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Ahmed Bassiouny Online Offline
- 98%Jobs Completed
- 100%On Budget
- 79%On Time
- 14%Repeat Hire Rate
A Windows program that makes data exchange between Android and a server
“One of the best programmers ever seen. i recommend him absolutly.”saeedfadavi 5 months ago.
Scrie un software
“loved working with him,he needed not so many explanations and I would hire him again without any problems.”Theo42 6 months ago.
Finding Eye gaze with regular web camera - open to bidding
“Ahmed is such a talented freelancer,very humble and friendly in his best to solve any query if you hire him without any will never disappoint you.”Sloopysafe 8 months ago.
create a software with rabbitmq, mysql and c#
“Just hire him, you will never be disappointed,”mehdisne 10 months ago.
Write some Software that will take certain information from racingpost.com and compile odds for horses based on that information.
“Excellent programmers. Had a tricky program to write and did extremely well, understood exactly what I was after!! Thanks!!”hiflyer007 10 months ago.
DeepWalk algorithm on NVIDA GPU
“I liked Ahmed's communication and willingness to fix bugs. We were disappointed in the documentation. We also believe we need to improve our requirements description to avoid confusion.”mecollado 11 months ago.
Research AssistantSep 2013 - Jul 2014 (10 months)
Part of the computer vision team
Bachelor of science2009 - 2013 (4 years)
IbTIECar Best Graduation Project award (2014)IbTIECar
Won 1st position in TIEC Graduation Projects Competition for virtual realitygraduation thesis "DreamOn".
Semantic Segmentation as Image Representation for Scene Recognition using
We introduce a novel approach towards scene recognition using semantic segmentation maps as image representation. Given a set of images and a list of possible categories for each image, our goal is to assign a category from that list to each image.
Facial Expression Recognition in the Wild Using Rich Deep Features
In this paper we present a novel approach towards facial expression recognition. We fuse rich deep features with domain knowledge through encoding discriminant facial patches. We conduct experiments on two of the most popular benchmark datasets; CK and TFE. Moreover, we present a novel dataset that, unlike its precedents, consists of natural - not acted - expression images.
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