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@egisg
Egypt旗标 Cairo, Egypt
会员,2011年11月13日加入
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egisg

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ISG- company working on software solutions online- high experienced in CMS wordpress, PHP frameworks and Mobile app development using phone-gap , ionic angular
$10 USD/hr
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3.6
  • 100%完工率
  • 100%预算內
  • 100%准时性
  • N/A重雇率

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经验

CIO

Sep 2009 - Mar 2012 (2 years)

• Provides technical direction for the development, design, and systems integration for client engagement from definition phase through implementation. • Applies significant knowledge of industry trends and developments to improve service to clients. • Reviews work of development team. • Easily recognizes system deficiencies and implements effective solutions. • Creates and executes development plans and revises as appropriate to meet changing needs and requirements. • Manages the process of innovative

Joomla as economic magazine portal

Mar 2009

work on a lot of online magazine portals with high level of profissional use in fron end and back end. and one of our good reference is [login to view URL]

Software Engineer consultant

Dec 2007 - Sep 2009 (1 year)

Microsoft –Vendor- Working in Localization live project developed in Egypt and managed by Microsoft –Redmond office team. I participate in Database and object design. And writing design documents following Microsoft standards Engineering Excellency http://eeg/. I am Key player in designing dynamic UI and implementing functionality of Localization live project. On this time period we are developing a version of LocLive to be proving of concept. And this version will guide the Microsoft PMs to plan to develop

教育

BS of computer engineering

1994 - 1999 (5 years)

Master computer engineering

2002 - 2006 (4 years)

Executive management diploma

2011 - 2013 (2 years)

刊物

Fabric fault classification using neural trees

In this paper, fabric faults classification using CNeT (Behnke and Karayiannis, 1998) is studied. The basic objectives are to improve the features selection used in CNeT (Behnke and Karayiannis, 1998) classifier and compare the results with other neural network classifiers. The algorithm adopted here is composed of three stages. The first stage is a preprocessing phase where defects are detected and localized. Since every detected defect has its different shape and size, all defects are normalized to a pred

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