Future University In Egypt (FUE)
Future University is one of most promising private universities in Egypt. Through excellence in teaching, research and service, Future University strives to provide a comprehensive, high-quality education that prepares our graduates to be future leaders.
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Altagamoa Al Khames, Main centre of town, end of 90th Street
New Cairo
Egypt

AMIRA MOHAMMED IBRAHIM IDREES

Basic information

Name : AMIRA MOHAMMED IBRAHIM IDREES
Title: part-time Lecturer
Google Schoolar Link
Personal Info: Prof Amira Mohamed Ibrahim Idrees works as a full-time Lecturer at the Faculty of Computer Science at Future University in Egypt. the head of the university requirements unit View More...

Education

Certificate Major University Year
PhD Computer science Cairo University - Faculty Of Computers And information 2010
Masters Computer and Information Science Institute of Statistical Studies and Research, Cairo University 2000
Diploma Computer Science Institute of Statistical Studies and Research, Cairo University 1995
Bachelor Communication Department Helwan University, Faculty of Engineering 1992

Teaching Experience

Name of Organization Position From Date To Date
Central Laboratory for Expert Systems, ARC Chair of Agricultural Expert System Development 01/01/2009 01/01/2012

Researches /Publications

INTELLIGENT PERSONALIZED SYSTEM FOR ENHANCING THE QUALITY OF LEARNING - 01/0

AMIRA MOHAMMED IBRAHIM IDREES

DALIA HASSAN AHMED HASSOUNA, AYMAN E. KHEDR, AHMED I. ELSEDDAWY

01/07/2020

This study proposes an approach for students’ learning style personalization. The study presents both the importance and the theoretical basis of learning styles. One of these perspectives is how self-reported learning style inventories are controversial, while others view learning styles as strategies that can be adjusted to specific tasks and conditions. To determine the efficacy of the proposed approach, an experiment has been applied and a set of Canadian Institute College students have been examined. This study concludes that we need to offer alternative ways to connect various learning styles when teaching the university students which would enable the teaching process to use different learning approaches and activities which would lead eventually to enhancing the learning process.

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Automated Ham-Spam Lexicon Generation Based on Semantic Relations Extraction - 01/0

AMIRA MOHAMMED IBRAHIM IDREES

Essam Shaaban,Ayman khedr

01/04/2020

One of the current essential methods for communication is electronic email (e-mail). It is currently considered the official method for different business activitiessuch as conducting agreements, the setup of official meetings, and team collaboration. This continuous interest in e-mails as a communication channel has drawn the attention to the need for eliminating spam which have a vital effect on both network resources and business activities. This research focuses on generating a ham-spam lexicon based on text analysis which is aimed to be one of the main resources for detecting personal spam e-mails. The lexicon generation is a key step to efficiently and economically successful spam elimination. The proposed framework has proven its applicability on a dataset of six groups and the classification algorithms have been examined to prove the efficient classification. The research is a step in a wider view for general intelligent business communication and collaboration framework.

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Automated Ham-Spam Lexicon Generation Based on Semantic Relations Extraction - 01/0

AMIRA MOHAMMED IBRAHIM IDREES

Ayman E. Khedr, Essam Shaaban

01/01/2020

One of the current essential methods for communication is electronic email (e-mail). It is currently considered the official method for different business activities such as conducting agreements, the setup of official meetings, and team collaboration. This continuous interest in e-mails as a communication channel has drawn the attention to the need for eliminating spam which have a vital effect on both network resources and business activities. This research focuses on generating a ham-spam lexicon based on text analysis which is aimed to be one of the main resources for detecting personal spam e-mails. The lexicon generation is a key step to efficiently and economically successful spam elimination. The proposed framework has proven its applicability on a dataset of six groups and the classification algorithms have been examined to prove the efficient classification. The research is a step in a wider view for general intelligent business communication and collaboration framework.

