The process of finding out the frequency of citations for various journals or articles or research papers is known as citation analysis. The impact of any research paper can be found by computing the number of citations, the particular journal or paper received. Many researchers quote concepts or ideas from various papers and the final outcome of the paper in their discussion. But it does not always mean that all the authors and papers referred to would be criticizing the referred paper with positive words. Sometimes if the referring paper has produced good results when compared to the referred paper then obviously the authors do not give much positive comments in their discussion. In order to address this problem, we propose the sentiment analysis can be performed to rate the citations based on the polarity level. The sentence parser can be used to identify the adjectives what the authors used in their discussion about the cited paper. Then the identified adjectives can be assigned a score to distinguish it from positive and negative. In between, if any paper is found without clearly mentioning the cited paper with identifiable adjective terms then in can be named as unknown or neutral. The citations can be ranked based on the computed polarity score. The ranking process helps to project the citations which have cited with higher positivity content in their discussion.
Blockchain has various merits such as decentralization, greater transparency and improved traceability. Nowadays blockchain is used at various numbers of applications such as financial related services, business & industry, integrity verification, governance, health and education sectors. Even though the blockchain technology has promising approaches for the building the future of internet systems and also extensive research is going on about the technical challenges. In this paper we presented the comprehensive survey on blockchain and the techniques that can be used to address the scalability issues with respect to storage and also different privacy preserving techniques that can be incorporated during the implementation of blockchain application.
Nowadays, skin cancer is one of the most dangerous forms of cancer found in humans. There are various types of skin cancer, like basal, melanoma, carcinoma, and the squamous cell from which the melanoma is unpredictable. Thus, skin cancer detection in the early stage is very useful to treat it successfully. Hence, this study introduces a new algorithm called social bat optimisation algorithm for skin cancer detection. Initially, the pre-processing is done for the input image to eliminate the noise and artefacts present in the image. Then, the pre-processed image is fed to the feature extraction step where the features are extracted based on convolutional neural network features, and the local pixel pattern-based texture feature (local PPBTF). Here, the PPBTF is the combination of texture features and pixel pattern-based features in which the equation of PPBTF is modified based on the local binary pattern. Subsequently, the classification is done based on the extracted features using a deep stacked auto-encoder, which is trained by the proposed social bat optimisation. The performance of skin cancer detection based on the proposed model is evaluated based on accuracy, sensitivity, and specificity. The proposed model achieves the maximal accuracy of 93.38%, maximal sensitivity of 95%, and the maximal specificity of 96% for K-fold.
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