Abnormal Item Detection Based on Time Window Merging for Recommender Systems

Qi, Ling; Huang, Hai; Wang, Peng; Wang, Ru Chuan

doi:10.1109/trustcom/bigdatase.2018.00047

Cited by 1 publication

(2 citation statements)

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“…Commonly used is logistic regression, support vector machines, decision trees, and so on. Tara [ 5 ] and Qi et al [ 7 ] used logistic regression to detect malicious users in Twitter and phishers in CNN and found that the name language pattern feature is the most significant feature that distinguishes malicious users from normal users. Jiang et al [ 12 ] used logistic regression to detect false reviews in Amazon.…”

Section: Supervised Algorithmmentioning

confidence: 99%

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Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning

Veeramakali

Shobanadevi

Nayak³

et al. 2022

Computational Intelligence and Neuroscience

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A key challenge in clinical recommendation systems is the problem of aberrant patient profiles in social networks. As a result of a person’s abnormal profile, numerous vests might be used to make fake remarks about them, cyber bullying, or cyber-attacks. Many clinical researchers have done extensive study on this topic. The most recent studies on this topic are summarized, and an overarching framework is provided. When it comes to the methods and datasets that make up the data collection, the feature presentation and algorithm selection layers provide an overview of the various types of algorithm selections available. The categorization and evaluation of diseases and disorders has been one of the major advantages of machine learning in medical. Because it was harder to predict, it rendered it more controllable. It might range from difficult-to-find cancers in the early stages to certain other illnesses spread through the bloodstream. In healthcare, we may pick methods in machine learning depending on reliable outcomes. To do so, we must run the findings through each method. The major issue arises during information training and validation. Because the dataset is so large, eliminating mistakes might be difficult. The providers, other characteristics, various algorithms, data labelling techniques, and assessment criteria are all presented and contrasted in depth. Detecting anomalous users in medical social networks, on the other hand, is a work in progress. The result evaluation layer provides an explanation of how to evaluate and mark up the results of the various algorithm selection layers. Finally, it looks forward to more study in this area.

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Section: Supervised Algorithmmentioning

confidence: 99%

“…About 5% of users are fake users. Some experts believe that this proportion is possible up to 10% [ 7 ].…”

Section: Introductionmentioning

confidence: 99%