Draft:Perception Anomaly Detection Algorithm

Draft article not currently submitted for review.

This is a draft Articles for creation (AfC) submission. It is not currently pending review. While there are no deadlines, abandoned drafts may be deleted after six months. To edit the draft click on the "Edit" tab at the top of the window.

To be accepted, a draft should:

Show the subject qualifies for a Wikipedia article by using multiple sources that meet four criteria. The sources should be (1) reliable (2) secondary (3) independent of the subject (4) talk about the subject in some depth. For some topics, there are alternative criteria.
Be written from a neutral point of view
Respect copyright and do not plagiarize. Do not copy-paste.

It is strongly discouraged to write about yourself, your business or employer. If you do so, you must declare it.

Where to get help

If you need help editing or submitting your draft, please ask us a question at the AfC Help Desk or get live help from experienced editors. These venues are only for help with editing and the submission process, not to get reviews.
If you need feedback on your draft, or if the review is taking a lot of time, you can try asking for help on the talk page of a relevant WikiProject. Some WikiProjects are more active than others so a speedy reply is not guaranteed.

How to improve a draft

Wikipedia:Contributing to Wikipedia – a basic overview on how to edit Wikipedia.
Help:Wikitext – how to use the markup
Help:Referencing for beginners – how to include references
Wikipedia:Article development – how to develop your article
Wikipedia:Writing better articles – how to improve your article
Wikipedia:Verifiability – make sure your article includes reliable third-party sources

You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article.

Improving your odds of a speedy review

To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags.

Add tags to your draft

Editor resources

Easy tools: Citation bot (help) | Advanced: Fix bare URLs

Last edited by 81.77.87.245 (talk | contribs) 4 months ago. (Update)

Submit the draft for review!

The Perception algorithm^[1]^[2]^[3] is an unsupervised parameter free machine learning technique designed to detect anomalies in univariate and multivariate data. The algorithm was inspired by elements of human perception, and builds on the idea that any event that is unexpected to occur, but does occur, is considered an anomaly with respect to some chosen measure. The algorithm is fast and efficient without the user having to supply any data specific parameters, as it adapts to the data distribution.

Algorithm

$\mathbb {E} (C_{n})={S \choose n}{\frac {1}{W^{n-1}}}<1$

Applications

The algorithm has been used in three known production use cases:

Detect insider threat activities in corporate networks.
Identify start and end points of promotional activity from EPOS data of retailers.
Detect unexpected number of support tickets being produced by a node in telecommunication applications.

References

^ Mohammad, Nassir (2022-05-13), "Anomaly Detection using Principles of Human Perception", arXiv:2103.12323 [cs.CR]
^ Mohammad, Nassir (2024-08-16), M-Nassir/perception, retrieved 2024-08-16
^ perception-nassir: A method for detecting anomalies in univariate and multivariate data, retrieved 2024-08-16

[:0-1] Mohammad, Nassir (2022-05-13), "Anomaly Detection using Principles of Human Perception", arXiv:2103.12323 [cs.CR]

[2] Mohammad, Nassir (2024-08-16), M-Nassir/perception, retrieved 2024-08-16

[3] perception-nassir: A method for detecting anomalies in univariate and multivariate data, retrieved 2024-08-16

[1]

[2]

[3]