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Mohamed Elhawary

Mohamed Elhawary

Mohamed is a Staff Software Engineer at Google. Prior to joining Google, Mohamed was a PhD student at Cornell University and an undergraduate student at Alexandria University. His work is around information retrieval, data mining, machine learning, large systems and wireless networks.
Authored Publications
Google Publications
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    Energy-Efficient Protocol for Cooperative Networks
    Zygmunt J. Haas
    IEEE/ACM TRANSACTIONS ON NETWORKING, vol. 19 (2011), pp. 561-574
    Preview abstract In cooperative networks, transmitting and receiving nodes recruit neighboring nodes to assist in communication. We model a cooperative transmission link in wireless networks as a transmitter cluster and a receiver cluster. We then propose a cooperative communication protocol for establishment of these clusters and for cooperative transmission of data. We derive the upper bound of the capacity of the protocol, and we analyze the end-to-end robustness of the protocol to data-packet loss, along with the tradeoff between energy consumption and error rate. The analysis results are used to compare the energy savings and the end-to-end robustness of our protocol to two non- cooperative schemes, as well as to another cooperative protocol published in the technical literature. The comparison results show that, when nodes are positioned on a grid, there is a reduction in the probability of packet delivery failure by two orders of magnitude for the values of parameters considered. Up to 80% in energy savings can be achieved for a grid topology, while for random node placement, our cooperative protocol can save up to 40% in energy consumption relative to the other protocols. The reduction in error rate and the energy savings translate into increased life time of cooperative sensor networks. View details
    Mining Arabic Business Reviews
    Mohamed Elfeky
    IEEE, pp. 1108-1113
    Preview abstract For languages with rich content over the web, business reviews are easily accessible via many known websites, e.g., Yelp.com. For languages with poor content over the web like Arabic, there are very few websites (we are actually aware of only one that is indeed unpopular) that provide business reviews. However, this does not mean that such reviews do not exist. They indeed exist unstructured in websites not originally intended for reviews, e.g., Forums and Blogs. Hence, there is a need to mine for those Arabic reviews from the web in order to provide them in the search results when a user searches for a business or a category of businesses. In this paper, we show how to extract the business reviews scattered on the web written in the Arabic language. The mined reviews are analyzed to also provide their sentiments (positive, negative or neutral). This way, we provide our users the information they need about the local businesses in the language they understand, and therefore provide a better search experience for the Middle East region, which mostly speaks Arabic. View details
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