Karoly Farkas

About Karoly Farkas

Karoly Farkas, With an exceptional h-index of 17 and a recent h-index of 9 (since 2020), a distinguished researcher at Budapesti Muszaki és Gazdaságtudományi Egyetem, specializes in the field of Future Internet, Mobile Networks, MANETs, Sensor Networks, Community Networks.

His recent articles reflect a diverse array of research interests and contributions to the field:

Simple Heuristics as a Viable Alternative to Machine Learning-Based Anomaly Detection in Industrial IoT

AREP: an adaptive, machine learning-based algorithm for real-time anomaly detection on network telemetry data

Cloud Native ChatOps Development

Best Practices of Cloud Native Application Development

Towards machine learning-based anomaly detection on time-series data

Participatory sensing framework

Karoly Farkas Information

University

Position

Associate Professor of Computer Science

Citations(all)

1175

Citations(since 2020)

296

Cited By

1042

hIndex(all)

17

hIndex(since 2020)

9

i10Index(all)

27

i10Index(since 2020)

9

Email

University Profile Page

Budapesti Muszaki és Gazdaságtudományi Egyetem

Google Scholar

View Google Scholar Profile

Karoly Farkas Skills & Research Interests

Future Internet

Mobile Networks

MANETs

Sensor Networks

Community Networks

Top articles of Karoly Farkas

Title

Journal

Author(s)

Publication Date

Simple Heuristics as a Viable Alternative to Machine Learning-Based Anomaly Detection in Industrial IoT

IEEE Internet of Things Magazine

Balint Bicski

Karoly Farkas

Adrian Pekar

2023/6/26

AREP: an adaptive, machine learning-based algorithm for real-time anomaly detection on network telemetry data

Neural Computing and Applications

Karoly Farkas

2023/3

Cloud Native ChatOps Development

Ádám Adamek

Károly Farkas

Gergely Szabó

2022/5/29

Best Practices of Cloud Native Application Development

Bachelor of profession’s thesis, Budapest University of Technology and Economics, Budapest

Dávid Richárd Kertész

Károly Farkas

Gergely Szabó

2021/5/23

Towards machine learning-based anomaly detection on time-series data

Infocommunications Journal

Daniel Vajda

Adrian Pekar

Karoly Farkas

2021

Participatory sensing framework

Károly Farkas

2020/1/1

See List of Professors in Karoly Farkas University(Budapesti Muszaki és Gazdaságtudományi Egyetem)