LIBS-Based Detection of Antioxidant Elements in Seeds of Emblica officinalis

The aim of the study was to determine the effect of the elements of the extract of seed from Emblica officinalis on antioxidant enzymes and osmotic fragility of erythrocytes membrane in normal as well as streptozotocin-induced severely diabetic albino Wister rats. The results revealed that the untreated diabetic rats exhibited increase in oxidative stress as indicated by significantly diminished activities of free radical scavenging enzymes such as catalase (CAT) and superoxide dismutase (SOD) by 37.5% (p

Detection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor Decomposition

Product reviews and ratings on e-commerce websites provide customers with detailed insights about various aspects of the product such as quality, usefulness, etc. Since they influence customers' buying decisions, product reviews have become a fertile ground for abuse by sellers (colluding with reviewers) to promote their own products or to tarnish the reputation of competitor's products. In this paper, our focus is on detecting such abusive entities (both sellers and reviewers) by applying tensor decomposition on the product reviews data. While tensor decomposition is mostly unsupervised, we formulate our problem as a semi-supervised binary multi-target tensor decomposition, to take advantage of currently known abusive entities. We empirically show that our multi-target semi-supervised model achieves higher precision and recall in detecting abusive entities as compared to unsupervised techniques. Finally, we show that our proposed stochastic partial natural gradient inference for our model empirically achieves faster convergence than stochastic gradient and Online-EM with sufficient statistics.Comment: Accepted to the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2019. Contains supplementary material. arXiv admin note: text overlap with arXiv:1804.0383