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.
We are now in Big Data era, and there is a growing demand for tools which can process and analyze it. Big data analytics deals with extracting valuable information from that complex data which can’t be handled by traditional data mining tools. This paper surveys the available tools which can handle large volumes of data as well as evolving data streams. The data mining tools and algorithms which can handle big data have also been summarized, and one of the tools has been used for mining of large datasets using distributed algorithms.
In this study, the authors aim to propose an optimized density-based algorithm for anomaly detection with focus on high-dimensional datasets. The optimization is achieved by optimizing the input parameters of the algorithm using firefly meta-heuristic. The performance of different similarity measures for the algorithm is compared including both L1 and L2 norms to identify the most efficient similarity measure for high-dimensional datasets. The algorithm is optimized further in terms of speed and scalability by using Apache Spark big data platform. The experiments were conducted on publicly available datasets, and the results were evaluated on various performance metrics like execution time, accuracy, sensitivity, and specificity.
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.
Recommendation System is an information filtering system which seeks to predict the “liking” of a user for an item, with the aim to suggest the user those items which he/she is most likely to select/buy. The focus of this paper is on rating prediction whose main objective is to predict the ratings the current user is going to give to the items which are yet to be rated/viewed by him/her. This paper uses a collaborative filtering based approach for generating recommendation, and the model used is a clustering-based model. In this approach all the existing users are clustered using whale optimization technique, instead of traditional clustering approaches like k-means, EM algorithm, etc. The appropriate cluster is then identified for the active user, and the ratings of the active user are predicted based on ratings given by other users belonging to the same cluster. Different measures like MAE, SD, RMSE and t-value are used for performance analysis of the proposed method and the results obtained are found to be highly accurate
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