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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]

Summary: Heart disease detection through early‐stage syndrome remains as a main confront in present world situation. If it is not detected appropriate time, then this turns out to be the major cause of death. Several existing heart disease detection techniques are developed with lower detection performance and therefore it is very significant to introduce a novel heart disease detection model that poses the potential to detect heart disease from input data. A novel detection approach named, social water cycle algorithm‐based deep residual network (SWCA‐based DRN) is proposed for classification of heart disease. The developed SWCA algorithm is a newly designed by the hybridization of social optimization algorithm and water cycle algorithm. Here, an input data is initially preprocessed and the feature fusion procedure is carried out RV coefficient enabled rider optimization algorithm‐based neural network. With the fused feature result, heart disease classification is performed utilizing a DRN classifier where training procedure of DRN is done by proposed optimization algorithm, named SWCA. Furthermore, developed SWCA‐enabled DRN technique outperformed different other present heart disease detection approaches and attained superior performance concerning the performance measures, like testing accuracy, sensitivity, and specificity with highest values of 0.941, 0.954, and 0.925. [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]

High Utility Itemset Mining (HUIM) is playing an important role in extracting meaningful knowledge that is profitable itemsets rather than their occurrence frequency. HUIM result thrives into a huge number of utility itemsets that makes it difficult in decision policy. The alternative procedure is condensed representation of HUI's that contains utility itemsets without any redundancy. Closed High Utility Itemsets (CHUI) and High Utility Generators (HUG) are lossless utility itemsets useful in recommendation systems. In this paper, we address how to extract Closed and Generator HUI's from the given dataset. For faster accessing, it is proposed hash based technique that store itemsets with the frequency, utility value, closed flag, and generator flag. Experimental results shows that it outperforms other approaches in time and space. [ABSTRACT FROM AUTHOR]