The 6th International Conference on Informatics and Systems (INFOS2008)

 27 – 29 March, 2008 Cairo, EGYPT


Prize of 1000 Egyptian Pound introduced 


Professor Aly Fahmy

the FCI Dean, Cairo University

for the best student paper submitted and presented at RSHIS2008





The 5th  International Workshop on

Rough Sets and Hybrid  Intelligent Systems  


Cairo, Egypt, March  29,  2008

Organized by:

Egyptian Rough Sets Working Society 



in  cooperation  with 

 Faculty of Computer and Information, Cairo university


Faculty of Computers and Information, The University of Menoufia

Workshop General Chairs

Dr. Aboul Ella Hassanien,  ERS Chair

                             Professor Nabil Abd El-ahid Ismail,                                                   Professor Aly Fahmy,

                         Faculty of Computers and Information,                                Faculty of Computer and Information,

                                Menoufia University                                                                                Cairo University


Workshop Co-Chairs and Program Chair

ERS Steering Committee

·         Dr. Mohamed Mohamed Ezzat Abdel-Monsef Mohamed, Tanta University

·         Dr. Amgad Salama Salem, Tanta University

·         Professor  A.M Kozae, Tanta University,

·         Prof. Abdel-Badeeh M. Salem, Ain Shams University

·         Dr. Yasser Fouad Mahmoud Hassan, Alexandria  University

·         Dr. Hala Shawky Own, NRIAG, Helwan

·         Dr. Nahla El-haggar, NRIAG,  Helwan

·         Dr. Wael Abd El-Kader Awad Suez Canal University

·         Prof.Dr. Farahat Farag Farhat,Sadat Academy

·         Dr Hussam Elbehiery,Egyptian Armed Forces Research Center

·         Dr. Tarek Gharib Fouad, Ain Shams University.

·         Professor Mahmoud Mohamed Hassan Gabr, Alexandria University


Honorary Chair

Janusz Kacprzyk

Janusz Kacprzyk
Professor, Ph.D., D.Sc.
Fellow of IEEE, IFSA
Systems Research Institute
Polish Academy of Sciences
ul. Newelska 6
01-447 Warsaw, Poland
Google: kacprzyk


Plenary talk

Binary data mining



Professor Václav Snášel,

Dean of FIT, VSB-Technical University of Ostrava -

 Department of Computer Science, Faculty of Electrical Engineering and Computer Science,

VSB-Technical University of Ostrava, 17. listopadu 15,

708 33 Ostrava - Poruba, Czech Republic,



Binary data have been occupying a special place in the domain of data analysis. Analysis of binary data sets, however, generally leads to NP-complete/hard problems. Consequently, the focus here is on effective heuristics for reducing the problem size. Matrix factorization or factor analysis is an important task helpful in the analysis of high dimensional real world data. There are several well known methods and algorithms for factorization of real data but many application areas including information retrieval, pattern recognition and data mining require processing of binary rather than real data see [4],[5],[7],[8],[11],[14]. Unfortunately, the methods used for real matrix factorization fail in the latter case. In this paper we introduce background for binary matrix factorization.In order to perform object recognition (no matter which one) it is necessary to learn representations of the underlying characteristic components. Such components correspond to object-parts, or features [10]. These data sets may comprise discrete attributes, such as those from market basket analysis, information retrieval, and bioinformatics, as well as continuous attributes such as those in scientific simulations, astrophysical measurements, and sensor networks. The feature extraction if applied on binary datasets, addresses many research and application fields, such as association rule mining [1], market basket analysis [2], discovery of regulation patterns in DNA microarray experiments [12], etc. Many of these problem areas have been described in tests of PROXIMUS framework (e.g. [7]). So called bars problem [13] is used as the benchmark. Set of artificial signals generated as a Boolean sum of given number of bars is analyzed by these methods. Here we will concentrate on the case of black and white pictures of bars combinations represented as binary vectors, so the complex feature extraction methods are unnecessary [6]. Many applications in computer and system science involve analysis of large scale and often high dimensional data. When dealing with such extensive information collections, it is usually very computationally expensive to perform some operations on the raw form of the data. Therefore, suitable methods approximating the data in lower dimensions or with lower rank are needed. In the following, we focus on the factorization of hight-dimensional binary data or high order binary tensors [3].  


