Future University In Egypt (FUE)
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Altagamoa Al Khames, Main centre of town, end of 90th Street
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AMIRA MOHAMMED IBRAHIM IDREES

Basic information

Name : AMIRA MOHAMMED IBRAHIM IDREES
Title: Professor & Head of IS Department
Google Schoolar Link
Personal Info: Amira M. Idrees, Professor, she received her B.Sc. from the Faculty of Engineering, Helwan University, Egypt. She finished her M.Sc. and Ph.D. degrees in Computer Science from the Faculty of Computers and Artificial Intelligence, Cairo University, Egypt in 2001 and 2010 respectively. She is currently a Professor in the Faculty of Computers and Information Technology, Future University in Egypt (FUE). The head of information systems department and the head of the university requirements unit, FUE. She has worked as the vice dean and the head of scientific departments, the Faculty of Computers and Information, Fayoum University. Her research interests include Knowledge Discovery, Text Mining, Opinion Mining, Sentimental Analysis, Cloud Computing, Software Engineering, Data warehousing, Data Science, and Big Data View More...

Education

Certificate Major University Year
PhD Computer science 2010
Masters 2000
Diploma Computer Science 1995
Bachelor 1992

Researches /Publications

Credibility aspects’ perceptions of social networks, a survey

AMIRA MOHAMMED IBRAHIM IDREES

Yehia Helmy; Ayman E. Khedr

31/07/2022

https://link.springer.com/article/10.1007/s13278-022-00924-6

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DDOS ATTACKS DEFENSE APPROACHES AND MECHANISM IN CLOUD ENVIROMENT

AMIRA MOHAMMED IBRAHIM IDREES

SARAH NAIEM;MOHAMED MARIE

15/07/2022

http://www.jatit.org/volumes/Vol100No13/1Vol100No13.pdf

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Credit Card Fraud Detection Using Machine Learning Techniques

AMIRA MOHAMMED IBRAHIM IDREES

Nermin Samy Elhusseny; Shimaa mohamed ouf

01/07/2022

https://digitalcommons.aaru.edu.jo/fcij/vol7/iss1/2/

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Exploratory Big Data Statistical Analysis The impact of People Life’s Characteristics on Their Educational Level

AMIRA MOHAMMED IBRAHIM IDREES

01/03/2022

http://www.jatit.org/volumes/Vol100No5/25Vol100No5.pdf

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DISTRIBUTED DENIAL OF SERVICES ATTACKS AND THEIR PREVENTION IN CLOUD SERVICES

AMIRA MOHAMMED IBRAHIM IDREES

SARAH NAIEM; MOHAMED MARIE

28/02/2022

http://www.jatit.org/volumes/Vol100No4/24Vol100No4.pdf

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A Statistical-Mining Techniques’ Collaboration for Minimizing Dimensionality in Ovarian Cancer Data

AMIRA MOHAMMED IBRAHIM IDREES

30/11/2021

https://digitalcommons.aaru.edu.jo/fcij/vol6/iss2/1/

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Enhancing the e-learning system based on a novel tasks’ classification load-balancing algorithm

AMIRA MOHAMMED IBRAHIM IDREES

Rashid Salem

01/09/2021

https://peerj.com/articles/cs-669/

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A proposed customer relationship framework based on information retrieval for effective Firms' competitiveness

AMIRA MOHAMMED IBRAHIM IDREES

Abdulwahab AliAlmazroia and Ayman E.Khedr

01/05/2021

Nowadays, firms are strongly racing to raise their competitive level in the international market. As this market has a natural connection, therefore, one of the vital roads for competing is exploring the users’ behaviour which continuously changes over time. This research proposes an intelligent information retrieval-based framework which applies a set of techniques to monitor the customers’ behaviour and determine the behaviour similarity. These techniques followed a determined opinion mining, knowledge discovery, weight measurement, and text analysis approaches. The aim of the proposed framework is to explore the suitable recommendations for the current customers and acquire new customers who could be selected from the customers’ social friends, which leads to the increase of the market share, a raise in the loyal customers’ segment, and finally in the firm’s competitiveness level. The framework has been successfully verified in two successful companies, the evaluation included different measures such as responding to change rate, and the customers’ segment share percentage. The evaluation presented an increase in the customers’ satisfaction level to be 97.91% and 97.31% for the two companies respectively while the willingness of new customers to join the customers’ segment has been raised by 83.15%. while However, the study could be further expanded in many directions such as the discovery of the customers’ opinion based on different sentiment levels.

