Christ University

About: Christ University is a education organization based out in Bengaluru, India. It is known for research contribution in the topics: Computer science & Convection. The organization has 2267 authors who have published 2715 publications receiving 14575 citations. The organization is also known as: Christ College & Christ University.

Folksonomy-based fuzzy user profiling for improved recommendations

Abstract: Genre is a major factor influencing user decisions to peruse an item in domains such as movies, books etc. Recommender systems, generally have, at their disposal, information regarding genres/categories that a movie/book belongs to. However, the degree of membership of the objects in these categories is typically unavailable. Such information, if available, would provide a better description of items and consequently lead to quality recommendations. In this paper, we propose an approach to infer the degree of genre presence in a movie by examining the various tags conferred on them by various users. Tags are user-defined metadata for items and embed abundant information about various facets of user likes, their opinion on the quality and the type of object tagged. Leveraging on tags to guide the genre degree determination exploits crowd sourcing to enrich item content description. Fuzzy logic naturally models human logic allowing for the nuanced representation of features of objects and thus is utilized to derive such gradual representation as well as for modeling user profiles. To the best of our knowledge ours is one of the first approaches to utilize such folksonomy information to infer genre degrees subsequently used for recommendations. The proposed method has the twin advantages of utilizing enriched content information for recommendation as well as squeezing the information from the user-item-tag and user-item ratings spaces and condensing them into fuzzy user profiles. The fuzzy user and object representations are leveraged both for the design of content-based as well as collaborative recommender systems. Experimental evaluations establish the effectiveness of the proposed approaches as compared to other baselines.

Spider Monkey Crow Optimization Algorithm With Deep Learning for Sentiment Classification and Information Retrieval

Abstract: The epidemic increase in online reviews’ growth made the sentiment classification a fascinating domain in academic and industrial research The reviews assist several domains, which is complicated to gather annotated training data Several sentiment classification methodologies are devised for performing the sentiment analysis, but retrieval of information is not accurately performed, less effective, and less convergence speed In this paper, we propose a sentiment paper proposes a sentiment classification model, namely Spider Monkey Crow Optimization algorithm (SMCA), for training the deep recurrent neural network (DeepRNN) In this method, the telecom review is employed to remove stop words and stemming to eliminate inappropriate data to minimize user’s seeking time Meanwhile, the feature extraction is performed using SentiWordNet to derive the sentiments from the reviews The extracted SentiWordNet features and other features, like elongated words, punctuation, hashtag, and numerical values, are employed in the DeepRNN for classifying sentiments To retrieve the required review, the Fuzzy K-Nearest neighbor (Fuzzy-KNN) is employed to retrieve the review based on a distance measure With rigorous assessments and experimentation, it is observed that the proposed SMCA-based DeepRNN performs better in terms of accuracy of 977%, precision of 955%, recall of 946%, and F1-score 967%, respectively

The Nainital-Cape Survey-IV

Abstract: The Nainital-Cape survey is a dedicated ongoing survey programme to search for and study pulsational variability in chemically peculiar (CP) stars to understand their internal structure and evolution. The main aims of this survey are to find new pulsating Ap and Am stars in the northern and southern hemisphere and to perform asteroseismic studies of these new pulsators. The survey is conducted using high-speed photometry. The candidate stars were selected on the basis of having Stromgren photometric indices similar to those of known pulsating CP stars. Over the last decade a total of 337 candidate pulsating CP stars were observed for the Nainital-Cape survey, making it one of the longest ground-based surveys for pulsation in CP stars in terms of time span and sample size. The previous papers of this series presented seven new pulsating variables and 229 null results. In this paper we present the light curves, frequency spectra and the various astrophysical parameters of the 108 additional CP stars observed since the last reported results. We have also tabulated the basic physical parameters of the known roAp stars. As part of establishing the detection limits in the Nainital-Cape survey, we investigated the scintillation noise level at the two observing sites used in this survey, Sutherland and Nainital, by comparing the combined frequency spectra stars observed from each location. Our analysis shows that both the sites permit the detection of variations on the order of 0.6 milli-magnitude (mmag) in the frequency range 1-4 mHz, with Sutherland being marginally better, on average.