I have a belief that the closest thing we have to superpower is programming. Since I was a little kid, I always wanted to be superhero and here I am now... on my own way to my superpower.
Working at GIS Technology Innovation Center at Umm Al-Qura University (UQU).
Worked As Business Intelligence for out sourcing project for Credit Agricole Egypt. The project was about updating the bank working environment from OFSAA(Oracle Financial Services Analytical Applications) 6 to OFSAA 12
Research Assistant under the supervision of Dr. Muhammed El-Sayed and Dr. Marwan El-Torky in Computer Vision specifically in Optical Handwriting Recognition in Arabic language. My work was focused on:
On both data set along side with INF_data set (Tunisian city names data set) and Digit data set (60K handwritten digit).
ACM is a world wide organization that is specialized in the field of computer engineering. For me as being the chair man of the branch in Alexandria, It is honor to serve here trying to improve the quality and the quantity of computer engineering, scientist , architects and so on. to improve the level of my country in the world wide competition.
Working on building a backend for https://www.facebook.com/help/community facebook help community to be more reliable, scalable, up to date using the Facebook technologies, support searching and integrable with Facebook products.
Objective-C developer working upon building the IPhone user interface for Intafeen project
This paper presents a comparative study for window-based descriptors on the application of Arabic handwritten alphabet recognition. We show a detailed experimental evaluation of different descriptors with several classifiers. The objective of the paper is to evaluate different window-based descriptors on the problem of Arabic letter recognition. Our experiments clearly show that they perform very well. Moreover, we introduce a novel spatial pyramid partitioning scheme that enhances the recognition accuracy for most descriptors. In addition, we introduce a novel dataset for Arabic handwritten isolated alphabet letters, which can serve as a benchmark for future research
In this paper, we introduce the first phase of a new dataset for offline Arabic handwriting recognition. The aim is to collect a very large dataset of isolated Arabic words that covers all letters of the alphabet in all possible shapes using a small number of simple words. The end goal is to collect a very large dataset of segmented letter images, which can be used to build and evaluate Arabic handwriting recognition systems that are based on segmented letter recognition. The current version of the dataset contains 25114 samples of 109 unique Arabic words that cover all possible shapes of all alphabet letters. The samples were collected from 907 writers. In its current form, the dataset can be used for the problem of closed-vocabulary word recognition. We evaluated a number of window-based descriptors and classifiers on this task and obtained an accuracy of 92.16\% using a SIFT-based descriptor and ANN.
Designing a search engine is one of the oldest Computer Science problems and still poses a number of challenges. Having ultra-huge companies which are taking over the control with money and power, the most important thing is satisfying users’ demands.
Due to the lack of Arabic information on the web, users are sometimes not satisfied with the results obtained from the common search engines. These demands the presence of intelligent search engines that understand the data given in hand and understand the user request to for a better service to the user.
We have designed Al-Ghaith to work specifically on Arabic language. That because Arabic language is integral to majority of the population of the Middle-East. The language distinct them from countries in other regions and it is also a language manifest in their faith. Arabic is the official language of hundreds of millions of people in twenty Middle East and northern African countries, and is the religious language of all Muslims of various ethnicities around the world. It is a Semitic language with 28 alphabet letters.
We have faced some challenges such as inherent properties of the Arabic language, the Arabic script (no short vowels and no capitalization), explosion of ambiguity (average 2.3 per word in other languages to 19.2 in Arabic), complex word structure and the problem of Normalization and losing distinction.
Al-Ghaith is working semantically not lexically and that makes it under- standing the metadata and queries too. Building a search engine to work semantically will lead to a lot of challenges such as handling morphological variations, handling concept matching, handling knowledge matching, the ability to enter queries freely, no special formats like quotes, or Boolean operators and the ability to operate without relying on statistics, user behavior, and other artificial means.
Computer Engineering, B.S.
Fall 2008 - Spring 2013
Alexandria University, Faculty of Engineering
Phun Is Phun (Alexandria University team) qualified and participated in ACM ICPC world finals 2013 in Saint Petersburg Russia.
Phun Is Phun (Alexandria University team) ranked as the second place in the ACM Arab CollegiateProgramming Contest (ACPC) and qualified to ACM ICPC (World Finals) in Saint Petersburg Russia.
Phun Is Phun(Alexandria University team) was awarded the "Solid Programmers" for solving 6 algorithmic problems from the first trial in ACM Egypt Collegiate Programming Contest (ECPC).
Phun Is Phun (Alexandria University team) ranked as the fifth place in the ACM Egypt Collegiate Programming Contest(ECPC).
Coursera August 2014
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Coursera September 2014
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