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A Collaborative Evaluation Metrics Approach for Classification Algorithms - 01/0

AMIRA MOHAMMED IBRAHIM IDREES

Fahad Kamal Alsheref

01/01/2020

Evaluating Algorithms is one of the critical steps which should be strongly considered as this is the pillar of most of the decisions. This research proposes a novel method for accurate algorithms’ evaluation according to the metrics’ relationships and weight. The weight of the evaluation metrics is determined according to their invariance level. The proposed method validity is confirmed by applying and evaluating the most famous and well-populated classification techniques. The results have been considered according to the calculated weight of the evaluation measures to reveal the final algorithm evaluation. As a case study, the most suitable classification technique for Tinnitus data are explored. This research considered the Tinnitus data as Tinnitus symptoms are not clearly recognized which highlights the difficulty of the patients to have a direct and fast diagnosis which highlighted the motivation in investigating intelligent methods for fast Tinnitus diagnosing. The research applied the experiment on a real dataset that is gathered in Egypt and the results highlighted that the Support Vector Machine classification algorithm is the most suitable technique for Tinnitus data classification with an accuracy equal to 90.1%.

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A Proposed Framework to Explore Semantic Relations for Learning Process Management - 01/0

AMIRA MOHAMMED IBRAHIM IDREES

Ayman E. Khedr, Fahad Kamal Alsheref

01/08/2019

This research argues that providing the students with the same material in different methods that correspond to their skills would guarantee their satisfaction as well as their level of success. This research focuses on the vital exploration of the suitable student learning style with respect to the student skills, the type of material and the impact of intelligent techniques. The research scope considers that students' skills are normally varied among individuals, this variation should be considered in the learning process. The proposed approach is based on the successful migration of different data across the components and a formal description of this data was presented to clarify the homogenous transformation according to the applied steps. The proposed framework has been applied on a set of students and the results revealed to a raise in the students' performance represented in their grades and their satisfaction level.

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Emotion Analysis for Opinion Mining From Text: A Comparative Study - 01/0

AMIRA MOHAMMED IBRAHIM IDREES

Hesham Ahmed Hassan

01/08/2019

In the past few years, web documents, blogs, and reviews have played an important role in many fields as organizations always aim to find consumer or public opinions about their products and services. On the other hand, individual consumers also seek the opinions or emotions of existing users of a certain product before purchasing it. This method is currently one of the most vital methods for adapting the organizations' plans. In this article, the authors provide a survey for different techniques and approaches for emotion analysis from the text. They also demonstrate the techniques and the methods that have been proposed by different researchers with criticizing many of these methods according to the applied approach and the accuracy level.

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Knowledge Discovery based Framework for Enhancing the House of Quality - 01/0

AMIRA MOHAMMED IBRAHIM IDREES

Ahmed I. ElSeddawy, Mohammed Ossama Zeidan

01/07/2019

Mining techniques proved to have a successful impact in different fields for many targets; one of these targets is to gain customers’ satisfaction through enhancing the products’ quality according to the voice of these customers. This research proposes a framework that is based on mining techniques and adopted Saaty method targeting to gain the customers’ satisfaction and consequently a competitive advantage in the real estate market. The proposed framework is applied during the design phase of a real estate residential building project as an improvement tool to design the building according to the customers’ requirements representing the voice of customers (VOC). The proposed Saaty method adaptation increased the number of the consistent sample which was incorrectly excluded using the traditional Saaty method. Saaty method adaptation has succeeded in enhancing the house of quality (HOQ) by achieving the consistent technical customers’ requirements for residential buildings, while customers’ segmentation succeeded in focusing on the homogeneous grouping of customers.

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A Proposed Model for Detecting Facebook News’ Credibility - 01/0

AMIRA MOHAMMED IBRAHIM IDREES

Fahad Kamal Alsheref, Ahmed I. Bahgat Elseddawy

01/01/2019

Social networks are currently one of the main News’ sources for most of their users. Moreover, News channels also consider social networks as main channels not only for spreading the news but also for measuring the feedback from their followers. Facebook Followers can comment or react to the news, which represents the follower’s feedback about this topic. Therefore, it is a fact that measuring the News’ credibility is one of the important tasks that could control the propagation of the fake news as well as the number of News’ followers. The proposed model in this research highlights the impact of the News’ followers on detecting the News’ polarity either it is fake or not. The proposed model focuses on applying an intelligent sentiment analysis using Vector Space Model (VSM) which is one of the most successful techniques on the users’ comments and reactions through the emoji. Then the degree of credibility is determined according to the correlation coefficient. An experimental study was applied using Facebook News dataset, which included the News and the followers’ feedbacks.