[1]     R. Agrawal, R. Srikant, Fast algorithms for mining association rules in large databases. In: VLDB ’94: Proceedings of the 20th International Conference on Very Large Data Bases, San Francisco, CA, USA, Morgan Kaufmann Publishers Inc. (1994) Pages 487-499

[2]     S. Brin, R. Motwani, J.D. Ullman, S. Tsur, Dynamic itemset counting and implication rules for market basket data. In: SIGMOD ’97: Proceedings of the 1997 ACM SIGMOD international conference on Management of data, New York, NY, USA, ACM Press (1997) Pages 255-264

[3]     L. Elden. Matrix Methods in Data Mining and Pattern Recognition. SIAM 2007.

[4]     A.A. Frolov, D. Husek, P. Muravjev, P. Polyakov, Boolean Factor Analysis by Attractor Neural Network. Neural Networks, IEEE Transactions 18(3) (2007)  Pages 698-670

[5]     H. Lu, J. Vaidya and V. Atluri, Optimal Boolean Matrix Decomposition: Application to Role Engineering, ICDE 2008, in print.

[6]     D. Húsek, P. Moravec, V. Snásel, A.A. Frolov, H. Rezanková, P. Polyakov: Comparison of Neural Network Boolean Factor Analysis Method with Some Other Dimension Reduction Methods on Bars Problem. Springer, LNCS 4815, PReMI 2007: 235-243

[7]     M. Koyuturk, A. Grama, N. Ramakrishnan, Nonorthogonal decomposition of binary matrices for bounded-error data compression and analysis.  ACM Trans. Math. Softw. 32(1) (2006) Pages 33-69

[8]     P. Miettinen, T. Mielikäinen, A. Gionis, G. Das, H. Mannila: The Discrete Basis Problem. PKDD 2006: 335-346


[9]     P. Moravec and V. Snášel. Dimension Reduction Methods for Image Retrieval. In Proceedings of the Conference on Intelligent Systems Design and Applications (ISDA2006), 6 pages, Jinan, Shandong, China, October 2006. IEEE Press.

[10]  V. Snášel, P. Moravec, and J. Pokorny. Using BFA with WordNet Based Model for Web Retrieval. Journal of Digital Information Management, 4(2):107-111, 2006.

[11]  V. Snášel, D. Húsek, Alexander A. Frolov, H. Řezanková, P. Moravec, P. Polyakov: Bars Problem Solving - New Neural Network Method and Comparison. Lecture Notes in Computer Science 4827, MICAI 2007: 671-682

[12]  Spellman, P.T., Sherlock, G., Zhang, M.Q., Anders, V.I.K., Eisen, M.B., Brown, P., Botstein, D., Futcher, B.: Comprehensive identification of cell cycle-regulated genes of the yeast saccharomyces cerevisiae by microarray hybridization. In: Molecular Biology of the Cell. (1998) Pages 3273-3297

[13]  M. W. Spratling: Learning Image Components for Object Recognition. Journal of Machine Learning Research 7 (2006) 793–815.

[14]  Z. Zhang, T. Li, Ch. Ding, X. Zhang, Binary Matrix Factorization with Applications, ICDM 2007


Plenary talk


Building and using  virtual environments




Professor   Michael R. M. Jenkin

Computer Science and Engineering
Faculty of Science and Engineering,
York University,
4700 Keele Street, Toronto, Ontario


      Abstract: Virtual reality and immersive environments have been proposed for a range of tasks, from training to entertainment. In this talk I will describe the development of three large-scale virtual reality devices; IVY - a six-sided immersive projective environment, MOOG - a stereo head mounted display equipped visual display coupled with a physical motion base, and the Active Desktop - a large scale immersive desk. Although each of these devices provides a compelling visual display, they do so in rather different ways and combine this visual display with other input modalities. Underlying these rather different display technologies is a common software infrastructure that allows software to be moved between the devices in a relatively straightforward manner and allows software development to take place using standard computer hardware. At York University one of the applications of virtual reality is to the generation of conflicting sensory inputs to aid in the study of basic perceptual processes with particular emphasis on the perception of self-motion and self-orientation. These are important questions on Earth where people make predictable errors in judgement given limited cues to their motion and orientation, and have applications in other domains including underwater and in outer space. I will conclude the talk with a review of some recent research into these questions and a discussion of how the virtual reality devices described in the talk (and other similar devices at York) are being used to investigate these questions in both 1g and in microgravity.


Michael Jenkin is a Professor of Computer Science and Engineering, and a member of the Centre for Vision Research at York University, Canada. Working in the fields of visually guided autonomous robots and virtual reality, he has published over 150 research papers including co-authoring Computational Principles of Mobile Robotics with Gregory Dudek and a series of co-edited books on human and machine vision with Laurence Harris. Michael Jenkin's current research intrests include work on sensing strategies for AQUA, an amphibious autonomous robot being developed as a collaboration between Dalhousie University, McGill University and York University; the development of tools and techniques to support crime scene investigation; and the understanding of the perception of self-motion and orientation in unusual environments including microgravity.


