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A good contrast is significant for analysis of medical images, and if the images have poor contrast, then some methods of contrast enhancement can be of much benefit. In this paper, a convolution neural network-based transfer learning approach is utilized for contrast enhancement of mammographic images. The experiments are conducted on ISP and MIAS datasets, where ISP dataset is used for training and MIAS dataset is used for testing (contrast enhancement). Experimental comparison of the proposed technique is done with the most popular direct and indirect contrast enhancement techniques such as CLAHE, BBHE, RMSHE, and contrast stretching. A qualitative comparison is done using mean square error (MSE), signal to noise ratio (SNR), and peak signal to noise ratio (PSNR). It is observed that the proposed technique outperforms the other techniques HE, RMSHE, CLAHE, BBHE, and contrast stretching. [ABSTRACT FROM AUTHOR]

Recommendation systems suggest relevant items to a user based on the similarity between users or between items. In a collaborative filtering approach for generating recommendations, there is a symmetry between the users. That is, if user A has similar interests with user B, then an item liked by B can be recommended to A and vice versa. To provide optimal and fast recommendations, a recommender system may generate and keep clusters of existing users/items. In this research work, a hybrid sparrow clustered (HSC) recommender system is developed, and is applied to the MovieLens dataset to demonstrate its effectiveness and efficiency. The proposed method (HSC) is also compared to other methods, and the results are compared. Precision, mean absolute error, recall, and accuracy metrics were used to figure out how well the movie recommender system worked for the HSC collaborative movie recommender system. The results of the experiment on the MovieLens dataset show that the proposed method is quite promising when it comes to scalability, performance, and personalized movie recommendations. [ABSTRACT FROM AUTHOR]

In today's world as the data on the web is increasing it becomes a challenge to identify the relevant information. Automatic text summarization (ATS) provides a significant answer to it. In this paper, fuzzy logic and shark smell optimization (SSO) based algorithm for extractive text summarization is proposed. Shark Smell Optimization has been used to assign a weight to eight different features to identify the less and more important text features of text summarization. Then, the Fuzzy Logic's inference system is utilized to generate fuzzy rules, and finally an automated summary is generated. The system generated summaries have been tested against the reference summaries from the DUC 2002, DUC 2003, DUC-2004 and TAC-11 dataset and ROUGE toolkit has been used for the evaluation of the proposed solution. Results of the proposed algorithm are compared against traditional methods and the rouge score suggested that the proposed algorithm generates better results than other methods. [ABSTRACT FROM AUTHOR]