The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Unraveling Data Complexity in the Metaverse for Anomaly Detection With Python on NYC Taxi
Abstract
In the dynamic environment of the Metaverse, where virtual interactions and transactions thrive, detecting data anomalies becomes imperative for maintaining integrity and security. This paper explores the application of Pythonbased anomaly detection models, including Inter Quartile Range (IQR), Median Absolute Deviation (MAD), and Local Outlier Factor (LOF), in identifying anomalies within NYC Taxi data. Through comprehensive analysis and experimentation, we investigate the effectiveness and comparative performance of these models in detecting outliers amidst the complex and diverse data landscape of the Metaverse. In the NYC Taxi Data, which contains 10320 data, it was analyzed with the mentioned algorithms and 2 anomalies (0.019%) were found with IQR. In the same data set, 1 anomaly (0.009%) was found with MAD model and 1032 anomalies (10%) were found with LOF
Related Content
|
Frederic Andres.
© 2027.
14 pages.
|
|
Kalsoom Safdar, Khairul Najmy Abdul Rani, Mohd Aminudin Jamlos, Siti Julia Rosli, Muhammad Usman Younus, Zanab Safdar.
© 2027.
27 pages.
|
|
Bani Adam, Binastya Anggara Sekti, Muhammad Adi Zacky Zahran.
© 2027.
24 pages.
|
|
Swetha Margaret T. A., Renuka Devi D..
© 2027.
31 pages.
|
|
Maurice Saluschke, Michael Schulz.
© 2027.
30 pages.
|
|
Mirjam Sepesy Maučec, Gregor Donaj.
© 2027.
16 pages.
|
|
Jorge A. Ruiz-Vanoye, Ocotlan Diaz-Parra, Ricardo A. Barrera-Cámara, Alejandro Fuentes-Penna, Francisco R. Trejo-Macotela, Jaime Aguilar-Ortiz, Eric Simancas-Acevedo.
© 2027.
21 pages.
|
|
|