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A proposed configurable approach for recommendation systems via data mining techniques - 01/0

AMIRA MOHAMMED IBRAHIM IDREES

Abd El-Fatah Hegazy and Samir El-Shewy

01/03/2017

This study presents a configurable approach for recommendations which determines the suitable recommendation method for each field based on the characteristics of its data, the method includes determining the suitable technique for selecting a representative sample of the provided data. Then selecting the suitable feature weighting measure to provide a correct weight for each feature based on its effect on the recommendations. Finally, selecting the suitable algorithm to provide the required recommendations. The proposed configurable approach could be applied on different domains. The experiments have revealed that the approach is able to provide recommendations with only 0.89 error rate percentage

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Using NLP Approach for Opinion Types Classifier - 01/0

AMIRA MOHAMMED IBRAHIM IDREES

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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Performance Tuning of K-Mean Clustering Algorithm a Step towards Efficient DSS - 01/0

AMIRA MOHAMMED IBRAHIM IDREES

Ahmed I. El Seddawy

01/01/2014

This research is the first step in building an efficient Decision Support System (DSS) which employs Data Mining (DM) predictive, classification, clustering, and association rules techniques. This step considers finding groups of members in the dataset that are very different from each other, and whose members are very similar to each other, therefore one DM task is applied which is clustering task. The main objective of the proposed research is to enhance the performance of one of the most well-known popular clustering algorithms (K-mean) to produce near-optimal decisions for telcos churn prediction and retention problems. Due to its performance in clustering massive data sets. The final clustering result of the k-mean clustering algorithm greatly depends upon the correctness of the initial centroids, which are selected randomly. This research will be followed by a serious of researches targeting the main objective which is an efficient DSS which will be applied on customer banking data. In this research a new method is proposed for finding the better initial centroids to provide an efficient way of assigning the data points to suitable clusters with reduced time complexity. The proposed algorithm is successfully developed an applied on customer banking data, and the evaluation results are presented.

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Sampling Technique Selection Framework for Knowledge Discovery - 01/0

AMIRA MOHAMMED IBRAHIM IDREES

Hesham Ahmed Hassan

01/03/2010

Knowledge Discovery in Databases (KDD) is a complex interactive and iterative process which involves many steps that must be done sequentially. Supporting the whole KDD process has enjoyed a great popularity in recent years, with advances in research. We however still lack of a generally accepted underlying framework and this hinders the further development of the field. We believe that the quest for such a framework is a promising research area. It is crucial to consider that the optimization of the mining process must take into account the pre-steps of knowledge discovery, thus a quite challenging problem is to consider an efficient method to estimate the optimal training set. In our work we aim in building an efficient knowledge discovery system for discovering interesting associations from databases, our approach improving. The utility of the data mining process by dividing the problem in two main tasks: sampling which considers The dataset to be mined and mining which considers the applied algorithm to mine the data.

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Diagnostic expert system using non-monotonic reasoning - 01/0

AMIRA MOHAMMED IBRAHIM IDREES

E.-S.EL-AZHARY, A.RAFEA

01/01/2002

The objective of this work is to develop an expert system for cucumber disorder diagnosis using non-monotonic reasoning to handle the situation when the system cannot reach a conclusion. One reason for this situation is when the information is incomplete. Another reason is when the domain knowledge itself is incomplete. Another reason is when the information is inconsistent. This method maintains the truth of the system in case of changing a piece of information. The proposed method uses two types of non-monotonic reasoning namely: ‘default reasoning’, and ‘reasoning in the presence of inconsistent information’ to achieve its goal

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Awards

Award Donor Date
NA NA 2014

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