Profilbillede af khaleedeng
@khaleedeng
Medlem siden 20. december 2014
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khaleedeng

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I am an expert network and system engineer with more than 11 years experience, I designed a network for hospital, ministries, and other companies. I designed and managed Data Centers for governmental and private sector. - Cisco Routers and switches - Mikrotik - Juinper - Ubiquity - PFsense and OPNsense - Fortigate - Cisco Meraki,. - ASA Firewall - Windows Servers 2003/2008/2012 R2 - Linux Centos, - PRTG - Splunk, - Squil - QNAP -TS-451+ and Western Digital EX4 - VOIP - Asterisk 13 - Radius and TACACS+ - SNORT - - Packet Tracer, - GNS3
$10 USD/hr
3 anmeldelser
2.3
  • 67%Jobs udført
  • 100%Indenfor Budget
  • 38%Til Tiden
  • N/AGenansættelsesfrekvens

Portfolio

Nylige bedømmelser

  • billede af Muhammad A. Project 18769039 has been deleted $27.00 USD

    “nothing”

  • billede af Gaurav G. Project for Khaled A. $125.00 AUD

    “I found his work really good. Very clean and to the point solution. Just a bit costly for me but appreciate his efforts to go extra mile and get things done. Looking forward to work again in future. All the best friend.”

  • billede af Dheeraj K. Project for Khaled A. ₹3700.00 INR

    “Superb work, Really appreciated will hire him again for my next work good 110%.”

Erfaring

Network and System Administrator

Mar 2015

I have experience in many network and system management such as: - Cisco Routers and switches - Mikrotik - Juinper - Ubiquity - PFsense and OPNsense - Fortigate - Cisco Meraki,. - ASA Firewall - Windows Servers 2003/2008/2012 R2 - Linux Centos, - PRTG - Splunk, - Squil - QNAP -TS-451+ and Western Digital EX4 - VOIP - Asterisk 13 - Radius and TACACS+ - SNORT - - Packet Tracer, - GNS3

Uddannelse

Master in computer engineering

2010 - 2014 (4 years)

bachelor of computer engineering

2001 - 2006 (5 years)

Publikationer

A Novel Clustering Algorithm Using K-means (CUK)

While K-means is one of the most well known methods to partition data set into clusters, it still has a problem when clusters are of different size and different density. K-means converges to one of many local minima. Many methods have been proposed to overcome these limitations of K-means, but most of these methods do not overcome the limitation of both different density and size in the same time. The previous methods success to overcome one of them while fails with the others. In this paper we propose a n

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