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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]

In single stage operation, high quality separation of similar size proteins was performed via batch ultrafiltration. Two permeable proteins, hemoglobin and ovalbumin, were separated using a 100 kDa polyethersulfone membrane for a volume concentration factor of 3.5. At low ionic strength, hemoglobin and ovalbumin showed their enhanced reverse selectivity of 11 and selectivity of 14 at pH 9 and 3.5, respectively. Protein adsorption on the membrane during ultrafiltration was carefully controlled by fine adjustment of pH and ionic strength of feed. The high separation factor was achieved as a result of pore constriction by protein adsorption and the electrostatic interaction between proteins and the membrane. [ABSTRACT FROM PUBLISHER]