Jaypee Institute of Information Technology

About: Jaypee Institute of Information Technology is a education organization based out in Noida, Uttar Pradesh, India. It is known for research contribution in the topics: Computer science & Cluster analysis. The organization has 2136 authors who have published 3435 publications receiving 31458 citations. The organization is also known as: JIIT Noida.

Cross domain recommendation using multidimensional tensor factorization

Abstract: In the era of social media, exponential growth of information generated by online social media and e-commerce applications demands expert and intelligent recommendation systems It has become one of the most valuable tools as it reduces problems such as information overload while selecting and suggesting friends, items, products, jobs etc according to users’ interests Recommendation uses Collaborative Filtering as one of the most popular approaches but the major limitations of this approach are sparsity and cold-start issues Mostly existing recommendation systems focus on a single domain, on the other end cross-domain collaborative filtering is able to alleviate the degree of sparsity and cold-start problems to a better extent To avoid these problems, cross domain evolution comes in limelight and has become an emerging topic of research nowadays This paper mainly discusses the notion of cross-domain recommendation, its techniques and proposes a generalized Cross Domain- Multi Dimension Tensor Factorization (CD-MDTF) approach to trade off influence among domains optimally Cross Domain recommendation system employs knowledge from source domain and commingles it to target domain which covers the aspect of intelligent behavior and brings it to the category of an expert system Finally, to evaluate the proposed CD-MDTF approach, experiments are performed on two real-world datasets, Movie-Lens and Book-Crossing Results validate that sparsity and cold start problem is reduced by 16% and 25% respectively in comparison to single-domain recommendation systems Further, the proposed CD-MDTF recommendation system accuracy is validated using precision and recall as evaluation performance metrics which shows an improvement of 41% in precision and 21% in recall The results show that embedding of multiple domains and multiple dimensions for recommendation helps in result improvement, thereby augmenting the recommendation system performance like an expert and intelligent system

An automatic nuclei segmentation method using intelligent gravitational search algorithm based superpixel clustering

Abstract: A reliable nuclei segmentation is still an open-ended problem, especially in the breast cancer histology images. For the same, this paper proposes an intelligent gravitational search algorithm based superpixel clustering method for automatic nuclei segmentation. In the proposed method, a novel variant of gravitational search algorithm, intelligent gravitational search algorithm, is employed to obtain the optimal cluster centroids. The experimental and statistical results evince that the proposed variant surpasses existing meta-heuristic algorithms on 47 benchmark functions belonging to different problem categories i.e., unimodal, multimodal, and real-parameter single objective optimization problems of CEC, 2013. Further, the segmentation accuracy of the proposed method is examined on H&E stained estrogen receptor positive (ER+) breast cancer images. Experiments affirm that the proposed method is comparatively an efficacious and accurate method for segmenting the nuclei within breast cancer histology images.