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Ferhat Ozgur Catak

University of Stavanger

27 papers hereh-index 231.4k citations68 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author5
  • first author11
  • middle author9
  • last author2

Across the 27 of 27 papers where every author was matched, so the position is known.

fields
  • cs.LG12
  • cs.CR10
  • cs.NI2
  • cs.CV1
  • eess.SP1
  • eess.SY1
affiliations
  • University of Stavanger
Homepage
same name
  • Ferhat Ozgur Catak — 15 papers, h 10

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20132023
most citedA Benchmark API Call Dataset for Windows PE Malware Classification

23 citations · 74 across the 26 of their papers we have counts for

collaborators
Showing 2016Show all

4 papers · 1 filter

cs.CR2016

Privacy Preserving PageRank Algorithm By Using Secure Multi-Party Computation

Ferhat Ozgur Catak

In this work, we study the problem of privacy preserving computation on PageRank algorithm. The idea is to enforce the secure multi party computation of the algorithm iteratively u…

cs.CR2016★ 10 cited

Secure Multi-Party Computation Based Privacy Preserving Extreme Learning Machine Algorithm Over Vertically Distributed Data

Ferhat Özgür Çatak

Especially in the Big Data era, the usage of different classification methods is increasing day by day. The success of these classification methods depends on the effectiveness of…

cs.LG2016

Robust Ensemble Classifier Combination Based on Noise Removal with One-Class SVM

Ferhat Özgür Çatak

In machine learning area, as the number of labeled input samples becomes very large, it is very difficult to build a classification model because of input data set is not fit in a…

cs.LG2016★ 23 cited

Classification with Boosting of Extreme Learning Machine Over Arbitrarily Partitioned Data

Ferhat Özgür Çatak

Machine learning based computational intelligence methods are widely used to analyze large scale data sets in this age of big data. Extracting useful predictive modeling from these…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.