Plenary talk

Intelligent Optimization








Dr. Crina D. Grosan


Department of Computer Science

Babes-Bolyai University

Cluj-Napoca, Romania

Abstract: Optimization problems are encountered daily in each of our lives. While most of us may fail to recognize the structure of these problems, they exist at many levels of complexity. Such optimization problems can vary from relatively simple, single input variable, single objective (SO) problems to multivariate, multiobjective optimization problems (MOPs) of great complexity. While obtaining the optimal solution to an MOP and hence solving it is the ultimate goal of any attempt to optimize an MOP, the desire of most researchers is to find an acceptable solution to MOPs. Since many real world problems are MOPs, this talk concentrates on finding acceptable solutions to MOPs using a relatively new, innovative, search approach.



Crina D. Grosan received BS and MS degrees in mathematics and a Ph.D. in computer science from  Babes-Bolyai University, Cluj-Napoca, Romania in 2005. She is currently a Lecturer in the Department of Computer Science, Babes-Bolyai University. Her recent research interests include optimization, mathematical programming, numerical analysis, computational intelligence, computational biology. Dr. Grosan has over 80 scientific publications including over 25 journal articles/book chapters and 6 books written or edited. She serves on the editorial board of a number of journals and on the program committee of several international conferences. More information at:


 Tutorial on

Hybrid Soft Computing: Reviews, Architectures and Perspectives


Professor Ajith Abraham

Norwegian Center of Excellence
Center of Excellence for Quantifiable Quality of Service
Norwegian University of Science and Technology,
O.S. Bragstads plass 2E,
N-7491 Trondheim


Abstract: Soft computing coined by Prof. Zadeh is now an established problem solving methodology. It is well known that the intelligent systems, which can provide human like expertise such as domain knowledge, uncertain reasoning, and adaptation to a noisy and time varying environment, are important in tackling practical computing problems. This tutorial introduces the basic ingredients of soft computing and then focus on some generic architectures of soft computing in a hybrid environment.


  • What is Soft Computing?

  • Different Soft Computing Architectures

  • Artificial Neural Networks

  • Neural Network Learning Paradigms

  • Need for Neural Network Optimization?

  • Global Optimization

  • Evolutionary Algorithms

  • Evolutionary Neural Networks

  • Fuzzy Logic

  • Fuzzy Reasoning and Inference System

  • Mamdani  and Takagi Sugeno fuzzy inference system

  • Evolutionary – Fuzzy Systems

  • Advantages of fuzzy inference systems

  • Neuro-fuzzy systems

  • Types of neuro-fuzzy systems

  • Cooperative neuro-fuzzy models

  • Concurrent neuro-fuzzy models

  • Integrated neuro-fuzzy models

  • Application areas with simple examples

  • Conclusions


We are delighted to invite you to participate in our 5th  workshop on  Rough Sets and Hybrid  Intelligent Systems, on 29 March.  2008.

Workshop  Objectives:


 Rough set theory, proposed by Zdzislaw Pawlak (1926-2006) in 1982, is a model of approximate reasoning. The main idea is based on the indiscernibility relation that describes indistinguishability of objects. Concepts are represented by lower and upper approximations. In applications, rough set methodology focuses on approximate representation of knowledge derivable from data. It leads to significant results in many areas including, for example, data mining, machine learning, finance, industry, multimedia, medicine, and most recently bioinformatics. 


The main objective of this workshop  is to provide a forum for mathematician, engineers, academicians, scientists and researchers to present the result of their research activities in the field of rough sets and their applications. The primary focus of the workshop is to create an effective medium for institutions and students to share ideas, innovations and problem solving techniques. On this basis, the Egyptian Rough Sets working group with the Faculty of Computers and Information, Menoufia University is calling for papers to be submitted to the 5th   workshop on Rough Sets and hybrid Intelligent Systems which  addressing theoretical, empirical and policy issues related to this theme, we would appreciate to receive  your paper  by 15 Feb, 2008.



Important information:

  •   The paper  must not exceed 8 pages in MS-word format

  •   For ERS member and nun-member (open)

  •   The paper  will published as a technical report published (on-line/hardcopy) by the ERS.

  •   The presentation time is 20  minuts including the disusssion

  •   Award will given to  the Best Student Presentation and to the Best Student paper.

