Implementation System of Network User Abnormal Behavior Detection Algorithm Based on Data Layering

Zhu, Hongjun; Gan, Ruijie

doi:10.1109/i-smac52330.2021.9640914

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…According to the classification of products and assessments, it emphasizes detecting 4 more dangerous faulty comment areas. It is found that the characteristics of response activities are the most effective for the problem, and Zhu [ 2 ] found that the overall score deviation is essential for detection. Fake comments have no effect.…”

Section: Supervised Algorithmmentioning

confidence: 99%

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%

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

Veeramakali

Shobanadevi

Nayak³

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

Automated Teller Machine Security and Robbery Prevention Based on Human Behaviour Analysis

2023

2023 Innovations in Power and Advanced Computing Technologies (I-Pact)

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Implementation System of Network User Abnormal Behavior Detection Algorithm Based on Data Layering

Cited by 2 publications

References 15 publications

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

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

Automated Teller Machine Security and Robbery Prevention Based on Human Behaviour Analysis

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