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A Configurable Mining Approach for Learning Services Customization

AMIRA MOHAMMED IBRAHIM IDREES

Aya M. Mostafa AMM , yehia M. Helmy YMH

01/12/2020

There is no doubt that this age is the age of data and technology. Moreover, there is tremendous development in all fields. The personalized material is a good approach in the different fields. It provides a fit material that matches the styles of readers. It supports readers in various reading domains. This research paper aims to support students in the educational system. Additionally, the research paper designs to increase education values for students. Furthermore, the research paper builds the smart appropriate materials through Egyptian Knowledge Banking (EKB) based on the learner question. The Egyptian Knowledge Bank (EKB) is a rich platform for data. The research paper is implemented in the faculty of Commerce and Business Administration, Business Information System program (BIS) at Helwan University, Egypt.

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A Literature Review for Contributing Mining Approaches for Business Process Reengineering

AMIRA MOHAMMED IBRAHIM IDREES

Noha Ahmed Bayomy, Laila A. Abd-Elmegid LAA ,Ayman Khedr

01/12/2020

Due to the changing dynamics of the business environment, organizations need to redesign or reengineer their business processes in order to provide services with the lowest cost and shortest response time while increasing quality. Thence, Business Process Re-engineering (BPR) provides a roadmap to achieve operational goals that leads to enhance flexibility and productivity, cost reduction, and quality of service/product. In this paper, we propose a literature review for the different proposed models for Business Process Reengineering. The models specify where the breakdowns occur in BPR implementation, justifies why such breakdowns occur, and propose techniques to prevent their occurrence again. The discussed models have been built based on different perspectives which are discussed, and consequently, different research gaps and issues have arisen which are also highlighted in this research

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A Proposed Architectural Framework for Generating Personalized Users' Query Response

AMIRA MOHAMMED IBRAHIM IDREES

Aya M. Mostafa, Ayman E. Khedr, Yehia M. Helmy

01/10/2020

Personality mining is a vital approach which is currently been on focus for retaining customers as well as extending the customers’ segment. Data in its varied forms such as personal profile, social networks data, as well as transactional data could be considered as key for the success of industries. Therefore, successful data analysis by applying intelligent techniques, such as mining, natural language processing techniques, weighting methods, and others, drive competitiveness to a higher level and, further, to uniqueness. One of the main competitive advantages is the customer satisfaction, therefore, utilizing such successful techniques and successfully analyzing these sources of data with exploring their varieties of relationships lead to the successful personalization and consequently the user satisfaction. This research proposes an adaptive framework for documents’ personalization. The proposed framework builds the most suitable documents’ contents following user’s queries as well as the preferred style for content presentation with respect to users’ personality characteristics. On the other hand, the research highlights the importance of the Egyptian Knowledge Bank as a source of professional documents for replying to the users’ queries. A preliminary investigation has been applied to explore the importance of the proposed framework in the educational field through conducting a survey that included ten staff members and fifty students. The results revealed that the current research is a vital objective in the education field with 91% and 95% respectively.

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An Architectural Framework for Generating Food Safety Key Performance Indicators

AMIRA MOHAMMED IBRAHIM IDREES

Fatma Abogabal, Shimaa M. Ouf and Ayman E. Khedr

01/10/2020

Information Technology proved its effectiveness in all industry fields, taking the competition to unexpectedly high levels. Identifying the essential parameters is vital to success. In different fields, business processes monitoring is also essential. In the food industry, for example, food hazards may occur in any stage of generating food, from agriculture to serving. This research uses data mining techniques to propose an architectural framework that can be utilized as a guide for food contamination prevention. The proposed framework aims at detecting the current food status, determining the suitability of the current conditions compared with the required conditions, and alerting users of near-threshold conditions. The framework predicts the available parameters for maintaining the food’s acceptability and includes a plan to follow. The research provides a prototype with a benchmark dataset for proving the applicability of the proposed framework.

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INTELLIGENT PERSONALIZED SYSTEM FOR ENHANCING THE QUALITY OF LEARNING

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

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

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

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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Knowledge Discovery based Framework for Enhancing the House of Quality

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

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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Emotion Analysis for Opinion Mining From Text: A Comparative Study

AMIRA MOHAMMED IBRAHIM IDREES

Hesham Ahmed Hassan

01/01/2019

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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A proposed configurable approach for recommendation systems via data mining techniques

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

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

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

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

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

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