  •  A certificate for each accepted paper

Workshop Place


Faculty of Computers and Information, The University of Menoufia




Papers on these and related subjects are particularly encouraged:

  • Rough set theory and applications

  • Fuzzy set theory and applications

  • Fuzzy-rough, rough-fuzzy and beyond

  • Knowledge discovery and data mining

The conference''s focus will also be on the following topics:

  • approximate reasoning

  • computational biology

  • data warehousing

  • decision support systems

  • distributed computing

  • evolutionary strategies

  • formal concept analysis

  • human-computer interaction

  • layered learning

  • machine intelligence

  • bioinformatics

  • multi-agent systems

  • multimedia mining

  • non standard logics

  • pattern recognition

  • semantic web and ontologies

  • web and text mining



Important date


                                 Submissions due:   5  March,   2008






Authors should submit the electronic version of their papers in MS-WORD  formats by email to



   Submission Guidelines and important dates

            Submission Requirements

  Papers should be original, previously unpublished work and should not identify the author's). Submissions should be no longer than 8 pages (including figures and references) in IEEE format. Email submissions (doc or pdf) are preferred and should be sent to  by midnight of the due date (5 March. 2008). Submissions should be in English. 



For further Information, please contact:




*1000 L.E.  for the best student paper

*The 4 top papers will published on    Telecommunication Systems journal" published by Springer Verlag, Germany

  *Selected papers will be fast-track reviewed for the editing book on < Foundation on Computational Intelligence>

To be published in "Studies in Computational Intelligence" published by Springer Verlag, Germany



Workshop Program


09:30-10.00 Registration

10.00-10.10 Welcome message by

                     Professor Nabil Abd El-ahid Ismail, FCI Dean, Menoufia University

10:10-10.50 plenary talk-I:

                 Building and using virtual environments by Professor Michael Jenkin

10:50-11:20 Plenary talk-II:

            Intelligent Optimization by Professor  Crina D. Grosan

11:30-12:10      Binary Data Mining by Professor Václav Snášel,


12.15  -  12.30  Coffee Break

12.30-2.00 Tutorial on Hybrid Soft Computing: Reviews, Architectures and Perspectives, by Professor Ajith Abraham.


2.00-3.00 Lunch Time



Session-I   Rough Sets and Applications


Time (3.00 -4.45)


Session chair:   Professor Hani Mahdi  - 

 Session co-chair: Dr. Aboul Ella Hassanien


Rough Sets Reduction for Binary Data

E.A.Rady, A.M.Koza, A.N.Abad, S.M.Attawey


Pre-topological Approach for Incomplete Information Systems

A . S. Salama


Novel Set Approximations in Generalized Multi-valued Decision

Information Systems (GMDIS)

S. Abd El-Badie



Decision of the new data

A.A. Abo Khadra  T. Medhat b, M. E. Ali


Missing values and covering rough sets

T. Medhat a, R. Mareay


New rough concepts based on semi topological rough sets

A.M.Kozae   and    Heba I. Mustafa


Data Reduction Relevant Effect of The Breast Cancer Tumor Benigncy or Malignancy Using RST

S. Abd El-Badie, M.M.E. Abd El-Monsef 2, A. M. Kozea  and E. A. Rady



Session-II Multimedia and hybrid intelligent systems


Time (3.00-4.45)


Session Chair: Professor Ajith Abraham  -  Session co-chair: Professor Dr. Waeil fathi


A Fully Automatic Texture Recognition System for Natural Gray

Images Coloring

Noura.A. Semary Mohyi.M.Hadhoud, Wail.S.El Kilani,  Nabil.A.Ismail


DNA Computing Approach for Project Evaluation Problems

Hala A. Omar, EL Sayed M.M.Zaky, Waiel F. Abd Elwahed,


A Hybrid Intelligent System for  Database Mining

Maha Mohamed Rshad Zeedan, Hoda Salah Sror,

Wail Fathi Abd El-Wahed


Dimensionality Reduction Using Rough Set Approach for Two

Neural Networks-Based Applications (invited)

M. Sammany, T. Medhat  


An Approach for the Discovery of Diagnosing Rules of Transformer Failure

Hossam A. Nabwey mohamed , A.M. Kozae E. A. Rady


Match Making in MASCE using Rough Sets

Hani Mahdi and Sally S. Attia



Fingerprint Feature Extraction and Matching using Contourlet Transform

M Asif Afzal Butt, Atif Bin Mansoor and Shoab A Khan


Closing and award announcement


Workshop web site