in which many of the questions are of same internal meaning but the formation of the question and the vocabulary used in the question are different.so recommending same answer to the near duplicate or duplicate threads are becoming really thought. In this project, the proposed system aim to develop a suitable online machine learning model to detect the question pair similarity so that the proposed system can easily detect that whether it is duplicate, near duplicate or not a duplicate question. If the question is duplicate or near duplicate of the previously existing answered question the proposed system can recommend similar answer to that duplicate thread. [ABSTRACT FROM AUTHOR]