Future University In Egypt (FUE)
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Altagamoa Al Khames, Main centre of town, end of 90th Street
New Cairo
Egypt

Mahmoud Sami Abd El-Aziz Othman

Basic information

Name : Mahmoud Sami Abd El-Aziz Othman
Title: Assistant Professor & Director of Quality Assurance Unit
Google Schoolar Link
Personal Info: Mahmoud Sami is a lecturer in Computer Science Department and Director of Quality Assurance and Continous Development Center. He graduated from Faculty of Computers and Information, Cairo University since 2008 ; he joined FUE in 2008. he got his M.Sc. and Ph.D. degree from the same faculty he graduated from. He received two awards as an outstanding Instructor for the academic years 2011-2012 and 2013-2014. His research directions are Artifcial Intelligence and Machine Learning and their Applications. View More...

Education

Certificate Major University Year
PhD Computer Science 2016
Masters Computer Science 2012
Bachelor Computer Sciences 2008

Researches /Publications

Deep Transfer Learning Driven Oral Cancer Detection and Classification Model

MAHMOUD SAMI ABDELAZIZ OTHMAN

Radwa Marzouk;Eatedal Alabdulkreem;Sami Dhahbi;Mohamed K. Nour;Mesfer Al Duhayyim;Manar Ahmed Hamza; Abdelwahed Motwakel; Ishfaq Yaseen;Mohammed Rizwanullah

16/07/2022

https://www.techscience.com/cmc/v73n2/48390

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Optimal Machine Learning Based Privacy Preserving Blockchain Assisted Internet of Things with Smart Cities Environment

MAHMOUD SAMI ABDELAZIZ OTHMAN

Al-Qarafi;Fadwa Alrowais;Saud S. Alotaibi;Nadhem Nemri;Fahd N. Al-Wesabi ;Mesfer Al Duhayyim;Radwa Marzouk;Al-Shabi

09/07/2022

https://www.mdpi.com/2076-3417/12/12/5893

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Textual Emotion Detection Approaches: A Survey

MAHMOUD SAMI ABDELAZIZ OTHMAN

Ahmed Abas;Ibrahim Elhenawy

30/06/2022

https://digitalcommons.aaru.edu.jo/fcij/vol7/iss1/3/

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An Empirical Study Towards an Automatic Phishing Attack Detection Using Ensemble Stacking Model

MAHMOUD SAMI ABDELAZIZ OTHMAN

Hesham Hassan

30/06/2022

https://digitalcommons.aaru.edu.jo/fcij/vol7/iss1/1/

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BERT-CNN: A Deep Learning Model for Detecting Emotions from Text

MAHMOUD SAMI ABDELAZIZ OTHMAN

Ahmed Abas;Ibrahim Elhenawy

07/12/2021

https://www.techscience.com/cmc/v71n2/45793

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Automatic Clustering of DNA Sequences With Intelligent Techniques

MAHMOUD SAMI ABDELAZIZ OTHMAN

Khaled Wassif

13/10/2021

https://ieeexplore.ieee.org/document/9568887

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A linguistic approach for opinionated documents summary

MAHMOUD SAMI ABDELAZIZ OTHMAN

Hesham Hassan

15/12/2018

https://www.sciencedirect.com/science/article/pii/S2314728817300260?via%3Dihub

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Using NLP Approach for Opinion Types Classifier

MAHMOUD SAMI ABDELAZIZ OTHMAN

Hesham Hassan,

01/09/2016

Information that are represented as text are either facts or opinions, whenever we need to make a decision, we often seek out the opinions of others which is one of the most influencing factors for our decisions. Traditionally, individuals can get opinions from friends and family while organizations use surveys, focus groups, opinion polls and consultants. Nowadays, opinions expressed through user generated content are considered as one of the important types of information which is available on the web, therefore, many resources have been emerged for expressing opinions including social media and others. This situation has revealed the necessity for robust, flexible Information Extraction (IE) systems, these systems have the availability to transform the web pages into program-friendly structures such as a relational database to reveal these opinions. In this paper, we propose an approach to classify the opinions of a document or a set of documents considering an object. The approach has been implemented and applied on a dataset of opinions. The proposed system discover the opinions provided for an object in a document or set of documents. The system discovers different types of opinionated statements, including the opinionated, comparative, superlative, and non- opinionated. The system has been applied on a set of 4000 sentences, and the results has been evaluated using the standard metrics, they are True positive, True negative, False positive, False negative, Precision, Recall, and F-score. We also provided a comparison of the presented work with previous work that has been presented in the same field. Index Terms—Opinion mining, opinion discovery, sentimental analysis, natural language processing

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Opinion Mining and Sentimental Analysis Approaches: A Survey

MAHMOUD SAMI ABDELAZIZ OTHMAN

Hesham Hassan, Abeer El-Korany

01/01/2014

The automatic extraction of information from unstructured sources has opened up new ways for querying, organizing, and analyzing data by building a clean semantics of structured databases from a huge number of unstructured data and the society became more data oriented with easy online access to both structured and unstructured data. New applications of structured extraction came around such as the paper topic opinion mining, which is a type of natural language processing for tracking the mood of the public about a particular topic. Opinion mining, which is also called sentiment analysis, involves building a system to collect and examine opinions about the product or topic made in blog posts, comments, reviews or tweets. Automated opinion mining often uses machine learning, which is a component of artificial intelligence (AI).

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Ontology-Based: Intelligent Document Management System

MAHMOUD SAMI ABDELAZIZ OTHMAN

Hesham Hassan

01/01/2011

As more and more knowledge and information becomes available through computers in the organizations, a critical capability of systems supporting knowledge management is the classification of documents into categories that are meaningful to the user. Today, text categorization is required due to the very large amount of text documents that we have to deal with daily. A text categorization system can be used in indexing documents to assist information retrieval tasks as well as in classifying documents of any specific Domain, This paper proposes an intelligent document management system that use the ontology of a specific domain to annotate the documents for classifying it automatically. The proposed system consists of seven main parts: Ontology Extractor, Ontology Parser, Document Parser, Annotator, Indexer, Data Repository, and Categorization Engine.

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Awards

Award Donor Date
Best Performance Future University 2018
Outstanding Service Future University 2016

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